diff --git a/modules/module1/ROADMAP.ipynb b/modules/module1/ROADMAP.ipynb index 8819cd2..6fc0bfc 100644 --- a/modules/module1/ROADMAP.ipynb +++ b/modules/module1/ROADMAP.ipynb @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -100,7 +100,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -114,7 +114,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/modules/module1/introducing_programming.ipynb b/modules/module1/introducing_programming.ipynb index 65ef52b..0106e54 100644 --- a/modules/module1/introducing_programming.ipynb +++ b/modules/module1/introducing_programming.ipynb @@ -200,7 +200,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.7.3" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/modules/module1/python_crash_course.ipynb b/modules/module1/python_crash_course.ipynb index 865fc4b..2f404c5 100644 --- a/modules/module1/python_crash_course.ipynb +++ b/modules/module1/python_crash_course.ipynb @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -115,11 +115,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"Hello, world\")\n", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, world, hello\n", + "\n", + "9\n" + ] + } + ], + "source": [ + "print(\"Hello, world, hello\", sep=\" \")\n", "print()\n", "print(5+4)" ] @@ -133,9 +143,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on built-in function print in module builtins:\n", + "\n", + "print(...)\n", + " print(value, ..., sep=' ', end='\\n', file=sys.stdout, flush=False)\n", + " \n", + " Prints the values to a stream, or to sys.stdout by default.\n", + " Optional keyword arguments:\n", + " file: a file-like object (stream); defaults to the current sys.stdout.\n", + " sep: string inserted between values, default a space.\n", + " end: string appended after the last value, default a newline.\n", + " flush: whether to forcibly flush the stream.\n", + "\n" + ] + } + ], "source": [ "help(print)" ] @@ -172,9 +201,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "thing 1\n", + "thing 1|thing 2\n", + "thing 1^thing 2^thing 3\n" + ] + } + ], "source": [ "print(\"thing 1\")\n", "print(\"thing 1\", \"thing 2\", sep=\"|\")\n", @@ -190,11 +229,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "thing 1^thing 2^thing 3\n", + "***********************\n" + ] + } + ], "source": [ - "print(\"thing 1\", \"thing 2\", \"thing 3\", sep=\"^\", end=\"\\n************\\n\")" + "print(\"thing 1\", \"thing 2\", \"thing 3\", sep=\"^\", end=\"\\n***********************\\n\")" ] }, { @@ -210,11 +258,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data+Meaning=Information" + ] + } + ], "source": [ - "print()" + "print(\"Data\",\"Meaning\",sep=\"+\",end=\"=Information\")" ] }, { @@ -246,9 +302,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hola\n", + "hola\n", + "Please type something inhola\n", + "hola\n", + "Please type something else inhola\n", + "hola\n" + ] + } + ], "source": [ "print(input())\n", "print(input(prompt=\"Please type something in\"))\n", @@ -291,9 +360,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] + } + ], "source": [ "a=4\n", "print(a)" @@ -301,9 +378,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "can't assign to literal (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m 5=\"five\"\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to literal\n" + ] + } + ], "source": [ "5=\"five\"" ] @@ -324,18 +410,40 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'This is a string literal'" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"This is a string literal\"" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'b is now a string variable'" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "b = \"b is now a string variable\"\n", "b" @@ -350,27 +458,60 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "int" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(5)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(\"b\")" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "float" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(4.)" ] @@ -386,11 +527,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "arg1 is a string\n", + "arg1 contains the correct value\n", + "arg2 is correctly a number\n", + "arg2 is correctly an integer\n" + ] + } + ], "source": [ - "literal_variables1(arg1=\"replace_me1\", arg2=\"replace_me2\")\n" + "a = \"327\"\n", + "literal_variables1(arg1=a, arg2=327)\n" ] }, { @@ -406,16 +559,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BRIAN CHAPMAN\n", + "brian chapman\n", + "George washington\n", + "George Washington\n", + "3\n" + ] + } + ], "source": [ "name = \"Brian Chapman\"\n", "print(name.upper())\n", "print(name.lower())\n", "print(\"george washington\".capitalize())\n", "print(\"george \".capitalize(), \"washington\".capitalize())\n", - "print(name.find(\"i\"))" + "print(name.find(\"a\"))" ] }, { @@ -427,9 +592,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on built-in function find:\n", + "\n", + "find(...) method of builtins.str instance\n", + " S.find(sub[, start[, end]]) -> int\n", + " \n", + " Return the lowest index in S where substring sub is found,\n", + " such that sub is contained within S[start:end]. Optional\n", + " arguments start and end are interpreted as in slice notation.\n", + " \n", + " Return -1 on failure.\n", + "\n" + ] + } + ], "source": [ "help(name.find)" ] @@ -445,27 +628,51 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "9.5\n", + "-1.8333333333333333\n", + "15\n", + "\n" + ] + } + ], "source": [ "a_num = 5\n", "a_num_also = 4.5\n", "\n", "rslt = a_num + a_num_also\n", + "print(type(rslt))\n", "print(rslt)\n", "a_num = 4 # assign a new value to a_num\n", "print((a_num-rslt)/3)\n", "\n", - "x = int(\"54\")\n", - "print(x)" + "x = int(\"10\",15)\n", + "print(x)\n", + "print(type(x))\n", + "#help(int)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BrianChapman\n", + "i\n" + ] + } + ], "source": [ "first_name = \"Brian\"\n", "last_name = \"Chapman\"\n", @@ -516,21 +723,55 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#1. INTEGER 5 --> invalid\n", + "#2. INTEGER 5\n", + "#3. STRING \"5.4\"\n", + "#4. FLOAT 5.4\n", + "#5. ERROR" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "ValueError", + "evalue": "could not convert string to float: 'five point four'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"five point four\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'five point four'" + ] + } + ], + "source": [ + "float(\"five point four\")" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "7338396147676878563" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "name.__hash__()\n", + "#name+name" + ] }, { "cell_type": "markdown", @@ -541,9 +782,97 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__add__',\n", + " '__class__',\n", + " '__contains__',\n", + " '__delattr__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__getitem__',\n", + " '__getnewargs__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " '__iter__',\n", + " '__le__',\n", + " '__len__',\n", + " '__lt__',\n", + " '__mod__',\n", + " '__mul__',\n", + " '__ne__',\n", + " '__new__',\n", + " '__reduce__',\n", + " '__reduce_ex__',\n", + " '__repr__',\n", + " '__rmod__',\n", + " '__rmul__',\n", + " '__setattr__',\n", + " '__sizeof__',\n", + " '__str__',\n", + " '__subclasshook__',\n", + " 'capitalize',\n", + " 'casefold',\n", + " 'center',\n", + " 'count',\n", + " 'encode',\n", + " 'endswith',\n", + " 'expandtabs',\n", + " 'find',\n", + " 'format',\n", + " 'format_map',\n", + " 'index',\n", + " 'isalnum',\n", + " 'isalpha',\n", + " 'isascii',\n", + " 'isdecimal',\n", + " 'isdigit',\n", + " 'isidentifier',\n", + " 'islower',\n", + " 'isnumeric',\n", + " 'isprintable',\n", + " 'isspace',\n", + " 'istitle',\n", + " 'isupper',\n", + " 'join',\n", + " 'ljust',\n", + " 'lower',\n", + " 'lstrip',\n", + " 'maketrans',\n", + " 'partition',\n", + " 'replace',\n", + " 'rfind',\n", + " 'rindex',\n", + " 'rjust',\n", + " 'rpartition',\n", + " 'rsplit',\n", + " 'rstrip',\n", + " 'split',\n", + " 'splitlines',\n", + " 'startswith',\n", + " 'strip',\n", + " 'swapcase',\n", + " 'title',\n", + " 'translate',\n", + " 'upper',\n", + " 'zfill']" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dir(name)" ] @@ -687,15 +1016,44 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your function seems to be correct\n" + ] + } + ], "source": [ "def quadratic(x,x0, a, b):\n", - " y = None\n", + " y = pow(x-x0,2)+a*x+b\n", + " return(y)\n", "print(test_quadratic(quadratic))" ] }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pow(1-2,2)+2*3+3" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -854,13 +1212,28 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 119, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Your function seems to be correct'" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def bmi(mass, height):\n", - " pass\n", - "test_bmi(bmi)\n" + " bmi = mass/pow(height,2)\n", + " return bmi\n", + "\n", + "test_bmi(bmi)\n", + "#bmi(6,1.7)\n", + "#help(bmi)" ] }, { @@ -880,19 +1253,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 121, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "overweight\n" + ] + } + ], "source": [ "bmi = 27.3\n", "\n", "if bmi < 18.5:\n", " print(\"underweight\")\n", - "elif 18.5 <= bmi < 25:\n", + "elif bmi < 25:\n", " print(\"normal\")\n", - "elif 25 <= bmi < 30:\n", + "elif bmi < 30:\n", " print(\"overweight\")\n", - "elif 30 <= bmi < 35:\n", + "elif bmi < 35:\n", " print(\"obesity\")\n", "else:\n", " print(\"extreme obesity\")" @@ -924,14 +1305,48 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 16)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m16\u001b[0m\n\u001b[0;31m switch(myage) {\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "#def pediatric_age(myage):\n", + "# if myage < 1:\n", + "# return \"NEONATES\"\n", + "# elif myage < 24:\n", + "# return \"INFANTS\"\n", + "# elif myage < 12*12:\n", + "# return \"CHILDREN\"\n", + "# elif myage < 12*16:\n", + "# return \"ADOLESCENTS\"\n", + "# else:\n", + "# return \"OTHER\"\n", + "# #\n", + "#\n", + "#test_pediatric_age(pediatric_age)\n", "def pediatric_age(myage):\n", - " pass\n", - "\n", - "test_pediatric_age(pediatric_age)" + " switch(myage) {\n", + " case 0:\n", + " return \"NEO\"\n", + " case range(1,24):\n", + " return \"INF\"\n", + " case range(24,12*12):\n", + " return \"CHILD\"\n", + " case range(12*12, 12*16):\n", + " return \"ADO\"\n", + " default:\n", + " return \"OTHER\"\n", + " }\n", + "test_pediatric_age(pediatric_age)\n", + "\n" ] }, { @@ -1029,10 +1444,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 156, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OIOO\n", + "O\n", + "I\n", + "O\n", + "O\n" + ] + } + ], + "source": [ + "string = input()\n", + "vowels=[\"a\",\"e\",\"i\",\"o\",\"u\"]\n", + "string=list(string)\n", + "for i in range(0,len(string)):\n", + " if string[i] in vowels or string[i].upper() in str(vowels).upper():\n", + " print(string[i])\n", + " else:\n", + " next" + ] }, { "cell_type": "markdown", @@ -1080,9 +1516,75 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['__doc__',\n", + " '__file__',\n", + " '__loader__',\n", + " '__name__',\n", + " '__package__',\n", + " '__spec__',\n", + " 'acos',\n", + " 'acosh',\n", + " 'asin',\n", + " 'asinh',\n", + " 'atan',\n", + " 'atan2',\n", + " 'atanh',\n", + " 'ceil',\n", + " 'copysign',\n", + " 'cos',\n", + " 'cosh',\n", + " 'degrees',\n", + " 'e',\n", + " 'erf',\n", + " 'erfc',\n", + " 'exp',\n", + " 'expm1',\n", + " 'fabs',\n", + " 'factorial',\n", + " 'floor',\n", + " 'fmod',\n", + " 'frexp',\n", + " 'fsum',\n", + " 'gamma',\n", + " 'gcd',\n", + " 'hypot',\n", + " 'inf',\n", + " 'isclose',\n", + " 'isfinite',\n", + " 'isinf',\n", + " 'isnan',\n", + " 'ldexp',\n", + " 'lgamma',\n", + " 'log',\n", + " 'log10',\n", + " 'log1p',\n", + " 'log2',\n", + " 'modf',\n", + " 'nan',\n", + " 'pi',\n", + " 'pow',\n", + " 'radians',\n", + " 'remainder',\n", + " 'sin',\n", + " 'sinh',\n", + " 'sqrt',\n", + " 'tan',\n", + " 'tanh',\n", + " 'tau',\n", + " 'trunc']" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import math\n", "dir(math)" @@ -1097,9 +1599,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "math.sin(math.pi/2)" ] @@ -1117,12 +1630,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Your function seems to be correct'" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "#help(random)\n", + "#print(random.random())\n", "def get_random_integer():\n", - " pass\n", + " a = random.randint(20,50)\n", + " \n", + " return a\n", + " \n", "test_random_integer(get_random_integer)" ] }, @@ -1130,6 +1660,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\n", "### Third-party packages\n", "\n", "Python comes with a lot, but it doesn't come with everything so people create third-party packages such as [numpy](http://www.numpy.org/), [scikit-learn](http://scikit-learn.org/stable/), and [Pandas](http://pandas.pydata.org/). These do not come with Python and so have to be **installed** separately. The Python package index (pypi) is a central listing of third-party packages. In a somewhat circular fashion, Python provides a third-party package [(pip)](https://pypi.python.org/pypi/pip) to help install third-party packages.\n", @@ -1153,33 +1684,134 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting package metadata: ...working... done\n", + "Solving environment: ...working... failed\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "SpecsConfigurationConflictError: Requested specs conflict with configured specs.\n", + " requested specs: \n", + " - ply==3.10\n", + " pinned specs: \n", + " - python=3.7\n", + "Use 'conda config --show-sources' to look for 'pinned_specs' and 'track_features'\n", + "configuration parameters. Pinned specs may also be defined in the file\n", + "/opt/conda/conda-meta/pinned.\n", + "\n", + "\n" + ] + }, + { + "ename": "CalledProcessError", + "evalue": "Command 'b'#pip uninstall nibabel\\n#pip install ply==3.10\\n#pip uninstall ply -y\\nconda install ply==3.10 -y\\n'' returned non-zero exit status 1.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mCalledProcessError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bash'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'#pip uninstall nibabel\\n#pip install ply==3.10\\n#pip uninstall ply -y\\nconda install ply==3.10 -y\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2350\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2351\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2352\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2353\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/IPython/core/magics/script.py\u001b[0m in \u001b[0;36mnamed_script_magic\u001b[0;34m(line, cell)\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscript\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshebang\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_error\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mCalledProcessError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_run_script\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_close\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mCalledProcessError\u001b[0m: Command 'b'#pip uninstall nibabel\\n#pip install ply==3.10\\n#pip uninstall ply -y\\nconda install ply==3.10 -y\\n'' returned non-zero exit status 1." + ] + } + ], "source": [ "%%bash\n", "#pip uninstall nibabel\n", - "pip install ply==3.11" + "#pip install ply==3.10\n", + "#pip uninstall ply -y\n", + "conda install ply==3.10 -y" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'It looks like nibabel is not installed'" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "test_nibabel()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'It looks like ply is installed properly'" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "test_ply()" ] }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "3\n", + "4\n", + "5\n", + "6\n", + "7\n", + "8\n", + "9\n" + ] + }, + { + "data": { + "text/plain": [ + "[None, None, None, None, None, None, None, None, None]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[print(i) for i in range(1,10)]" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1294,7 +1926,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.7.3" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/modules/module1/strings.ipynb b/modules/module1/strings.ipynb index 29955a3..4728f3b 100644 --- a/modules/module1/strings.ipynb +++ b/modules/module1/strings.ipynb @@ -53,15 +53,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CHRONIC HEPATITIS C WITHOUT HEPATIC COMA\n", + "070.54\n", + "Help on built-in function ascii in module builtins:\n", + "\n", + "ascii(obj, /)\n", + " Return an ASCII-only representation of an object.\n", + " \n", + " As repr(), return a string containing a printable representation of an\n", + " object, but escape the non-ASCII characters in the string returned by\n", + " repr() using \\\\x, \\\\u or \\\\U escapes. This generates a string similar\n", + " to that returned by repr() in Python 2.\n", + "\n" + ] + } + ], "source": [ "description = 'CHRONIC HEPATITIS C WITHOUT HEPATIC COMA'\n", "code = \"\"\"070.54\"\"\"\n", "\n", "print(description)\n", - "print(code)" + "print(code)\n", + "\n", + "help(ascii)" ] }, { @@ -77,9 +98,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Y\n", + "ᘪ\n", + "339\n", + "97\n" + ] + } + ], "source": [ "print(chr(89))\n", "print(chr(5674))\n", @@ -98,14 +130,81 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "True\n" + ] + } + ], "source": [ "print('a' < 'A')\n", "print(\"aBc\" < \"abc\")" ] }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['A',\n", + " 'B',\n", + " 'C',\n", + " 'D',\n", + " 'E',\n", + " 'F',\n", + " 'G',\n", + " 'H',\n", + " 'I',\n", + " 'J',\n", + " 'K',\n", + " 'L',\n", + " 'M',\n", + " 'N',\n", + " 'O',\n", + " 'P',\n", + " 'Q',\n", + " 'R',\n", + " 'S',\n", + " 'T',\n", + " 'U',\n", + " 'V',\n", + " 'W',\n", + " 'X',\n", + " 'Y',\n", + " 'Z']" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def num_range(star, stop):\n", + " a=\"\"\n", + " for i in range(star,stop):\n", + " a=a+chr(i)\n", + " return a\n", + "num_range(97,100)\n", + "\n", + "\n", + "def char_range(star, stop=\"Z\"):\n", + " star_ord = ord(star)\n", + " stop_ord = ord(stop)\n", + " return [chr(i) for i in range(star_ord, stop_ord+1)]\n", + "\n", + "char_range(\"A\")\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -142,11 +241,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "string_index1_quiz(\"replace me with the character at mystring[19]\")" + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "- is the correct value\n" + ] + } + ], + "source": [ + "from quizzes.string_quizzes import *\n", + "mystring = \"\"\"Termination of Swan-Ganz catheter within the proximal right main pulmonary.\"\"\"\n", + "#print(mystring[-1])\n", + "string_index1_quiz(mystring[19])" ] }, { @@ -167,11 +277,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Termination of Swan-Ganz catheter within the proximal right main pulmonary.\n", + "a is the correct value\n" + ] + } + ], "source": [ - "string_index2_quiz(\"replace me with your guess of the result for mystring[-4]\")" + "\n", + "string_index2_quiz(mystring[-4])" ] }, { @@ -191,9 +311,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CRNCHPTTSCWTOTHPTCCM\n" + ] + } + ], "source": [ "print(description)\n", "print(description[0])\n", @@ -275,37 +403,86 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on built-in function split:\n", + "\n", + "split(sep=None, maxsplit=-1) method of builtins.str instance\n", + " Return a list of the words in the string, using sep as the delimiter string.\n", + " \n", + " sep\n", + " The delimiter according which to split the string.\n", + " None (the default value) means split according to any whitespace,\n", + " and discard empty strings from the result.\n", + " maxsplit\n", + " Maximum number of splits to do.\n", + " -1 (the default value) means no limit.\n", + "\n" + ] + } + ], "source": [ "a = '1,2,3,4,5'\n", + "\n", "help(a.split)" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['1,2,3,4,5']" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "a.split()" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 74, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5]\n" + ] + } + ], "source": [ - "a.split(',')" + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['resp', 'care', 'pt', 'received', 'on', 'psv', 'mode,', 'per', 'team', 'peep', 'placed', 'back', 'on', 'at', '5', 'cmH20.', 'initially', 'pt', 'requiring', 'ps', '12,', 'now', 'on', '8', 'for', 'progression', 'of', 'weaning.', 'tolerating', 'fair', 'with', 'rr', 'approx', '25-32', 'range.', \"mdi's\", 'given', 'q4h,', 'flovent', 'started', 'at', '8', 'p.', 'bid.', 'cuff', 'leak', 'seems', 'more', 'constant', 'today,', '?worse', 'with', 'peep', 'on,', 'cuff', 'pressure', 'kept', 'at', '30', 'cmH20', 'with', '10', \"cc's\", 'in', 'cuff,', 'to', 'seal', 'it', 'would', 'require', 'cuff', 'pressure', 'of', '45', 'cmh20.', 'IP', 'evaluated', 'and', 'chooses', 'not', 'to', 'replace', 'trach', 'at', 'this', 'time,', 'maintain', 'cuff', 'pressure', 'at', '30.', 'c/w', 'slow', 'wean,', 'progress', 'to', 'trach', 'mask', 'as', 'soon', 'as', 'possible.']\n" + ] + } + ], "source": [ "note =\"\"\"resp care\n", "pt received on psv mode, per team peep placed back on at 5 cmH20. initially pt requiring ps 12, now on 8 for progression of weaning. tolerating fair with rr approx 25-32 range. mdi's given q4h, flovent started at 8 p. bid. cuff leak seems more constant today, ?worse with peep on, cuff pressure kept at 30 cmH20 with 10 cc's in cuff, to seal it would require cuff pressure of 45 cmh20. IP evaluated and chooses not to replace trach at this time, maintain cuff pressure at 30. c/w slow wean, progress to trach mask as soon as possible.\"\"\"\n", @@ -324,9 +501,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['1', '2', '3', '4', '5']\n", + "12345\n" + ] + } + ], "source": [ "number_list = a.split(\",\")\n", "print(number_list)\n", @@ -335,18 +521,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 2 3 4 5\n" + ] + } + ], "source": [ "print( ' '.join(number_list))" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1,2,3,4,5\n", + "1, 2, 3, 4, 5\n", + "1this will look messy2this will look messy3this will look messy4this will look messy5\n" + ] + } + ], "source": [ "print(','.join(number_list))\n", "\n", @@ -498,7 +702,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/modules/module1/true_false.ipynb b/modules/module1/true_false.ipynb index 839b73c..819ebe6 100644 --- a/modules/module1/true_false.ipynb +++ b/modules/module1/true_false.ipynb @@ -180,10 +180,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#1. T\n", + "#2. F\n", + "#3. T\n", + "#4. T\n", + "x = 1; y = 0; z = 2\n", + "bool(x) and bool(y) or bool(z)\n" + ] }, { "cell_type": "markdown", @@ -198,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -312,7 +330,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/modules/module1/why_python.ipynb b/modules/module1/why_python.ipynb index 5ddbde1..cfb313b 100644 --- a/modules/module1/why_python.ipynb +++ b/modules/module1/why_python.ipynb @@ -93,7 +93,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.7.3" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/modules/module2/PythonBasicNumericTypes.ipynb b/modules/module2/PythonBasicNumericTypes.ipynb index 5a4a7c0..753dc07 100644 --- a/modules/module2/PythonBasicNumericTypes.ipynb +++ b/modules/module2/PythonBasicNumericTypes.ipynb @@ -75,9 +75,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9223372036854775807 9223372036854775807\n" + ] + } + ], "source": [ "import sys\n", "print(sys.maxsize, 2**63 - 1)" @@ -92,11 +100,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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+ ] + } + ], "source": [ - "print(sys.maxsize+1)" + "print(sys.maxsize**1000)" ] }, { @@ -126,9 +142,19 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "maximum float 1.7976931348623157e+308\n", + "minimum float 2.2250738585072014e-308\n", + "epislon 2.220446049250313e-16\n" + ] + } + ], "source": [ "import sys\n", "print(\"maximum float\",sys.float_info.max)\n", @@ -605,7 +631,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.2" + "version": "3.7.3" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/modules/module2/blood_pressure_summary.png b/modules/module2/blood_pressure_summary.png index cbc59e5..facfe18 100644 Binary files a/modules/module2/blood_pressure_summary.png and b/modules/module2/blood_pressure_summary.png differ diff --git a/modules/module2/numeric_data_characterization.ipynb b/modules/module2/numeric_data_characterization.ipynb index 5c4153e..27b5d2b 100644 --- a/modules/module2/numeric_data_characterization.ipynb +++ b/modules/module2/numeric_data_characterization.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -100,12 +100,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n", + " \n" + ] + }, + { + "data": { + "text/html": [ + "
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heart_rate
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count483.000000
mean92.857143
std14.398532
min75.000000
25%85.000000
50%90.000000
75%99.000000
max303.000000
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" + ], + "text/plain": [ + " heart_rate\n", + "count 483.000000\n", + "mean 92.857143\n", + "std 14.398532\n", + "min 75.000000\n", + "25% 85.000000\n", + "50% 90.000000\n", + "75% 99.000000\n", + "max 303.000000" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hr.describe()" ] @@ -337,6 +754,106 @@ "hr.head()" ] }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on class range in module builtins:\n", + "\n", + "class range(object)\n", + " | range(stop) -> range object\n", + " | range(start, stop[, step]) -> range object\n", + " | \n", + " | Return an object that produces a sequence of integers from start (inclusive)\n", + " | to stop (exclusive) by step. range(i, j) produces i, i+1, i+2, ..., j-1.\n", + " | start defaults to 0, and stop is omitted! range(4) produces 0, 1, 2, 3.\n", + " | These are exactly the valid indices for a list of 4 elements.\n", + " | When step is given, it specifies the increment (or decrement).\n", + " | \n", + " | Methods defined here:\n", + " | \n", + " | __bool__(self, /)\n", + " | self != 0\n", + " | \n", + " | __contains__(self, key, /)\n", + " | Return key in self.\n", + " | \n", + " | __eq__(self, value, /)\n", + " | Return self==value.\n", + " | \n", + " | __ge__(self, value, /)\n", + " | Return self>=value.\n", + " | \n", + " | __getattribute__(self, name, /)\n", + " | Return getattr(self, name).\n", + " | \n", + " | __getitem__(self, key, /)\n", + " | Return self[key].\n", + " | \n", + " | __gt__(self, value, /)\n", + " | Return self>value.\n", + " | \n", + " | __hash__(self, /)\n", + " | Return hash(self).\n", + " | \n", + " | __iter__(self, /)\n", + " | Implement iter(self).\n", + " | \n", + " | __le__(self, value, /)\n", + " | Return self<=value.\n", + " | \n", + " | __len__(self, /)\n", + " | Return len(self).\n", + " | \n", + " | __lt__(self, value, /)\n", + " | Return self integer -- return number of occurrences of value\n", + " | \n", + " | index(...)\n", + " | rangeobject.index(value, [start, [stop]]) -> integer -- return index of value.\n", + " | Raise ValueError if the value is not present.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Static methods defined here:\n", + " | \n", + " | __new__(*args, **kwargs) from builtins.type\n", + " | Create and return a new object. See help(type) for accurate signature.\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors defined here:\n", + " | \n", + " | start\n", + " | \n", + " | step\n", + " | \n", + " | stop\n", + "\n" + ] + } + ], + "source": [ + "help(range)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -350,9 +867,96 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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heart_rateonerangeinverse rangerange_diff
08810483483
18411482481
28712481479
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heart_rateonerangeinverse rangerange_diffrange_diff2
08810483483483
18411482481480
28712481479477
37813480477474
48514479475471
\n", + "
" + ], + "text/plain": [ + " heart_rate one range inverse range range_diff range_diff2\n", + "0 88 1 0 483 483 483\n", + "1 84 1 1 482 481 480\n", + "2 87 1 2 481 479 477\n", + "3 78 1 3 480 477 474\n", + "4 85 1 4 479 475 471" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "hr[\"range_diff2\"] = hr.apply(lambda row: row[\"range_diff\"] - row[\"range\"], \n", " axis=1)\n", @@ -419,10 +1116,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/html": [ + "
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heart_rateonerangeinverse rangerange_diffrange_diff2normalized hrnormalized_hr
08810483483483-0.337336-0.530326
18411482481480-0.615142-0.542867
28712481479477-0.406788-0.532951
37813480477474-1.031851-0.561711
48514479475471-0.545691-0.538865
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" + ], + "text/plain": [ + " heart_rate one range inverse range range_diff range_diff2 \\\n", + "0 88 1 0 483 483 483 \n", + "1 84 1 1 482 481 480 \n", + "2 87 1 2 481 479 477 \n", + "3 78 1 3 480 477 474 \n", + "4 85 1 4 479 475 471 \n", + "\n", + " normalized hr normalized_hr \n", + "0 -0.337336 -0.530326 \n", + "1 -0.615142 -0.542867 \n", + "2 -0.406788 -0.532951 \n", + "3 -1.031851 -0.561711 \n", + "4 -0.545691 -0.538865 " + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#dir(hr)\n", + "#hr.mean\n", + "#hr.std\n", + "\n", + "hr[\"normalized hr\"] = (hr[\"heart_rate\"] - hr[\"heart_rate\"].mean())/hr[\"heart_rate\"].std()\n", + "\n", + "hr[\"normalized_hr\"] = hr.apply(lambda num: (num[\"heart_rate\"]-num.mean()) / num.std(), axis=1)\n", + "\n", + "hr.head()" + ] }, { "cell_type": "markdown", @@ -455,22 +1274,10 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.4" - }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, diff --git a/modules/module2/original_bp_data.ipynb b/modules/module2/original_bp_data.ipynb index 4eedbe5..9730326 100644 --- a/modules/module2/original_bp_data.ipynb +++ b/modules/module2/original_bp_data.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -49,9 +49,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enter password for MIMIC2 database········\n" + ] + } + ], "source": [ "conn = pymysql.connect(host=\"mysql\",port=3306,\n", " user=\"jovyan\",passwd=getpass.getpass(\"Enter password for MIMIC2 database\"),\n", @@ -60,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -79,9 +87,176 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idicustay_iditemidcharttimeelemidrealtimecgidcuidvalue1value1numvalue1uomvalue2value2numvalue2uomresultstatusstopped
0152321892162984-12-06 04:00:0002984-12-06 04:32:00-154139139.0mmHg4646.0mmHgNoneNotStopd
1232922894163423-04-21 21:00:0003423-04-21 21:00:00-169NoneNaNNoneNoneNaNNoneNoneD/C'd
2293334370763081-04-04 23:00:0003081-04-04 23:00:00-11NoneNaNNoneNoneNaNNoneNoneD/C'd
334512682-09-07 19:15:0002682-09-07 21:40:00-16900.0mmHg00.0mmHgNoneNotStopd
434512682-09-07 20:00:0002682-09-07 21:40:00-1697878.0mmHg4949.0mmHgNoneNotStopd
\n", + "
" + ], + "text/plain": [ + " subject_id icustay_id itemid charttime elemid \\\n", + "0 15232 18921 6 2984-12-06 04:00:00 0 \n", + "1 23292 28941 6 3423-04-21 21:00:00 0 \n", + "2 29333 43707 6 3081-04-04 23:00:00 0 \n", + "3 3 4 51 2682-09-07 19:15:00 0 \n", + "4 3 4 51 2682-09-07 20:00:00 0 \n", + "\n", + " realtime cgid cuid value1 value1num value1uom value2 \\\n", + "0 2984-12-06 04:32:00 -1 54 139 139.0 mmHg 46 \n", + "1 3423-04-21 21:00:00 -1 69 None NaN None None \n", + "2 3081-04-04 23:00:00 -1 1 None NaN None None \n", + "3 2682-09-07 21:40:00 -1 69 0 0.0 mmHg 0 \n", + "4 2682-09-07 21:40:00 -1 69 78 78.0 mmHg 49 \n", + "\n", + " value2num value2uom resultstatus stopped \n", + "0 46.0 mmHg None NotStopd \n", + "1 NaN None None D/C'd \n", + "2 NaN None None D/C'd \n", + "3 0.0 mmHg None NotStopd \n", + "4 49.0 mmHg None NotStopd " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bloodpressure.head()" ] @@ -96,7 +271,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -110,7 +285,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/modules/module2/visualizing_numeric_data.ipynb b/modules/module2/visualizing_numeric_data.ipynb index 0d00ff3..ff27384 100644 --- a/modules/module2/visualizing_numeric_data.ipynb +++ b/modules/module2/visualizing_numeric_data.ipynb @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +84,7 @@ "metadata": {}, "outputs": [], "source": [ - "#%matplotlib inline" + "#%matplotlib inline\n" ] }, { @@ -98,7 +98,7 @@ }, { "cell_type": "code", - 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'26693.txt',\n", + " '7307.txt',\n", + " '15476.txt',\n", + " ...]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "os.listdir(HRDIR)" ] @@ -130,9 +1141,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3903\n", + "4474.txt\n" + ] + } + ], "source": [ "print(len(hr_files))\n", "print(hr_files[-1])" @@ -140,9 +1160,87 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(367, 1)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n", + " \n" + ] + }, + { + "data": { + 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "hr[\"heart rate\"].plot(color=\"LightSalmon\")" ] @@ -209,24 +1377,70 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig1, ax1 = plt.subplots(1)\n", "hr[\"heart rate\"].plot(color=(1,0,0, 0.25), ax=ax1)\n", "ax1.set_xlabel(\"Time Point\")\n", "ax1.set_ylabel(\"Heart Rate (beats/minute)\")\n", - "ax1.set_ylim((40, 150))" + "ax1.set_ylim((55, 150))" ] }, { @@ -240,22 +1454,349 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function read_table in module pandas.io.parsers:\n", + "\n", + "read_table(filepath_or_buffer, sep=False, delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal=b'.', lineterminator=None, quotechar='\"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True, delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None)\n", + " Read general delimited file into DataFrame.\n", + " \n", + " .. deprecated:: 0.24.0\n", + " Use :func:`pandas.read_csv` instead, passing ``sep='\\t'`` if necessary.\n", + " \n", + " Also supports optionally iterating or breaking of the file\n", + " into chunks.\n", + " \n", + " Additional help can be found in the online docs for\n", + " `IO Tools `_.\n", + " \n", + " Parameters\n", + " ----------\n", + " filepath_or_buffer : str, path object, or file-like object\n", + " Any valid string path is acceptable. The string could be a URL. Valid\n", + " URL schemes include http, ftp, s3, and file. For file URLs, a host is\n", + " expected. A local file could be: file://localhost/path/to/table.csv.\n", + " \n", + " If you want to pass in a path object, pandas accepts either\n", + " ``pathlib.Path`` or ``py._path.local.LocalPath``.\n", + " \n", + " By file-like object, we refer to objects with a ``read()`` method, such as\n", + " a file handler (e.g. via builtin ``open`` function) or ``StringIO``.\n", + " sep : str, default '\\\\t' (tab-stop)\n", + " Delimiter to use. If sep is None, the C engine cannot automatically detect\n", + " the separator, but the Python parsing engine can, meaning the latter will\n", + " be used and automatically detect the separator by Python's builtin sniffer\n", + " tool, ``csv.Sniffer``. In addition, separators longer than 1 character and\n", + " different from ``'\\s+'`` will be interpreted as regular expressions and\n", + " will also force the use of the Python parsing engine. Note that regex\n", + " delimiters are prone to ignoring quoted data. Regex example: ``'\\r\\t'``.\n", + " delimiter : str, default ``None``\n", + " Alias for sep.\n", + " header : int, list of int, default 'infer'\n", + " Row number(s) to use as the column names, and the start of the\n", + " data. Default behavior is to infer the column names: if no names\n", + " are passed the behavior is identical to ``header=0`` and column\n", + " names are inferred from the first line of the file, if column\n", + " names are passed explicitly then the behavior is identical to\n", + " ``header=None``. Explicitly pass ``header=0`` to be able to\n", + " replace existing names. The header can be a list of integers that\n", + " specify row locations for a multi-index on the columns\n", + " e.g. [0,1,3]. Intervening rows that are not specified will be\n", + " skipped (e.g. 2 in this example is skipped). Note that this\n", + " parameter ignores commented lines and empty lines if\n", + " ``skip_blank_lines=True``, so ``header=0`` denotes the first line of\n", + " data rather than the first line of the file.\n", + " names : array-like, optional\n", + " List of column names to use. If file contains no header row, then you\n", + " should explicitly pass ``header=None``. Duplicates in this list will cause\n", + " a ``UserWarning`` to be issued.\n", + " index_col : int, sequence or bool, optional\n", + " Column to use as the row labels of the DataFrame. If a sequence is given, a\n", + " MultiIndex is used. If you have a malformed file with delimiters at the end\n", + " of each line, you might consider ``index_col=False`` to force pandas to\n", + " not use the first column as the index (row names).\n", + " usecols : list-like or callable, optional\n", + " Return a subset of the columns. If list-like, all elements must either\n", + " be positional (i.e. integer indices into the document columns) or strings\n", + " that correspond to column names provided either by the user in `names` or\n", + " inferred from the document header row(s). For example, a valid list-like\n", + " `usecols` parameter would be ``[0, 1, 2]`` or ``['foo', 'bar', 'baz']``.\n", + " Element order is ignored, so ``usecols=[0, 1]`` is the same as ``[1, 0]``.\n", + " To instantiate a DataFrame from ``data`` with element order preserved use\n", + " ``pd.read_csv(data, usecols=['foo', 'bar'])[['foo', 'bar']]`` for columns\n", + " in ``['foo', 'bar']`` order or\n", + " ``pd.read_csv(data, usecols=['foo', 'bar'])[['bar', 'foo']]``\n", + " for ``['bar', 'foo']`` order.\n", + " \n", + " If callable, the callable function will be evaluated against the column\n", + " names, returning names where the callable function evaluates to True. An\n", + " example of a valid callable argument would be ``lambda x: x.upper() in\n", + " ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster\n", + " parsing time and lower memory usage.\n", + " squeeze : bool, default False\n", + " If the parsed data only contains one column then return a Series.\n", + " prefix : str, optional\n", + " Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...\n", + " mangle_dupe_cols : bool, default True\n", + " Duplicate columns will be specified as 'X', 'X.1', ...'X.N', rather than\n", + " 'X'...'X'. Passing in False will cause data to be overwritten if there\n", + " are duplicate names in the columns.\n", + " dtype : Type name or dict of column -> type, optional\n", + " Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32,\n", + " 'c': 'Int64'}\n", + " Use `str` or `object` together with suitable `na_values` settings\n", + " to preserve and not interpret dtype.\n", + " If converters are specified, they will be applied INSTEAD\n", + " of dtype conversion.\n", + " engine : {'c', 'python'}, optional\n", + " Parser engine to use. The C engine is faster while the python engine is\n", + " currently more feature-complete.\n", + " converters : dict, optional\n", + " Dict of functions for converting values in certain columns. Keys can either\n", + " be integers or column labels.\n", + " true_values : list, optional\n", + " Values to consider as True.\n", + " false_values : list, optional\n", + " Values to consider as False.\n", + " skipinitialspace : bool, default False\n", + " Skip spaces after delimiter.\n", + " skiprows : list-like, int or callable, optional\n", + " Line numbers to skip (0-indexed) or number of lines to skip (int)\n", + " at the start of the file.\n", + " \n", + " If callable, the callable function will be evaluated against the row\n", + " indices, returning True if the row should be skipped and False otherwise.\n", + " An example of a valid callable argument would be ``lambda x: x in [0, 2]``.\n", + " skipfooter : int, default 0\n", + " Number of lines at bottom of file to skip (Unsupported with engine='c').\n", + " nrows : int, optional\n", + " Number of rows of file to read. Useful for reading pieces of large files.\n", + " na_values : scalar, str, list-like, or dict, optional\n", + " Additional strings to recognize as NA/NaN. If dict passed, specific\n", + " per-column NA values. By default the following values are interpreted as\n", + " NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',\n", + " '1.#IND', '1.#QNAN', 'N/A', 'NA', 'NULL', 'NaN', 'n/a', 'nan',\n", + " 'null'.\n", + " keep_default_na : bool, default True\n", + " Whether or not to include the default NaN values when parsing the data.\n", + " Depending on whether `na_values` is passed in, the behavior is as follows:\n", + " \n", + " * If `keep_default_na` is True, and `na_values` are specified, `na_values`\n", + " is appended to the default NaN values used for parsing.\n", + " * If `keep_default_na` is True, and `na_values` are not specified, only\n", + " the default NaN values are used for parsing.\n", + " * If `keep_default_na` is False, and `na_values` are specified, only\n", + " the NaN values specified `na_values` are used for parsing.\n", + " * If `keep_default_na` is False, and `na_values` are not specified, no\n", + " strings will be parsed as NaN.\n", + " \n", + " Note that if `na_filter` is passed in as False, the `keep_default_na` and\n", + " `na_values` parameters will be ignored.\n", + " na_filter : bool, default True\n", + " Detect missing value markers (empty strings and the value of na_values). In\n", + " data without any NAs, passing na_filter=False can improve the performance\n", + " of reading a large file.\n", + " verbose : bool, default False\n", + " Indicate number of NA values placed in non-numeric columns.\n", + " skip_blank_lines : bool, default True\n", + " If True, skip over blank lines rather than interpreting as NaN values.\n", + " parse_dates : bool or list of int or names or list of lists or dict, default False\n", + " The behavior is as follows:\n", + " \n", + " * boolean. If True -> try parsing the index.\n", + " * list of int or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3\n", + " each as a separate date column.\n", + " * list of lists. e.g. If [[1, 3]] -> combine columns 1 and 3 and parse as\n", + " a single date column.\n", + " * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call\n", + " result 'foo'\n", + " \n", + " If a column or index cannot be represented as an array of datetimes,\n", + " say because of an unparseable value or a mixture of timezones, the column\n", + " or index will be returned unaltered as an object data type. For\n", + " non-standard datetime parsing, use ``pd.to_datetime`` after\n", + " ``pd.read_csv``. To parse an index or column with a mixture of timezones,\n", + " specify ``date_parser`` to be a partially-applied\n", + " :func:`pandas.to_datetime` with ``utc=True``. See\n", + " :ref:`io.csv.mixed_timezones` for more.\n", + " \n", + " Note: A fast-path exists for iso8601-formatted dates.\n", + " infer_datetime_format : bool, default False\n", + " If True and `parse_dates` is enabled, pandas will attempt to infer the\n", + " format of the datetime strings in the columns, and if it can be inferred,\n", + " switch to a faster method of parsing them. In some cases this can increase\n", + " the parsing speed by 5-10x.\n", + " keep_date_col : bool, default False\n", + " If True and `parse_dates` specifies combining multiple columns then\n", + " keep the original columns.\n", + " date_parser : function, optional\n", + " Function to use for converting a sequence of string columns to an array of\n", + " datetime instances. The default uses ``dateutil.parser.parser`` to do the\n", + " conversion. Pandas will try to call `date_parser` in three different ways,\n", + " advancing to the next if an exception occurs: 1) Pass one or more arrays\n", + " (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the\n", + " string values from the columns defined by `parse_dates` into a single array\n", + " and pass that; and 3) call `date_parser` once for each row using one or\n", + " more strings (corresponding to the columns defined by `parse_dates`) as\n", + " arguments.\n", + " dayfirst : bool, default False\n", + " DD/MM format dates, international and European format.\n", + " iterator : bool, default False\n", + " Return TextFileReader object for iteration or getting chunks with\n", + " ``get_chunk()``.\n", + " chunksize : int, optional\n", + " Return TextFileReader object for iteration.\n", + " See the `IO Tools docs\n", + " `_\n", + " for more information on ``iterator`` and ``chunksize``.\n", + " compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'\n", + " For on-the-fly decompression of on-disk data. If 'infer' and\n", + " `filepath_or_buffer` is path-like, then detect compression from the\n", + " following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no\n", + " decompression). If using 'zip', the ZIP file must contain only one data\n", + " file to be read in. Set to None for no decompression.\n", + " \n", + " .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression.\n", + " \n", + " thousands : str, optional\n", + " Thousands separator.\n", + " decimal : str, default '.'\n", + " Character to recognize as decimal point (e.g. use ',' for European data).\n", + " lineterminator : str (length 1), optional\n", + " Character to break file into lines. Only valid with C parser.\n", + " quotechar : str (length 1), optional\n", + " The character used to denote the start and end of a quoted item. Quoted\n", + " items can include the delimiter and it will be ignored.\n", + " quoting : int or csv.QUOTE_* instance, default 0\n", + " Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of\n", + " QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).\n", + " doublequote : bool, default ``True``\n", + " When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate\n", + " whether or not to interpret two consecutive quotechar elements INSIDE a\n", + " field as a single ``quotechar`` element.\n", + " escapechar : str (length 1), optional\n", + " One-character string used to escape other characters.\n", + " comment : str, optional\n", + " Indicates remainder of line should not be parsed. If found at the beginning\n", + " of a line, the line will be ignored altogether. This parameter must be a\n", + " single character. Like empty lines (as long as ``skip_blank_lines=True``),\n", + " fully commented lines are ignored by the parameter `header` but not by\n", + " `skiprows`. For example, if ``comment='#'``, parsing\n", + " ``#empty\\na,b,c\\n1,2,3`` with ``header=0`` will result in 'a,b,c' being\n", + " treated as the header.\n", + " encoding : str, optional\n", + " Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python\n", + " standard encodings\n", + " `_ .\n", + " dialect : str or csv.Dialect, optional\n", + " If provided, this parameter will override values (default or not) for the\n", + " following parameters: `delimiter`, `doublequote`, `escapechar`,\n", + " `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to\n", + " override values, a ParserWarning will be issued. See csv.Dialect\n", + " documentation for more details.\n", + " tupleize_cols : bool, default False\n", + " Leave a list of tuples on columns as is (default is to convert to\n", + " a MultiIndex on the columns).\n", + " \n", + " .. deprecated:: 0.21.0\n", + " This argument will be removed and will always convert to MultiIndex\n", + " \n", + " error_bad_lines : bool, default True\n", + " Lines with too many fields (e.g. a csv line with too many commas) will by\n", + " default cause an exception to be raised, and no DataFrame will be returned.\n", + " If False, then these \"bad lines\" will dropped from the DataFrame that is\n", + " returned.\n", + " warn_bad_lines : bool, default True\n", + " If error_bad_lines is False, and warn_bad_lines is True, a warning for each\n", + " \"bad line\" will be output.\n", + " delim_whitespace : bool, default False\n", + " Specifies whether or not whitespace (e.g. ``' '`` or ``' '``) will be\n", + " used as the sep. Equivalent to setting ``sep='\\s+'``. If this option\n", + " is set to True, nothing should be passed in for the ``delimiter``\n", + " parameter.\n", + " \n", + " .. versionadded:: 0.18.1 support for the Python parser.\n", + " \n", + " low_memory : bool, default True\n", + " Internally process the file in chunks, resulting in lower memory use\n", + " while parsing, but possibly mixed type inference. To ensure no mixed\n", + " types either set False, or specify the type with the `dtype` parameter.\n", + " Note that the entire file is read into a single DataFrame regardless,\n", + " use the `chunksize` or `iterator` parameter to return the data in chunks.\n", + " (Only valid with C parser).\n", + " memory_map : bool, default False\n", + " If a filepath is provided for `filepath_or_buffer`, map the file object\n", + " directly onto memory and access the data directly from there. Using this\n", + " option can improve performance because there is no longer any I/O overhead.\n", + " float_precision : str, optional\n", + " Specifies which converter the C engine should use for floating-point\n", + " values. The options are `None` for the ordinary converter,\n", + " `high` for the high-precision converter, and `round_trip` for the\n", + " round-trip converter.\n", + " \n", + " Returns\n", + " -------\n", + " DataFrame or TextParser\n", + " A comma-separated values (csv) file is returned as two-dimensional\n", + " data structure with labeled axes.\n", + " \n", + " See Also\n", + " --------\n", + " to_csv : Write DataFrame to a comma-separated values (csv) file.\n", + " read_csv : Read a comma-separated values (csv) file into DataFrame.\n", + " read_fwf : Read a table of fixed-width formatted lines into DataFrame.\n", + " \n", + " Examples\n", + " --------\n", + " >>> pd.read_table('data.csv') # doctest: +SKIP\n", + "\n" + ] + } + ], "source": [ - "os.path.exists(os.path.join(BPDIR, \"1000.txt\"))" + "#os.path.exists(os.path.join(BPDIR, \"1000.txt\"))\n", + "help(pd.read_table)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + }, + { + "ename": "TypeError", + "evalue": "Empty 'DataFrame': no numeric data to plot", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m header=None, names=[\"systolic\", \n\u001b[1;32m 3\u001b[0m \"diastolic\"], keep_default_na=False)\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mbp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"systolic\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2740\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2741\u001b[0m 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secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1996\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1997\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1998\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1999\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1799\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m 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"execute_result" + }, + { + "data": { + "image/png": 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UwyU2K0FPRKMB/BbAeUKIamV7ZyKKGp8PBdAPQFFbzXuLtqDv7972fZza69qFp/SdzhW3560KerfJWOld4KUNxQxDo06zqDRelvqG9N9G/+MTfDdHO6wb8l4HWeB/9+HpOPWejwCknovTCKgQyluhFML1RrKwKUu2FeYCPshUx2gkohWW+415klgkYrbttIApj370q3ZUWeY/7JOx9uP9PBPVdPPN7lqDR97x4l45EcB0AP2JaAMRXQvgAQCtAXxgc6M8CcB8IpoH4GUAPxFCZDUV//zMddp0wpn4YtVO9B43CXdOWmxu++8Xa7O5vM10Y/3NTaPXsWN/LY6/ewqWbalMnRdJ7cwp2k51DfMaqeplL+kL3JBBu7FrqEIILN1SiRk5ela4EmABL5mxepe5aLop6B00+kJEGBd66B+EdC6ZRy0CtfXp91seEotmmoyVZ3BH3uek103C/Kw73o9bsGoSiuUppYIbrhJLCHGZZvPjDvu+AuCVXAsFAONsCb+8cNfbSV/lRz9djVvGDACgz2PthV+9NN/8bG9z2Sws/OHSbdi89wAe/jjlq5sQAv+assLxxfVhuTETO3mRK1ImZRIYjTm5qFICct5Cykavv1+FEMr2zmNb5QEMu3MK7rhgIH4w/JDsT9wIN/+l2etRFo3ggsE9sz5HQ1ygtiGOWIS0DgWxCKXmnxw1evfKqueWHYt89+3H++nP1ee3YONeVNc1oEV5YRf7C1VkrE57yjSpeHi31hh9RDftb7uqUjY4+3n9+NK+MGsd1u6sMs09lbXW6L2/T17ueGxM8eXKp2YotRMpnHTayMOfWC1uhZ4/mrJkK2av2RVok42d52asw3oj2ZbO1AUUxgRlP+e/pyZ9yp2yjD7zxVozh3vG8xp/SZsPMj/8+uX5+KUmCCqtLBnuWzwhUNuQQEUsgl+f1T/td6LU5Lj9XdU53TjdG7XzPmB4o83fsBeLNu1Nex/8PGd75//ynA3eD86SwAt6PxNPXkwQKjed2R//uXyI+3lt370I+ukrdyKREPjtKwtw0YOfo6Uh6NU89251s/iye1TpvXQIsqH50Tb311r9xFdt35/XBVKufSrp7VBKkbG/e22BqfU5tbNcO+h4QuCdBZstbcV+zhbKnNGaHdZcR/M37MGtry/E2KedXYIlqSRhORQ4T2S6b5+v3InVO6rQoiKGswemK2rxhJKO2DjNok17MXXpVrMdq/dz7U69oJcpSYRIZbIEgDH/Sl8vws9ztjeVygOFj8EIvKD3Ncmh2TeTMPOat8L+EL1ksLvs0S9MIbCzqs50B5MBSW5lA4BmZSnPArf7ILWw9bvcNTfpTZDJRm9HzQcuhMCoez8OxAIpQcF5MjY3Qf/4Z6tw3bNzccvrC03N097ptm+RimKWS2ZKzntgGoDkCkluyE42CDnXM921LfsOYOrSbejSugLNy9O9bxIJYbZxef/H/OszXDMhtTqU+li8dNL2iHP7c7WfY8W2/dhWeQBbNSnGl221JjOrqm1ATV08rZPOJ4EU9Is2pTRFP5F/Og05U6OVv60ZPwZrxo/JcF7v51TRBV1VKS+pOn+g6zvU4/fW1ONAfdy1c3h+1nrXcjVkodGrGk0h06tWFyE9dD5wnIzNcapj9Y6kcH9uxjozuddvXp5v2Ue1JZdlMX8kCZJG79Q/qo4QXVpXoJnGzTIuhGm6cWrj6lanfeREaSQCHLB15HbBrj6DjXtqcPp9H2PYnVNw3F1TUF3XYL73S7fsw/pdVvfrmvo4fvrsHJxyz0emR06+V7gLpKCftmKH+dmPMNJpT7Goc6v1mlva3oF4HQnUK2+5LoJSFfptmltzy9g56x+f4PBb38VNL+rtm35eTjlR5ScUW9UI7RplPjnt3o8Ldu5CUijTjS53kT3BnLp2QrlD42zjwVMsSEYzJxOe2sy7tmmm1egbEsJUEB0FvUWjT305rEsr87NMj9y2eZllRAukpw9Rn//uKquP/YDb3sMRf3gP1XUNmL4yPXdPLEL40DAT1TYksH5XNQbc9h5emJW+6Hm2BE7Qr9hWaUkuVFufQO9xk/DU52tcj9U900zat9c0odlq9PUNGkHfkC7oLzqmJwYd1A4A0KFledoxKq9/tQkA8Mf/LcKwOyc7ljETKY3eu7o5cWaq0RVysi5o/OCxGfieh+hTpz4zV0EvJwHN7/VxfGfIQZZt6iioPJZ6Nm/N32R+PqpXOwgh8M1b38WT01ZrrxWEiFiJU1E6tqowP3dpXaGNkE0khNnGJ3y+Bi9qRrlqRyI7g7svOhLv/iKVtWWbkSGzPBqxjGiBdEuDKvidxMOmPQe0UfVRxemiuq4BK7cnzW9vzc9fwsTACfrJtmANOVHxt/eWuR6r1egzaO1elwVL87rxeJyqKcjGtEkJld5n1O2kfp3N33dV1eHT35wKIHMekwmfrzEbImB9Scf861N8vbVSd5ilLLJx+n2/7dpNtvz6pXl4Yda6QK+B+tmKHZZFWHR0MoSPTlDmGgVpX+ns6idn4aXZ69GuRRnOPao7AKugL4tGcKA+jt7jJuHnz6UW0a5rSGDl9v2oqY/jH5O/1l7L9LoJgO3GqdPp0jol6Du3aaYtazwhLMnJ7v8wvb7q6aXQHtanA2LRCO64YCAAYJshlOMivc3bNfpnlHgdJ0Vo454abXrlMsXqUFMfNy0N+XTNDZSgnzBtNWbZAnLki+L04N+ctwn/m7fJ2Ce1fcaqnXj8s9UZhbJXAZPudeOwX4YJmkzv+4i+HTF7zW7z+0EdWuBbR/fwVDaJ2hkt2rQP932wHFv3HcDtby1OazCyA1q6pdLyQnhF1TJz0QJfmrMBv31lQdaxDkGhf7fkcF+9zZPmb8ak+Ztzdq+0a5LTV+1EZW0DYhHCcYd2BADUKPbc8ljEMscleWH2emzck1QyDu7QQn8xaaP3WcaHPnLO4+6EfdUolWdnrHW8b+qIt52DuTOeEHhxdsplUWfHl6ffvLcGf3ozGWAplcJ+hvlGpjJJJAT22FIe2M2eHyzean52ynB599tLsEmTHkV9d2vq4lnNobkRKEH/xzcXY8pSq0YvBZFTna+f+CX+b+KXxj6pnb77yBe4/a3FGQ2PtR6FXJrXjYPGYy9jnSblqZ1hfTqgS5tmaV4Rp3+zi6eyOV07IQR+8/J8PP7ZasxYZdVI5bKDALBye5Vvl0ZV+OQjJ06+RgjFQtrF1Xbys+fm4mfPzc3ZdONk+49FImY7VN+ZsmjEYgpQue+DZMxGy4p0wQekzBlOCn1tQxwTpq3G/toGPD19DRIJASEE/vLuUv0BGXhgqn5UAQC3vLbQsUWqNnmnetjz0Ojm4qSC8puX55sxM3LlKvu83mcrdlhG4kB2AYVLt1Rq82DFE6l7XlMfN026+YxCD5Sg1yG1PZ0wsmuj2oCpDA9EF0atxXZap6Gt/fqqy53d1iq5eIg1lf9VI3oDsAZL2XHTCgDg0693pCakhEAiIfDJ8u0QQmDH/tSLkI3fuiqYpdCfvnKnZXLZD8VKoKZj454aLM9g9tIhE2zp2l+uWplTRxGLkukUoPphl0cjlrkXdRJ23vrkWqVOUZjCptGv3lGFtTuTLn8H6uO4ZsIs/PHNxRh+1xTc9sYifLBkq6fR2OcrdqDW1v6dOiOJU1tqrrgct3Soh+rMAeidDmRdVYEtNXpd2eyLqthNNyqZnvmO/emmGyGEec9r6uKors2/4hN4QS+FgK69T1QmWRIJoXVlW7rFeQFerwLG/tycNB57GdVG9Phn+gmwo3slJ2GlBv/H845wLc+I8VNdr11dF8cXq1KNc8Lna3DFEzPx6tyNWLer2pzEWrez2rd5QfUEqW2IY3dVHS579IuMefoz4VU7Wr+r2ho4lBCucQNb9h5wHDE0xBNpGtbI8VNx5t8/8VQeSUWZDItPP2euphsnmVEWjWgVjvJYBDV1qhNA+rEtNJ4qgOpemTzvqfd8hJP/9hGAZHCYXO1Jel3tq6lP88BasGEvhBDYX9uAHftrsWxLJb732Az8+c3Flv1Uga3DHpktaakEh7V0SC5oX01O9/ylgqO6OkpB7yX/jM7te3tlLarrGrSCvk2zGDq0LE8rS8vyKOIJYSp2e2vqscXwvc9nGpzAC/p6U6NPR9WY31+8VWsvtts4VRxtlTbsWq/Ty5tJo9dpKJ/8+lT079YaAPDYlcdi1V3nmL/16dTSU9mcrg1YV8iRmQlvemke9tbUm4uJj30mJZxvPvtwrL77nLTz2FF9iuvjwjz3p1/vcDrEsWz2z06s2r4fJ/71Qzyo2IPvn7oCJ/71Q1Pr1DH87in48TP6Duiut5di5Pip2gkyP3MPqunmT28uxkilI87VdON0fDRCWq2yLBqxrHalE0hOuZrMyVjNb7pRTjwh0rTPbz3wGZ75Yi1OvecjDL1jMvbXJm3xizdbFa7m5dmJnqGHtDc/Owl6O93aNEvbJgQwa80uS6csha2b23X/rq3TRjJnDOiKY++cjO/8Z7q2PUcjhAP18bSONxIhxIVA+5bJ+YZNe2ow3+iomrl0hn4IvqCXjVnT3tXZ6pmrd/nOxyJdGt3wel77O/mL51M+7/bhYEUsgoM7WjsadeJ4QI826NE2vYF6vbYdu0uomj875W2R1OY+/NUpGc+laiX1DQlsMib53LQ0FVWLl8dnQpqbPlTs0dNXJTsWt/z/TgFeU5cmJ9COVdxUJX1u9p7aWmr0CQG8s3CL5bdc59MyBfPohPjiTfssHVsiATx9zTBrmRx9y51t9DqX4rihuduZu3a32XmWGZ2gvVNyaytOGWLPVHJTtTRGJvNuOxMf3HCS47n6dm6Vtu3GF+elRa1K27ybRr9sayVue2ORZZu8p4s27cOq7emKR4TI9I762al9LdsTCWGaoTbuqcGuKsPbJ6yTsTrq4smbo7MlVxnaRJfWFdhZVVuwPCl27c5Jy7JvVwXQEptG09YlQAoADunoXavPpDkKpGspVYpLXtx4CaVbmFtwjUXQxxPmaKUsQ3CaHdVstmWf+0It0txQXRfHfe8vw7n3f2pqYE72Ujet3O09OszjOgaqjd4+NM/dRq/fXhaNmEpQLEJobWi3M9fYbMmJBLoqGu2hnVs6BsopnuBpv+mebDwh8N6iLWnb1UVBpKC3m+cqbIL+8sdnWL7LeYc3f36CtqxASqNv26IM/bq2xsQfDbf8LhWWF2bro8XlqFYSNW30/m0mexQvIvvoBbB2nt3aNrdcM6G4b27cU2N6JMUTAnUNCYwcP9X0LMyWwubG9IHTSynNH/a2uWDDXnO2v0ubClQeaPClPT3wvcE+ypb5u7nd++U9hfr7WYjYre52jYwoVQ/7XIVbQ1fNYbUNCceJ5kyogWN+0h4s3rzPfJFO/kZyCUonYarb/PDHKxGLRjBj1U6sU+z78UT6ugB2gfjH/y3SJnIzU9cm0kPXM2nJbjw/c12agiCJRckUntee0AdXj+yDGat3pmmpFbGoJTlelChDtKi+rP/+cEWa3RtIThz+c0q694w6MpXnsrcxe6CTk9lvYE/nhTnso4Lj+3ZEtzbNTBu3m/nTfh9kp5TJEcIJ1fynm49T51OaKXWPUNJ0I82hG3fXYG9Nsg01JAT21NRh454a/N/EL3GeT5drlcBo9E6Nz/S6sUnXCx+cZn5u06wM+2rqfQXeHN7NuQH98IQ+lu92wZ6WD9xoWE5atWpXlHhJI+AnuZSuo5SCSwiRNrHz2BVDzc9SYMjLuQWE2TX6TPMgTqjBZF68DHRaqLzfU5ZutUSBSnRt6u53luL2txbjfcXvGUgKaLtniJ0Jn6/B7LW707ZLoZUQQuPmmvwrRKqdeCXTmgyxCJmdZVk0gm5tm+H8QT0tJsLrRx2GV64bYbH1RiNWQV/XkMBf3l2KfQfqzayK9qfvFKy4cJO+E2rTPKU/SuuSXaP3auZTBeR/fnCM5TddO+3UujxtmxN205c8XdTHyFSSKS4AsN5TdTQTTyTw3Ix1povn3pp67FPOZQ+Yy5bACHqn4aQUCPZf1f1bN4thT009dtpyTEj+8d1BadsyydAbz7Quc2sX4Pay3PTSvOR2B3l3ydCD9D+44GcIqetkzMVIYO00Tu2yI0F8AAAgAElEQVTf2bKEmT3zoltqCHWyr95YBMIvbypD0Xkb9jjuJ4TAM1+sxZfr0gWsHN7/94t1ZhRoTV0cL8/ZACGEr4nQqtq4JcjGD17dK73kYfdKLBIxhWdZVNXYU/vceMY3MKBHG0vAUCxqFfRvzd+Ehz5aib+9uwxTjDmLKUu3abMu2nnTwZzw8Mcpjxm3pRb9MHpgd9d9qny4JtoXdJedilv7P3NAV811Mytu5xyZKruq0e+utnYQe2vqURdPmKa4fOWVCoygd3op60yNXn9ceTSCNs3K0tKzSjq0LNeuZuMlq6XEfmm79iwfslMddIJQt2CCnzLa0fWTZnkUrxsgGRiiel7YtS23DmZ3Vapxqhr9vgMNnkZVB+rjuNNYDQxI5fS49Nj0DnH9rhrc+vpCM3pRRfcS3Pn2YvzqpXn4fOVOf4LeJVtgJnt/eSw1GWvHEvWYx8CwWJRQZwiqMiW/jdVskvxcoZpuIhGLkiSb2MY9NXh17kYAyc7pkjytDSw7lfq4sLyjhZlNS45ivOLU+Ti1/1GHd8Hxh3bUei1lShDYqVUFOrVKjTQyrVAnBX8HY3+141q13TklihuBEfTOGr3+YcgG2q5FmWvmR+3xmm3d2jRDNEKadSYzu1cK2187R/ayevf0bNccPzvVvUF6EfSpyGGdaSP1+Y2vUtoXwaq12O2nbtfdVpnS9uriCYtf/RvzNrqW2Un7uVDTIa/PsDKSLn2D9OCprrO6srmleqg80ICKDGlJ7dqfitPycsltGS+bNWXRCI7vm0yBcFyfDuZ2nfBS7eFRSgrfeEJg5/5aU9ufaotId1qMwy/mamYNCZx+XyozqRDZp8/o1yXdi0Zy/iDvSxT+w2F1NyevmyeuOhYTxw73vJToL07rByCZ7VY1qcUiERzUoXlah6JmHpWpHlQFZNS9H2dtyvGyOPgTRLSNiBYq2zoQ0QdE9LXxt73y281EtIKIlhHRWV4LEnd4kZx6XemOdPGQXhmHhbIx2W2COmH22W9PxdLbR6fZs91s9Jm2XzWid5obp9e5Hi+Wmz+/tVhbRhW7NxKRVfOTQswcurpceMf+OtMLpq4hYbHZ7/cwdHaafI1pBO2aDD7yOuErNciYzRbttorP7qo6i+ZrJ9O6CFIwuGn0+RT6zcoiOPkbnbHkz6Mx5JCUoNcFB6l27lgkgnhC4M5JSzDkjsl5iUrOZG+Xoz1d1dX74WeR7Hd+cSKW33G29jc/5s6VGjdIIHNqc8A5FbQd6YUWJbLULxohTL3pFCy9fbRlf3W9345S0NuUogF/eNfTte14KfEEAKNt28YBmCKE6AdgivEdRDQAwKUAjjCOeZCIPM26OGX5c2qIvdo3R++OLfCrM/tbNEw78qzTbx6FGb87zdyuU1pj0YiRK8RmunHT6I3vOkHfV6N9eDXJeGm0kwyzRybtSPdT1CLo9ZNSdtRidzSGlvbJ2Ftt65ZeP/FLHHrzJPP7JQ9Px4l//RAAcMEgqxeB7mX/ap2z/V7XNqRwj0bIck+qXcwm6ipgOuSycjpksbU2+jxK91m3nG6uPSw1cXs+9kwT4y3Ko+Zk7Ctzk/MRmUYqXlHL8L3jDrb8Js2W6ffGqn74WRvBbnrMN26BSl6vLasciZDlfYtGCGWGrFFRJ7E7OAj6bJuTa4mFEJ8AsGfXOR/AU8bnpwBcoGx/XghRK4RYDWAFgGHwwM0OHgZO2npdPIEjerZFJEKeIuTatSi3+BNnkrX20HL7vbW3SSlQdA+hV7vmadu86hx2r4InrzoWxxxsHR3IS2Z6T+zlIpDFdCO9N+QWp1w+6nlk6oYD9QlU18fRU6nnqHs/Mifz3py3yVI2NWfIyMM6Wc6v69j2ZPBmsC/K8e8PV1g0b1Wjr3GwwcvOZldVbcZ7qObjtyPvVyFy3ah0bl1hzks45ZiRo6W3rrf6n79y3QhMvekUxKLJQCvpBpqPeQNVcenU0ur1IvNJ6d6NYuS//+Xp/Vz3sQtgv79LZCcfIbLIEKfD1fiDDi2T6Zh1I9Ete/15bwHZ2+i7CiE2A4DxV6Za7AlAjU7YYGxz5QObu5tE1drUAKT6eMK0qf5+zADH8zq1JT85t9O9bvReOLoXvWf7dEGvM1HosGu4p/TvjFP6W7Na7qqqc/Uw0f2mmo+yGb4PNyallm7eh6raBjOEGwBWba/CR8u2ZTg6STtbwIquCgfq4xYPIRW7sPvbe8tMd766hoRFcI9/Z5kZCavSs31zEAH7DzRkzLmTyTYqBZ2u/Eu3+EuQ5pWv1qePdP45+Ws8YSwqMrBnW8tvQw5pj25tmyX9thOppfaySUR3/2WDLR2J2om2rIjhmWuHmbnyZYyF3fQlRHFWtDpD4zHjF6nRuwUIyvYXjZAlzbFuRP/W9SdYRgrSdLNAE7exzsO60HbyPf7R1Vz7PIloLBHNJqLZ27c7D4tVjf6qJ2aak2r1DcLsWdXFkdMu7iAA/QS/uQVMmc4tmkv1tGn0lwzthYcvH+Lpur8fM8A8vlOrChCRVmg/MHUFNmfo5XXHqBq9zB/jJ6inQ8ty9GrfHC/OXo+pS7elZRJs3cx9gtyeg0RnB6+tT6Bt85jZ8C2/abRaGRk60RZsNHnJVsvi0JL2LcohBPCvqSsyZiRUzRMDe7bBZcNSJgp523T3ebWDHdiJhngCD0z92lUA6+7VtJXuuYZiEcIixf89G0F/7lHdLR2JGmXdpnkZTuzXGScZwWzSlKQzEeWaB8iJP3xrgCVORCWbYCg70kZfoclzrzLEiJ8hsioKujIM7NnWYjpsa8g0nVDfX1uPB6Z+7WstiWxrvZWIugOA8VeqbxsAqD5yvQBonW2FEI8IIYYKIYZWtHLOOaNqWV9v2497P1iO9xdtwZZ9B0y3smxWxPGzHJ69Qc5cbU2GJH+Xf49WJl/tZqU/nTdQm3tDR+fWFfjrxUcly2vagdP3u/eD5ZijCeSRxBPWzpDIaiaxp2D1QovyKMoiEXOVrFYVMfz0lFQODy8CxD7pZbfT1jbEMXPNLsu8ya3nOo/eVKYs3YYrnpjpul8r5fnYPU8sZVFs37edewTuvuhI87t8b3Vya6GyCIhOC7fz+lebcM/7y7URpyo6k5CXIKRohCz3+bFPV7seY0e+b+POPjxtgRzZ3qXQcpp4FyicR9LVI/vgdAfN3et6z5mQmrebsqhOxqqdoVNfM6BHauQq75/OdPPXd5fhnveXY9IC70sNZlvt/wG40vh8JYA3lO2XElEFEfUB0A+A69u2NsNQxG6jf+ijlWbGRS+2sutH6W1yuWj0G/fU4FQjfav6uxfbmV+FQgp4s7hZvB0NiYRFMMjkZWnXcjnPN7qmOqhWFTHLCKBFRcwy9LQL+kRCpJlG7KYpe8i6TBf86dc7zGetC1YBgO4+EsCpqDbqTC+ONEHcf9lgDFPcGYHUUFynofrtROUQ3z66kK56El2H76Vl2DvXXGz0Pzm5L+6/zJpK5IAh0OSEZnUBF5LPBl2u+e5tm5npNJxQE5FJjd4tgjyieLGpqTGcnCyOObg9ju7VFhcN7mnus18j6PcYvvZ+gtC8uFdOBDAdQH8i2kBE1wIYD+AMIvoawBnGdwghFgF4EcBiAO8C+JkQwndLWjN+jPlZl6hf4hbBtmb8GPzopEP1P+Yg6AGrXVv+fPF/rEEmlyvuUpcNSw50vC5ILpEjj0wavRu60Pxs6Neltfm5RXnMlmo4gX01qUa5q6rOItjrEwnLGrdAeoNPmqdS31VfbimgnLwzvCSJ0zHAwf5vR4a46+yyqclY9/O4TUBKDVhN4/uto3vghjOs0dpjjnSPEtXhtuBHrvQzlAHpfrvfYRI86Udf0KJo0Xl2TfvtKDx1TbrPiJrc79dnHW5+VjX1TMj2TUQYYcQ8OJVB8sbPT8B93x1k7qMLCpRmOz+upF68bi4TQnQXQpQJIXoJIR4XQuwUQpwmhOhn/N2l7H+nEKKvEKK/EOIdzyWxIRfIti8WruLHJcuOrzwyLrqSEELbu6rP4Y4LjsS8P5zpeSLWfg4p8LOxa8YT3rw/3ExgA3q0wbDeSW22fcsyi+vgeUf3xM6qlCD/19QVlnxE9XFhydMOJG2VR9tiDJxGafK6LR0WzWhnmKbUCMTUOfX1euW6Eea6qzpG9O2Iubeegb6dW5qdjq588hl58SJxm/iWGrbTKlCSm2xpOlQyJezLIo2LZ+beegYGH5y0S0szkpMJTxj/NTZ24fjXi4/SauZL/jwaM285HUfaJrUBoNywzbtr9PKa1jQoXmSPtOPrBX3Km8crgYmMteOUj1pF9wLLbHdSg3bCT3t3SuokWbql0joENrMAWn1ns9E67Y0pm1cjkRCIC2HeLz/zEyrXndwXD3x/MCb+aDi6t21u0chaVcTQxjYBu3BjatJv4B/eSztfNEqYcNWxlm1O0al3XDgQk/7vBHTRLCKRvH7y2sf27pD2W2+HdM9d2yRd2L64+TTt7xWxCDq0LLdMnukm4CI+NHq3BHBy0k61t+ueViaFwWmJPSB3jX7KTSc7/qYu3C0nr+2mB3WOJY+ep56xC/rWDq7ZzcujaFYWxbM/Og6Tb7Tmus9ko39UmQRWc+fYZYEbmVzGpVnPraNRCayg93IzdIENsgfu1T7z6lF+ekMvKyed98BnaduyyWttJ+XbnvybjUb/xLTVSCSEmd/eaSk5t1sSiRC6tG5mht6ro4SKsgjGnX14ms02E1FK7/zKNMEo//nBMaiIRXFEj+Sznfij4bjy+EMs+0jFIKYJeHMSijJ1RjcH+758oVNDcGBo7/RMpJkCpuzYff/tmIJeeUba4D5N2/LS2tTjdJ5MbugcCT644SS8+tMRlm2yo3r9K6svhhyZ5ZICIRfsbcOts2nTrAyHKSZLIKVg6mTIMEXRUAOmMpVBh07RlR540iTqR7wEVtB7mWjVhSLLntOPIM8Hutwg+SiBrE9qMtbbcWqq5aVbKhEXAmcO6IpfnNYPv/foueKGRdDHImhZEUvzwshENEJpL4FOgB3cwaqRH9+3Iw6yLQPZsiIpWMpsEbHJcuq1aCdtTlJuy0p50xnfsCgXT159LJ686ljzGXkxj2XS6IUQ5iIZbu0/WyVCzRH1wo+Pz+ocdvp1bY1jDrZ2gPaFRSTqgMKrmP/7d4/Ga7aOJFv8zpHpqDA1ek1nq9ZP6E0s6rObYLQhO7rR/3eG9gKQciv2I+MCs/AIAPzunMPNXCleGvIVx/dO2/azUw/Dnuq6tFBsyVvXn4Anp63JevLOD36GVk6YXjcZPDt0jD35UDymLIAgRFJ42Cf1LNfyWTZVoLr5FOvQCXWdgPOycpXX9UNV1OH0w5cPSVtbVr7QMuviwTYT0KlG8JoM9vMSeJZpkZZ9iplDrbGu9hkFVoafpM33F6f1w2EZkoPZ+dGJfdKC9TKhW6cVUILLkD4Z++D3j8G0FTvw7AxrFPKFg3t5vq4baq75Yb074LRveq+TpMz0utGcX3kucdOW7ryP0z3VafStbG3czxxloDT6c4/qgRsNQeTW8w7o3sYMKlDp3KoCD35/iKMgH9izLe695Oi8CGE38jGoSM+k6byvOsJRj5M+9Pke5ahlyZQnxgn50vXv2hq/H/NNAPo8IrpOf/TAbpb6tjLs0gkh0lNYeHgfzlLWIpXIOskXyslDRxav1sMCLLqkYxK12G5Fzrb9yuvbO8afnNxXt7vJLWMGpKWsyEQ0Qvjk16dqt0tURaFnu+Y458juuPPCI9OOyYXjbZPt6jvw7I+Oy2oBbtlGdXNdav3iDhq9l2dnF+pAeiR5fT7dKxsTVcNzuhnDD03awG4xBEOmcxQL2XzzIVilF8n5Rk6WTEJL1SjVjlLmuHa1hvksr+p1kynRk5OnjHxW791wEn54YtINVqe967T8Xu1bYPmdqQyGLYwXI6kpWm+SbhT07WPStcRDOrbAUb1SXhb2OjnNbcjnvGKbe7qDXVV1jitNqdvVMnsNCPz2kGSdvtG1teM+snOWnZh8BpneG7tnlFcO7pg+T6Z6KKmPJR/zWToevsIaha5exmvOGjtSwdCmFlGelbQYps0Zeairbl6pnU15zZRRNa1cnvdsBOw3ZM34MY4pg+1CVIZcN4am7oacELanJ86GXu1bYPGfzzIXKhl8sLdz6jqZfN8b1aVUfXZ2n2TZIfz4pENNzd1+jEQXHu6WNhZITf5pA4ls21bffQ7u+c5Raft9/OtT8cbPRprf7eYop1GLvNW3vrHItZxXPTkLw+6aov3t9Ps+MT+r99brUzvv6B5YM35MWtoNFSmcpLCS65BmalcP+Jhgt2OfB1E7rcaYii2ztadsPc5U5CigriGBNePHmKkOAGtHIu+1vZ1n+x7aLRh+Mo8GStDrXvL+trVd5c2zy7HHrhiKmb/Tu8k1NqMO74LPx43SmgOyoUV5zHxBvE526ta9dDOHZfpVd6i6Tqa6XJ3dl93J3U/3vL0Kf8mhRjRtapWn9MZv30I2dzf7b1eN6G05p8Rp1FKIif/f29I95wtpN5ba7F8uPgozbzkNp32zKz4fN0p7jJeO1olTDne2gasjr0LlvbGPEPPxqKSX1nYjmPPZHx5n/qa2X+lu2t82wvI7IXyIMTKyuy/7yXUTqMlYnXB6+pph2F5Zi06tyrGnpt5MZ2x/ucpjEUcf68amf7fW6JFBq8oVdaV7J3Q2Pj/D40M7tcSqHZkTcrWuiKGytgGv/nSERds4okdbtCiPmlGeV43ojXs/WA4BqzeGrjg6bSfTUPfVn47Aht01ZlmFSA/D8evGJzXizrYOy2nCueAeXnk8vRSoZWYGxgi6tE6+Nz3aNcfkG09Gq4oYht+dGnXk4qlyz3eOQpRSbpbyTEJYR1+F8rRU27w9dXO2dG6VjL+QphTVzq8qEAN7tsULY4fjmEOsHkl+zVQTfzQcO/fXueaGykTANPr0G9C5dQUG9GiDLm2a4RtdW6dsi4UM8cuBa0/o4+rD31i8+8sTcfPZqdBtN4Gk/mxPr6w78n/Xn4DHrhia5loHpOYUAKsmrA7ldVq17h3I9KzbtSjHwJ5tUWYcqBPGfm2xV43sjdsvGIjvHWf11ffi/aPy3i9PwtOa0HrJzv21uOTh6Y42e0k+zA0SadZ16jwP69IqLa4gm6SBkopY1DKytZpuUoKqUD71dsErv+ZixYxECI9eMRSvK2Y+J447tGNa+/Mr6Lu3bYYje7VNe3/9pBcPvKC3c+8lR+O6U/piUK/c7d+FQDfJl2+8vneHd2tjMfX4aWD2TIi6l71Pp5aOWQLHjdZPlru5Qeq0Ry+C+tg+HfDtY3phrCa30U1n9sc1I/u4nkO93uXDD0m7X04CT5cY7OAOLdC/W2tz7sjO+4u24PlZ6zFz9S48MW2N57LlSsLBE8TOjYobbq4Tpa00roI19XHcNSm1QHxjRcmmckflVqczBnQ1AxD94jvflRkbZN2eKFWN3kuD6t62OX47+vCCT7r+7eKjcPXI3maYvFcKucSZ5F8+JsfUsHS3e6ZqjnYPE793u60tLTKQ1NpGHuacWwZICSDVU8eLAtCpVQXuveRo7SIl7VqU4bZv5SdITMf+2vRVsNxGnGOfmaME1GQ+fz4tQwmP1/y/0/qZbSDXIKNmmnQOT05bbYmaLZSNHkiuAfHv7x1j2VYMn40JVx+L0Ud08yy7bjt3gOmEAfhztbYTKBt9rr1sPvmOkYTohVnrXfa04nXh4Fw4tncHnD+oB974Spvq34L6ktlf2CGHtEd5NILpq3amHde83O4tkWVhFYRwT9Yl510fvWIolm+txF3vLM1Zo/SSpz0XdKlkvXROcQf3u0KirqnrRiqEP7drNtOY05Zv3W/5XkiN/q8XH21+jkUIEfK+rkE+OaV/F19BZ9co0e1A+jvop3MMlEYfRJwWTnCiMTR6QP9iqD7gOux90CvXjcA/Lh1kflcbkt33XV2hPlfOOsJ5OTcpgOJC4KqRfbD8jrNzVgDUifHTMniBOGEPurFzlMaM6CV5mJxQ31lVl3E/tfbDNEnb/JAwhbcHQQ/vnUImmpWl7oXTo2ysvDeRCGHV3WO0UfX5ZMxR2aWRzkRoNPogMqJvR3y+Ml3jdaKxBP3gg9rhzXlJjf6EwzrhsxU7cM93jjYX69ChEz5Or/Axh7QHlBQKt+WgAdknEx/6/hBHbcRPJkgn5Kl7tmuOjXtqTHfPr+88OyszxLM/PC6j9qQLKPKi0csFx5+bsQ4/P/Uwx/3UIj8/dnhOZg6n/Cv6fY3r5zgZ7CX6tJCmm8Ym23bmhv2Z+ekcWaN34cHvH5MWTDI6g3+8X8+MbLl6ZG/z86NXDMW0caNcNa9WFZlfOPXoc47sjsk3plLS5sOsZkYMR8gxo6Qp6PMwln/kiiGYNm6UWfayaCSruZ1M5ZXYO3i/XmFbXTxv/JQlEzL7ZBsPacDl0na5mm4qVI3eodMIj5jPvp25kTYZ60PQs0bvQrsW5TiiRxt8uS613ueAHm3w7qIt2v0bS6NXBW/z8ih6ljfHGhe/d5mz3Xoi5/39JL3SMeWmkyGEwEfLnBd/tyM7q3xoeK0qYhmjRPPJ5BtOxkl/+9D87jcVR6Z+LZ/ulXdeeCQuGNwTh3pYt3jCVcOwdMu+rBLWqXjS6IuRnD6PyLZeSOzKlg/vyuBo9Bcd07PYRXDE/qJlchG0h1w3Jm7D8ZYajV6tW75Hm307t0rL5e2GlI9eUv66n6vxJjnteV10oyunjKpA45kumpdHHV0+7bRtUZZxBS6vqJOxzjb61OdMsQdBJZu27pf0dDAlqNGfMzD/kxf5wt44nZJ0AY2ba+fv3z3akh7ArY/xsmpXIfHSLvNhozfPVcS8R7q0Ddee0AfP2VLwSjJ1bAFyRssKL+ZMtfZeO6KmRvpkbAkK+iBjb6bZ5D4vBPY83e42+nTTjdp28mkiyJaUoM+HRp/zKbJG9ywydbSvzd3o+FupC3ov8zthmowtFLl43WRtZyCi/kT0lfJvHxH9koj+SEQble3neDtftiVpXL59TC+tCSQIuJkqdBGKbrf96pG9G9XPO582+kJ4Pnjlco07aiZbt1xZKuw4PRIW9O6QTVo3ikYvhFgGYBAAEFEUwEYArwG4GsDfhRD3+DlfkAW91EhuO3cArjmhD2ZoAoyCgJugb+E2KaY5/A/fOgJ/+NYROZQqRXq6MU0R8mijL1YA3prxY7Tbsw+mC/DLkSdYzruT7l7p49g8leE0ACuFEGvzdL5AIu+r3XQjs+IVOgLTDTfNW2ezDlI0MqAsNZcPG32wqtZorrdBxt7evrz1DAAs6L0QBPfKSwFMVL7/nIiuADAbwE1CiN1uJwiCfdgJNVcLYBX07/ziRHyzext8/OtT0vJFNzbZCDZy+FwszMjYEvO68UK2/u8Bq0bemHLTyaY58dDO1gRhs39/umXxFabIk7FEVA7gPAA3G5seAnA7kgrw7QDuBXCN5rixAMYCQHk356jAIKJ63cgkWtlmsssnQVhdSwf50NJN000+JmMb+X689tMRaKNZq3jKTSejUpMPxyvBfKrZodZFBm89dc0wHNHDmoyuUyt/yQSbAvYO348ffT40+rMBzBVCbAUA+TdZMHoUwFu6g4QQjwB4BAAquvcTQW7N9tFGtmtNFhqnycfnxw63rAalYvG6KZDq6OesUbNTKD2vm8GavPxASqAxek5md0pP5JICIR+C/jIoZhsi6i6E2Gx8vRCApzXRAiznFdNNccvhhmwIdnk9PEPQS2OYzC459iB8uX4Prh/lPnJLmW5yv27QTDfZEpJqAAhXXRqbXEw3OammRNQCwBkAXlU2/5WIFhDRfACnArghl2sEgcuGHYyW5VGcfWQyx01QG2uuQbmFqlarihjuv2wwOnoYjl85ojdalEcxKossk3Ya0y20kAR5/sovYapLY5M+Gev92Jw0eiFENYCOtm2XZ3OuoHl/qBzWpRUW/Xm0+T2ojTUrv/GAVeWb3dtgsXKvcyHATYphfGOXkSWZj76k3smAFlYO7c5Tlg90w2qjz3eJiksQTTdrxo/BmvFjfOWVD2A1siZMdSk2fkzJwYjlLzGC2lgjEcLcW88oek6boBBEQS8Ji1mJKQ5lUSpRjb6E2n2Qi9qhZbkvryCLH32QK+aDXu2TqYmDLEv9CPoAV8M3YapLMSmPRnzFmgRG9Quq3TvsBHluJFte/skIzNuwJ9B185MvKcj1YIpDeSyCt+Zvdt/RIDCCvpQI64sXls62W9tm6NbWeRWwIHDXhUeib+dWePCjlcUuSqPwxFVDk5Gu4WhiRePhy4dg1upd+Hj5duyu1sfG6GDTTRaUUFFdCVNdSomOrSrwm9GH4/Lhh+DRK4aa23VZL8PAqMO7YnSA15woFc46oht+f+4A1PkMNAmMoC8lSqlTciPMXjelwO0XDMQZA7pavkvUdYHDQlhGjcVmd1Wdr/0DI+hL6fFzY2UakzB1wGGqSzGprPWXOykwgr6UZGeYGit3WsFFPht+Rowdv+lYgiPomaIQpk6LCT7c3IpDYLxuWGspPmH1Jio1pt88Crur6vHK3A0AuDNmcic4gr6EGnMplZUpDV65bgRqG+IAgO5tm6N72+Z4dW7ytzA1N1YmikNgBH0pEabRh8XrpnjFaPIMOUSfy55h8kFgbPSlJGRYKWEaA9nOwtTeQlSVkiI4gr6EWnPplNSdMI1OGIbRExhBzxQHDpgKLlL5KSUlyI0QVaWkCIygL6UGEKYXjwk+YWptPIIsDoER9KVEmJqqJU1xqGpW+vDTYPJFYAR9KTXqMCn0PDoJMGT7GwbCVJcSIjiCvoQaQFiFY0irxTChw++7mpMfPRGtAVAJIA6gQQgxlIg6AHgBQG8AawBcIoTYnct1mMLBsj24hDHXDSsT+SFChHgjLyV4qhBikBBCJtUeB2CKEKIfgCnGd+pxjOUAAAmuSURBVA9wCygGHDAVfFg4MnaiPhtFIUw35wN4yvj8FIALvBzEjZlhrITxnQhhlYqC37aRq6AXAN4nojlENNbY1lUIsRkAjL9ddAcS0Vgimk1Es3MsA5MD6nxDGAVLKRPGuVgmP0R8vqy55roZKYTYRERdAHxAREu9HiiEeATAIwBQ0b2f4MbMMHrC1AGH1ZGhsYk0pkYvhNhk/N0G4DUAwwBsJaLuAGD83eblXNwAggA/gyDBrwTjRJ/OLX3tn7WgJ6KWRNRafgZwJoCFAP4H4EpjtysBvJHtNRiGCZnXTbELEBImXD0M//nBEM/752K66QrgNUMTjwF4TgjxLhHNAvAiEV0LYB2A73g5GTeA4sMaZLAIk4CXcBvLD51aVWD0wG6e989a0AshVgE4WrN9J4DTsj0vwzBJwpimmCkOHBnLmPAjCCZhei5hHKWUAsER9NwAGMYCvxFMvgiMoGeKD3s+BZQQPZcQVaWkCIyg5wbAMDb4pWDyRGAEPVN8WKwEE34uTK6woGcYhgk5gRH0PEotPvwMmELDbaw4BEbQMwyjJ0zCkb3rikNgBD03gOITJoHCMEyK4Ah6FjIME3r4PS8OgRH0TPHhUVUw8bFiHMNoCYyg556eYcIPv+bFITiCnptA8eFHEEhYCWJyJTCCnmGY8MNpNopDYAQ9P//iw4+AYcJJcAR9sQvAMEzB4fe8OARG0DPFh4fVTKHhJlYcAiPouQEwDMMUhsAIeqb4cF/LFBoeNRaHAAl6bgAMwzCFIGtBT0QHEdGHRLSEiBYR0S+M7X8koo1E9JXx7xxv58u2JEy+4GcQMDgklskTsRyObQBwkxBiLhG1BjCHiD4wfvu7EOKe3IvHMAzD5ErWgl4IsRnAZuNzJREtAdAz2/OxMll8ODo5YPAQi8kTebHRE1FvAIMBzDA2/ZyI5hPRE0TU3uGYsUQ0m4hmG9/zURSGYRjGRs6CnohaAXgFwC+FEPsAPASgL4BBSGr89+qOE0I8IoQYKoQYmmsZmPzAfS3DhJOcBD0RlSEp5J8VQrwKAEKIrUKIuBAiAeBRAMM8nSuXgjBMGOHJWCZP5OJ1QwAeB7BECHGfsr27stuFABZ6O1+2JWHyBT8ChgknuXjdjARwOYAFRPSVse13AC4jokEABIA1AH6cUwkZhmGYnMjF6+Yz6JXAt7M5H3t8BAB+BAwTSgIUGcswjAW2ZzJ5IjCCntt08eFRVcDgyVgmTwRG0DMMwzCFgQU9Y8KjKoYJJ4ER9CxkGIZhCkOABD1L+mLDTyBg8DvB5InACHqGYWzwZCyTJwIj6Fl3KT48qmKYcBIcQc8yhmEYpiAERtAzxYc722DC8Q1MrgRG0HNjZhg9AmyrZ3IjMIKeKT7c1TJMOAmMoGezAcMwTGEIjqAvdgEY7mwZJqQERtAzDKOH56+YXAmOoOe2HAD4ITBMGAmMoGethWEYpjAERtAzxYdt9MGE3SuZXAmMoGchwzAMUxgKJuiJaDQRLSOiFUQ0znX/QhWE8Qw/g2DCZk0mVwoi6IkoCuDfAM4GMADAZUQ0oBDXYhiGYTJTKI1+GIAVQohVQog6AM8DOD/TAZw5sfjwM2CYcFIoQd8TwHrl+wZjG8MwHolFk69nNDAzaUypEivQeXWqocV1gIjGAhgLAG16HIryWGm15n9/7xi0rIgWuxh54Y4LBmLBhr3o37V1sYvCKFw4uCfW76rG+YPCpSPdccFAHNmzbbGL0aQgUYBVbIjoeAB/FEKcZXy/GQCEEHfr9h86dKiYPXt23svBMAwTZohojhBiqNt+hVKjZwHoR0R9iKgcwKUA/legazEMwzAZKIjpRgjRQEQ/B/AegCiAJ4QQiwpxLYZhGCYzhbLRQwjxNoC3C3V+hmEYxhulNQPKMAzD+IYFPcMwTMhhQc8wDBNyWNAzDMOEHBb0DMMwIacgAVO+C0FUCWBZsctRQDoB2FHsQhQQrl9pw/UrXQ4RQnR226lg7pU+WeYluqtUIaLZXL/ShetX2oS9fl5g0w3DMEzIYUHPMAwTcoIi6B8pdgEKDNevtOH6lTZhr58rgZiMZRiGYQpHUDR6hmEYpkAUXdD7XUQ8aBDRQUT0IREtIaJFRPQLY3sHIvqAiL42/rZXjrnZqO8yIjqreKX3DhFFiehLInrL+B6a+hFROyJ6mYiWGs/x+JDV7wajbS4koolE1KzU60dETxDRNiJaqGzzXSciGkJEC4zf/kVhXU9TCFG0f0imMF4J4FAA5QDmARhQzDJlUYfuAI4xPrcGsBzJBdH/CmCcsX0cgL8YnwcY9awA0Meof7TY9fBQzxsBPAfgLeN7aOoH4CkAPzQ+lwNoF5b6IbmE52oAzY3vLwK4qtTrB+AkAMcAWKhs810nADMBHI/kqnjvADi72HUrxL9ia/S+FxEPGkKIzUKIucbnSgBLkHy5zkdSgMD4e4Hx+XwAzwshaoUQqwGsQPI+BBYi6gVgDIDHlM2hqB8RtUFSaDwOAEKIOiHEHoSkfgYxAM2JKAagBYBNKPH6CSE+AbDLttlXnYioO4A2QojpIin1n1aOCRXFFvShWkSciHoDGAxgBoCuQojNQLIzANDF2K0U6/wPAL8BkFC2haV+hwLYDuBJwzT1GBG1REjqJ4TYCOAeAOsAbAawVwjxPkJSPxt+69TT+GzfHjqKLehdFxEvFYioFYBXAPxSCLEv066abYGtMxGdC2CbEGKO10M02wJbPyS13WMAPCSEGAygCslhvxMlVT/DTn0+kiaLHgBaEtEPMh2i2RbY+nnEqU5hrKuWYgv6DQAOUr73QnJYWVIQURmSQv5ZIcSrxuatxtAQxt9txvZSq/NIAOcR0RokTWujiOi/CE/9NgDYIISYYXx/GUnBH5b6nQ5gtRBiuxCiHsCrAEYgPPVT8VunDcZn+/bQUWxBX/KLiBuz9I8DWCKEuE/56X8ArjQ+XwngDWX7pURUQUR9APRDckIokAghbhZC9BJC9Eby+UwVQvwA4anfFgDriai/sek0AIsRkvohabIZTkQtjLZ6GpLzSGGpn4qvOhnmnUoiGm7cmyuUY8JFsWeDAZyDpKfKSgC3FLs8WZT/BCSHe/MBfGX8OwdARwBTAHxt/O2gHHOLUd9lKKFZfgCnIOV1E5r6ARgEYLbxDF8H0D5k9fsTgKUAFgJ4Bknvk5KuH4CJSM451COpmV+bTZ0ADDXuy0oAD8AIIg3bP46MZRiGCTnFNt0wDMMwBYYFPcMwTMhhQc8wDBNyWNAzDMOEHBb0DMMwIYcFPcMwTMhhQc8wDBNyWNAzDMOEnP8Ha34XjB8J5C8AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "bp = pd.read_table(os.path.join(BPDIR, \"1000.txt\"), \n", " header=None, names=[\"systolic\", \"diastolic\"], \n", @@ -298,9 +1870,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig2, ax2 = plt.subplots(1)\n", "bp[\"systolic\"].plot(color=(1,0,0, 0.25), ax=ax2)\n", @@ -338,10 +1956,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig2, ax2 = plt.subplots(1)\n", + "bp[\"systolic\"].hist(ax=ax2, color=\"#90ee90\", bins=100)\n", + "bp[\"diastolic\"].hist(ax=ax2, bins=100)\n", + "\n", + "ax2.set_xlabel(\"Time Point\")\n", + "ax2.set_ylabel(\"Blood pressure\")\n", + "ax2.legend([\"Sistolic\",\"Disatolic\"])\n", + "#ax1.set_ylim((55, 150))" + ] }, { "cell_type": "markdown", @@ -354,12 +2004,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " systolic diastolic\n", + "0 146 64\n", + "1 134 51\n", + "2 134 54\n", + "3 131 60\n", + "4 133 55" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "all_bp = [pd.read_table(os.path.join(BPDIR, f), \n", " header=None, \n", @@ -384,7 +2134,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -395,9 +2145,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(3876, 3)" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sdata = np.array(summary_data)\n", "sdata.shape" @@ -405,9 +2166,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'. Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig_summary, ax_summary = plt.subplots(1)\n", "fig_summary.set_size_inches(12,12)\n", @@ -450,7 +2231,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -464,7 +2245,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/modules/module3/Football.ipynb b/modules/module3/Football.ipynb index 421e645..82d35c3 100644 --- a/modules/module3/Football.ipynb +++ b/modules/module3/Football.ipynb @@ -11,16 +11,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: matplotlib_venn in /opt/conda/lib/python3.7/site-packages (0.11.5)\n", + "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from matplotlib_venn) (3.0.3)\n", + "Requirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from matplotlib_venn) (1.2.1)\n", + "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from matplotlib_venn) (1.16.4)\n", + "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->matplotlib_venn) (0.10.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->matplotlib_venn) (1.1.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->matplotlib_venn) (2.4.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->matplotlib_venn) (2.8.0)\n", + "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from cycler>=0.10->matplotlib->matplotlib_venn) (1.12.0)\n", + "Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->matplotlib_venn) (41.0.1)\n" + ] + } + ], "source": [ "!pip install matplotlib_venn" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -31,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -51,27 +68,61 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dict" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(teams)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dict_values([['Bobby Abrams', 'David Adams', 'Flozell Adams', 'Keith Adams', 'Vashone Adams', 'Herb Adderley', 'Margene Adkins', 'Tommie Agee', 'Troy Aikman', 'Chris Akins', 'Alex Albright', 'Vince Albritton', 'Ray Alexander', 'Darnell Alford', 'Gary Allen', 'Larry Allen', 'Lance Alworth', 'Antonio Anderson', 'Deon Anderson', 'Richie Anderson', 'George Andrie', 'Scott Ankrom', 'David Arkin', 'Jimmy Armstrong', 'Tyji Armstrong', 'Jim Arneson', 'Bob Asher', 'Dowe Aughtman', 'Miles Austin', 'Rob Awalt', 'Akin Ayodele', 'Remi Ayodele', 'Gene Babb', 'John Babinecz', 'Robert Bailey', 'Dan Bailey', 'Jesse Baker', 'Jon Baker', 'Matt Baker', 'Sam Baker', 'Brian Baldinger', 'Alex Barron', 'Gordon Banks', 'Marion Barber III', 'Rod Barksdale', 'Benny Barnes', 'Darian Barnes', 'Gary Barnes', 'Reggie Barnes', 'Rodrigo Barnes', 'Micheal Barrow', 'Marv Bateman', 'Bill Bates', 'Michael Bates', 'Michael Batiste', 'Craig Baynham', 'Arliss Beach', 'Bob Belden', 'Jason Bell', 'Martellus Bennett', 'Darren Benson', 'Bob Bercich', 'Adam Bergen', 'Joe Berger', 'Justin Beriault', 'Larry Bethea', 'Steve Beuerlein', 'Erik Bickerstaff', 'Dick Bielski', 'Terry Billups', 'Don Bishop', 'Eric Bjornson', 'Alois Blackwell', 'Kelly Blackwell', 'Willie Blade', 'Ricky Blake', 'Drew Bledsoe', 'Alvin Blount', 'Jim Boeke', 'Rocky Boiman', 'Chris Boniol', 'Nate Borden', 'Rich Borresen', 'Anthony Bowden', 'Joe Bowden', 'Tom Braatz', 'Byron Bradfute', 'Kerry Brady', 'Chris Brazzell', 'Bob Breunig', 'Alundis Brice', 'Zach Bridgeman', 'Greg Briggs', 'Lester Brinkley', 'Larry Brinson', 'Clyde Brock', 'Keith Brooking', 'Jamal Brooks', 'Jermaine Brooks', 'Kevin Brooks', 'Michael Brooks', 'Bob Brotzki', 'Willie Broughton', 'Courtney Brown', 'Eric Brown', 'Guy Brown', 'Larry Brown', 'Otto Brown', 'Darrick Brownlow', 'Antonio Bryant', 'Dez Bryant', 'Chris Brymer', 'David Buehler', 'Amos Bullocks', 'Cornell Burbage', 'Jackie Burkett', 'Kevin Burnett', 'Dave Burnette', 'Ron Burton', 'Bill Butler', 'Quincy Butler', 'Lee Roy Caffey', 'Dan Campbell', 'Alan Campos', 'Billy Cannon', 'Jr.', 'Barry Cantrell', 'Chris Canty', 'Warren Capone', 'Glenn Carano', 'Harold Carmichael', 'Bobby Carpenter', 'Duane Carrell', 'Leonardo Carson', 'Jon Carter', 'Quincy Carter', 'Shante Carver', 'Scott Case', 'Tony Casillas', 'Aveion Cason', 'Quinton Caver', 'Marc Cerqua', 'Sal Cesario', 'Robert Chancey', 'Thornton Chandler', 'Seronsa Charles', 'Louis Cheek', 'Randy Chevrier', 'Chris Chewning', 'Darrin Chiaverini', 'Ray Childress', 'Tashard Choice', 'Steve Cisowski', 'Morris Claiborne', 'Darryl Clack', 'Mike Clark', 'Monte Clark', 'Phil Clark', 'Franklin Clarke', 'Antonio Clay', 'Hayward Clay', 'DeVone Claybrooks', 'Dextor Clinkscale', 'Dexter Coakley', 'Garry Cobb', 'Larry Cole', 'Anthony Coleman', 'Kenyon Coleman', 'Lincoln Coleman', 'Marcus Coleman', 'Ralph Coleman', 'Reggie Collier', 'Javiar Collins', 'Jerome Collins', 'Marc Colombo', 'Jim Colvin', 'Bill Conaty', 'Jon Condo', 'Fred Cone', 'Mike Connelly', 'Bobby Joe Conrad', 'Chris Cooper', 'Jim Cooper', 'Reggie Cooper', 'Roger Cooper', 'Terrance Copper', 'Frank Cornish', 'Fred Cornwell', 'José Cortéz', 'Quentin Coryatt', 'Doug Cosbie', 'Vince Courville', 'Patrick Crayton', 'William Creeden', 'Willis Crockett', 'Gene Cronin', 'Tom Crowder', 'Billy Cundiff', 'Randall Cunningham', 'Richie Cunningham', 'Tony Curtis', 'Andy Cvercko', 'Tim Daniel', 'Dick Daniels', 'Woodrow Dantzler', 'Billy Davis', 'Donnie Davis', 'Keith Davis', 'Kyle Davis', 'Leonard Davis', 'Nathan Davis', 'Sonny Davis', 'Wendell Davis', 'Andrew Davison', 'Jack Del Rio', 'Pat Dennis', 'Doug Dennison', 'Steve DeOssie', 'Harold Deters', 'Buddy Dial', 'Jorge Diaz', 'Anthony Dickerson', 'Paul Dickson', 'John Diehl', 'Gennaro DiNapoli', 'Mike Ditka', 'James Dixon', 'Tony Dixon', 'Fred Doelling', 'Ray Donaldson', 'Doug Donley', 'Leon Donohue', 'Pat Donovan', 'Jim Doran', 'Karl Dorrell', 'Tony Dorsett', 'Char-ron Dorsey', 'Merrill Douglas', 'Mike Dowdle', 'Michael Downs', 'Kenny Duckett', 'Fred Dugan', 'Chris Duliban', 'Perry Dunn', 'L. G. Dupree', 'Billy Joe Dupree', 'John Dutton', 'Mike Dwyer', 'Ricky Easmon', 'Ron East', 'Chad Eaton', 'Dave Edwards', 'Dixon Edwards', 'Kelvin Edwards', 'Mario Edwards', 'Jim Eidson', 'Ebenezer Ekuban', 'Abram Elam', 'Onzy Elam', 'Lin Elliot', 'Greg Ellis', 'Demetric Evans', 'Thomas Everett', 'Jason Fabini', 'Mike Falls', 'Anthony Fasano', 'Ron Fellows', 'Jason Ferguson', 'Anthony Fieldings', 'Aaron Fields', 'Filip Filipović', 'Joe Fishback', 'Ray Fisher', 'John Fitzgerald', 'Harry Flaherty', 'John Flannery', 'Cory Fleming', 'Ryan Flinn', 'Richmond Flowers', 'Nick Folk', 'Lee Folkins', 'Steve Folsom', 'Bernard Ford', 'Ryan Fowler', 'Todd Fowler', 'Ron Francis', 'Tom Franckhauser', 'Bill Frank', 'Lance Frazier', 'Paul Frasier Andy Frederick', 'Tyler Fredrickson', 'Doug Free', 'Ben Fricke', 'Byron Frisch', 'Toni Fritsch', 'Ken Frost', 'Bob Fry', 'Jean Fugett', 'Scott Fujita', 'Oronde Gadsden', 'Mike Gaechter', 'Derrick Gainer', 'Scott Galbraith', 'Joey Galloway', 'Kenneth Gant', 'Kelvin Garmon', 'Jason Garrett', 'Judd Garrett', 'Walt Garrison', 'Everett Gay', 'Peter Gent', 'Eddie George', 'John Gesek', 'Sonny Gibbs', 'Aaron Gibson', 'Aaron Glenn', 'Terry Glenn', \"La'Roi Glover\", 'Junior Glymph', 'Randall Godfrey', 'Kevin Gogan', 'John Gonzaga', 'Daniel Gonzalez', 'Leon Gonzalez', 'Dwayne Goodrich', 'Mike Goolsby', 'Cornell Gowdy', 'Toby Gowin', 'Martín Gramática', 'Charlie Granger', 'Norm Granger', 'Orantes Grant', 'Jeff Grau', 'Clinton Greathouse', 'Alex Green', 'Allen Green', 'Cornell Green', 'Skyler Green', 'Forrest Gregg', 'Bill Gregory', 'Glynn Gregory', 'Bob Grottkau', 'Andre Gurode', 'Buzz Guy', 'Halvor Hagen', 'Charles Haley', 'Chris Hall', 'Lemanski Hall', 'Shea Halligan', 'Darren Hambrick', 'Troy Hambrick', 'Dean Hamel', 'Ken Hamlin', 'Ryan Hannam', 'Wayne Hansen', 'Linc Hardin', 'Darryl Hardy', 'Kevin Hardy', 'Alvin Harper', 'Dave Harper', 'Roger Harper', 'Reggie Harrell', 'Cliff Harris', 'Duriel Harris', 'Jackie Harris', 'Jimmy Harris', 'Rod Harris', 'Jason Hatcher', 'Duane Hawthorne', 'Bob Hayes', 'Wendell Hayes', 'Tommy Haynes', 'Harold Hays', 'Ennis Haywood', 'Don Healy', 'George Hegamin', 'Mike Hegman', 'Don Heinrich', 'Dale Hellestrae', 'Nate Hemsley', 'Thomas Henderson', 'Steve Hendrickson', 'Manny Hendrix', 'Tim Hendrix', 'Chad Hennings', 'Anthony Henry', 'Drew Henson', 'Bill Herchman', 'Efren Herrera', 'Edward Hervey', 'Mark Higgs', 'Alonzo Highsmith', 'Jon Hilbert', 'Bill Hill', 'Calvin Hill', 'Rod Hill', 'Tony Hill', 'Tony Hill', 'Damon Hodge', 'Tommy Hodson', 'Gary Hogeboom', 'Jesse Holley', 'Montrae Holland', 'Johnny Holloway', 'Ronnie Holmes', 'Clayton Holmes', 'Issiac Holt', 'Dennis Homan', 'Mitch Hoopes', 'Ray Horton', 'John Houser', 'Bill Houston', 'Carl Howard', 'David Howard', 'Percy Howard', 'Ron Howard', 'Chuck Howley', 'Billy Howton', 'Lynn Hoyem', 'Oliver Hoyte', 'Johnny Huggins', 'Randy Hughes', 'Tyrone Hughes', 'Buddy Humphrey', 'John Hunt', 'Monty Hunter', 'Pete Hunter', 'Jeff Hurd', 'Sam Hurd', 'Eric Hurt', 'Ed Husmann', 'Ken Hutcherson', 'Chad Hutchinson', 'Bruce Huther', 'Tony Hutson', 'Michael Irvin', 'Joe Isbell', 'Raghib Ismail', 'Alcender Jackson', 'Tim Jackson', 'Willie Jackson', 'Bradie James', 'Cedric James', 'Vontrell Jamison', 'Garth Jax', 'Jim Jeffcoat', 'Patrick Jeffers', 'Michael Jefferson', 'Mike Jenkins', 'Keith Jennings', 'Jim Jensen', 'John Jett', 'Al Johnson', 'Brad Johnson', 'Butch Johnson', 'Keyshawn Johnson', 'Mike Johnson', 'Mitch Johnson', 'Thomas Johnson', 'Undra Johnson', 'Walter Johnson', 'Daryl Johnston', 'Adam Jones', 'Dale Jones', 'E. J. Jones', 'Ed Jones', 'Felix Jones', 'James Jones', 'Jermaine Jones', 'Jimmie Jones', 'Julius Jones', 'Nathan Jones', 'Robert Jones', 'Bobby Jonsson', 'Lee Roy Jordan Eric Jackson Douglas Jackson', 'Jason Kaiser', 'Mike Keller', 'Derek Kennard', 'Crawford Ker', 'Gene Killian', 'Keylon Kincade', 'Steve Kiner', 'Angelo King', 'Mike Kiselak', 'Jon Kitna', 'Syd Kitson', 'Dick Klein', 'Frank Klein', 'Ricky Kloppe', 'Micah Knorr', 'Bernie Kosar', 'Kyle Kosier', 'Walt Kowalczyk', 'Craig Kupp', 'Jake Kupp', 'Aaron Kyle', 'Sean Lee', 'L.P. Ladouceur', 'David LaFleur', 'Scott Laidlaw', 'Bill Lang', 'Kareem Larrimore', 'Derrick Lassic', 'Babe Laufenberg', 'Robert Lavette', 'Burton Lawless', 'Ryan Leaf', 'Eddie LeBaron', 'ReShard Lee', 'Sean Lee', 'Matt Lehr', 'Tim Lester', 'Leon Lett', 'D.D. Lewis', 'Woodley Lewis', 'George Lilja', 'Bob Lilly', 'Kevin Lilly', 'Tony Liscio', 'Bruce Livingston', 'Warren Livingston', 'J. W. Lockett', 'Eugene Lockhart', 'Obert Logan', 'Bob Long', 'Dustin Long', 'Clint Longley', 'Billy Lothridge', 'Pete Lougheed', 'Reggie Love', 'Mike Lucky', 'Anthony Lucas', 'Wade Lynch', 'Brandon Mahoney', 'Arthur Mack', 'Louis Mackey', 'Dave Manders', 'Wade Manning', 'Brock Marion', 'Greg Marquess', 'Amos Marsh', 'James Marten', 'Harvey Martin', 'Jamar Martin', 'Kelvin Martin', 'Carlos Martinez', 'Russell Maryland', 'Ray Mathews', 'Kevin Mathis', 'Mat McBriar', 'Hurvin McCormack', 'Bob McCreary', 'David McDaniels', 'Paul McDonald', 'Tommy McDonald', 'Marques McFadden', 'Zachary McEwen', 'Tony McGee', 'Don McIlhenny', 'Toddrick McIntosh', 'Everett McIver', 'Jason McKie', 'Jeremy McKinney', 'Dennis McKinnon', 'James McKnight', 'Scott McLean', 'Ryan McNeil', 'Pat McQuistan', 'Chuck McSwain', 'Dale Memmelaar', 'Don Meredith', 'John Meyers', 'Joey Mickey', 'Hugh Millen', 'Anthony Miller', 'Jim Miller', 'Ernie Mills', 'Dwayne Missouri', 'Aaron Mitchell', 'Johnny Mitchell', 'Singor Mobley', 'Dickey Moegle', 'Jim Molinaro', 'Mike Montgomery', 'Matt Moore', 'Jim Mooty', 'Dennis Morgan', 'Quincy Morgan', 'Craig Morton', 'Lee Murchison', 'DeMarco Murray', 'Eddie Murray', 'Adrian Murrell', 'Greg Myers', 'Michael Myers', 'Godfrey Myles', 'Tom Myslinski Dennis Mitchell', 'Ralph Neely', 'Jim Neidhart', 'Ryan Neufeld', 'Robert Newhouse', 'Terence Newman', 'Timmy Newsome', 'Nate Newton', 'Dat Nguyen', 'John Niland', 'John Nix', 'Brandon Noble', 'Dick Nolan', 'Ben Noll', 'Danny Noonan', 'Pettis Norman', 'Anthony Norris', 'Jerry Norton', 'Ken Norton', 'Jay Novacek', 'Ed Nutting', 'Blaine Nye', 'Eric Ogbogu', 'Jeff Ogden', 'Igor Olshansky', \"Keith O'Neil\", 'Paul Oswald', 'Bob Otto', 'Jerry Overton', 'Billy Owens', 'Brig Owens', 'Terrell Owens', 'Akwasi Owusu-Ansah', 'Paul Olson', 'Craig Page', 'Solomon Page', 'Paul Palmer', 'Billy Parks', 'James Parrish', 'Tony Parrish', 'Jack Patera', 'Elvis Patterson', 'Drew Pearson', 'Preston Pearson', 'Rodney Peete', 'Steve Pelluer', 'Jesse Penn', 'George Peoples', 'Mac Percival', 'Don Perkins', 'Ray Perkins', 'Bob Perryman', 'Stephen Peterman', 'Kurt Petersen', 'Calvin Peterson', 'Rob Petitti', 'Kirk Phillips', 'Carl Pickens', 'Brett Pierce', 'Willie Pile', 'Cyril Pinder', 'Kavika Pittman', 'Kurt Ploeger', 'Lance Poimboeuf', 'Lousaka Polite', 'David Ponder', 'Garry Porterfield', 'Karl Powe', 'Jemeel Powell', 'Russell Powell', 'Phil Pozderac', 'Clayton Prewitt', 'Jim Price', 'Peerless Price', 'Cory Procter', 'Mickey Pruitt', 'Jethro Pugh', 'Duane Putnam', 'Mike Quinn', 'Tom Rafferty', 'Ken-Yon Rambo', 'Tom Randall', 'Sonny Randle', 'Jay Ratliff', 'Jamaica Rector', 'Beasley Reece', 'Guy Reese', 'Izell Reese', 'Danielle Rusler', 'Jacques Reeves', 'Mel Renfro', 'Mike Renfro', 'Lance Rentzel', 'Jerry Rhome', 'Curvin Richards', 'Golden Richards', 'Howard Richards', 'Gloster Richardson', 'Jamel Richardson', 'Colin Ridgway', 'Jim Ridlon', 'Earl Riley', 'Marco Rivera', 'John Roach', 'Alfredo Roberts', 'Jeff Robinson', 'Larry Robinson', 'Cliff Robertson Bill Roe', 'Bill Rogers', 'Jacob Rogers', 'Jeff Rohrer', 'Tony Romo', 'John Roper', 'Derek Ross', 'Dominique Ross', 'Oliver Ross', 'Reggie Rucker', 'Roger Ruzek', 'Sean Ryan', 'Jay Saldi', 'Brian Salonen', 'Jeff Sanchez', 'Bill Sandeman', 'Deion Sanders', 'O. J. Santiago', 'Broderick Sargent', 'Buzz Sawyer', 'Mike Saxon', 'Orlando Scandrick', 'Noel Scarlett', 'Greg Schaum', 'Ray Schoenke', 'Chris Schultz', 'Jim Schwantz', 'Steve Scifres', 'Chuck Scott', 'Darnay Scott', 'Herbert Scott', 'John Scott', 'Kevin Scott', 'Lynn Scott', 'Schad Scott Victor Scott', 'Scott Secules', 'Tim Seder', 'Ron Sellers', 'Gerald Sensabaugh', 'Rafael Septien', 'Scott Shanle', 'Randy Shannon', 'Robert Shaw', 'Joe Shearin', 'Derrick Shepard', 'Dave Sherer', 'Mike Sherrard', 'Joe Shields', 'Clay Shiver', 'Les Shy', 'Cleo Simmons', 'Dave Simmons', 'Victor Simmons', 'Al Singleton', 'Tony Slaton', 'Stan Smagala', 'Don Smerek', 'Artie Smith', 'Darrin Smith', 'Daryle Smith', 'Donald Smith', 'Emmitt Smith', 'J. D. Smith', 'Jackie Smith', 'Jim Ray Smith', 'Jimmy Smith', 'Kevin Smith', 'Myron Smith', 'Shaun Smith', 'Tarik Smith', 'Timmy Smith', 'Tody Smith', 'Tyron Smith', 'Vinson Smith', 'Waddell Smith', 'Zuriel Smith', 'Shannon Snell', 'Loren Snyder', 'Joe Soboleski', 'Jesse Solomon', 'Roland Solomon', 'Mike Solwold', 'Phillippi Sparks', 'Marcus Spears', 'Alonzo Spellman', 'Anthony Spencer', 'Sebron Spivey', 'Danny Spradlin', 'Ron Springs', 'Dave Stalls', 'Isaiah Stanback', 'Montavious Stanley', 'Roger Staubach', 'Markus Steele', 'Robert Steele', 'Larry Stephens', 'Mark Stepnoski', 'Curtis Stewart', 'Daleroy Stewart', 'Junior Siavii', 'Jim Stiger', 'Bryan Still', 'Tom Stincic', 'Clint Stoerner', 'Sim Stokes', 'Ron Stone', 'Cliff Stoudt', 'Omar Stoutmire', 'Otto Stowe', 'Les Strayhorn', 'Fred Strickland', 'Danny Stubbs', 'Darren Studstill', 'Oscar Sturgis', 'Andy Stynchula', 'Nicky Sualua', 'Shaun Suisham', 'Mike Sullivan', 'Russ Swan', 'Kevin Sweeney', 'Reggie Swinton', 'Don Talbert', 'Matt Tarullo', 'Junior Tautalatasi', 'Johnathan Taylor', 'Tony Taylor', 'George Teague', 'Derek Tennell', 'Vinny Testaverde', 'Anthony Thomas', 'Bill Thomas', 'Blair Thomas', 'Broderick Thomas', 'Dave Thomas', 'Duane Thomas', 'Ike Thomas', 'Joey Thomas', 'Robert Thomas', 'Broderick Thompson', 'Tyson Thompson', 'Andy Thorn', 'Bruce Thornton (CB)', 'Bruce Thornton (DL)', 'Kalen Thornton', 'Dennis Thurman', 'Kirk Timmer', 'Ken Tippins', 'Glen Titensor', 'Brandon Tolbert', 'Tony Tolbert', 'J. R. Tolver', 'Pat Toomay', 'Willie Townes', 'Greg Tremble', 'Billy Truax', 'Jerry Tubbs', 'B. J. Tucker', 'Jason Tucker', 'Ross Tucker', 'Torrin Tucker', 'Mark Tuinei', 'Jimmie Turner', 'Stephen Tyler', 'Mike Ulufale', 'Dimitrius Underwood', 'Jerheme Urban', 'Matt Vanderbeek', 'Mike Vanderjagt', 'Dick Van Raaphorst', 'Alan Veingrad', 'Tony Vidal', 'Danny Villanueva', 'Kurt Vollers Dustin Vaughan', 'Mark Walen', 'Gary Walker', 'Louie Walker', 'Malcolm Walker', 'Herschel Walker', 'Rodney Wallace', 'Everson Walls', 'Steve Walsh', 'Mike Walter', 'Tyson Walter', 'Bruce Walton', 'Dedric Ward', 'DeMarcus Ware', 'Derek Ware', 'Chris Warren', 'John Warren', 'James Washington', 'Mark Washington', 'Charlie Waters', 'Kendall Watkins', 'Randy Watts', 'Russell Wayt', 'Colston Weatherington', 'Claxton Welch', 'Norm Wells', 'Gary Westberry', 'Bryant Westbrook', 'James Whalen', 'Kenny Wheaton', 'Bob White', 'Chris White', 'Danny White', 'Gerald White', 'Randy WhiteTerry white RB', 'A.D. Whitfield', 'Fred Whittingham', 'Ron Widby', 'Dave Widell', 'John Wilbur', 'Sam Wilder', 'Marcellus Wiley', 'Michael Wiley', 'Charlie Williams', 'Kevin Williams', 'Erik Williams', 'Joe Williams', 'John Williams', 'Lenny Williams', 'Randal Williams', 'Robert Williams', 'Roy E. Williams', 'Roy L. Williams', 'Sherman Williams', 'Stepfret Williams', 'Tyrone Williams (WR)', 'Tyrone Williams (CB)', 'Ken Willis', 'Mitch Willis', 'Robert Wilson', 'Steve Wilson', 'Wade Wilson', 'Gary Wisener', 'Terry Witherspoon', 'Jason Witten', 'Darren Woodson', 'Rolly Woolsey', 'Barron Wortham', 'Alexander Wright', 'Anthony Wright', 'Brad Wright', 'Charles Wright', 'Rayfield Wright', 'Steve Wright', 'Maury Youmans', 'Cecil Young Charley Young', 'Ryan Young', 'Peppi Zellner', 'Luis Zendejas', 'Mike Zentic', 'Jeff Zimmerman'], ['Anthony Adams', 'Chester Adams', 'Gaines Adams', 'Al Afalava', 'Armando Allen', 'Jermaine Allen', 'Jerry Angelo*', 'Brad Anderson', 'Heartley Anderson*', 'Mark Anderson', 'Neal Anderson', 'Tom Andrews', 'Neill Armstrong*', 'Devin Aromashodu', 'Johan Asiata', 'Zac Atterberry', 'Brendon Ayanbadejo', 'Ervin Baldwin', 'Cody Balogh', 'Kirk Barton', 'Brett Basanez', 'Brian Baschnagel', 'Kurt Becker', 'Josh Beekman', 'Kahlil Bell', 'Earl Bennett', 'Cedric Benson', 'George Blanda', 'Marty Booker', 'Mark Bortz', 'Zackary Bowman', 'Mark Bradley', 'Zeke Bratkowski', 'Lance Briggs', 'Kevin Brock', 'John Broussard', 'Alex Brown', 'Ed Brown', 'Mike Brown', 'Dan Buenning*', 'Maury Buford', 'Rudy Bukich', 'Josh Bullocks', 'Rudy Burgess', 'Dick Butkus', 'Kevin Butler', 'Brian Cabral', 'Desmond Clark', \"'Joe Clermond'\", 'Marc Colombo', 'Curtis Conway', 'Jimmy Conzelman', 'Jim Covert', 'Jay Cutler', 'Jason Davis', 'Kellen Davis', 'Rashied Davis', 'Wendell Davis', 'Dahna Deleston', 'Richard Dentp', 'Mike Ditka', 'Jim Dooley', 'Chuck Dressen', 'Paddy Driscoll', 'Dave Duerson', 'Dusty Dvoracek', 'Candy Delazzer', 'Faust Delazzer', 'Paul \"Tiny\" Engebretsen', 'Curtis Enis', 'Earl Evans', 'Gary Famiglietti', 'Gary Fencik', 'Matt Forté', 'Leslie Frazier', 'Andy Frederick', 'Marcus Freeman', 'Steve Fuller', 'Thomas Gafford*', 'Justin Gage', 'Michael Gaines', 'Wentford Gaines', 'Roberto Garza', 'Willie Gault', 'Shaun Gayle', 'Dennis Gentry', 'Abe Gibron', 'Jarron Gilbert', 'Robbie Gould', 'Corey Graham', 'Ryan Grice-Mullen*', 'Brian Griese', 'Rex Grossman', 'George Halas*', 'Dan Hampton', 'Marcus Hamilton', 'Caleb Hanie', 'Tommie Harris', 'Marcus Harrison', 'Mike Hartenstine', 'Michael Haynes', 'Craig Heyward', 'Devin Hester', 'Jay Hilgenberg', 'Hunter Hillenmeyer', 'Merril Hoge', 'Stefan Humphries', 'Israel Idonije', 'Juaquin Iglesias', 'Dick Jauron', 'Luke Johnsos', 'Kevin Jones', 'Ralph Jones', 'Thomas Jones', 'Ken Kavanaugh', 'Tyrone Keys', 'Derek Kinder', 'Johnny Knox', 'Olin Kreutz', 'Joey LaRocque', 'Chris Leak*', 'Brandon Lloyd', 'Lance Louis', 'Sid Luckman', 'Johnny Lujack', 'Kevin Malast', 'Patrick Mannelly', 'James Manness', 'Danieal Manning', 'Ricky Manning, Jr.', 'Ken Margerum', 'Wilber Marshall', 'James Marten', 'Bernard Masterson', 'Brad Maynard', 'Henry Melton', 'Terrence Metcalf', 'Fontel Mines', 'Keith Molesworth', 'Marcus Monk', 'D.J. Moore', 'Emery Moorehead', 'Bam Morris', 'Johnny Morris', 'Jim Morrissey', 'Brad Muster', 'George McAfee', 'Trumaine McBride', 'Darrell McClover', 'Richmond McGee', 'Brandon McGowan', 'Jason McKie', 'Dennis McKinnon', 'Jim McMahon', 'Steve McMichael', 'Cade McNown', 'Bronko Nagurski', 'Anthony Oakley', 'Adewale Ogunleye', 'Greg Olsen', 'Frank Omiyale', 'Keith Ortego', 'Kyle Orton', 'Orlando Pace', 'Glenn Pakulak*', 'Jack Pardee', 'Frank Pauly', 'Kevin Payne', 'Walter \"Sweetness\" Payton', 'Eric Peterman', 'William Perry', 'Adrian N. Peterson', 'Reggie Phillips', 'Cyril J. Pontillo', 'Terrance K Porter', 'Jonathan Quinn', 'Dan Rains', 'Donovan Raiola', 'Tyler Reed', 'Mike Richardson', 'Brandon Rideau', 'Ron Rivera*', 'Nick Roach', 'Buddy Ryan', 'John St. Clair', 'Thomas Sanders', 'Gale Sayers', 'Kevin Shaffer', 'Tim Shaw', 'Mike \"Samurai\" Singeltary', 'Cole Slade', 'Samuel Slade', 'Lovie Smith*', 'Craig Steltz', 'Matt Suhey', 'John Tait', \"Will Ta'ufo'ou\", 'Ken Taylor', 'David Terrell', 'Tom Thayer', 'Cliff Thrift', 'Calvin Thomas', 'Charles Tillman', 'Pisa Tinoisamoa', 'Matt Toeaina', 'Mike Tomczak', 'Woodny Turenne', 'Bulldog Turner', 'Chester Taylor', 'Keith Traylor', 'Brian Urlacher', 'Keith Van Horne', 'Nathan Vasher', 'Bill Wade', 'Bobby Wade', 'Henry Waechter', 'Dave Wannstedt', 'Chris Williams', 'Jamar Williams', 'Otis Wilson', 'Rod Wilson', 'Garrett Wolfe', 'Donnell Woolford', 'Tim Wrightman', 'Chris Zorich'], ['Walter Abercrombie', 'Ed Adamchik', 'Bob Adams', 'Flozell Adams', 'Mike Adams', 'Mike Adams', 'Ben Agajanian', 'Dick Alban', 'Tom Alberghini', 'Art Albrecht', 'John Alderton', 'Brent Alexander', 'Chuck Allen', 'Cortez Allen', 'Duane Allen', 'Jimmy Allen', 'Lou Allen', 'Will Allen', 'Don Alley', 'John Allred', 'Lyneal Alston', 'Rudy Andabaker', 'Anthony Anderson', 'Art Anderson', 'Chet Anderson', 'Fred Anderson', 'Gary Anderson', 'Jesse Anderson', 'Larry Anderson', 'Mel Anderson', 'Ralph Anderson', 'Steve Apke', 'Dri Archer', 'Al Arndt', 'Dick Arndt', 'Brian Arnfelt', 'David Arnold', 'Jahine Arnold', 'Jay Arnold', 'Corwin \"Corrie\" Artman', 'Willie Asbury', 'Bert Askson', 'Dale Atkeson', 'Frank Atkinson', 'Steve August', 'Gene Augusterfer', 'Ocie Austin', 'Steve Avery', 'Buddy Aydelette', 'Rich Badar', 'Matt Bahr', 'Henry Bailey', 'Patrick Bailey', 'Rodney Bailey', 'Conway Baker', 'Dallas Baker', 'John Baker', 'Tim Baker', 'Lou Baldacci', 'Gary Ballman', 'Bob Balog', 'John Banaszak', 'Warren Bankston', 'Vince Banonis', 'Pete Barbolak', 'Ed Barker', 'Johnnie Barnes', 'Reggie Barnes', 'Walt Barnes', 'Tom Barnett', 'Fred Barry', 'Earl Bartlett', 'Mike Basrak', 'Dick Bassi', 'Baron Batch', 'Charlie Batch', 'Marco Battaglia', 'Arnaz Battle', 'Ainsley Battles', 'Kelvin Beachum', 'Byron Beams', 'Tom Beasley', 'Chuck Beatty', 'Ed Beatty', 'Wayland Becker', 'Mark Behning', 'Bert Bell', 'Kendrell Bell', \"Le'Veon Bell\", 'Myron Bell', 'Richard Bell', 'Theo Bell', 'George Belotti', 'Albert Bentley', 'Mitch Berger', 'Dave Bernard', 'Ed Bernet', 'Greg Best', 'Jerome Bettis', 'Tom Bettis', 'Frank Billock', 'Craig Bingham', 'John Binotto', 'Don Bishop', 'Harold Bishop', 'Charlie Bivins', 'Todd Blackledge', 'Will Blackwell', 'Antwon Blake', 'Brian Blankenship', 'Greg Blankenship', 'Rocky Bleier', 'Jeremy Bloom', 'Mel Blount', 'Fred Bohannon', 'Rocky Boiman', 'Nick Bolkovac', 'Randal Bond', 'Ernie Bonelli', 'Steve Bono', 'Ulish Booker', 'Clarence Booth', 'Kirk Botkin', 'Emil Boures', 'Tony Bova', 'R.J. Bowers', 'Bill Bowman', 'Sam Boyd', 'Jim Boyle', 'Ed Bradley', 'Charlie Bradshaw', 'Jim Bradshaw', 'Terry Bradshaw', 'Jeff Brady', 'Pat Brady', 'Jamaal Branch', 'Art Brandau', 'Jim Brandt', 'Maury Bray', 'Dave Brazil', 'Bill Breeden', 'Rod Breedlove', 'Gene Breen', 'Ed Brett', 'Pete Brewster', 'Eugene Bright', 'Bubby Brister', 'Jessie Britt', 'Ralph Britt', 'Barrett Brooks', 'Dorian Brooks', 'Al Brosky', 'Fred Broussard', 'Angelo Brovelli', 'Anthony Brown', 'Antonio Brown', 'Chad Brown', 'Chris Brown', 'Curtis Brown', 'Dante Brown', 'Dave Brown', 'Deauntae Brown', 'Dee Brown', 'Demetrius Brown', 'Ed Brown', 'Ernie Brown', 'J. B. Brown', 'Justin Brown', 'John Brown', 'Kris Brown', 'Lance Brown', 'Larry Brown', 'Levi Brown', 'Tom Brown', 'Bryant Browning', 'Henry \"Hank\" Bruder', 'Mark Bruener', 'Boyd Brumbaugh', 'Jim Brumfield', 'Dewey Brundage', 'Fred Bruney', 'John Bruno', 'Corbin Bryant', 'Fernando Bryant', 'Hubie Bryant', 'Martavis Bryant', 'Felix Bucek', 'Brentson Buckner', 'Carl Buda', 'Rudy Bukich', 'Chester \"Chet\" Bulger', 'Amos Bullocks', 'John Burleson', 'Joe Burnett', 'Len Burnett', 'Tom Burnette', 'Josh Burr', 'John Burrell', 'Plaxico Burress', 'Bill Butler', 'Crezdon Butler', 'Drew Butler', 'Jack Butler', 'Jim Butler', 'John Butler', 'Frank Bykowski', 'Larry Cabrelli', 'Bill Cahill', 'Ralph Calcagni', 'Dean Caliguire', 'Jack Call', 'Lee Calland', 'Chris Calloway', 'Tom Calvin', 'Paul Cameron', 'Bob Campbell', 'Dick Campbell', 'Don Campbell', 'Glenn Campbell', 'John Campbell', 'Leon Campbell', 'Russ Campbell', 'Scott Campbell', 'Rocco Canale', 'Wayne Capers', 'Jason Capizzi', 'Dick Capp', 'Dom Cara', 'Joe Cardwell', 'Preston Carpenter', 'Gregg Carr', 'Rodney Carter', 'Tyrone Carter', 'Chris Carter', 'Keith Cash', 'Cy Casper', 'Mark Catano', 'Drew Caylor', 'John Cenci', 'Garth Chamberlain', 'Lynn Chandnois', 'Justin Cheadle', 'Ernie Cheatham', 'Edgar Cherry', 'Chuck Cherundolo', 'Dick Christy', 'Joe Cibulas', 'Ben Ciccone', 'Gene Cichowski', 'Gus Cifelli', 'Bob Cifers', 'Jim Clack', 'Kendrick Clancy', 'Gail Clark', 'James Clark', 'Mike Clark', 'Reggie Clark', 'Ryan Clark', 'Spark Clark', 'John Clay', 'Harvey Clayton', 'Henry Clement', 'Johnny Clement', 'Kyle Clement', 'Jackie Cline', 'Tony Cline', 'Joey Clinkscales', 'Marvin Cobb', 'Zamir Cobb', 'Patrick Cobbs', 'Nakia Codie', 'Ricardo Colclough', 'Reggie Coldagelli', 'Robin Cole', 'Terry Cole', 'Andre Coleman', 'LaMonte Coleman', 'Max Coley', 'Mike Collier', 'Reggie Collier', 'Jack Collins', 'Willie Colon', 'Craig Colquitt', 'Chris Combs', 'Tony Compagno', 'Dick Compton', 'Merlyn Condit', 'Steve Conley', 'Dick Conn', 'Mike Connelly', 'Rameel Connor', 'Chris Conrad', 'Enio Conti', 'Joe Coomer', 'Adrian Cooper', 'Marquis Cooper', 'Sam Cooper', 'Lou Cordileone', 'Anthony Corley', 'Bob Coronado', 'Thomas Cosgrove', 'Jerricho Cotchery', 'Russell Cotton', 'Steve Courson', 'Brad Cousino', 'Russ Craft', 'Bill Cregar', 'Carl Crennel', 'Larry Critchfield', 'Winfield Croft', \"Da'Mon Cromartie-Smith\", 'Marshall Cropper', 'Joe Cugliari', 'Bennie Cunningham', 'Ron Curl', 'Don Currivan', 'Roy Curry', 'Matt Cushing', 'Randy Cuthbert', 'Bernard Dafney', 'Anthony Daigle', 'Ted Dailey', 'Ken Dallafior', 'Willie Daniel', 'Charles Davenport', 'Najeh Davenport', 'Bill Davidson', 'Kenny Davidson', 'Art Davis', 'Bruce Davis', 'Carey Davis', 'Charlie Davis', 'Dave Davis', 'Hall Davis', 'Henry Davis', 'Lorenzo Davis', 'Paul Davis', 'Robert Davis', 'Russell Davis', 'Sam Davis', 'Steve Davis', 'Travis Davis', 'Tommy Dawkins', 'Dermontti Dawson', 'Len Dawson', 'Nick DeCarbo', 'Art DeCarlo', 'David DeCastro', 'Jonathan Dekker', 'Harry Deligianis', 'Jack Deloplaine', 'DeLuca', 'George Demko', 'John Dempsey', 'Carmine DePascal', 'Henry DePaul', 'Dick Deranek', 'Dean Derby', 'Darrell Dess', 'Buddy Dial', 'Charlie Dickey', 'Richard Dickinson', 'Chuck Dicus', 'Mark Didio', 'Luby DiMeolo', 'Dean Dingman', 'Johnnie Dirden', 'Dennis Dixon', 'George Dobash', 'John Dockery', 'Dale Dodrill', 'Les Dodson', 'John Doehring', 'Chris Doering', 'Cliff Dolaway', 'Richard \"Dick\" Dolly', 'Allen \"Al\" Donelli', 'Rick Donnalley', 'Thom Dornbrook', 'Forrest Douds', 'Bob Dougherty', 'Bob Douglas', 'Larry Douglas', 'Dick Doyle', 'Theo Doyle', 'Al Drulis', 'Rick Druschel', 'Bill Dudley', 'Vontez Duff', 'Roger Duffy', 'Len Dugan', 'Gilford \"Cliff\" Duggan', 'Paul Duhart', 'Chuckie Dukes', 'Craig Dunaway', 'Karl Dunbar', 'Maurice Duncan', 'Tony Dungy', 'David Dunn', 'Gary Dunn', 'Bill Dutton', 'Jonathan Dwyer', 'Nick Eason', 'Vic Eaton', 'Terry Echols', 'Shayne Edge', 'Dave Edwards', 'Glen Edwards', 'Troy Edwards', 'Donnie Elder', 'Larry Elkins', 'Jim Elliott', 'Marv Ellstrom', 'Leo Elter', 'Nik Embernate', 'Carlos Emmons', 'Paul Engebretsen', 'Rick Engles', 'Rich Erenberg', 'Paul Ernster', 'Trai Essex', 'Tim Euhus', 'Donald Evans', 'Jon Evans', 'Ray Evans', 'Walt Evans', 'Thomas Everett', 'Ron Fair', 'Alan Faneca', 'Hebron Fangupo', 'John Farquhar', 'Venice Farrar', 'Ed Farrell', 'James Farrior', 'Kris Farris', \"Ta'ase Faumui\", 'Steve Fedell', 'Nick Feher', 'Bob Ferguson', 'Jim Ferranti', 'Lou Ferry', 'John Fiala', 'Ralph Fife', 'Deon Figures', 'Dan Fike', 'Francis Filchock', 'Jim Files', 'Jim Finks', 'Mike Finn', 'Doug Fisher', 'Everett Fisher', 'Ray Fisher', 'Max Fiske', 'Dick Flanagan', 'Tom Fletcher', 'Lethon Flowers', 'Fred Foggie', 'Lee Folkins', 'Vernon Foltz', 'Larry Foote', 'Darryl Ford', 'Henry Ford', 'Moses Ford', 'Todd Fordham', 'John Foruria', 'Barry Foster', 'Jayson Foster', 'Ramon Foster', 'Sid Fournet', 'Keyaron Fox', 'Sam Francis', 'Joe Frank', 'Andre Frazier', 'Lorenzo Freeman', 'Ernest French', 'Len Frketich', 'Chris Fuamatu-Maafala', 'Dick Fugler', 'Randy Fuller', 'Ed Fullerton', 'John \"Frenchy\" Fuqua', 'Steve Furness', 'Bob Gaddis', 'Bobby Gage', 'Larry Gagner', 'Wentford Gaines', 'Joey Galloway', 'Kendall Gammon', 'Wayne Gandy', 'Bob Gaona', 'Chris Gardocki', 'Bill Garnaas', 'Reggie Garrett', 'Gregg Garrity', 'Terence Garvin', 'Keith Gary', 'Joe Gasparella', 'Charlie Gauer', 'Jason Gavadza', 'William Gay', 'Cory Geason', 'Byron Gentry', 'Chris George', 'Matt George', 'Roy Gerela', 'Joe Geri', 'Oliver Gibson', 'Thaddeus Gibson', 'Marcus Gilbert', 'John Gildea', 'Jason Gildon', 'Scoop Gillespie', 'Joe Gilliam', 'Harry Gilmer', 'David Gilreath', 'Earl Girard', 'Joe Glamp', 'Glenn Glass', 'Fred Glatz', 'Gary Glick', 'Junior Glymph', 'Clark Goff', 'Robert Golden', 'George Gonda', 'Pete Gonzalez', 'John Goodman', 'John Goodson', 'Joey Goodspeed', 'Walt Gorinski', 'Preston Gothard', 'Mace Gouldsby', 'Cornell Gowdy', 'Theo Grabinski', 'Bruce Gradkowski', 'Neil Graff', 'Jeff Graham', 'Kenny Graham', 'Kent Graham', 'Russell Graham', 'Gordon Gravelle', 'Ray Graves', 'Tom Graves', 'Sam Gray', 'Bobby Joe Green', 'Eric Green', 'Johnny Green', 'Isaiah Green', 'Joe Greene', 'Kevin Greene', 'Tracey Greene', 'Norm Greeney', 'L. C. Greenwood', 'Larry Griffin', 'John Grigas', 'Tyler Grisham', 'Earl Gros', 'Randy Grossman', 'Bob Gunderman', 'Riley Gunnels', 'John Guy', 'Walt Hackett', 'Elmer Hackney', 'Clark Haggans', 'Mike Haggerty', 'Byron Haines', 'Russell Hairston', 'Dick Haley', 'Delton Hall', 'Ronnie Hall', 'Alan Haller', 'Jack Ham', 'Casey Hampton', 'Phil Handler', 'Bob Hanlon', 'Craig Hanneman', 'Terry Hanratty', 'Tom Hanson', 'Harbes', 'Lem Harkey', 'Matt Harper', 'Maurice Harper', 'Billy Harris', 'Franco Harris', 'Lou Harris', 'Orien Harris', 'Ra\\'Shon \"Sonny\" Harris', 'Tim Harris', 'Chester David \"Tuff\" Harris', 'Arnold Harrison', 'Bob Harrison', 'James Harrison', 'Nolan Harrison', 'Reggie Harrison', 'Jeff Hartings', 'Howard Hartley', 'Tom Hartnett', 'Justin Hartwig', 'Carlton Haselrig', 'Jim Haslett', 'Andre Hastings', 'Courtney Hawkins', 'Greg Hawthorne', 'Henry Hayduk', 'Jonathan Hayes', 'Rudy Hayes', 'Verron Haynes', 'George Hays', 'Ken Hebert', 'Ernie Hefferle', 'Bill Hegarty', 'Don Heinrich', 'Paul Held', 'Warren Heller', 'Jon Henderson', 'Dick Hendley', 'Kevin Henry', 'Mike Henry', 'Urban Henry', 'Dick Hensley', 'Ken Henson', 'Anthony Henton', 'Donald Herron', 'Noah Herron', 'Bill Hewitt', 'Cameron Heyward', 'Howard Hickey', 'Derek Hill', 'Harlon Hill', 'Jimmy Hill', 'Jerry Hillebrand', 'Tony Hills', 'John Hilton', 'Glen Ray Hines', 'Bryan Hinkle', 'Jack Hinkle', 'Mike Hinnant', 'Hal Hinte', 'Chuck Hinton', 'Claude Hipps', 'Joe Hoague', 'Dick Hoak', 'Pat Hodgson', 'Bob Hoel', 'Dave Hoffmann', 'Darrell Hogan', 'Merril Hoge', 'Bob Hohn', 'Chris Hoke', 'Bill Holcomb', 'Ed Holler', 'Corey Holliday', 'Joe Hollingsworth', 'Bernard \"Tony\" Holm', 'Walt Holmer', 'Earl Holmes', 'Ernie Holmes', 'Melvin Holmes', 'Santonio Holmes', 'Evander \"Ziggy\" Hood', 'Frank Hood', 'Allen Hooker', 'Dwayne Hooper', 'Chris Hope', 'Bill Hornick', 'Garry Howe', 'Glen Howe', 'Cal Hubbard', 'Chris Hubbard', 'Gene Hubka', 'Alan Huff', 'Chris Hughes', 'Connor Hughes', 'David Hughes', 'Dennis Hughes', 'Dick Hughes', 'George Hughes', 'Jed Hughes', 'Jay Hull', 'Cedric Humes', 'Mike Humpal', 'Al Humphrey', 'Art Hunter', 'Hal Hunter', 'Richard Huntley', 'Bill Hurley', 'Tunch Ilkin', 'John Itzel', 'Corey Ivy', 'Frank Ivy', 'Khori Ivy', 'Mortty Ivy', 'Chidi Iwuoma', 'George Izo', 'Alonzo Jackson', 'Earnest Jackson', 'John Jackson', 'Kenny Jackson', 'Lenzie Jackson', 'Cam Jacobs', 'Omar Jacobs', 'Dan James', 'Clarence Janecek', 'Val Jansante', 'Toimi Jarvi', 'Ralph Jecha', 'Roy Jefferson', 'Tom Jelley', 'A. J. Jenkins', 'John Jenkins', 'Erik Jensen', 'Tony Jeter', 'Johnson', 'Bill Johnson', 'Brandon Johnson', 'Charles Johnson', 'D. J. Johnson', 'David Johnson', 'Jason Johnson', 'John Henry Johnson', 'Jovon Johnson', 'Malcolm Johnson', 'Norm Johnson', 'Ron Johnson', 'Tim Johnson', 'Troy Johnson', 'Will Johnson', 'Chester \"Swede\" Johnston', 'Rex Johnston', 'Aaron Jones', 'Art Jones', 'Bruce Jones', 'Donta Jones', 'Felix Jones', 'Gary Jones', 'George Jones', 'Jarvis Jones', 'Mike Jones', 'Victor Jones', 'Darin Jordan', 'Tim Jorden', 'Terrance Joseph', 'Royal Kahler', 'George Kakasic', 'Dave Kalina', 'Todd Kalis', 'John Kapele', 'Jeremy Kapinos', 'John Karcis', 'Ed Karpowich', 'Ted Karras', 'George Kavel', 'Tom Keane', 'Tom Keating', 'Brett Keisel', 'Craig Keith', 'Marv Kellum', 'Jim Kelly', 'Chad Kelsay', 'Mose Kelsch', 'Chris Kemoeatu', 'Jack Kemp', 'Ray Kemp', 'George Kennard', 'John Kennerson', 'Gary Kerkorian', 'Brady Keys', 'Walter Kichefski', 'Max Kielbasa', 'Walt Kiesling', 'George Kiick', 'Pat Killorin', 'Frank Kilroy', 'Frank Kimble', 'Carlos King', 'Justin King', 'Phil King', 'Mark Kirchner', 'Ken Kirk', 'Levon Kirkland', 'Travis Kirschke', 'Ben Kish', 'Ed Kissell', 'Earl Klapstein', 'Dick Klein', 'Jack Klotz', 'John Klumb', 'Daryl Knox', 'Bob Kohrs', 'Jon Kolb', 'Elmer Kolberg', 'Chris Kolodziejski', 'John Kondrla', 'Ken Kortas', 'Jules Koshlap', 'Rich Kotite', 'Martin Kottler', 'Matt Kranchick', 'Dan Kreider', 'Joe Kresky', 'Clint Kriewaldt', 'Bill Krisher', 'Mike Kruczek', 'Joe Krupa', 'Larry Krutko', 'John Kuhn', 'Justin Kurpeikis', 'Roy Kurrasch', 'Zvonimir Kvaternik', 'Steve Lach', 'Dave LaCrosse', 'Bo Lacy', 'Pete Ladygo', 'Bill Lajousky', 'Carnell Lake', 'Joe Lamas', 'Frank Lambert', 'Jack Lambert', 'Lowell Lander', 'Mose Lantz', 'Chuck Lanza', 'Dan LaRose', 'Lou Lassahn', 'Dick Lasse', 'Johnny Lattner', 'Ted Laux', 'Hubbard Law', 'Ben Lawrence', 'Bobby Layne', 'Paul Lea', 'Bob Leahy', 'Gerald Leahy', 'Bernard Lee', 'Danzell Lee', 'Greg Lee', 'Herman Lee', 'John Lee', 'Dick Leftridge', 'Byron Leftwich', 'Doug Legursky', 'Ray Lemek', 'Matt Lentz', 'Jim Leonard', 'Tim Lester', 'John Letsinger', 'Lou Levanti', 'Jim Levey', 'Darcy Levy', 'Art Lewis', 'Frank Lewis', 'Joe Lewis', 'Keenan Lewis', 'Roy Lewis', 'Tim Lewis', 'Dave Liddick', 'Mike Lind', 'Louis Lipps', 'Gene Lipscomb', 'David Little', 'Carl Littlefield', 'Greg Lloyd', 'Steve Loch', 'Charles Lockett', 'Chuck Logan', 'Mike Logan', 'Stefan Logan', 'Bill Long', 'Jim Long', 'Joe Long', 'Terry Long', 'Ken Longenecker', 'Don Looney', 'Andre Lott', 'John Lott', 'Kamil Loud', 'Love', 'Duval Love', 'Reggie Lowe', 'Jack Lowther', 'Dick Lucas', 'Jeff Lucas', 'John Lucente', 'Alex Lukachick', 'Bob Luna', 'Booth Lusteg', 'Mitch Lyons', 'Bill Mack', 'Rico Mack', 'Bill Mackrides', 'Tommy Maddox', 'Anthony Madison', 'L.E. Madison', 'Mike Magac', 'George Magulick', 'Sean Mahan', 'Frank Maher', 'John Malecki', 'Joe Malkovich', 'Fran Mallick', 'Mark Malone', 'Jim Mandich', 'Ray Mansfield', 'Edgar Manske', 'Rod Manuel', 'Bobby Maples', 'Joseph Maras', 'Basilio Marchi', 'Ted Marchibroda', 'Jerry Marion', 'Henry Marker', 'Jeff Markland', 'Lou Marotti', 'Curtis Marsh', 'Marshie', 'Paul Martha', 'Emerson Martin', 'Johnny Martin', 'Ricky Martin', 'Tee Martin', 'Vernon Martin', 'Grant Mason', 'Bob Masters', 'Walt Masters', 'John Mastrangelo', 'Ed Matesic', 'Joe Matesic', 'Ray Mathews', 'Terance Mathis', 'Frank Mattioli', 'Marv Matuszak', 'Alvin Maxson', 'Ray May', 'Hayden Mayhew', 'Alvoid Mays', 'Damon Mays', 'Lee Mays', 'Jerry Mazzanti', 'Fred McAfee', 'Ryan McBean', 'Richie McCabe', 'Art McCaffray', 'Brice McCain', 'Don McCall', 'David McCann', 'John McCarthy', 'Jack McClairen', 'Willie McClung', 'Glenn McCombs', 'Dewey McConnell', 'Jamie McCoy', 'Antwon McCray', 'Daniel McCullers', 'Hugh McCullough', 'Karl McDade', 'Edward McDonald', 'Shaun McDonald', 'Coley McDonough', 'Paul McDonough', 'Kenny McEntyre', 'Bryant McFadden', 'Marv McFadden', 'Ben McGee', 'Rob McGovern', 'Thurman McGraw', 'Tyrone McGriff', 'Sean McHugh', 'Tim McKyer', 'Leon McLaughlin', 'Steve McLendon', 'John McMakin', 'Johnny \"Blood\" McNally', 'Ed McNamara', 'Bill McPeak', 'Tom McWilliams', 'Ed Meadows', 'Bryant Meeks', 'Charles Mehelich', 'Steve Meilinger', 'Rashard Mendenhall', 'Jamon Meredith', 'Albert \"Elmer\" Merkovsky', 'Mike Merriweather', 'Max Messner', 'Dennis Meyer', 'Fred Meyer', 'John Meyer', 'Ron Meyer', 'Bill Meyers', 'Bill Michael', 'Ed Michaels', 'Lou Michaels', 'Art Michalik', 'John Michelosen', 'Kelvin Middleton', 'Lou Midler', 'Barron Miles', 'Eddie Miles', 'Heath Miller', 'Jim Miller', 'Josh Miller', 'Mike Miller', 'Tom Miller', 'Ernie Mills', 'Freddie Milons', 'Henry Minarik', 'Tom Miner', 'Gene Mingo', 'Frank Minini', 'Michael Minter', 'Arthur Moats', 'Dick Modzelewski', 'Ed Modzelewski', 'Dicky Moegle', 'Tony Momsen', 'Bucky Moore', 'Lance Moore', 'Mewelde Moore', 'Red Moore', 'Gonzalo Morales', 'Sean Morey', 'Bob Morgan', 'Quincy Morgan', 'Tom Moriarty', 'Earl Morrall', 'Byron Morris', 'Jack Morris', 'Steve Morse', 'Rick Moser', 'Clure Mosher', 'Norm Mosley', 'Paul Moss', 'Marion Motley', 'Norm Mott', 'Derek Moye', 'Sam Mudie', 'Mike Mularkey', 'Lee Mulleneaux', 'Gerry Mullins', 'Ryan Mundy', 'Dick Murley', 'George Murphy', 'Earl Murray', 'Tom Myslinski', 'Gern Nagler', 'John Naioti', 'Martin Nance', 'Dick Nardi', 'Greasy Neale', 'Bill Nelsen', 'Darrell Nelson', 'Edmund Nelson', 'Carl Nery', 'Tom Newberry', 'Harry Newsome', 'Armand Niccolai', 'Allen Nichols', 'Bob Nichols', 'Elbie Nickel', 'Hardy Nickerson', 'George Nicksich Jr', 'George Nicksich Sr', 'John Nisby', 'Kent Nix', 'Mike Nixon', 'Mathias Nkwenti', 'Jeffrey Noble', 'Leo Nobile', 'Terry Nofsinger', 'Dan Nolan', 'John Noppenberg', 'Mark Nori', 'John Nosich', 'Shaun Nua', 'Buzz Nutter', 'Jerry Nuzum', \"Fran O'Brien\", \"Jack O'Brien\", 'Mel Odelli', 'Henry Odom', \"Pat O'Donahue\", \"Neil O'Donnell\", 'John Oehler', 'John Oelerich', 'A.J. Ofodile', 'Chukky Okobi', 'Chris Oldham', 'Ray Oldham', \"Dan O'Leary\", 'Stan Olejniczak', 'Oliver', 'Clarence Oliver', 'Jerry Olsavsky', 'Al Olszewski', \"Joe O'Malley\", \"Bob O'Neil\", 'Dennis Onkotz', 'David Opfar', 'Tony Orlandini', 'Bo Orlando', 'Ken Ormiston', 'Jimmy Orr', 'Chuck Ortmann', \"Terry O'Shea\", 'Paul Oswald', 'Darrick Owens', 'Lonnie Palelei', 'Tyler Palko', 'Jonathan Palmer', 'Michael Palmer', 'Tom Palmer', 'George Papach', 'Babe Parilli', 'Frank Parker', 'Willie Parker', 'Jeremy Parquet', 'James Parrish', 'Gordon Paschka', 'Frank Pastin', 'Frank Patrick[disambiguation needed]', 'John Patrick', 'Billy Patterson', 'Stan Pavkov', 'Scott Paxson', 'Clarence Peaks', 'Morgan Pears', 'Barry Pearson', 'Preston Pearson', 'Erric Pegram', 'Leon Pense', 'John Perko', 'George Perles', 'Pete Perreault', 'Darren Perry', 'Lowell Perry', 'John Petchel', 'Ted Petersen', 'Todd Peterson', 'John Petrella', 'Ken Phares', 'Marvin Philip', 'Joe Pierre', 'Roger Pillath', 'Ed Pine', 'Ray Pinney', 'Rocco Pirro', 'Mel Pittman', 'Dick Plasman', 'George Platukis', 'Mark Plevelich', 'Kirk Pointer', 'Frank Pokorny', 'Troy Polamalu', 'Frank Pollard', 'John Popovich', 'Joey Porter', 'Al Postus', 'Hank Poteat', 'Myron Pottios', 'Bill Potts', 'Mike Potts', 'Ernie Pough', 'Maurkice Pouncey', 'Shar Pourdanesh', 'Ryan Powdrell', 'Tim Powell', 'John Powers', 'Bill Priatko', 'Jimond Pugh', 'Rollin Putzier', 'Jesse Quatse', 'Jerry Quick', 'Jeff Quinn', 'Mike Quinn', 'Carroll Raborn', 'Alex Rado', 'George Rado', 'Vince Ragunas', 'Matt Raich', 'Pete Rajkovich', 'Buster Ramsey', 'Antwaan Randle El', 'Walt Rankin', 'Walter Rasby', 'Leo Raskowski', 'Randy Rasmussen', 'Eric Ravotti', 'Israel Raybon', 'Dave Reavis', 'Bert Rechichar', 'Redding', 'Isaac Redman', 'Jeff Reed', 'Dan Reeder', 'Cad Reese', 'Jerry Reese', 'Jordan Reffet', 'John Reger', 'Willie Reid', 'Will Renfro', 'Joe Repko', 'Jared Retkofsky', 'Randy Reutershan', 'Ray Reutt', 'Billy Reynolds', 'Jim Reynolds', 'Reznichak', 'Don Rhodes', 'Loran \"Dave\" Ribble', 'Perry Richards', 'Huey Richardson', 'Terry Richardson', 'Rodney Richmond', 'Tom Ricketts', 'Bill Riehl', 'Jay Riemersma', 'John Rienstra', 'Dick Riffle', 'Avon Riley', 'Cameron Riley', 'Gabriel Rivera', 'Jack Roberts', 'Bill Robinson', 'Ed Robinson', 'Gil Robinson', 'Jack Robinson', 'Willy Robinson', 'Marshall Robnett', 'Mike Rodak', 'Mark Rodenhauser', 'John Rodgers', 'Ben Roethlisberger', 'Fran Rogel', 'Cullen Rogers', 'Rondour', 'Gene Ronzani', 'Harvey Rooker', 'J.P. Rooney', 'Andres Roosna', 'Dedrick Roper', 'Jim Rorison', 'Oliver Ross', 'Allen Rossum', 'Pete Rostosky', 'Tom Rouen', 'Bob Rowley', 'John Rowser', 'Mark Royals', 'Orpheus Roye', 'Aubrey Rozzell', 'Eddie Rucinski', 'Grey Ruegamer', 'Guy Ruff', 'Ernie Ruple', 'Andy Russell', 'Gary Russell', 'Rod Rust', 'Rod Rutherford', 'Ed Ryan', 'Steve Sader', 'Troy Sadowski', 'Paul Salata', 'Chris Samp', 'John Sample', 'Don Samuel', 'Carl Samuelson', 'Kuan Sanchez', 'Lupe Sanchez', 'Sigurd Sandberg', 'Emmanuel Sanders', 'Wayne Sandefur', 'Chuck Sanders', 'John \"Jack\" Sanders', 'Watts Sanderson', 'Curtis Sandig', 'Mike Sandusky', 'Theron Sapp', 'Bill Saul', 'Sylvan Saumer', 'Weslye Saunders', 'Charley Scales', 'Shawn Scales', 'Jack Scarbath', 'Bernard Scherer', 'John Schiechl', 'John Schmidt', 'Ricky Schmitt', 'Bob Schmitz', 'Mike Schneck', 'Bob Schnelker', 'Karl Schuelke', 'Eberle Schultz', 'Elmer Schwartz', 'John Schweder', 'Glenn Scolnik', 'Chad Scott', 'Chris Scott', 'Jonathan Scott', 'Patrick Scott', 'Wilbert Scott', 'Joe Scudero', 'Todd Seabaugh', 'Charley Seabright', 'Ray Seals', 'Leon Searcy', 'Vic Sears', 'Mike Sebastian', 'Richard Seigler', 'Warren Seitz', 'Bernie Semes', 'Daniel Sepulveda', 'Brent Sexton', 'George Shaffer', 'Ron Shanklin', 'Rick Sharp', 'Bobby Shaw', 'Ryan Shazier', 'Chris Sheffield', 'Donnie Shell', 'Richard Shelton', 'Charlie Shepard', 'Leslie Shepherd', 'Stan Sheriff', 'Alex \"Allie\" Sherman', 'Bob Sherman', 'Ray Sherman', 'Dezmond Sherrod', 'Burrell Shields', 'Scott Shields', 'Dick Shiner', 'Jerry Shipkey', 'A.Q. Shipley', 'Bill Shockley', 'Jim Shorter', 'Bret Shugarts', 'Hubert Shurtz', 'Don Shy', 'John Simerson', 'Tracy Simien', 'Milt Simington', 'Jason Simmons', 'Jerry Simmons', 'Kendall Simmons', 'Bob Simms', 'Jackie Simpson', 'Tim Simpson', 'Willie Simpson', 'Darryl Sims', 'Frank Sinkovitz', 'George Sirochman', 'Vince Sites', 'Paul Skansi', 'Joe Skladany', 'Nick Skorich', 'Ed Skoronski', 'Walt Slater', 'Stan Smagala', 'Alex Smail', 'Fred Small', 'Joe Smaltz', 'Aaron Smith', 'Anthony Smith', 'Ben Smith', 'Billy Ray Smith, Sr.', 'Bobby Smith', 'Dave Smith', 'Hal Smith', 'Jeff Smith', 'Jim Smith', 'Kevin Smith', 'Laverne Smith', 'Marvel Smith', 'Ron Smith', 'Steve Smith', 'Stu Smith', 'Truett Smith', 'Ray Snell', 'Bill Snyder', 'Bob Snyder', 'John Sodaski', 'Bob Soleau', 'Ariel Solomon', 'George Somers', 'Ross Sorce', 'Jamil Soriano', 'Wilbur \"Bill\" Sortet', 'Frank Souchak', 'Souffley', 'Matt Spaeth', 'Chad Spann', 'Todd Spencer', 'John Spezzaferro', 'Jack Spinks', 'Francis St. Paul', 'Brian St. Pierre', 'Jeremy Staat', 'Jon Staggers', 'Brenden StaI', 'Duce Staley', 'John Stallworth', 'Ronald Stanley', 'Jack Stanton', 'Darnell Stapleton', 'Pat Stark', 'Rohn Stark', 'Max Starks', 'Ben Starret', 'Larry Station', 'Ernie Stautner', 'Joel Steed', 'Ernest Steele', 'Ron Stehouwer', 'Brian Stenger', 'Paul Stenn', 'Jamain Stephens', 'Kent Stephenson', 'Dean Steward', 'George Stewart', 'Kordell Stewart', 'Ken Stilley', 'John Stock', 'Mark Stock', 'Ed Stofko', 'Dwight Stone', 'Matt Storm', 'Cliff Stoudt', 'Glen Stough', 'Tyronne Stowe', 'Eli Strand', 'Rick Strom', 'Walter Strosser', 'George Strugar', 'Art Strutt', 'Dan Stryzinski', 'Justin Strzelczyk', 'Nick Studen', 'Russell Stuvaints', 'Steve Suhey', 'Shaun Suisham', 'George Sulima', 'Chris Sullivan', 'Frank Sullivan', 'Robert Sullivan', 'Frank Summers', 'Charley Sumner', 'Sal Sunseri', 'Don Sutherin', 'Mike Sutton', 'Ricky Sutton', 'John Swain', 'Lynn Swann', 'Limas Sweed', 'Calvin Sweeney', 'Jim Sweeney', 'Willie Sydnor', 'Stevenson Sylvester', 'Walt Szot', 'Bill Tanguay', 'Maa Tanuvasa', 'George Tarasovic', 'Jess Tatum', 'Eric Taylor', 'Hugh Taylor', 'Ike Taylor', 'Jim Taylor', 'Lionel Taylor', 'Mike Taylor', 'Lou Tepe', 'Nat Terry', 'Ray Tesser', 'Larry Tharpe', 'Ryan Thelwell', 'Yancey Thigpen', 'Ben Thomas', 'Cam Thomas', 'Clendon Thomas', 'J. T. Thomas', 'Mark Thomas', 'Shamarko Thomas', 'Tuffy Thompson', 'Donnel Thompson', 'Leroy Thompson', 'Tommy Thompson', 'Weegie Thompson', 'Sidney Thornton', 'Bob Thurbon', 'Andrae Thurman', 'Morgan Tiller', 'Lawrence Timmons', 'Sid Tinsley', 'George Titus', 'Silas J. Titus', 'Loren Toews', 'Charley Tolar', 'Lou Tomasetti', 'Andy Tomasic', 'Mike Tomczak', 'Dick Tomlinson', 'Clarence Tommerson', 'LaVern Torgeson', 'Brandon Torrey', 'John Tosi', 'Erik Totten', 'Deshea Townsend', 'Tom Tracy', 'David Trout', 'Lou Tsoutsouvas', 'B.J. Tucker', 'Erroll Tucker', 'Anthony Tuggle', 'Stephon Tuitt', 'Jerame Tuman', 'Dan Turk', 'John Turley', 'Trevis Turner', 'Rich Tylski', 'Joe Tyrrell', 'Tim Tyrrell', 'David Upchurch', 'Paul Uram', 'Kraig Urbik', 'Valenti', 'Zack Valentine', 'Bob Valesente', 'Bruce Van Dyke', 'Lenny Vandermade', 'Frank Varrichione', 'Harp Vaughan', 'Elton Veals', 'Craig Veasey', 'Ross Ventrone', 'Vic Vidoni', 'Justin Vincent', 'Keydrick Vincent', 'Shawn Vincent', 'Stahle Vincent', 'Kimo von Oelhoffen', 'Lloyd Voss', 'Mike Vrabel', 'Bob Wade', 'Tommy Wade', 'Clint Wager', 'Mike Wagner', 'Bobby Walden', 'Gerran Walker', 'Richard Walker', 'Sammy Walker', 'J. T. Wall', 'Cody Wallace', 'Mike Wallace', 'Ray Wallace', 'Rian Wallace', 'Will Walls', 'Bill Walsh', 'Frank Walton', 'Joe Walton', 'Ted Walton', 'Hines Ward', 'Buist \"Buss\" Warren', 'Greg Warren', 'Xavier Warren', 'Anthony Washington', 'Clarence Washington', 'Dewayne Washington', 'Nate Washington', 'Robert Washington', 'Sam Washington', 'Tom Watkins', 'Allan Watson', 'Sid Watson', 'B. W. Webb', 'Elnardo Webster', 'George Webster', 'Mike Webster', 'Thurlow Weed', 'Henry Weinberg', 'Izzy Weinstock', 'Henry \"Heinie\" Weisenbaugh', 'Billy Wells', 'Joe Wendlick', 'Ralph Wenzel', 'Ralph Wenzel', 'Isaac West', 'Ed Westfall', 'Damon Wetzel', 'Markus Wheaton', 'Ernie Wheeler', 'Tommy Whelan', 'Mark Whipple', 'Byron White', 'Dwight White', 'Paul White', 'Hans Wiederkehr', 'Joe Wiehl', 'Paul Wiggins', 'J.R. Wilburn', 'Solomon Wilcots', 'Jack Wiley', 'Eric Wilkerson', 'Albert Williams', 'Brandon Williams', 'Dave Williams', 'Don Williams', 'Eric Williams', 'Erwin Williams', 'Gerald Williams', 'Jerrol Williams', 'Joe Williams', 'Joe J. Williams', 'John L. Williams', 'Payton Williams', 'Ray Williams', 'Renauld Williams', 'Robert Williams', 'Sidney Williams', 'Vince Williams', 'Warren Williams', 'Willie Williams', 'Fred Williamson', 'Keith Willis', 'Billy Wilson', 'Cedrick Wilson', 'Frank Wilson', 'Gordon Wilson', 'Kion Wilson', 'Chuck Winfrey', 'Brad Wing', 'Blake Wingle', 'Dennis Winston', 'Al Wistert', 'Mike Withycombe', 'Jon Witman', 'Jim Wolf', 'Craig Wolfley', 'Will Wolford', 'Joe Womack', 'Ken Woodard', 'David Woodley', 'LaMarr Woodley', 'Dwayne Woodruff', 'Al Woods', 'Donovan Woods', 'Rick Woods', 'Marv Woodson', 'Rod Woodson', 'Donnell Woolford', 'Jason Worilds', 'Tim Worley', 'John \"Dutch\" Woudenberg', 'Lud Wray', 'Lowe Wren', 'Anthony Wright', 'Destry Wright', 'Al Wukits', 'Frank Wydo', 'Al Young', 'Albert Young', 'Dick Young', 'Theo Young', 'Walter Young', 'Paul Younger', 'John Yurchey', 'Stan Zajdel', 'Silvio Zaninelli', 'Zeher', 'Amos Zereoue', 'Jeff Zgonina', 'Leroy Zimmerman', 'Joe Zombek', 'Frank Zoppetti']])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "teams.keys()" + "teams.keys()\n", + "teams.values()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "list" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(teams['steelers'])" ] @@ -94,9 +145,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n", + "1668\n" + ] + } + ], "source": [ "print(len(teams))\n", "print(len(teams['steelers']))" @@ -113,9 +173,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1666" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "steelers_set = set(teams['steelers'])\n", "len(steelers_set)" @@ -136,9 +207,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[('Mike Adams', 2),\n", + " ('Ralph Wenzel', 2),\n", + " ('Walter Abercrombie', 1),\n", + " ('Ed Adamchik', 1),\n", + " ('Bob Adams', 1),\n", + " ('Flozell Adams', 1),\n", + " ('Ben Agajanian', 1),\n", + " ('Dick Alban', 1),\n", + " ('Tom Alberghini', 1),\n", + " ('Art Albrecht', 1)]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from collections import Counter\n", "\n", @@ -162,9 +253,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(225, 954)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "bears_set = set(teams['bears'])\n", "cowboys_set = set(teams['cowboys'])\n", @@ -181,12 +283,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "venn3([steelers_set, cowboys_set, bears_set],\n", - " (\"Steelers\",\"Cowboys\",\"Bears\"))" + "venn3([steelers_set, cowboys_set, bears_set],(\"Steelers\",\"Cowboys\",\"Bears\"))" ] }, { @@ -278,7 +400,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -292,7 +414,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/modules/module3/ROADMAP.ipynb b/modules/module3/ROADMAP.ipynb index 7f160be..53fbf0f 100644 --- a/modules/module3/ROADMAP.ipynb +++ b/modules/module3/ROADMAP.ipynb @@ -80,7 +80,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -94,7 +94,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/modules/module4/Gene_Expression_Analysis_with_Pandas.ipynb b/modules/module4/Gene_Expression_Analysis_with_Pandas.ipynb index 15f8daf..916331b 100644 --- a/modules/module4/Gene_Expression_Analysis_with_Pandas.ipynb +++ b/modules/module4/Gene_Expression_Analysis_with_Pandas.ipynb @@ -2,11 +2,11 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "% matplotlib inline" + "%matplotlib inline" ] }, { @@ -88,9 +88,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + }, + { + "data": { + "text/plain": [ + "'/home/gastonq/DATA/Bioinf'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os \n", "import time \n", @@ -151,9 +169,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n", + " \n" + ] + } + ], "source": [ "geneexp_path = os.path.join(DATADIR,\"PANCAN12.IlluminaHiSeq_RNASeqV2.geneExp.tumor_whitelist\")\n", "geneexpression_data = pd.read_table(geneexp_path, index_col=0, header=0)\n" @@ -176,9 +203,10 @@ "metadata": {}, "outputs": [], "source": [ - "subdata = pd.read_table(geneexp_path,\n", + "subdata = pd.read_csv(geneexp_path,\n", " nrows=5,\n", - " usecols=range(20,40))\n", + " usecols=range(20,40),\n", + " sep=\"\\t\")\n", "subdata" ] }, @@ -215,9 +243,1883 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(20501, 3271)\n" + ] + }, + { + "data": { + "text/html": [ + "
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20501 rows × 3271 columns

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265.966003 \n", + "ZUFSP 116.372803 187.842300 126.138000 112.516403 \n", + "ZW10 408.967407 438.663696 434.350098 418.229401 \n", + "ZWILCH 248.128998 635.093079 272.829712 268.429993 \n", + "ZWINT 256.020203 646.768921 233.880798 118.621902 \n", + "ZXDA 24.937000 19.715200 91.224800 55.386002 \n", + "ZXDB 174.559204 198.795197 607.414429 699.084229 \n", + "ZXDC 901.612305 1163.745850 986.579102 1045.355347 \n", + "ZYG11A 2.216600 11.500500 18.395100 20.497200 \n", + "ZYG11B 628.413330 691.128113 1474.237427 870.039185 \n", + "ZYX 12845.897461 6062.979004 4024.401611 2172.263428 \n", + "ZZEF1 1238.539429 1553.669189 1164.899048 1238.988159 \n", + "ZZZ3 463.274689 468.784210 815.391785 887.483582 \n", + "psiTPTE22 21.612101 34.501598 72.454201 316.615814 \n", + "\n", + "[20501 rows x 3271 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "print(geneexpression_data.shape)\n", "geneexpression_data" @@ -300,17 +2202,171 @@ "metadata": {}, "source": [ "## Exercise 1\n", - "\n", - "Use the method above(.ix) to only view the columns **3000 through 3005** and rows **8000 through 8010** " + " Use the method above(.ix) to only view the columns **3000 through 3005** and rows **8000 through 8010** " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n", + ".ix is deprecated. Please use\n", + ".loc for label based indexing or\n", + ".iloc for positional indexing\n", + "\n", + "See the documentation here:\n", + "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/html": [ + "
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TCGA-DQ-5631TCGA-DQ-7588TCGA-DQ-7589TCGA-DQ-7590TCGA-DQ-7591
#probe
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HOXC685.63760421.60330043.806702234.436905248.885895
HOXC8113.27480310.166300117.07099940.09090044.666901
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HOXD10369.7459117.62470034.58269922.73200012.181900
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HOXD1330.1591002.11800027.77960083.0749970.507600
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" + ], + "text/plain": [ + " TCGA-DQ-5631 TCGA-DQ-7588 TCGA-DQ-7589 TCGA-DQ-7590 TCGA-DQ-7591\n", + "#probe \n", + "HOXC5 25.566401 13.788000 24.919399 24.798500 55.062099\n", + "HOXC6 85.637604 21.603300 43.806702 234.436905 248.885895\n", + "HOXC8 113.274803 10.166300 117.070999 40.090900 44.666901\n", + "HOXC9 45.594898 75.823402 58.960701 73.568901 92.886803\n", + "HOXD10 369.745911 7.624700 34.582699 22.732000 12.181900\n", + "HOXD11 297.079102 14.825800 13.322900 13.639200 24.363800\n", + "HOXD12 0.712400 0.000000 0.000000 0.000000 0.507600\n", + "HOXD13 30.159100 2.118000 27.779600 83.074997 0.507600\n", + "HOXD1 47.019699 5.930300 7.937000 4.133100 5.583400\n", + "HOXD3 6.411800 1.270800 7.086600 4.959700 4.568200" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "Parsed_Data = \n", + "Parsed_Data = geneexpression_data.ix[8000:8010,3000:3005]\n", "Parsed_Data" ] }, @@ -327,9 +2383,375 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5125, 1)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:5: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n", + " \"\"\"\n" + ] + }, + { + "data": { + "text/html": [ + "
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Cancer
Sample
TCGA-A6-2670COAD
TCGA-A6-2671COAD
TCGA-A6-2672COAD
TCGA-A6-2674COAD
TCGA-A6-2675COAD
TCGA-A6-2676COAD
TCGA-A6-2677COAD
TCGA-A6-2678COAD
TCGA-A6-2679COAD
TCGA-A6-2680COAD
TCGA-A6-2681COAD
TCGA-A6-2682COAD
TCGA-A6-2683COAD
TCGA-A6-2684COAD
TCGA-A6-2685COAD
TCGA-A6-2686COAD
TCGA-A6-3807COAD
TCGA-A6-3808COAD
TCGA-A6-3809COAD
TCGA-A6-3810COAD
TCGA-A6-4105COAD
TCGA-A6-4107COAD
TCGA-A6-5656COAD
TCGA-A6-5657COAD
TCGA-A6-5659COAD
TCGA-A6-5660COAD
TCGA-A6-5661COAD
TCGA-A6-5662COAD
TCGA-A6-5664COAD
TCGA-A6-5665COAD
......
TCGA-61-2087OV
TCGA-61-2088OV
TCGA-61-2092OV
TCGA-61-2094OV
TCGA-61-2095OV
TCGA-61-2096OV
TCGA-61-2097OV
TCGA-61-2098OV
TCGA-61-2101OV
TCGA-61-2102OV
TCGA-61-2104OV
TCGA-61-2109OV
TCGA-61-2110OV
TCGA-61-2111OV
TCGA-61-2113OV
TCGA-61-2610OV
TCGA-61-2611OV
TCGA-61-2612OV
TCGA-61-2613OV
TCGA-61-2614OV
TCGA-72-4231OV
TCGA-72-4232OV
TCGA-72-4233OV
TCGA-72-4234OV
TCGA-72-4235OV
TCGA-72-4236OV
TCGA-72-4237OV
TCGA-72-4238OV
TCGA-72-4240OV
TCGA-72-4241OV
\n", + "

5125 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " Cancer\n", + "Sample \n", + "TCGA-A6-2670 COAD\n", + "TCGA-A6-2671 COAD\n", + "TCGA-A6-2672 COAD\n", + "TCGA-A6-2674 COAD\n", + "TCGA-A6-2675 COAD\n", + "TCGA-A6-2676 COAD\n", + "TCGA-A6-2677 COAD\n", + "TCGA-A6-2678 COAD\n", + "TCGA-A6-2679 COAD\n", + "TCGA-A6-2680 COAD\n", + "TCGA-A6-2681 COAD\n", + "TCGA-A6-2682 COAD\n", + "TCGA-A6-2683 COAD\n", + "TCGA-A6-2684 COAD\n", + "TCGA-A6-2685 COAD\n", + "TCGA-A6-2686 COAD\n", + "TCGA-A6-3807 COAD\n", + "TCGA-A6-3808 COAD\n", + "TCGA-A6-3809 COAD\n", + "TCGA-A6-3810 COAD\n", + "TCGA-A6-4105 COAD\n", + "TCGA-A6-4107 COAD\n", + "TCGA-A6-5656 COAD\n", + "TCGA-A6-5657 COAD\n", + "TCGA-A6-5659 COAD\n", + "TCGA-A6-5660 COAD\n", + "TCGA-A6-5661 COAD\n", + "TCGA-A6-5662 COAD\n", + "TCGA-A6-5664 COAD\n", + "TCGA-A6-5665 COAD\n", + "... ...\n", + "TCGA-61-2087 OV\n", + "TCGA-61-2088 OV\n", + "TCGA-61-2092 OV\n", + "TCGA-61-2094 OV\n", + "TCGA-61-2095 OV\n", + "TCGA-61-2096 OV\n", + "TCGA-61-2097 OV\n", + "TCGA-61-2098 OV\n", + "TCGA-61-2101 OV\n", + "TCGA-61-2102 OV\n", + "TCGA-61-2104 OV\n", + "TCGA-61-2109 OV\n", + "TCGA-61-2110 OV\n", + "TCGA-61-2111 OV\n", + "TCGA-61-2113 OV\n", + "TCGA-61-2610 OV\n", + "TCGA-61-2611 OV\n", + "TCGA-61-2612 OV\n", + "TCGA-61-2613 OV\n", + "TCGA-61-2614 OV\n", + "TCGA-72-4231 OV\n", + "TCGA-72-4232 OV\n", + "TCGA-72-4233 OV\n", + "TCGA-72-4234 OV\n", + "TCGA-72-4235 OV\n", + "TCGA-72-4236 OV\n", + "TCGA-72-4237 OV\n", + "TCGA-72-4238 OV\n", + "TCGA-72-4240 OV\n", + "TCGA-72-4241 OV\n", + "\n", + "[5125 rows x 1 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "DATADIR2 = os.path.join(os.getcwd(), \"Resources\")\n", "DATADIR2\n", @@ -360,9 +2782,216 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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1339.768311 792.278015 27.799200 \n", + "TCGA-AX-A1CP 5422.242188 707.956604 871.609375 62.387001 \n", + "\n", + "[3271 rows x 20502 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "geneexpression_data_sorted = geneexpression_data_classes.sort_values(['Cancer'], ascending=True)\n", "geneexpression_data_sorted" @@ -419,9 +5030,50 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BRCA 817\n", + "KIRC 470\n", + "LUAD 353\n", + "UCEC 333\n", + "HNSC 303\n", + "OV 262\n", + "LUSC 220\n", + "COAD 192\n", + "GBM 154\n", + "BLCA 96\n", + "READ 71\n", + "Name: Cancer, dtype: int64\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + 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+ "text/plain": [ + "
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geneexpression_data_sorted.loc[geneexpression_data_sorted['Cancer'].isin(['LUAD'])]\n", "print(LUAD.shape)\n", @@ -468,12 +6902,1788 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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"LUAD_LUSC" ] }, @@ -517,9 +8727,314 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n", + ".ix is deprecated. Please use\n", + ".loc for label based indexing or\n", + ".iloc for positional indexing\n", + "\n", + "See the documentation here:\n", + "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/html": [ + "
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50%80.7645030.0000000.00000099.1701971.10990018985.478516317.3398130.8791000.000000608.311218...4848.990234531.1447751015.977417637.518677152.504807276.6897891572.5302731015.714294372.398804263.606293
75%161.1566010.0000000.936500135.4987034.34070029576.404297498.2047121.8951000.357400735.350098...7039.890137726.2974851396.878540782.764221196.351898355.5893861901.9981691263.687866659.407715376.424805
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415.669137 495.050827 186.154886 \n", + "min 23.046101 716.965393 151.228699 6.168600 28.673800 \n", + "25% 218.556702 1340.959961 808.457275 157.725296 173.896500 \n", + "50% 276.689789 1572.530273 1015.714294 372.398804 263.606293 \n", + "75% 355.589386 1901.998169 1263.687866 659.407715 376.424805 \n", + "max 945.775818 3814.414307 5147.004395 3490.022705 1162.816528 \n", + "\n", + "[8 rows x 99 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "first_100_genes = LUAD.ix[:,0:100]\n", "stats = first_100_genes.describe()\n", @@ -553,9 +9068,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Frequency')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "EGFR = geneexpression_data_sorted[['EGFR']]\n", "EGFR.hist(sharey=True, figsize= (5,5), xlabelsize=11, ylabelsize=14)\n", @@ -836,21 +9372,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.13" + "pygments_lexer": "ipython3", + "version": "3.7.3" } }, "nbformat": 4, diff --git a/modules/module4/Pandas-DataWrangling.ipynb b/modules/module4/Pandas-DataWrangling.ipynb index cb9c9a2..0dfcaa9 100644 --- a/modules/module4/Pandas-DataWrangling.ipynb +++ b/modules/module4/Pandas-DataWrangling.ipynb @@ -21,18 +21,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "% matplotlib inline" + "%matplotlib inline" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", "import sqlite3 as sqlite\n", @@ -42,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -51,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -76,18 +87,519 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + } + ], "source": [ "elevation = pd.read_table(os.path.join(DATADIR,\"elevation.txt\"))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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RankStateHighest elevationLowest elevationAverage elevation
01Colorado14,440 feet3,315 feet6,800 feet
12Wyoming13,804 feet3,099 feet6,700 feet
23Utah13,528 feet2,000 feet6,100 feet
34New Mexico13,161 feet2,842 feet5,700 feet
45Nevada13,140 feet479 feet5,500 feet
56Idaho12,662 feet710 feet5,000 feet
67Arizona12,633 feet70 feet4,100 feet
78Montana12,799 feet1,800 feet3,400 feet
89Oregon11,239 feetSea level3,300 feet
910Hawaii13,796 feetSea level3,030 feet
1011California14,494 feet-282 feet2,900 feet
1112Nebraska5,424 feet840 feet2,600 feet
1213South Dakota7,242 feet966 feet2,200 feet
1314Kansas4,039 feet679 feet2,000 feet
1415Alaska20,320 feetSea level1,900 feet
1516North Dakota3,506 feet750 feet1,900 feet
1617Washington14,410 feetSea level1,700 feet
1718Texas8,749 feetSea level1,700 feet
1819West Virginia4,863 feet240 feet1,500 feet
1920Oklahoma4,973 feet289 feet1,300 feet
2021Minnesota2,301 feet602 feet1,200 feet
2122Pennsylvania3,213 feetSea level1,100 feet
2223Iowa1,670 feet480 feet1,100 feet
2324Wisconsin1,951 feet581 feet1,050 feet
2425New Hampshire6,288 feetSea level1,000 feet
2526New York5,344 feetSea level1,000 feet
2627Vermont4,393 feet95 feet1,000 feet
2728Virginia5,729 feetSea level950 feet
2829Tennessee6,643 feet178 feet900 feet
2930Michigan1,979 feet572 feet900 feet
3031Ohio1,549 feet455 feet850 feet
3132Missouri1,772 feet230 feet800 feet
3233Kentucky4,139 feet257 feet750 feet
3334North Carolina6,684 feetSea level700 feet
3435Indiana1,257 feet320 feet700 feet
3536Arkansas2,753 feet55 feet650 feet
3637Maine5,276 feetSea level600 feet
3738Georgia4,784 feetSea level600 feet
3839Illinois1,235 feet279 feet600 feet
3940Massachusetts3,487 feetSea level500 feet
4041Alabama2,407 feetSea level500 feet
4142Connecticut2,380 feetSea level500 feet
4243South Carolina3,560 feetSea level350 feet
4344Maryland3,360 feetSea level350 feet
4445Mississippi806 feetSea level300 feet
4546New Jersey1,803 feetSea level250 feet
4647Rhode Island812 feetSea level200 feet
4748Louisiana535 feet-8 feet100 feet
4849Florida345 feetSea level100 feet
4950Delaware450 feetSea level60 feet
\n", + "
" + ], + "text/plain": [ + " Rank State Highest elevation Lowest elevation Average elevation\n", + "0 1 Colorado 14,440 feet 3,315 feet 6,800 feet\n", + "1 2 Wyoming 13,804 feet 3,099 feet 6,700 feet\n", + "2 3 Utah 13,528 feet 2,000 feet 6,100 feet\n", + "3 4 New Mexico 13,161 feet 2,842 feet 5,700 feet\n", + "4 5 Nevada 13,140 feet 479 feet 5,500 feet\n", + "5 6 Idaho 12,662 feet 710 feet 5,000 feet\n", + "6 7 Arizona 12,633 feet 70 feet 4,100 feet\n", + "7 8 Montana 12,799 feet 1,800 feet 3,400 feet\n", + "8 9 Oregon 11,239 feet Sea level 3,300 feet\n", + "9 10 Hawaii 13,796 feet Sea level 3,030 feet\n", + "10 11 California 14,494 feet -282 feet 2,900 feet\n", + "11 12 Nebraska 5,424 feet 840 feet 2,600 feet\n", + "12 13 South Dakota 7,242 feet 966 feet 2,200 feet\n", + "13 14 Kansas 4,039 feet 679 feet 2,000 feet\n", + "14 15 Alaska 20,320 feet Sea level 1,900 feet\n", + "15 16 North Dakota 3,506 feet 750 feet 1,900 feet\n", + "16 17 Washington 14,410 feet Sea level 1,700 feet\n", + "17 18 Texas 8,749 feet Sea level 1,700 feet\n", + "18 19 West Virginia 4,863 feet 240 feet 1,500 feet\n", + "19 20 Oklahoma 4,973 feet 289 feet 1,300 feet\n", + "20 21 Minnesota 2,301 feet 602 feet 1,200 feet\n", + "21 22 Pennsylvania 3,213 feet Sea level 1,100 feet\n", + "22 23 Iowa 1,670 feet 480 feet 1,100 feet\n", + "23 24 Wisconsin 1,951 feet 581 feet 1,050 feet\n", + "24 25 New Hampshire 6,288 feet Sea level 1,000 feet\n", + "25 26 New York 5,344 feet Sea level 1,000 feet\n", + "26 27 Vermont 4,393 feet 95 feet 1,000 feet\n", + "27 28 Virginia 5,729 feet Sea level 950 feet\n", + "28 29 Tennessee 6,643 feet 178 feet 900 feet\n", + "29 30 Michigan 1,979 feet 572 feet 900 feet\n", + "30 31 Ohio 1,549 feet 455 feet 850 feet\n", + "31 32 Missouri 1,772 feet 230 feet 800 feet\n", + "32 33 Kentucky 4,139 feet 257 feet 750 feet\n", + "33 34 North Carolina 6,684 feet Sea level 700 feet\n", + "34 35 Indiana 1,257 feet 320 feet 700 feet\n", + "35 36 Arkansas 2,753 feet 55 feet 650 feet\n", + "36 37 Maine 5,276 feet Sea level 600 feet\n", + "37 38 Georgia 4,784 feet Sea level 600 feet\n", + "38 39 Illinois 1,235 feet 279 feet 600 feet\n", + "39 40 Massachusetts 3,487 feet Sea level 500 feet\n", + "40 41 Alabama 2,407 feet Sea level 500 feet\n", + "41 42 Connecticut 2,380 feet Sea level 500 feet\n", + "42 43 South Carolina 3,560 feet Sea level 350 feet\n", + "43 44 Maryland 3,360 feet Sea level 350 feet\n", + "44 45 Mississippi 806 feet Sea level 300 feet\n", + "45 46 New Jersey 1,803 feet Sea level 250 feet\n", + "46 47 Rhode Island 812 feet Sea level 200 feet\n", + "47 48 Louisiana 535 feet -8 feet 100 feet\n", + "48 49 Florida 345 feet Sea level 100 feet\n", + "49 50 Delaware 450 feet Sea level 60 feet" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "elevation" ] @@ -101,9 +613,501 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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RankStateHighest elevationLowest elevationAverage elevation
01Colorado14,440 feet3,315 feet6,800 feet
12Wyoming13,804 feet3,099 feet6,700 feet
23Utah13,528 feet2,000 feet6,100 feet
34New Mexico13,161 feet2,842 feet5,700 feet
45Nevada13,140 feet479 feet5,500 feet
56Idaho12,662 feet710 feet5,000 feet
67Arizona12,633 feet70 feet4,100 feet
78Montana12,799 feet1,800 feet3,400 feet
89Oregon11,239 feet0 feet3,300 feet
910Hawaii13,796 feet0 feet3,030 feet
1011California14,494 feet-282 feet2,900 feet
1112Nebraska5,424 feet840 feet2,600 feet
1213South Dakota7,242 feet966 feet2,200 feet
1314Kansas4,039 feet679 feet2,000 feet
1415Alaska20,320 feet0 feet1,900 feet
1516North Dakota3,506 feet750 feet1,900 feet
1617Washington14,410 feet0 feet1,700 feet
1718Texas8,749 feet0 feet1,700 feet
1819West Virginia4,863 feet240 feet1,500 feet
1920Oklahoma4,973 feet289 feet1,300 feet
2021Minnesota2,301 feet602 feet1,200 feet
2122Pennsylvania3,213 feet0 feet1,100 feet
2223Iowa1,670 feet480 feet1,100 feet
2324Wisconsin1,951 feet581 feet1,050 feet
2425New Hampshire6,288 feet0 feet1,000 feet
2526New York5,344 feet0 feet1,000 feet
2627Vermont4,393 feet95 feet1,000 feet
2728Virginia5,729 feet0 feet950 feet
2829Tennessee6,643 feet178 feet900 feet
2930Michigan1,979 feet572 feet900 feet
3031Ohio1,549 feet455 feet850 feet
3132Missouri1,772 feet230 feet800 feet
3233Kentucky4,139 feet257 feet750 feet
3334North Carolina6,684 feet0 feet700 feet
3435Indiana1,257 feet320 feet700 feet
3536Arkansas2,753 feet55 feet650 feet
3637Maine5,276 feet0 feet600 feet
3738Georgia4,784 feet0 feet600 feet
3839Illinois1,235 feet279 feet600 feet
3940Massachusetts3,487 feet0 feet500 feet
4041Alabama2,407 feet0 feet500 feet
4142Connecticut2,380 feet0 feet500 feet
4243South Carolina3,560 feet0 feet350 feet
4344Maryland3,360 feet0 feet350 feet
4445Mississippi806 feet0 feet300 feet
4546New Jersey1,803 feet0 feet250 feet
4647Rhode Island812 feet0 feet200 feet
4748Louisiana535 feet-8 feet100 feet
4849Florida345 feet0 feet100 feet
4950Delaware450 feet0 feet60 feet
\n", + "
" + ], + "text/plain": [ + " Rank State Highest elevation Lowest elevation Average elevation\n", + "0 1 Colorado 14,440 feet 3,315 feet 6,800 feet\n", + "1 2 Wyoming 13,804 feet 3,099 feet 6,700 feet\n", + "2 3 Utah 13,528 feet 2,000 feet 6,100 feet\n", + "3 4 New Mexico 13,161 feet 2,842 feet 5,700 feet\n", + "4 5 Nevada 13,140 feet 479 feet 5,500 feet\n", + "5 6 Idaho 12,662 feet 710 feet 5,000 feet\n", + "6 7 Arizona 12,633 feet 70 feet 4,100 feet\n", + "7 8 Montana 12,799 feet 1,800 feet 3,400 feet\n", + "8 9 Oregon 11,239 feet 0 feet 3,300 feet\n", + "9 10 Hawaii 13,796 feet 0 feet 3,030 feet\n", + "10 11 California 14,494 feet -282 feet 2,900 feet\n", + "11 12 Nebraska 5,424 feet 840 feet 2,600 feet\n", + "12 13 South Dakota 7,242 feet 966 feet 2,200 feet\n", + "13 14 Kansas 4,039 feet 679 feet 2,000 feet\n", + "14 15 Alaska 20,320 feet 0 feet 1,900 feet\n", + "15 16 North Dakota 3,506 feet 750 feet 1,900 feet\n", + "16 17 Washington 14,410 feet 0 feet 1,700 feet\n", + "17 18 Texas 8,749 feet 0 feet 1,700 feet\n", + "18 19 West Virginia 4,863 feet 240 feet 1,500 feet\n", + "19 20 Oklahoma 4,973 feet 289 feet 1,300 feet\n", + "20 21 Minnesota 2,301 feet 602 feet 1,200 feet\n", + "21 22 Pennsylvania 3,213 feet 0 feet 1,100 feet\n", + "22 23 Iowa 1,670 feet 480 feet 1,100 feet\n", + "23 24 Wisconsin 1,951 feet 581 feet 1,050 feet\n", + "24 25 New Hampshire 6,288 feet 0 feet 1,000 feet\n", + "25 26 New York 5,344 feet 0 feet 1,000 feet\n", + "26 27 Vermont 4,393 feet 95 feet 1,000 feet\n", + "27 28 Virginia 5,729 feet 0 feet 950 feet\n", + "28 29 Tennessee 6,643 feet 178 feet 900 feet\n", + "29 30 Michigan 1,979 feet 572 feet 900 feet\n", + "30 31 Ohio 1,549 feet 455 feet 850 feet\n", + "31 32 Missouri 1,772 feet 230 feet 800 feet\n", + "32 33 Kentucky 4,139 feet 257 feet 750 feet\n", + "33 34 North Carolina 6,684 feet 0 feet 700 feet\n", + "34 35 Indiana 1,257 feet 320 feet 700 feet\n", + "35 36 Arkansas 2,753 feet 55 feet 650 feet\n", + "36 37 Maine 5,276 feet 0 feet 600 feet\n", + "37 38 Georgia 4,784 feet 0 feet 600 feet\n", + "38 39 Illinois 1,235 feet 279 feet 600 feet\n", + "39 40 Massachusetts 3,487 feet 0 feet 500 feet\n", + "40 41 Alabama 2,407 feet 0 feet 500 feet\n", + "41 42 Connecticut 2,380 feet 0 feet 500 feet\n", + "42 43 South Carolina 3,560 feet 0 feet 350 feet\n", + "43 44 Maryland 3,360 feet 0 feet 350 feet\n", + "44 45 Mississippi 806 feet 0 feet 300 feet\n", + "45 46 New Jersey 1,803 feet 0 feet 250 feet\n", + "46 47 Rhode Island 812 feet 0 feet 200 feet\n", + "47 48 Louisiana 535 feet -8 feet 100 feet\n", + "48 49 Florida 345 feet 0 feet 100 feet\n", + "49 50 Delaware 450 feet 0 feet 60 feet" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "elevation.replace(\"Sea level\",\"0 feet\")\n" ] @@ -117,7 +1121,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -127,9 +1131,501 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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RankStateHighest elevationLowest elevationAverage elevation
01Colorado14,4403,3156,800
12Wyoming13,8043,0996,700
23Utah13,5282,0006,100
34New Mexico13,1612,8425,700
45Nevada13,1404795,500
56Idaho12,6627105,000
67Arizona12,633704,100
78Montana12,7991,8003,400
89Oregon11,23903,300
910Hawaii13,79603,030
1011California14,494-2822,900
1112Nebraska5,4248402,600
1213South Dakota7,2429662,200
1314Kansas4,0396792,000
1415Alaska20,32001,900
1516North Dakota3,5067501,900
1617Washington14,41001,700
1718Texas8,74901,700
1819West Virginia4,8632401,500
1920Oklahoma4,9732891,300
2021Minnesota2,3016021,200
2122Pennsylvania3,21301,100
2223Iowa1,6704801,100
2324Wisconsin1,9515811,050
2425New Hampshire6,28801,000
2526New York5,34401,000
2627Vermont4,393951,000
2728Virginia5,7290950
2829Tennessee6,643178900
2930Michigan1,979572900
3031Ohio1,549455850
3132Missouri1,772230800
3233Kentucky4,139257750
3334North Carolina6,6840700
3435Indiana1,257320700
3536Arkansas2,75355650
3637Maine5,2760600
3738Georgia4,7840600
3839Illinois1,235279600
3940Massachusetts3,4870500
4041Alabama2,4070500
4142Connecticut2,3800500
4243South Carolina3,5600350
4344Maryland3,3600350
4445Mississippi8060300
4546New Jersey1,8030250
4647Rhode Island8120200
4748Louisiana535-8100
4849Florida3450100
4950Delaware450060
\n", + "
" + ], + "text/plain": [ + " Rank State Highest elevation Lowest elevation Average elevation\n", + "0 1 Colorado 14,440 3,315 6,800 \n", + "1 2 Wyoming 13,804 3,099 6,700 \n", + "2 3 Utah 13,528 2,000 6,100 \n", + "3 4 New Mexico 13,161 2,842 5,700 \n", + "4 5 Nevada 13,140 479 5,500 \n", + "5 6 Idaho 12,662 710 5,000 \n", + "6 7 Arizona 12,633 70 4,100 \n", + "7 8 Montana 12,799 1,800 3,400 \n", + "8 9 Oregon 11,239 0 3,300 \n", + "9 10 Hawaii 13,796 0 3,030 \n", + "10 11 California 14,494 -282 2,900 \n", + "11 12 Nebraska 5,424 840 2,600 \n", + "12 13 South Dakota 7,242 966 2,200 \n", + "13 14 Kansas 4,039 679 2,000 \n", + "14 15 Alaska 20,320 0 1,900 \n", + "15 16 North Dakota 3,506 750 1,900 \n", + "16 17 Washington 14,410 0 1,700 \n", + "17 18 Texas 8,749 0 1,700 \n", + "18 19 West Virginia 4,863 240 1,500 \n", + "19 20 Oklahoma 4,973 289 1,300 \n", + "20 21 Minnesota 2,301 602 1,200 \n", + "21 22 Pennsylvania 3,213 0 1,100 \n", + "22 23 Iowa 1,670 480 1,100 \n", + "23 24 Wisconsin 1,951 581 1,050 \n", + "24 25 New Hampshire 6,288 0 1,000 \n", + "25 26 New York 5,344 0 1,000 \n", + "26 27 Vermont 4,393 95 1,000 \n", + "27 28 Virginia 5,729 0 950 \n", + "28 29 Tennessee 6,643 178 900 \n", + "29 30 Michigan 1,979 572 900 \n", + "30 31 Ohio 1,549 455 850 \n", + "31 32 Missouri 1,772 230 800 \n", + "32 33 Kentucky 4,139 257 750 \n", + "33 34 North Carolina 6,684 0 700 \n", + "34 35 Indiana 1,257 320 700 \n", + "35 36 Arkansas 2,753 55 650 \n", + "36 37 Maine 5,276 0 600 \n", + "37 38 Georgia 4,784 0 600 \n", + "38 39 Illinois 1,235 279 600 \n", + "39 40 Massachusetts 3,487 0 500 \n", + "40 41 Alabama 2,407 0 500 \n", + "41 42 Connecticut 2,380 0 500 \n", + "42 43 South Carolina 3,560 0 350 \n", + "43 44 Maryland 3,360 0 350 \n", + "44 45 Mississippi 806 0 300 \n", + "45 46 New Jersey 1,803 0 250 \n", + "46 47 Rhode Island 812 0 200 \n", + "47 48 Louisiana 535 -8 100 \n", + "48 49 Florida 345 0 100 \n", + "49 50 Delaware 450 0 60 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "elevation.replace(\"Sea level\",\"0 feet\").replace(r2,\"\").to_csv(os.path.join(DATADIR,\n", " \"elevation2.txt\"),\n", @@ -147,9 +1643,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14,440 \n", + "\n" + ] + } + ], "source": [ "print(elevation2[\"Highest elevation\"][0])\n", "print(type(elevation2[\"Highest elevation\"][0]))\n" @@ -168,14 +1673,567 @@ "metadata": {}, "outputs": [], "source": [ - "help(elevation2.convert_objects)" + "help(elevation2.convert_objects)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n", + "For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/html": [ + "
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RankStateHighest elevationLowest elevationAverage elevation
01ColoradoNaNNaNNaN
12WyomingNaNNaNNaN
23UtahNaNNaNNaN
34New MexicoNaNNaNNaN
45NevadaNaN479.0NaN
56IdahoNaN710.0NaN
67ArizonaNaN70.0NaN
78MontanaNaNNaNNaN
89OregonNaN0.0NaN
910HawaiiNaN0.0NaN
1011CaliforniaNaN-282.0NaN
1112NebraskaNaN840.0NaN
1213South DakotaNaN966.0NaN
1314KansasNaN679.0NaN
1415AlaskaNaN0.0NaN
1516North DakotaNaN750.0NaN
1617WashingtonNaN0.0NaN
1718TexasNaN0.0NaN
1819West VirginiaNaN240.0NaN
1920OklahomaNaN289.0NaN
2021MinnesotaNaN602.0NaN
2122PennsylvaniaNaN0.0NaN
2223IowaNaN480.0NaN
2324WisconsinNaN581.0NaN
2425New HampshireNaN0.0NaN
2526New YorkNaN0.0NaN
2627VermontNaN95.0NaN
2728VirginiaNaN0.0950.0
2829TennesseeNaN178.0900.0
2930MichiganNaN572.0900.0
3031OhioNaN455.0850.0
3132MissouriNaN230.0800.0
3233KentuckyNaN257.0750.0
3334North CarolinaNaN0.0700.0
3435IndianaNaN320.0700.0
3536ArkansasNaN55.0650.0
3637MaineNaN0.0600.0
3738GeorgiaNaN0.0600.0
3839IllinoisNaN279.0600.0
3940MassachusettsNaN0.0500.0
4041AlabamaNaN0.0500.0
4142ConnecticutNaN0.0500.0
4243South CarolinaNaN0.0350.0
4344MarylandNaN0.0350.0
4445Mississippi806.00.0300.0
4546New JerseyNaN0.0250.0
4647Rhode Island812.00.0200.0
4748Louisiana535.0-8.0100.0
4849Florida345.00.0100.0
4950Delaware450.00.060.0
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" + ], + "text/plain": [ + " Rank State Highest elevation Lowest elevation \\\n", + "0 1 Colorado NaN NaN \n", + "1 2 Wyoming NaN NaN \n", + "2 3 Utah NaN NaN \n", + "3 4 New Mexico NaN NaN \n", + "4 5 Nevada NaN 479.0 \n", + "5 6 Idaho NaN 710.0 \n", + "6 7 Arizona NaN 70.0 \n", + "7 8 Montana NaN NaN \n", + "8 9 Oregon NaN 0.0 \n", + "9 10 Hawaii NaN 0.0 \n", + "10 11 California NaN -282.0 \n", + "11 12 Nebraska NaN 840.0 \n", + "12 13 South Dakota NaN 966.0 \n", + "13 14 Kansas NaN 679.0 \n", + "14 15 Alaska NaN 0.0 \n", + "15 16 North Dakota NaN 750.0 \n", + "16 17 Washington NaN 0.0 \n", + "17 18 Texas NaN 0.0 \n", + "18 19 West Virginia NaN 240.0 \n", + "19 20 Oklahoma NaN 289.0 \n", + "20 21 Minnesota NaN 602.0 \n", + "21 22 Pennsylvania NaN 0.0 \n", + "22 23 Iowa NaN 480.0 \n", + "23 24 Wisconsin NaN 581.0 \n", + "24 25 New Hampshire NaN 0.0 \n", + "25 26 New York NaN 0.0 \n", + "26 27 Vermont NaN 95.0 \n", + "27 28 Virginia NaN 0.0 \n", + "28 29 Tennessee NaN 178.0 \n", + "29 30 Michigan NaN 572.0 \n", + "30 31 Ohio NaN 455.0 \n", + "31 32 Missouri NaN 230.0 \n", + "32 33 Kentucky NaN 257.0 \n", + "33 34 North Carolina NaN 0.0 \n", + "34 35 Indiana NaN 320.0 \n", + "35 36 Arkansas NaN 55.0 \n", + "36 37 Maine NaN 0.0 \n", + "37 38 Georgia NaN 0.0 \n", + "38 39 Illinois NaN 279.0 \n", + "39 40 Massachusetts NaN 0.0 \n", + "40 41 Alabama NaN 0.0 \n", + "41 42 Connecticut NaN 0.0 \n", + "42 43 South Carolina NaN 0.0 \n", + "43 44 Maryland NaN 0.0 \n", + "44 45 Mississippi 806.0 0.0 \n", + "45 46 New Jersey NaN 0.0 \n", + "46 47 Rhode Island 812.0 0.0 \n", + "47 48 Louisiana 535.0 -8.0 \n", + "48 49 Florida 345.0 0.0 \n", + "49 50 Delaware 450.0 0.0 \n", + "\n", + " Average elevation \n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 NaN \n", + "4 NaN \n", + "5 NaN \n", + "6 NaN \n", + "7 NaN \n", + "8 NaN \n", + "9 NaN \n", + "10 NaN \n", + "11 NaN \n", + "12 NaN \n", + "13 NaN \n", + "14 NaN \n", + "15 NaN \n", + "16 NaN \n", + "17 NaN \n", + "18 NaN \n", + "19 NaN \n", + "20 NaN \n", + "21 NaN \n", + "22 NaN \n", + "23 NaN \n", + "24 NaN \n", + "25 NaN \n", + "26 NaN \n", + "27 950.0 \n", + "28 900.0 \n", + "29 900.0 \n", + "30 850.0 \n", + "31 800.0 \n", + "32 750.0 \n", + "33 700.0 \n", + "34 700.0 \n", + "35 650.0 \n", + "36 600.0 \n", + "37 600.0 \n", + "38 600.0 \n", + "39 500.0 \n", + "40 500.0 \n", + "41 500.0 \n", + "42 350.0 \n", + "43 350.0 \n", + "44 300.0 \n", + "45 250.0 \n", + "46 200.0 \n", + "47 100.0 \n", + "48 100.0 \n", + "49 60.0 " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "elevation2.convert_objects(convert_numeric=True)" ] @@ -194,9 +2252,571 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + }, + { + "data": { + "text/html": [ + "
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Unnamed: 0RankHighest elevationLowest elevationAverage elevation
State
Colorado011444033156800
Wyoming121380430996700
Utah231352820006100
New Mexico341316128425700
Nevada45131404795500
Idaho56126627105000
Arizona6712633704100
Montana781279918003400
Oregon891123903300
Hawaii9101379603030
California101114494-2822900
Nebraska111254248402600
South Dakota121372429662200
Kansas131440396792000
Alaska14152032001900
North Dakota151635067501900
Washington16171441001700
Texas1718874901700
West Virginia181948632401500
Oklahoma192049732891300
Minnesota202123016021200
Pennsylvania2122321301100
Iowa222316704801100
Wisconsin232419515811050
New Hampshire2425628801000
New York2526534401000
Vermont26274393951000
Virginia272857290950
Tennessee28296643178900
Michigan29301979572900
Ohio30311549455850
Missouri31321772230800
Kentucky32334139257750
North Carolina333466840700
Indiana34351257320700
Arkansas3536275355650
Maine363752760600
Georgia373847840600
Illinois38391235279600
Massachusetts394034870500
Alabama404124070500
Connecticut414223800500
South Carolina424335600350
Maryland434433600350
Mississippi44458060300
New Jersey454618030250
Rhode Island46478120200
Louisiana4748535-8100
Florida48493450100
Delaware4950450060
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Rank Highest elevation Lowest elevation \\\n", + "State \n", + "Colorado 0 1 14440 3315 \n", + "Wyoming 1 2 13804 3099 \n", + "Utah 2 3 13528 2000 \n", + "New Mexico 3 4 13161 2842 \n", + "Nevada 4 5 13140 479 \n", + "Idaho 5 6 12662 710 \n", + "Arizona 6 7 12633 70 \n", + "Montana 7 8 12799 1800 \n", + "Oregon 8 9 11239 0 \n", + "Hawaii 9 10 13796 0 \n", + "California 10 11 14494 -282 \n", + "Nebraska 11 12 5424 840 \n", + "South Dakota 12 13 7242 966 \n", + "Kansas 13 14 4039 679 \n", + "Alaska 14 15 20320 0 \n", + "North Dakota 15 16 3506 750 \n", + "Washington 16 17 14410 0 \n", + "Texas 17 18 8749 0 \n", + "West Virginia 18 19 4863 240 \n", + "Oklahoma 19 20 4973 289 \n", + "Minnesota 20 21 2301 602 \n", + "Pennsylvania 21 22 3213 0 \n", + "Iowa 22 23 1670 480 \n", + "Wisconsin 23 24 1951 581 \n", + "New Hampshire 24 25 6288 0 \n", + "New York 25 26 5344 0 \n", + "Vermont 26 27 4393 95 \n", + "Virginia 27 28 5729 0 \n", + "Tennessee 28 29 6643 178 \n", + "Michigan 29 30 1979 572 \n", + "Ohio 30 31 1549 455 \n", + "Missouri 31 32 1772 230 \n", + "Kentucky 32 33 4139 257 \n", + "North Carolina 33 34 6684 0 \n", + "Indiana 34 35 1257 320 \n", + "Arkansas 35 36 2753 55 \n", + "Maine 36 37 5276 0 \n", + "Georgia 37 38 4784 0 \n", + "Illinois 38 39 1235 279 \n", + "Massachusetts 39 40 3487 0 \n", + "Alabama 40 41 2407 0 \n", + "Connecticut 41 42 2380 0 \n", + "South Carolina 42 43 3560 0 \n", + "Maryland 43 44 3360 0 \n", + "Mississippi 44 45 806 0 \n", + "New Jersey 45 46 1803 0 \n", + "Rhode Island 46 47 812 0 \n", + "Louisiana 47 48 535 -8 \n", + "Florida 48 49 345 0 \n", + "Delaware 49 50 450 0 \n", + "\n", + " Average elevation \n", + "State \n", + "Colorado 6800 \n", + "Wyoming 6700 \n", + "Utah 6100 \n", + "New Mexico 5700 \n", + "Nevada 5500 \n", + "Idaho 5000 \n", + "Arizona 4100 \n", + "Montana 3400 \n", + "Oregon 3300 \n", + "Hawaii 3030 \n", + "California 2900 \n", + "Nebraska 2600 \n", + "South Dakota 2200 \n", + "Kansas 2000 \n", + "Alaska 1900 \n", + "North Dakota 1900 \n", + "Washington 1700 \n", + "Texas 1700 \n", + "West Virginia 1500 \n", + "Oklahoma 1300 \n", + "Minnesota 1200 \n", + "Pennsylvania 1100 \n", + "Iowa 1100 \n", + "Wisconsin 1050 \n", + "New Hampshire 1000 \n", + "New York 1000 \n", + "Vermont 1000 \n", + "Virginia 950 \n", + "Tennessee 900 \n", + "Michigan 900 \n", + "Ohio 850 \n", + "Missouri 800 \n", + "Kentucky 750 \n", + "North Carolina 700 \n", + "Indiana 700 \n", + "Arkansas 650 \n", + "Maine 600 \n", + "Georgia 600 \n", + "Illinois 600 \n", + "Massachusetts 500 \n", + "Alabama 500 \n", + "Connecticut 500 \n", + "South Carolina 350 \n", + "Maryland 350 \n", + "Mississippi 300 \n", + "New Jersey 250 \n", + "Rhode Island 200 \n", + "Louisiana 100 \n", + "Florida 100 \n", + "Delaware 60 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.read_table(os.path.join(DATADIR,\"elevation2.txt\"),\n", " thousands=\",\",\n", @@ -205,9 +2825,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('en_US', 'UTF-8')\n" + ] + } + ], "source": [ "import locale\n", "print(locale.getlocale())\n" @@ -215,18 +2843,602 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function setlocale in module locale:\n", + "\n", + "setlocale(category, locale=None)\n", + " Set the locale for the given category. The locale can be\n", + " a string, an iterable of two strings (language code and encoding),\n", + " or None.\n", + " \n", + " Iterables are converted to strings using the locale aliasing\n", + " engine. Locale strings are passed directly to the C lib.\n", + " \n", + " category may be given as one of the LC_* values.\n", + "\n" + ] + } + ], "source": [ "help(locale.setlocale)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on function atof in module locale:\n", + "\n", + "atof(string, func=)\n", + " Parses a string as a float according to the locale settings.\n", + "\n" + ] + } + ], + "source": [ + "help(locale.atof)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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RankStateHighest elevationLowest elevationAverage elevation
01Colorado14440.03315.06800.0
12Wyoming13804.03099.06700.0
23Utah13528.02000.06100.0
34New Mexico13161.02842.05700.0
45Nevada13140.0479.05500.0
56Idaho12662.0710.05000.0
67Arizona12633.070.04100.0
78Montana12799.01800.03400.0
89Oregon11239.00.03300.0
910Hawaii13796.00.03030.0
1011California14494.0-282.02900.0
1112Nebraska5424.0840.02600.0
1213South Dakota7242.0966.02200.0
1314Kansas4039.0679.02000.0
1415Alaska20320.00.01900.0
1516North Dakota3506.0750.01900.0
1617Washington14410.00.01700.0
1718Texas8749.00.01700.0
1819West Virginia4863.0240.01500.0
1920Oklahoma4973.0289.01300.0
2021Minnesota2301.0602.01200.0
2122Pennsylvania3213.00.01100.0
2223Iowa1670.0480.01100.0
2324Wisconsin1951.0581.01050.0
2425New Hampshire6288.00.01000.0
2526New York5344.00.01000.0
2627Vermont4393.095.01000.0
2728Virginia5729.00.0950.0
2829Tennessee6643.0178.0900.0
2930Michigan1979.0572.0900.0
3031Ohio1549.0455.0850.0
3132Missouri1772.0230.0800.0
3233Kentucky4139.0257.0750.0
3334North Carolina6684.00.0700.0
3435Indiana1257.0320.0700.0
3536Arkansas2753.055.0650.0
3637Maine5276.00.0600.0
3738Georgia4784.00.0600.0
3839Illinois1235.0279.0600.0
3940Massachusetts3487.00.0500.0
4041Alabama2407.00.0500.0
4142Connecticut2380.00.0500.0
4243South Carolina3560.00.0350.0
4344Maryland3360.00.0350.0
4445Mississippi806.00.0300.0
4546New Jersey1803.00.0250.0
4647Rhode Island812.00.0200.0
4748Louisiana535.0-8.0100.0
4849Florida345.00.0100.0
4950Delaware450.00.060.0
\n", + "
" + ], + "text/plain": [ + " Rank State Highest elevation Lowest elevation \\\n", + "0 1 Colorado 14440.0 3315.0 \n", + "1 2 Wyoming 13804.0 3099.0 \n", + "2 3 Utah 13528.0 2000.0 \n", + "3 4 New Mexico 13161.0 2842.0 \n", + "4 5 Nevada 13140.0 479.0 \n", + "5 6 Idaho 12662.0 710.0 \n", + "6 7 Arizona 12633.0 70.0 \n", + "7 8 Montana 12799.0 1800.0 \n", + "8 9 Oregon 11239.0 0.0 \n", + "9 10 Hawaii 13796.0 0.0 \n", + "10 11 California 14494.0 -282.0 \n", + "11 12 Nebraska 5424.0 840.0 \n", + "12 13 South Dakota 7242.0 966.0 \n", + "13 14 Kansas 4039.0 679.0 \n", + "14 15 Alaska 20320.0 0.0 \n", + "15 16 North Dakota 3506.0 750.0 \n", + "16 17 Washington 14410.0 0.0 \n", + "17 18 Texas 8749.0 0.0 \n", + "18 19 West Virginia 4863.0 240.0 \n", + "19 20 Oklahoma 4973.0 289.0 \n", + "20 21 Minnesota 2301.0 602.0 \n", + "21 22 Pennsylvania 3213.0 0.0 \n", + "22 23 Iowa 1670.0 480.0 \n", + "23 24 Wisconsin 1951.0 581.0 \n", + "24 25 New Hampshire 6288.0 0.0 \n", + "25 26 New York 5344.0 0.0 \n", + "26 27 Vermont 4393.0 95.0 \n", + "27 28 Virginia 5729.0 0.0 \n", + "28 29 Tennessee 6643.0 178.0 \n", + "29 30 Michigan 1979.0 572.0 \n", + "30 31 Ohio 1549.0 455.0 \n", + "31 32 Missouri 1772.0 230.0 \n", + "32 33 Kentucky 4139.0 257.0 \n", + "33 34 North Carolina 6684.0 0.0 \n", + "34 35 Indiana 1257.0 320.0 \n", + "35 36 Arkansas 2753.0 55.0 \n", + "36 37 Maine 5276.0 0.0 \n", + "37 38 Georgia 4784.0 0.0 \n", + "38 39 Illinois 1235.0 279.0 \n", + "39 40 Massachusetts 3487.0 0.0 \n", + "40 41 Alabama 2407.0 0.0 \n", + "41 42 Connecticut 2380.0 0.0 \n", + "42 43 South Carolina 3560.0 0.0 \n", + "43 44 Maryland 3360.0 0.0 \n", + "44 45 Mississippi 806.0 0.0 \n", + "45 46 New Jersey 1803.0 0.0 \n", + "46 47 Rhode Island 812.0 0.0 \n", + "47 48 Louisiana 535.0 -8.0 \n", + "48 49 Florida 345.0 0.0 \n", + "49 50 Delaware 450.0 0.0 \n", + "\n", + " Average elevation \n", + "0 6800.0 \n", + "1 6700.0 \n", + "2 6100.0 \n", + "3 5700.0 \n", + "4 5500.0 \n", + "5 5000.0 \n", + "6 4100.0 \n", + "7 3400.0 \n", + "8 3300.0 \n", + "9 3030.0 \n", + "10 2900.0 \n", + "11 2600.0 \n", + "12 2200.0 \n", + "13 2000.0 \n", + "14 1900.0 \n", + "15 1900.0 \n", + "16 1700.0 \n", + "17 1700.0 \n", + "18 1500.0 \n", + "19 1300.0 \n", + "20 1200.0 \n", + "21 1100.0 \n", + "22 1100.0 \n", + "23 1050.0 \n", + "24 1000.0 \n", + "25 1000.0 \n", + "26 1000.0 \n", + "27 950.0 \n", + "28 900.0 \n", + "29 900.0 \n", + "30 850.0 \n", + "31 800.0 \n", + "32 750.0 \n", + "33 700.0 \n", + "34 700.0 \n", + "35 650.0 \n", + "36 600.0 \n", + "37 600.0 \n", + "38 600.0 \n", + "39 500.0 \n", + "40 500.0 \n", + "41 500.0 \n", + "42 350.0 \n", + "43 350.0 \n", + "44 300.0 \n", + "45 250.0 \n", + "46 200.0 \n", + "47 100.0 \n", + "48 100.0 \n", + "49 60.0 " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "locale.setlocale(locale.LC_NUMERIC, '') # I'm a little confused by this\n", "elevation2['Lowest elevation'] = \\\n", @@ -243,9 +3455,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "('en_US', 'UTF-8')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "conversion = locale.localeconv()\n", "locale.getlocale()" @@ -260,9 +3483,604 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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RankStateHighest elevationLowest elevationAverage elevationRange elevation
01Colorado14440.03315.06800.011125.0
12Wyoming13804.03099.06700.010705.0
23Utah13528.02000.06100.011528.0
34New Mexico13161.02842.05700.010319.0
45Nevada13140.0479.05500.012661.0
56Idaho12662.0710.05000.011952.0
67Arizona12633.070.04100.012563.0
78Montana12799.01800.03400.010999.0
89Oregon11239.00.03300.011239.0
910Hawaii13796.00.03030.013796.0
1011California14494.0-282.02900.014776.0
1112Nebraska5424.0840.02600.04584.0
1213South Dakota7242.0966.02200.06276.0
1314Kansas4039.0679.02000.03360.0
1415Alaska20320.00.01900.020320.0
1516North Dakota3506.0750.01900.02756.0
1617Washington14410.00.01700.014410.0
1718Texas8749.00.01700.08749.0
1819West Virginia4863.0240.01500.04623.0
1920Oklahoma4973.0289.01300.04684.0
2021Minnesota2301.0602.01200.01699.0
2122Pennsylvania3213.00.01100.03213.0
2223Iowa1670.0480.01100.01190.0
2324Wisconsin1951.0581.01050.01370.0
2425New Hampshire6288.00.01000.06288.0
2526New York5344.00.01000.05344.0
2627Vermont4393.095.01000.04298.0
2728Virginia5729.00.0950.05729.0
2829Tennessee6643.0178.0900.06465.0
2930Michigan1979.0572.0900.01407.0
3031Ohio1549.0455.0850.01094.0
3132Missouri1772.0230.0800.01542.0
3233Kentucky4139.0257.0750.03882.0
3334North Carolina6684.00.0700.06684.0
3435Indiana1257.0320.0700.0937.0
3536Arkansas2753.055.0650.02698.0
3637Maine5276.00.0600.05276.0
3738Georgia4784.00.0600.04784.0
3839Illinois1235.0279.0600.0956.0
3940Massachusetts3487.00.0500.03487.0
4041Alabama2407.00.0500.02407.0
4142Connecticut2380.00.0500.02380.0
4243South Carolina3560.00.0350.03560.0
4344Maryland3360.00.0350.03360.0
4445Mississippi806.00.0300.0806.0
4546New Jersey1803.00.0250.01803.0
4647Rhode Island812.00.0200.0812.0
4748Louisiana535.0-8.0100.0543.0
4849Florida345.00.0100.0345.0
4950Delaware450.00.060.0450.0
\n", + "
" + ], + "text/plain": [ + " Rank State Highest elevation Lowest elevation \\\n", + "0 1 Colorado 14440.0 3315.0 \n", + "1 2 Wyoming 13804.0 3099.0 \n", + "2 3 Utah 13528.0 2000.0 \n", + "3 4 New Mexico 13161.0 2842.0 \n", + "4 5 Nevada 13140.0 479.0 \n", + "5 6 Idaho 12662.0 710.0 \n", + "6 7 Arizona 12633.0 70.0 \n", + "7 8 Montana 12799.0 1800.0 \n", + "8 9 Oregon 11239.0 0.0 \n", + "9 10 Hawaii 13796.0 0.0 \n", + "10 11 California 14494.0 -282.0 \n", + "11 12 Nebraska 5424.0 840.0 \n", + "12 13 South Dakota 7242.0 966.0 \n", + "13 14 Kansas 4039.0 679.0 \n", + "14 15 Alaska 20320.0 0.0 \n", + "15 16 North Dakota 3506.0 750.0 \n", + "16 17 Washington 14410.0 0.0 \n", + "17 18 Texas 8749.0 0.0 \n", + "18 19 West Virginia 4863.0 240.0 \n", + "19 20 Oklahoma 4973.0 289.0 \n", + "20 21 Minnesota 2301.0 602.0 \n", + "21 22 Pennsylvania 3213.0 0.0 \n", + "22 23 Iowa 1670.0 480.0 \n", + "23 24 Wisconsin 1951.0 581.0 \n", + "24 25 New Hampshire 6288.0 0.0 \n", + "25 26 New York 5344.0 0.0 \n", + "26 27 Vermont 4393.0 95.0 \n", + "27 28 Virginia 5729.0 0.0 \n", + "28 29 Tennessee 6643.0 178.0 \n", + "29 30 Michigan 1979.0 572.0 \n", + "30 31 Ohio 1549.0 455.0 \n", + "31 32 Missouri 1772.0 230.0 \n", + "32 33 Kentucky 4139.0 257.0 \n", + "33 34 North Carolina 6684.0 0.0 \n", + "34 35 Indiana 1257.0 320.0 \n", + "35 36 Arkansas 2753.0 55.0 \n", + "36 37 Maine 5276.0 0.0 \n", + "37 38 Georgia 4784.0 0.0 \n", + "38 39 Illinois 1235.0 279.0 \n", + "39 40 Massachusetts 3487.0 0.0 \n", + "40 41 Alabama 2407.0 0.0 \n", + "41 42 Connecticut 2380.0 0.0 \n", + "42 43 South Carolina 3560.0 0.0 \n", + "43 44 Maryland 3360.0 0.0 \n", + "44 45 Mississippi 806.0 0.0 \n", + "45 46 New Jersey 1803.0 0.0 \n", + "46 47 Rhode Island 812.0 0.0 \n", + "47 48 Louisiana 535.0 -8.0 \n", + "48 49 Florida 345.0 0.0 \n", + "49 50 Delaware 450.0 0.0 \n", + "\n", + " Average elevation Range elevation \n", + "0 6800.0 11125.0 \n", + "1 6700.0 10705.0 \n", + "2 6100.0 11528.0 \n", + "3 5700.0 10319.0 \n", + "4 5500.0 12661.0 \n", + "5 5000.0 11952.0 \n", + "6 4100.0 12563.0 \n", + "7 3400.0 10999.0 \n", + "8 3300.0 11239.0 \n", + "9 3030.0 13796.0 \n", + "10 2900.0 14776.0 \n", + "11 2600.0 4584.0 \n", + "12 2200.0 6276.0 \n", + "13 2000.0 3360.0 \n", + "14 1900.0 20320.0 \n", + "15 1900.0 2756.0 \n", + "16 1700.0 14410.0 \n", + "17 1700.0 8749.0 \n", + "18 1500.0 4623.0 \n", + "19 1300.0 4684.0 \n", + "20 1200.0 1699.0 \n", + "21 1100.0 3213.0 \n", + "22 1100.0 1190.0 \n", + "23 1050.0 1370.0 \n", + "24 1000.0 6288.0 \n", + "25 1000.0 5344.0 \n", + "26 1000.0 4298.0 \n", + "27 950.0 5729.0 \n", + "28 900.0 6465.0 \n", + "29 900.0 1407.0 \n", + "30 850.0 1094.0 \n", + "31 800.0 1542.0 \n", + "32 750.0 3882.0 \n", + "33 700.0 6684.0 \n", + "34 700.0 937.0 \n", + "35 650.0 2698.0 \n", + "36 600.0 5276.0 \n", + "37 600.0 4784.0 \n", + "38 600.0 956.0 \n", + "39 500.0 3487.0 \n", + "40 500.0 2407.0 \n", + "41 500.0 2380.0 \n", + "42 350.0 3560.0 \n", + "43 350.0 3360.0 \n", + "44 300.0 806.0 \n", + "45 250.0 1803.0 \n", + "46 200.0 812.0 \n", + "47 100.0 543.0 \n", + "48 100.0 345.0 \n", + "49 60.0 450.0 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "elevation2['Range elevation'] = \\\n", "elevation2.apply(lambda row: row['Highest elevation'] - \n", @@ -279,18 +4097,848 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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RankStateHighest elevationLowest elevationAverage elevationRange elevation
01Colorado14440.03315.06800.011125.0
12Wyoming13804.03099.06700.010705.0
23Utah13528.02000.06100.011528.0
34New Mexico13161.02842.05700.010319.0
45Nevada13140.0479.05500.012661.0
56Idaho12662.0710.05000.011952.0
67Arizona12633.070.04100.012563.0
78Montana12799.01800.03400.010999.0
89Oregon11239.00.03300.011239.0
910Hawaii13796.00.03030.013796.0
1011California14494.0-282.02900.014776.0
1415Alaska20320.00.01900.020320.0
1617Washington14410.00.01700.014410.0
1718Texas8749.00.01700.08749.0
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" + ], + "text/plain": [ + " Rank State Highest elevation Lowest elevation Average elevation \\\n", + "0 1 Colorado 14440.0 3315.0 6800.0 \n", + "1 2 Wyoming 13804.0 3099.0 6700.0 \n", + "2 3 Utah 13528.0 2000.0 6100.0 \n", + "3 4 New Mexico 13161.0 2842.0 5700.0 \n", + "4 5 Nevada 13140.0 479.0 5500.0 \n", + "5 6 Idaho 12662.0 710.0 5000.0 \n", + "6 7 Arizona 12633.0 70.0 4100.0 \n", + "7 8 Montana 12799.0 1800.0 3400.0 \n", + "8 9 Oregon 11239.0 0.0 3300.0 \n", + "9 10 Hawaii 13796.0 0.0 3030.0 \n", + "10 11 California 14494.0 -282.0 2900.0 \n", + "14 15 Alaska 20320.0 0.0 1900.0 \n", + "16 17 Washington 14410.0 0.0 1700.0 \n", + "17 18 Texas 8749.0 0.0 1700.0 \n", + "\n", + " Range elevation \n", + "0 11125.0 \n", + "1 10705.0 \n", + "2 11528.0 \n", + "3 10319.0 \n", + "4 12661.0 \n", + "5 11952.0 \n", + "6 12563.0 \n", + "7 10999.0 \n", + "8 11239.0 \n", + "9 13796.0 \n", + "10 14776.0 \n", + "14 20320.0 \n", + "16 14410.0 \n", + "17 8749.0 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "elevation2[elevation2['Highest elevation'] > 8000]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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RankStateHighest elevationLowest elevationAverage elevationRange elevation
01Colorado14440.03315.06800.011125.0
12Wyoming13804.03099.06700.010705.0
23Utah13528.02000.06100.011528.0
34New Mexico13161.02842.05700.010319.0
45Nevada13140.0479.05500.012661.0
56Idaho12662.0710.05000.011952.0
67Arizona12633.070.04100.012563.0
78Montana12799.01800.03400.010999.0
89Oregon11239.00.03300.011239.0
910Hawaii13796.00.03030.013796.0
1011California14494.0-282.02900.014776.0
1112Nebraska5424.0840.02600.04584.0
1213South Dakota7242.0966.02200.06276.0
1314Kansas4039.0679.02000.03360.0
1415Alaska20320.00.01900.020320.0
1516North Dakota3506.0750.01900.02756.0
1617Washington14410.00.01700.014410.0
1718Texas8749.00.01700.08749.0
1819West Virginia4863.0240.01500.04623.0
1920Oklahoma4973.0289.01300.04684.0
2021Minnesota2301.0602.01200.01699.0
2122Pennsylvania3213.00.01100.03213.0
2223Iowa1670.0480.01100.01190.0
2324Wisconsin1951.0581.01050.01370.0
2425New Hampshire6288.00.01000.06288.0
2526New York5344.00.01000.05344.0
2627Vermont4393.095.01000.04298.0
2728Virginia5729.00.0950.05729.0
2829Tennessee6643.0178.0900.06465.0
2930Michigan1979.0572.0900.01407.0
3031Ohio1549.0455.0850.01094.0
3132Missouri1772.0230.0800.01542.0
3233Kentucky4139.0257.0750.03882.0
3334North Carolina6684.00.0700.06684.0
3435Indiana1257.0320.0700.0937.0
3536Arkansas2753.055.0650.02698.0
3637Maine5276.00.0600.05276.0
3738Georgia4784.00.0600.04784.0
3839Illinois1235.0279.0600.0956.0
3940Massachusetts3487.00.0500.03487.0
4041Alabama2407.00.0500.02407.0
4142Connecticut2380.00.0500.02380.0
4243South Carolina3560.00.0350.03560.0
4344Maryland3360.00.0350.03360.0
4445Mississippi806.00.0300.0806.0
4546New Jersey1803.00.0250.01803.0
4647Rhode Island812.00.0200.0812.0
4748Louisiana535.0-8.0100.0543.0
4849Florida345.00.0100.0345.0
4950Delaware450.00.060.0450.0
\n", + "
" + ], + "text/plain": [ + " Rank State Highest elevation Lowest elevation \\\n", + "0 1 Colorado 14440.0 3315.0 \n", + "1 2 Wyoming 13804.0 3099.0 \n", + "2 3 Utah 13528.0 2000.0 \n", + "3 4 New Mexico 13161.0 2842.0 \n", + "4 5 Nevada 13140.0 479.0 \n", + "5 6 Idaho 12662.0 710.0 \n", + "6 7 Arizona 12633.0 70.0 \n", + "7 8 Montana 12799.0 1800.0 \n", + "8 9 Oregon 11239.0 0.0 \n", + "9 10 Hawaii 13796.0 0.0 \n", + "10 11 California 14494.0 -282.0 \n", + "11 12 Nebraska 5424.0 840.0 \n", + "12 13 South Dakota 7242.0 966.0 \n", + "13 14 Kansas 4039.0 679.0 \n", + "14 15 Alaska 20320.0 0.0 \n", + "15 16 North Dakota 3506.0 750.0 \n", + "16 17 Washington 14410.0 0.0 \n", + "17 18 Texas 8749.0 0.0 \n", + "18 19 West Virginia 4863.0 240.0 \n", + "19 20 Oklahoma 4973.0 289.0 \n", + "20 21 Minnesota 2301.0 602.0 \n", + "21 22 Pennsylvania 3213.0 0.0 \n", + "22 23 Iowa 1670.0 480.0 \n", + "23 24 Wisconsin 1951.0 581.0 \n", + "24 25 New Hampshire 6288.0 0.0 \n", + "25 26 New York 5344.0 0.0 \n", + "26 27 Vermont 4393.0 95.0 \n", + "27 28 Virginia 5729.0 0.0 \n", + "28 29 Tennessee 6643.0 178.0 \n", + "29 30 Michigan 1979.0 572.0 \n", + "30 31 Ohio 1549.0 455.0 \n", + "31 32 Missouri 1772.0 230.0 \n", + "32 33 Kentucky 4139.0 257.0 \n", + "33 34 North Carolina 6684.0 0.0 \n", + "34 35 Indiana 1257.0 320.0 \n", + "35 36 Arkansas 2753.0 55.0 \n", + "36 37 Maine 5276.0 0.0 \n", + "37 38 Georgia 4784.0 0.0 \n", + "38 39 Illinois 1235.0 279.0 \n", + "39 40 Massachusetts 3487.0 0.0 \n", + "40 41 Alabama 2407.0 0.0 \n", + "41 42 Connecticut 2380.0 0.0 \n", + "42 43 South Carolina 3560.0 0.0 \n", + "43 44 Maryland 3360.0 0.0 \n", + "44 45 Mississippi 806.0 0.0 \n", + "45 46 New Jersey 1803.0 0.0 \n", + "46 47 Rhode Island 812.0 0.0 \n", + "47 48 Louisiana 535.0 -8.0 \n", + "48 49 Florida 345.0 0.0 \n", + "49 50 Delaware 450.0 0.0 \n", + "\n", + " Average elevation Range elevation \n", + "0 6800.0 11125.0 \n", + "1 6700.0 10705.0 \n", + "2 6100.0 11528.0 \n", + "3 5700.0 10319.0 \n", + "4 5500.0 12661.0 \n", + "5 5000.0 11952.0 \n", + "6 4100.0 12563.0 \n", + "7 3400.0 10999.0 \n", + "8 3300.0 11239.0 \n", + "9 3030.0 13796.0 \n", + "10 2900.0 14776.0 \n", + "11 2600.0 4584.0 \n", + "12 2200.0 6276.0 \n", + "13 2000.0 3360.0 \n", + "14 1900.0 20320.0 \n", + "15 1900.0 2756.0 \n", + "16 1700.0 14410.0 \n", + "17 1700.0 8749.0 \n", + "18 1500.0 4623.0 \n", + "19 1300.0 4684.0 \n", + "20 1200.0 1699.0 \n", + "21 1100.0 3213.0 \n", + "22 1100.0 1190.0 \n", + "23 1050.0 1370.0 \n", + "24 1000.0 6288.0 \n", + "25 1000.0 5344.0 \n", + "26 1000.0 4298.0 \n", + "27 950.0 5729.0 \n", + "28 900.0 6465.0 \n", + "29 900.0 1407.0 \n", + "30 850.0 1094.0 \n", + "31 800.0 1542.0 \n", + "32 750.0 3882.0 \n", + "33 700.0 6684.0 \n", + "34 700.0 937.0 \n", + "35 650.0 2698.0 \n", + "36 600.0 5276.0 \n", + "37 600.0 4784.0 \n", + "38 600.0 956.0 \n", + "39 500.0 3487.0 \n", + "40 500.0 2407.0 \n", + "41 500.0 2380.0 \n", + "42 350.0 3560.0 \n", + "43 350.0 3360.0 \n", + "44 300.0 806.0 \n", + "45 250.0 1803.0 \n", + "46 200.0 812.0 \n", + "47 100.0 543.0 \n", + "48 100.0 345.0 \n", + "49 60.0 450.0 " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "elevation2" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] File b'./Resources/mimic2_radreports_100.txt' does not exist: b'./Resources/mimic2_radreports_100.txt'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDATADIR\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"mimic2_radreports_100.txt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 700\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[0;32mdef\u001b[0m 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\u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File b'./Resources/mimic2_radreports_100.txt' does not exist: b'./Resources/mimic2_radreports_100.txt'" + ] + } + ], "source": [ "pd.read_table(os.path.join(DATADIR,\"mimic2_radreports_100.txt\"))" ] @@ -306,9 +4954,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] File b'./Resources/mimic2_radreports_100.txt' does not exist: b'./Resources/mimic2_radreports_100.txt'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m pd.read_table(\n\u001b[1;32m 2\u001b[0m os.path.join(DATADIR,\n\u001b[0;32m----> 3\u001b[0;31m \"mimic2_radreports_100.txt\")).dropna(how=\"all\")\n\u001b[0m", + "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, 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\u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File b'./Resources/mimic2_radreports_100.txt' does not exist: b'./Resources/mimic2_radreports_100.txt'" + ] + } + ], "source": [ "pd.read_table(\n", " os.path.join(DATADIR,\n", @@ -317,9 +4992,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n", + " after removing the cwd from sys.path.\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] File b'./Resources/mimic2_radreports_100.txt' does not exist: b'./Resources/mimic2_radreports_100.txt'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m os.path.join(DATADIR,\n\u001b[1;32m 3\u001b[0m \"mimic2_radreports_100.txt\"),\n\u001b[0;32m----> 4\u001b[0;31m usecols=[\"subject_id\", \"charttime\", \"text\"]).dropna(how=\"all\")\n\u001b[0m", + "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 700\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 897\u001b[0m \u001b[0;32mdef\u001b[0m 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\u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File b'./Resources/mimic2_radreports_100.txt' does not exist: b'./Resources/mimic2_radreports_100.txt'" + ] + } + ], "source": [ "reports = pd.read_table(\n", " os.path.join(DATADIR,\n", @@ -356,7 +5058,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -370,7 +5072,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/modules/module4/ROADMAP.ipynb b/modules/module4/ROADMAP.ipynb index 61d4c14..4bb3018 100644 --- a/modules/module4/ROADMAP.ipynb +++ b/modules/module4/ROADMAP.ipynb @@ -58,7 +58,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -72,7 +72,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/modules/module4/Resources/elevation2.txt b/modules/module4/Resources/elevation2.txt new file mode 100644 index 0000000..da56a43 --- /dev/null +++ b/modules/module4/Resources/elevation2.txt @@ -0,0 +1,51 @@ + Rank State Highest elevation Lowest elevation Average elevation +0 1 Colorado 14,440 3,315 6,800 +1 2 Wyoming 13,804 3,099 6,700 +2 3 Utah 13,528 2,000 6,100 +3 4 New Mexico 13,161 2,842 5,700 +4 5 Nevada 13,140 479 5,500 +5 6 Idaho 12,662 710 5,000 +6 7 Arizona 12,633 70 4,100 +7 8 Montana 12,799 1,800 3,400 +8 9 Oregon 11,239 0 3,300 +9 10 Hawaii 13,796 0 3,030 +10 11 California 14,494 -282 2,900 +11 12 Nebraska 5,424 840 2,600 +12 13 South Dakota 7,242 966 2,200 +13 14 Kansas 4,039 679 2,000 +14 15 Alaska 20,320 0 1,900 +15 16 North Dakota 3,506 750 1,900 +16 17 Washington 14,410 0 1,700 +17 18 Texas 8,749 0 1,700 +18 19 West Virginia 4,863 240 1,500 +19 20 Oklahoma 4,973 289 1,300 +20 21 Minnesota 2,301 602 1,200 +21 22 Pennsylvania 3,213 0 1,100 +22 23 Iowa 1,670 480 1,100 +23 24 Wisconsin 1,951 581 1,050 +24 25 New Hampshire 6,288 0 1,000 +25 26 New York 5,344 0 1,000 +26 27 Vermont 4,393 95 1,000 +27 28 Virginia 5,729 0 950 +28 29 Tennessee 6,643 178 900 +29 30 Michigan 1,979 572 900 +30 31 Ohio 1,549 455 850 +31 32 Missouri 1,772 230 800 +32 33 Kentucky 4,139 257 750 +33 34 North Carolina 6,684 0 700 +34 35 Indiana 1,257 320 700 +35 36 Arkansas 2,753 55 650 +36 37 Maine 5,276 0 600 +37 38 Georgia 4,784 0 600 +38 39 Illinois 1,235 279 600 +39 40 Massachusetts 3,487 0 500 +40 41 Alabama 2,407 0 500 +41 42 Connecticut 2,380 0 500 +42 43 South Carolina 3,560 0 350 +43 44 Maryland 3,360 0 350 +44 45 Mississippi 806 0 300 +45 46 New Jersey 1,803 0 250 +46 47 Rhode Island 812 0 200 +47 48 Louisiana 535 -8 100 +48 49 Florida 345 0 100 +49 50 Delaware 450 0 60 diff --git a/modules/module4/Visualization-Maps.ipynb b/modules/module4/Visualization-Maps.ipynb index 2ec86f4..3aba076 100644 --- a/modules/module4/Visualization-Maps.ipynb +++ b/modules/module4/Visualization-Maps.ipynb @@ -22,9 +22,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting bokeh==0.12.16\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/cd/47/201408029628164342e65a4552ee00abc79ea7be1b64031281b81b0e2f4d/bokeh-0.12.16.tar.gz (14.7MB)\n", + "\u001b[K |████████████████████████████████| 14.7MB 130kB/s eta 0:00:01 |██▍ | 1.1MB 971kB/s eta 0:00:14 |███████████████▋ | 7.1MB 971kB/s eta 0:00:08\n", + "\u001b[?25hRequirement already satisfied: six>=1.5.2 in /opt/conda/lib/python3.7/site-packages (from bokeh==0.12.16) (1.12.0)\n", + "Requirement already satisfied: PyYAML>=3.10 in /opt/conda/lib/python3.7/site-packages (from bokeh==0.12.16) (5.1)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.7/site-packages (from bokeh==0.12.16) (2.8.0)\n", + "Requirement already satisfied: Jinja2>=2.7 in /opt/conda/lib/python3.7/site-packages (from bokeh==0.12.16) (2.10.1)\n", + "Requirement already satisfied: numpy>=1.7.1 in /opt/conda/lib/python3.7/site-packages (from bokeh==0.12.16) (1.16.4)\n", + "Requirement already satisfied: packaging>=16.8 in /opt/conda/lib/python3.7/site-packages (from bokeh==0.12.16) (19.0)\n", + "Requirement already satisfied: tornado>=4.3 in /opt/conda/lib/python3.7/site-packages (from bokeh==0.12.16) (6.0.2)\n", + "Requirement already satisfied: MarkupSafe>=0.23 in /opt/conda/lib/python3.7/site-packages (from Jinja2>=2.7->bokeh==0.12.16) (1.1.1)\n", + "Requirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging>=16.8->bokeh==0.12.16) (2.4.0)\n", + "Building wheels for collected packages: bokeh\n", + " Building wheel for bokeh (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Stored in directory: /home/gastonq/.cache/pip/wheels/ff/28/51/22e8d08e9d5383ee1de981aaa8ff7bc53c7d65022e5101400f\n", + "Successfully built bokeh\n", + "Installing collected packages: bokeh\n", + " Found existing installation: bokeh 1.0.4\n", + " Uninstalling bokeh-1.0.4:\n", + " Successfully uninstalled bokeh-1.0.4\n", + "Successfully installed bokeh-0.12.16\n" + ] + } + ], + "source": [ + "!pip install bokeh=='0.12.16'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], "source": [ "import os\n", "import sqlite3 as sqlite\n", @@ -37,10 +82,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ + "\n", "from bokeh.io import output_notebook" ] }, @@ -53,9 +99,298 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id !== undefined) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var element_id = msg.content.text.trim();\n", + " Bokeh.index[element_id].model.document.clear();\n", + " delete Bokeh.index[element_id];\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(\"582c9885-3d8e-4668-aa4e-bf5372cd5d18\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };var element = document.getElementById(\"582c9885-3d8e-4668-aa4e-bf5372cd5d18\");\n", + " if (element == null) {\n", + " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '582c9885-3d8e-4668-aa4e-bf5372cd5d18' but no matching script tag was found. \")\n", + " return false;\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(\"582c9885-3d8e-4668-aa4e-bf5372cd5d18\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"582c9885-3d8e-4668-aa4e-bf5372cd5d18\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"582c9885-3d8e-4668-aa4e-bf5372cd5d18\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '582c9885-3d8e-4668-aa4e-bf5372cd5d18' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"582c9885-3d8e-4668-aa4e-bf5372cd5d18\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "output_notebook()" ] @@ -69,23 +404,436 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
LongitudeLatitudeNumber_of_CasualtiesDate
0NaNNaN118/01/1979
1NaNNaN101/01/1979
2NaNNaN301/01/1979
3NaNNaN201/01/1979
4NaNNaN101/01/1979
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" + ], + "text/plain": [ + " Longitude Latitude Number_of_Casualties Date\n", + "0 NaN NaN 1 18/01/1979\n", + "1 NaN NaN 1 01/01/1979\n", + "2 NaN NaN 3 01/01/1979\n", + "3 NaN NaN 2 01/01/1979\n", + "4 NaN NaN 1 01/01/1979" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = pd.read_csv(os.path.join(DATADIR,\n", - " \"Accidents7904.csv\"),\n", - " usecols = ['Longitude',\"Latitude\",\"Date\"]).dropna()\n", + " \"Accidents7904.csv\"),nrows=5000,\n", + " usecols = ['Longitude',\"Latitude\",\"Date\",\"Number_of_Casualties\"])#.dropna()\n", "data.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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"data.dtypes" + "data.describe()\n", + "#data.dtypes" ] }, { @@ -97,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -108,9 +856,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'Range1d' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m plot = GMapPlot(\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mx_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRange1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0my_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRange1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mmap_options\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmap_options\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'Range1d' is not defined" + ] + } + ], "source": [ "from bokeh.io import output_file, show\n", "from bokeh.models import (\n", @@ -124,8 +884,8 @@ " map_type=\"roadmap\", zoom=6)\n", "\n", "plot = GMapPlot(\n", - " x_range=DataRange1d(), \n", - " y_range=DataRange1d(), \n", + " x_range=Range1d(), \n", + " y_range=Range1d(), \n", " map_options=map_options\n", ")\n", "plot.title.text = \"U.K. Road Accidents\"\n", @@ -158,7 +918,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -172,7 +932,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.4" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/modules/module4/pandas_hdf5.ipynb b/modules/module4/pandas_hdf5.ipynb index 5d2895d..66a8f1f 100644 --- a/modules/module4/pandas_hdf5.ipynb +++ b/modules/module4/pandas_hdf5.ipynb @@ -9,18 +9,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "% matplotlib inline" + "%matplotlib inline" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], "source": [ "import os\n", "import shutil\n", @@ -116,9 +124,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: read_table is deprecated, use read_csv instead, passing sep='\\t'.\n", + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time to read original file 42.98869180679321\n" + ] + } + ], "source": [ "import time\n", "url_txt = \\\n", @@ -140,9 +164,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time to read hdf5 file 1.1325137615203857\n" + ] + } + ], "source": [ "url_txt = os.path.join(DATADIR,\"PANCAN12.IlluminaHiSeq_RNASeqV2.geneExp.tumor_whitelist.hdf5\")\n", "start = time.time()\n", @@ -160,9 +192,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'data' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined" + ] + } + ], "source": [ "data[0:2]\n" ] @@ -183,18 +227,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting package metadata: done\n", + "Solving environment: done\n", + "\n", + "\n", + "==> WARNING: A newer version of conda exists. <==\n", + " current version: 4.6.14\n", + " latest version: 4.7.5\n", + "\n", + "Please update conda by running\n", + "\n", + " $ conda update -n base conda\n", + "\n", + "\n", + "\n", + "# All requested packages already installed.\n", + "\n" + ] + } + ], "source": [ "!conda install pytables -y" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HDF5 92.4795 x faster than traditional read\n" + ] + } + ], "source": [ "url_hdf = os.path.join(DATADIR, \"PANCAN12.IlluminaHiSeq_RNASeqV2.geneExp.tumor_whitelist.hdf5\")\n", "start = time.time()\n", @@ -205,18 +280,1867 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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#probeTCGA-02-0047TCGA-02-0055TCGA-02-2483TCGA-02-2485TCGA-02-2486TCGA-04-1348TCGA-04-1357TCGA-04-1362TCGA-04-1364...TCGA-HD-7831TCGA-HD-7832TCGA-HD-7917TCGA-HN-A2NLTCGA-HQ-A2OETCGA-IQ-7630TCGA-IQ-7631TCGA-IQ-7632TCGA-J2-8192TCGA-J2-8194
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2A2BP1244.629501137.351105111.028999257.1429144.2683000.2689000.6510004.30250010.600600...46.1959990.0000000.4209000.0000000.2310000.6658003.8791000.0000002.2525000.000000
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5A2M34012.42187542876.26171921058.5195317798.53320340971.4257815899.8281259384.4404303350.4206541455.231567...13529.6318361277.7901612233.9772955096.6293952753.2270511538.41931212355.9404301815.98584026093.91601623738.701172
6A4GALT36.264198487.73651186.965698155.23809843.90240192.498001298.177094697.991882147.984207...1530.8214111234.3038331365.31994672.5539021116.886108602.5836181796.5748292748.630859751.196594211.513306
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14AADACL40.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.424000...0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
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16AADAT233.56590399.789803259.208405190.000000217.07319641.94680021.484400107.892502210.739700...78.53330227.70960057.659901350.506714134.67309616.31300050.98239994.19500079.586998109.027496
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22AARSD1325.762909432.235504627.335083470.000000460.975586243.076096427.734406364.716492378.228912...352.244812664.679077539.141418519.458374769.923828463.756287725.944824601.314270407.320496533.362427
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25AASDH241.556305294.884399230.501297229.523804248.170700165.904800171.223999273.040894262.894592...155.911697238.512802360.269409170.995499297.297302226.052094158.488693154.435898410.699188243.349304
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..................................................................
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20501 rows × 3272 columns

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