Lab Comprension listas Indira Percastigui

your-code/.ipynb_checkpoints/main-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# List Comprehensions Lab\n",
+    "\n",
+    "Complete the following set of exercises to solidify your knowledge of list comprehensions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Use a list comprehension to create and print a list of consecutive integers starting with 1 and ending with 50."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls_int = [i for i in range (1,51)]\n",
+    "\n",
+    "print(ls_int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Use a list comprehension to create and print a list of even numbers starting with 2 and ending with 200."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls_even = [i for i in range (2,201,2)] \n",
+    "print(ls_even)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Use a list comprehension to create and print a list containing all elements of the 10 x 4 Numpy array below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([[0.84062117, 0.48006452, 0.7876326 , 0.77109654],\n",
+    "       [0.44409793, 0.09014516, 0.81835917, 0.87645456],\n",
+    "       [0.7066597 , 0.09610873, 0.41247947, 0.57433389],\n",
+    "       [0.29960807, 0.42315023, 0.34452557, 0.4751035 ],\n",
+    "       [0.17003563, 0.46843998, 0.92796258, 0.69814654],\n",
+    "       [0.41290051, 0.19561071, 0.16284783, 0.97016248],\n",
+    "       [0.71725408, 0.87702738, 0.31244595, 0.76615487],\n",
+    "       [0.20754036, 0.57871812, 0.07214068, 0.40356048],\n",
+    "       [0.12149553, 0.53222417, 0.9976855 , 0.12536346],\n",
+    "       [0.80930099, 0.50962849, 0.94555126, 0.33364763]])\n",
+    "\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls_matriz = [n for vector in a for n in vector]\n",
+    "\n",
+    "print(ls_matriz)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Add a condition to the list comprehension above so that only values greater than or equal to 0.5 are printed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls_matriz = [n for vector in a for n in vector if n>=0.5]\n",
+    "print(ls_matriz)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Use a list comprehension to create and print a list containing all elements of the 5 x 2 x 3 Numpy array below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = np.array([[[0.55867166, 0.06210792, 0.08147297],\n",
+    "        [0.82579068, 0.91512478, 0.06833034]],\n",
+    "\n",
+    "       [[0.05440634, 0.65857693, 0.30296619],\n",
+    "        [0.06769833, 0.96031863, 0.51293743]],\n",
+    "\n",
+    "       [[0.09143215, 0.71893382, 0.45850679],\n",
+    "        [0.58256464, 0.59005654, 0.56266457]],\n",
+    "\n",
+    "       [[0.71600294, 0.87392666, 0.11434044],\n",
+    "        [0.8694668 , 0.65669313, 0.10708681]],\n",
+    "\n",
+    "       [[0.07529684, 0.46470767, 0.47984544],\n",
+    "        [0.65368638, 0.14901286, 0.23760688]]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls_matriz2 = [n for matriz in b \n",
+    "              for vector in matriz \n",
+    "              for n in vector]\n",
+    "\n",
+    "print (ls_matriz2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Add a condition to the list comprehension above so that the last value in each subarray is printed, but only if it is less than or equal to 0.5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ls_matriz3 = [n for matriz in b \n",
+    "              for vector in matriz \n",
+    "              for n in vector if vector[-1]==n and n<=0.5]   #Ultimo elemento sobre el vector\n",
+    "\n",
+    "print (ls_matriz3)\n",
+    "\n",
+    "ls_matriz4 = [n for matriz in b \n",
+    "              for vector in matriz \n",
+    "              for n in vector if matriz[-1][-1]==n and n<=0.5]   #Ultimo elemento sobre la matriz \n",
+    "\n",
+    "print (ls_matriz4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Use a list comprehension to select and print the names of all CSV files in the */data* directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files = r\"C:\\Users\\indir\\Documents\\carpeta_Bootcamp\\lab-list-comprehension\\data\"\n",
+    "\n",
+    "file_list = [f for f in os.listdir(files) if f.endswith(\".csv\")]\n",
+    "\n",
+    "print(file_list)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 7. Use a list comprehension and the Pandas `read_csv` and `concat` methods to read all CSV files in the */data* directory and combine them into a single data frame. Display the top 10 rows of the resulting data frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 8. Use a list comprehension to select and print the column numbers for columns from the data set whose median is less than 0.48."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 9. Use a list comprehension to add a new column (20) to the data frame whose values are the values in column 19 minus 0.1. Display the top 10 rows of the resulting data frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 10. Use a list comprehension to extract and print all values from the data set that are between 0.7 and 0.75."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}