diff --git a/your-code/main.ipynb b/your-code/main.ipynb
deleted file mode 100755
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--- a/your-code/main.ipynb
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@@ -1,420 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Before your start:\n",
-    "- Read the README.md file\n",
-    "- Comment as much as you can and use the resources in the README.md file\n",
-    "- Happy learning!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Import reduce from functools, numpy and pandas"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Challenge 1 - Mapping\n",
-    "\n",
-    "#### We will use the map function to clean up words in a book.\n",
-    "\n",
-    "In the following cell, we will read a text file containing the book The Prophet by Khalil Gibran."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Run this code:\n",
-    "\n",
-    "location = '../data/58585-0.txt'\n",
-    "with open(location, 'r', encoding=\"utf8\") as f:\n",
-    "    prophet = f.read().split(' ')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Let's remove the first 568 words since they contain information about the book but are not part of the book itself. \n",
-    "\n",
-    "Do this by removing from `prophet` elements 0 through 567 of the list (you can also do this by keeping elements 568 through the last element)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you look through the words, you will find that many words have a reference attached to them. For example, let's look at words 1 through 10."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### The next step is to create a function that will remove references. \n",
-    "\n",
-    "We will do this by splitting the string on the `{` character and keeping only the part before this character. Write your function below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def reference(x):\n",
-    "    '''\n",
-    "    Input: A string\n",
-    "    Output: The string with references removed\n",
-    "    \n",
-    "    Example:\n",
-    "    Input: 'the{7}'\n",
-    "    Output: 'the'\n",
-    "    '''\n",
-    "    \n",
-    "    # your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have our function, use the `map()` function to apply this function to our book, The Prophet. Return the resulting list to a new list called `prophet_reference`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another thing you may have noticed is that some words contain a line break. Let's write a function to split those words. Our function will return the string split on the character `\\n`. Write your function in the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def line_break(x):\n",
-    "    '''\n",
-    "    Input: A string\n",
-    "    Output: A list of strings split on the line break (\\n) character\n",
-    "        \n",
-    "    Example:\n",
-    "    Input: 'the\\nbeloved'\n",
-    "    Output: ['the', 'beloved']\n",
-    "    '''\n",
-    "    \n",
-    "    # your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Apply the `line_break` function to the `prophet_reference` list. Name the new list `prophet_line`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you look at the elements of `prophet_line`, you will see that the function returned lists and not strings. Our list is now a list of lists. Flatten the list using list comprehension. Assign this new list to `prophet_flat`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Challenge 2 - Filtering\n",
-    "\n",
-    "When printing out a few words from the book, we see that there are words that we may not want to keep if we choose to analyze the corpus of text. Below is a list of words that we would like to get rid of. Create a function that will return false if it contains a word from the list of words specified and true otherwise."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def word_filter(x):\n",
-    "    '''\n",
-    "    Input: A string\n",
-    "    Output: True if the word is not in the specified list \n",
-    "    and False if the word is in the list.\n",
-    "        \n",
-    "    Example:\n",
-    "    word list = ['and', 'the']\n",
-    "    Input: 'and'\n",
-    "    Output: False\n",
-    "    \n",
-    "    Input: 'John'\n",
-    "    Output: True\n",
-    "    '''\n",
-    "    \n",
-    "    word_list = ['and', 'the', 'a', 'an']\n",
-    "    \n",
-    "    # your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the `filter()` function to filter out the words speficied in the `word_filter()` function. Store the filtered list in the variable `prophet_filter`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Bonus Challenge\n",
-    "\n",
-    "Rewrite the `word_filter` function above to not be case sensitive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def word_filter_case(x):\n",
-    "   \n",
-    "    word_list = ['and', 'the', 'a', 'an']\n",
-    "    \n",
-    "    # your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Challenge 3 - Reducing\n",
-    "\n",
-    "#### Now that we have significantly cleaned up our text corpus, let's use the `reduce()` function to put the words back together into one long string separated by spaces. \n",
-    "\n",
-    "We will start by writing a function that takes two strings and concatenates them together with a space between the two strings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def concat_space(a, b):\n",
-    "    '''\n",
-    "    Input:Two strings\n",
-    "    Output: A single string separated by a space\n",
-    "        \n",
-    "    Example:\n",
-    "    Input: 'John', 'Smith'\n",
-    "    Output: 'John Smith'\n",
-    "    '''\n",
-    "    \n",
-    "    # your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the function above to reduce the text corpus in the list `prophet_filter` into a single string. Assign this new string to the variable `prophet_string`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Bonus Challenge 2 - Applying Functions to DataFrames\n",
-    "\n",
-    "#### Our next step is to use the apply function to a dataframe and transform all cells.\n",
-    "\n",
-    "To do this, we will connect to Ironhack's database and retrieve the data from the *pollution* database. Select the *beijing_pollution* table and retrieve its data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's look at the data using the `head()` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next step is to create a function that divides a cell by 24 to produce an hourly figure. Write the function below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def hourly(x):\n",
-    "    '''\n",
-    "    Input: A numerical value\n",
-    "    Output: The value divided by 24\n",
-    "        \n",
-    "    Example:\n",
-    "    Input: 48\n",
-    "    Output: 2.0\n",
-    "    '''\n",
-    "    \n",
-    "    # your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Apply this function to the columns `Iws`, `Is`, and `Ir`. Store this new dataframe in the variable `pm25_hourly`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Our last challenge will be to create an aggregate function and apply it to a select group of columns in our dataframe.\n",
-    "\n",
-    "Write a function that returns the standard deviation of a column divided by the length of a column minus 1. Since we are using pandas, do not use the `len()` function. One alternative is to use `count()`. Also, use the numpy version of standard deviation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sample_sd(x):\n",
-    "    '''\n",
-    "    Input: A Pandas series of values\n",
-    "    Output: the standard deviation divided by the number of elements in the series\n",
-    "        \n",
-    "    Example:\n",
-    "    Input: pd.Series([1,2,3,4])\n",
-    "    Output: 0.3726779962\n",
-    "    '''\n",
-    "    \n",
-    "    # your code here"
-   ]
-  }
- ],
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-  "kernelspec": {
-   "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": "3.7.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}