Python_Introduction_Answer_Key.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python: A Tutorial in Jupyter Notebook\n",
    "### Adapted from scipy lectures: https://scipy-lectures.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why Python?\n",
    "\n",
    "The scientist's needs\n",
    "- Get data (simulation, experiment control),\n",
    "- Manipulate and process data,\n",
    "- Visualize results, quickly to understand, but also with high quality figures, for reports or publications.\n",
    "\n",
    "Python's strengths\n",
    "- Batteries included: Rich collection of already existing bricks of classic numerical methods, plotting or data processing tools. We don’t want to re-program the plotting of a curve, a Fourier transform or a fitting algorithm. Don’t reinvent the wheel!\n",
    "\n",
    "- Easy to learn: Most scientists are not paid as programmers, neither have they been trained so. They need to be able to draw a curve, smooth a signal, do a Fourier transform in a few minutes.\n",
    "\n",
    "- Easy communication: To keep code alive within a lab or a company it should be as readable as a book by collaborators, students, or maybe customers. Python syntax is simple, avoiding strange symbols or lengthy routine specifications that would divert the reader from mathematical or scientific understanding of the code.\n",
    "\n",
    "- Efficient code: Python numerical modules are computationally efficient. But needless to say that a very fast code becomes useless if too much time is spent writing it. Python aims for quick development times and quick execution times.\n",
    "\n",
    "- Universal: Python is a language used for many different problems. Learning Python avoids learning a new software for each new problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Installing Python and Anaconda\n",
    "\n",
    "Python comes in many flavors, and there are many ways to install it. However, we recommend to install a scientific-computing distribution that comes readily with optimized versions of scientific modules.\n",
    "\n",
    "There are several fully-featured scientific Python distributions. We strongly recommend the usage of Anaconda and the Anaconda Navigator. An overview and installation instructions can be found at the following links:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://docs.anaconda.com/anaconda/navigator/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://docs.anaconda.com/anaconda/navigator/install/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Installation Instructions (depending on your operating system)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Windows: https://docs.anaconda.com/anaconda/install/windows/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mac OS: https://docs.anaconda.com/anaconda/install/mac-os/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linux: https://docs.anaconda.com/anaconda/install/linux/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On Windows, macOS, and Linux, it is best to install Anaconda for the local user, which does not require administrator permissions and is the most robust type of installation. However, if you need to, you can install Anaconda system wide, which does require administrator permissions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have installed Anaconda or Anaconda Navigator, you can use Jupyter notebooks! These are interactive Python (and R) shells where you can write code and recieve real-time results. These are highly recommended for performing all single-cell analysis, as they allow for reproducibility of analysis and quick customization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information can be found at: https://jupyter.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have installed Anacoda and created a conda environment on the command line, such as using the Terminal application, then you can start jupyter notebooks from the terminal using the command: ```jupyter notebook```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are working in the Anaconda Navigator, you can use Jupyter notebooks directly in the Anaconda Navigator. To do this, use the guide here: https://docs.anaconda.com/anaconda/user-guide/tasks/use-jupyter-notebook-extensions/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have installed Python and Anaconda, it is time to familiarize yourself with the basics of Python programming and programming language syntax! To do this, study the scipy lectures tutorial provided in the first link and answer the programming questions below. Hint: if you are unsure about how to answer one of the questions, please read the corresponding sections of the scipy lectures tutorial indicated as \"necessary material.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q0) Install Anaconda or Anaconda Navigator for your operating system (Windows, MacOS, or Linux). Are you able to open this Jupyter notebook as an interactive shell?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start with a question 0 to give the opportunity to remind you that Python is ZERO INDEXED! In other words, if you have a list of items, the first item in the list is accessed using the zeroth index, the second item in the list is accessed using the first index, and so on!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If you can, run the following code cell by pressing shift+enter or the \"Run\" button at the top of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, world!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello, world!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For questions Q1 to Q10, please read through the corresponding sections of the scipy lecture notes (https://scipy-lectures.org/) to learn the material and provide your answers in a notebook cell."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get started by reviewing the basics of an interactive session and Python in **section 1**: https://scipy-lectures.org/intro/index.html."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Topics**\n",
    "* **Basic types (integers, floats, strings): https://scipy-lectures.org/intro/language/basic_types.html**\n",
    "* **Containers (lists, tuples, dictionaries): https://scipy-lectures.org/intro/language/basic_types.html**\n",
    "* **Control flow (if/else statements, for loops, list comprehensions): https://scipy-lectures.org/intro/language/control_flow.html**\n",
    "* **Functions: https://scipy-lectures.org/intro/language/functions.html**\n",
    "* **Packages: https://scipy-lectures.org/intro/language/reusing_code.html#packages**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1) Define a list containing the following floats: ```1, 4.2, 3, 5.1, 8, 2, 0.7```. Write a for-loop to iterate through each item in the list. For each item, if the value is greater than 5, add 1, and otherwise add 2. Append the sum for each item to a new list. Print the new list to the console output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Necessary material: sections 1.2.2 and 1.2.3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 6.2, 5, 6.1, 9, 4, 2.7]\n"
     ]
    }
   ],
   "source": [
    "my_list = [1, 4.2, 3, 5.1, 8, 2, 0.7]\n",
    "new_list = []\n",
    "for item in my_list:\n",
    "    if item > 5:\n",
    "        new_list.append(item+1) \n",
    "    else:\n",
    "        new_list.append(item+2)\n",
    "print(new_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2) Define a dictionary with the following key-value pairs: ```(\"one-squared\", 1), (\"two\"squared\", 2), (\"three-squared\", 3), (\"four-squared\", 4), (\"five-squared\", 5)```. Write a for loop or list comprehension to iterate over each key-value pair and update the values to be equal to their square (i.e. to the power of two). Print the resulting dictionary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Necessary material: section 1.2.3**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'one-squared': 1, 'two-squared': 4, 'three-squared': 9, 'four-squared': 16, 'five-squared': 25}\n"
     ]
    }
   ],
   "source": [
    "my_dict = {\"one-squared\":1, \"two-squared\":2, \"three-squared\":3, \"four-squared\":4, \"five-squared\":5}\n",
    "for k, v in my_dict.items():\n",
    "    my_dict[k] = v**2\n",
    "print(my_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Topics**\n",
    "* **Numpy arrays (indexing, slicing, copying, masking): https://scipy-lectures.org/intro/numpy/array_object.html**\n",
    "* **Numpy arrays (broadcasting, reshaping, resizing): https://scipy-lectures.org/intro/numpy/operations.html**\n",
    "* **Additional numpy exercises: https://scipy-lectures.org/intro/numpy/exercises.html**\n",
    "* **Matplotlib data visualization: https://scipy-lectures.org/intro/matplotlib/index.html**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q3) Define a numpy array consisting of the numbers 1 to 20 (hint: use ```np.arange``` to assign the values). Write a function ```reverse()``` that takes a numpy array and returns an array containing the items in a reverse order (hint: you might need to use a for-loop)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Bonus: is there a way to reverse an array in one line of code?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Necessary material: sections 1.2.3, 1.2.4, 1.4.1, 1.4.2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19., 18., 17., 16., 15., 14., 13., 12., 11., 10.,  9.,  8.,  7.,\n",
       "        6.,  5.,  4.,  3.,  2.,  1.,  0.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def reverse(arr):\n",
    "    new_arr = np.zeros((len(arr)))\n",
    "    for i in range(0, len(arr)):\n",
    "        new_arr[len(arr)-i-1] = i\n",
    "    return new_arr\n",
    "        \n",
    "my_arr = np.arange(0, 20)\n",
    "reverse(my_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19, 18, 17, 16, 15, 14, 13, 12, 11, 10,  9,  8,  7,  6,  5,  4,  3,\n",
       "        2,  1,  0])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arr[::-1] # one liner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q4) Below, we define a 4x4 numpy array. Using this array, compute the mean for each column. Plot a histogram of the column means using matplotlib (code provided)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Necessary material: sections 1.4.1, 1.4.2, optional: 1.5**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_arr = np.array([[1.0, 22.0, 12.0, 22.0, 27.0, 14.0, 15.0, 14.0, 16.0, 5.0, 10.0, 4.0, 0.0, 24.0, 15.0, 14.0, 27.0, 2.0, 14.0, 24.0, 13.0, 8.0, 0.0, 12.0, 35.0, 3.0, 19.0, 11.0, 11.0, 9.0, 15.0, 11.0, 7.0, 7.0, 22.0, 13.0, 7.0, 18.0, 17.0, 18.0, 0.0, 3.0, 10.0, 8.0, 19.0, 9.0, 11.0, 21.0, 5.0, 10.0, 7.0, 15.0, 28.0, 29.0, 1.0, 8.0, 16.0, 2.0, 12.0, 5.0, 16.0, 14.0, 0.0, 27.0, 1.0, 28.0, 10.0, 21.0, 4.0, 8.0, 4.0, 10.0, 13.0, 26.0, 26.0, 8.0, 1.0, 4.0, 15.0, 11.0, 10.0, 21.0, 13.0, 26.0, 21.0, 16.0, 7.0, 13.0, 8.0, 1.0, 3.0, 10.0, 6.0, 4.0, 4.0, 25.0, 18.0, 18.0, 6.0, 0.0],\n",
    "[21.0, 13.0, 27.0, 8.0, 25.0, 18.0, 0.0, 5.0, 4.0, 24.0, 9.0, 18.0, 14.0, 29.0, 1.0, 2.0, 17.0, 20.0, 0.0, 15.0, 17.0, 11.0, 17.0, 12.0, 17.0, 7.0, 18.0, 5.0, 26.0, 30.0, 17.0, 12.0, 22.0, 6.0, 26.0, 1.0, 26.0, 24.0, 0.0, 19.0, 1.0, 10.0, 4.0, 11.0, 2.0, 8.0, 32.0, 6.0, 12.0, 12.0, 21.0, 18.0, 12.0, 12.0, 9.0, 4.0, 14.0, 0.0, 12.0, 1.0, 29.0, 10.0, 15.0, 10.0, 23.0, 16.0, 7.0, 30.0, 29.0, 27.0, 0.0, 17.0, 11.0, 2.0, 11.0, 7.0, 27.0, 14.0, 13.0, 16.0, 9.0, 7.0, 33.0, 0.0, 15.0, 1.0, 13.0, 8.0, 23.0, 11.0, 11.0, 27.0, 3.0, 7.0, 20.0, 0.0, 7.0, 19.0, 20.0, 16.0],\n",
    "[12.0, 10.0, 9.0, 11.0, 16.0, 1.0, 13.0, 15.0, 16.0, 21.0, 0.0, 24.0, 0.0, 9.0, 18.0, 15.0, 20.0, 16.0, 0.0, 0.0, 19.0, 28.0, 23.0, 16.0, 24.0, 13.0, 34.0, 18.0, 10.0, 15.0, 7.0, 14.0, 22.0, 1.0, 16.0, 7.0, 30.0, 1.0, 29.0, 17.0, 0.0, 15.0, 2.0, 8.0, 8.0, 0.0, 0.0, 9.0, 14.0, 18.0, 11.0, 11.0, 17.0, 2.0, 25.0, 7.0, 22.0, 21.0, 5.0, 7.0, 21.0, 22.0, 10.0, 19.0, 7.0, 0.0, 8.0, 12.0, 0.0, 0.0, 16.0, 25.0, 20.0, 9.0, 15.0, 5.0, 14.0, 0.0, 11.0, 1.0, 27.0, 0.0, 11.0, 3.0, 17.0, 8.0, 22.0, 28.0, 13.0, 2.0, 1.0, 18.0, 3.0, 16.0, 16.0, 7.0, 1.0, 4.0, 16.0, 2.0],\n",
    "[11.0, 12.0, 2.0, 7.0, 0.0, 0.0, 5.0, 8.0, 0.0, 0.0, 11.0, 5.0, 0.0, 0.0, 11.0, 25.0, 19.0, 16.0, 0.0, 0.0, 20.0, 0.0, 10.0, 14.0, 7.0, 1.0, 12.0, 12.0, 6.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 14.0, 6.0, 0.0, 17.0, 7.0, 0.0, 1.0, 7.0, 4.0, 14.0, 2.0, 0.0, 7.0, 0.0, 0.0, 13.0, 0.0, 8.0, 23.0, 11.0, 30.0, 6.0, 0.0, 0.0, 17.0, 0.0, 0.0, 0.0, 5.0, 0.0, 0.0, 3.0, 16.0, 6.0, 3.0, 0.0, 5.0, 8.0, 22.0, 22.0, 17.0, 0.0, 3.0, 0.0, 11.0, 11.0, 21.0, 15.0, 6.0, 1.0, 6.0, 0.0, 0.0, 11.0, 6.0, 15.0, 5.0, 8.0, 2.0, 5.0, 10.0, 8.0, 7.0, 0.0],\n",
    "[23.0, 12.0, 12.0, 24.0, 1.0, 7.0, 7.0, 16.0, 0.0, 39.0, 13.0, 1.0, 24.0, 0.0, 0.0, 10.0, 8.0, 0.0, 15.0, 12.0, 0.0, 27.0, 0.0, 17.0, 9.0, 24.0, 2.0, 7.0, 8.0, 14.0, 20.0, 15.0, 12.0, 9.0, 7.0, 19.0, 4.0, 8.0, 0.0, 5.0, 3.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 12.0, 24.0, 15.0, 0.0, 1.0, 10.0, 0.0, 8.0, 8.0, 19.0, 13.0, 20.0, 24.0, 0.0, 20.0, 4.0, 0.0, 7.0, 12.0, 17.0, 16.0, 21.0, 0.0, 5.0, 21.0, 0.0, 5.0, 1.0, 20.0, 0.0, 0.0, 3.0, 19.0, 10.0, 23.0, 0.0, 19.0, 25.0, 6.0, 1.0, 26.0, 8.0, 0.0, 9.0, 4.0, 0.0, 0.0, 13.0, 17.0, 0.0, 0.0, 9.0, 10.0],\n",
    "[6.0, 10.0, 12.0, 5.0, 32.0, 0.0, 20.0, 18.0, 37.0, 10.0, 18.0, 0.0, 0.0, 0.0, 10.0, 20.0, 27.0, 0.0, 22.0, 5.0, 3.0, 4.0, 24.0, 0.0, 10.0, 0.0, 20.0, 13.0, 23.0, 2.0, 30.0, 25.0, 15.0, 0.0, 25.0, 14.0, 10.0, 15.0, 6.0, 27.0, 11.0, 14.0, 18.0, 1.0, 0.0, 4.0, 2.0, 10.0, 12.0, 0.0, 0.0, 12.0, 8.0, 0.0, 8.0, 4.0, 5.0, 19.0, 13.0, 9.0, 23.0, 9.0, 15.0, 20.0, 20.0, 0.0, 22.0, 20.0, 18.0, 6.0, 15.0, 25.0, 17.0, 12.0, 20.0, 4.0, 7.0, 9.0, 0.0, 0.0, 17.0, 9.0, 6.0, 21.0, 0.0, 0.0, 0.0, 0.0, 24.0, 6.0, 9.0, 7.0, 14.0, 10.0, 9.0, 8.0, 15.0, 7.0, 4.0, 0.0],\n",
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    "[17.0, 11.0, 0.0, 6.0, 17.0, 2.0, 0.0, 26.0, 19.0, 6.0, 20.0, 11.0, 13.0, 9.0, 11.0, 0.0, 19.0, 33.0, 9.0, 18.0, 2.0, 11.0, 5.0, 18.0, 14.0, 19.0, 9.0, 32.0, 6.0, 17.0, 16.0, 8.0, 10.0, 10.0, 8.0, 16.0, 39.0, 34.0, 0.0, 16.0, 17.0, 14.0, 24.0, 15.0, 5.0, 0.0, 18.0, 6.0, 11.0, 14.0, 13.0, 15.0, 15.0, 19.0, 10.0, 8.0, 0.0, 0.0, 14.0, 18.0, 14.0, 8.0, 8.0, 18.0, 0.0, 12.0, 5.0, 19.0, 22.0, 17.0, 14.0, 0.0, 0.0, 7.0, 30.0, 18.0, 11.0, 11.0, 23.0, 5.0, 21.0, 25.0, 17.0, 30.0, 9.0, 5.0, 13.0, 0.0, 0.0, 10.0, 8.0, 3.0, 9.0, 27.0, 16.0, 31.0, 20.0, 2.0, 6.0, 0.0],\n",
    "[15.0, 0.0, 21.0, 23.0, 31.0, 0.0, 6.0, 18.0, 8.0, 3.0, 23.0, 0.0, 23.0, 19.0, 22.0, 19.0, 13.0, 13.0, 4.0, 4.0, 30.0, 0.0, 4.0, 4.0, 5.0, 14.0, 0.0, 1.0, 0.0, 0.0, 19.0, 15.0, 14.0, 0.0, 14.0, 16.0, 30.0, 9.0, 0.0, 1.0, 14.0, 35.0, 1.0, 10.0, 9.0, 9.0, 0.0, 30.0, 15.0, 7.0, 3.0, 0.0, 23.0, 0.0, 0.0, 12.0, 9.0, 16.0, 26.0, 17.0, 6.0, 0.0, 4.0, 0.0, 26.0, 15.0, 13.0, 0.0, 2.0, 11.0, 3.0, 22.0, 36.0, 8.0, 16.0, 11.0, 0.0, 5.0, 0.0, 4.0, 1.0, 3.0, 0.0, 5.0, 14.0, 14.0, 17.0, 0.0, 15.0, 14.0, 4.0, 0.0, 22.0, 18.0, 11.0, 0.0, 5.0, 0.0, 15.0, 22.0],\n",
    "[0.0, 15.0, 7.0, 16.0, 15.0, 15.0, 5.0, 4.0, 0.0, 5.0, 5.0, 1.0, 12.0, 8.0, 25.0, 0.0, 0.0, 3.0, 0.0, 7.0, 12.0, 19.0, 3.0, 8.0, 1.0, 18.0, 0.0, 0.0, 0.0, 10.0, 21.0, 5.0, 7.0, 1.0, 13.0, 0.0, 6.0, 0.0, 13.0, 13.0, 9.0, 25.0, 0.0, 13.0, 3.0, 0.0, 6.0, 8.0, 7.0, 0.0, 0.0, 3.0, 14.0, 4.0, 0.0, 18.0, 0.0, 16.0, 15.0, 0.0, 0.0, 7.0, 3.0, 0.0, 0.0, 0.0, 5.0, 0.0, 3.0, 19.0, 0.0, 5.0, 0.0, 2.0, 2.0, 0.0, 10.0, 0.0, 21.0, 1.0, 20.0, 3.0, 8.0, 22.0, 0.0, 2.0, 0.0, 0.0, 22.0, 2.0, 0.0, 7.0, 0.0, 1.0, 5.0, 2.0, 16.0, 12.0, 18.0, 7.0],\n",
    "[5.0, 17.0, 25.0, 0.0, 5.0, 8.0, 6.0, 8.0, 15.0, 0.0, 8.0, 15.0, 10.0, 0.0, 8.0, 1.0, 3.0, 5.0, 13.0, 11.0, 10.0, 0.0, 6.0, 8.0, 7.0, 0.0, 0.0, 0.0, 0.0, 0.0, 6.0, 15.0, 0.0, 5.0, 5.0, 17.0, 11.0, 30.0, 17.0, 12.0, 15.0, 26.0, 9.0, 0.0, 1.0, 0.0, 15.0, 13.0, 0.0, 1.0, 6.0, 8.0, 20.0, 1.0, 0.0, 0.0, 0.0, 21.0, 4.0, 5.0, 0.0, 11.0, 7.0, 7.0, 0.0, 6.0, 15.0, 19.0, 9.0, 8.0, 4.0, 16.0, 7.0, 0.0, 9.0, 19.0, 0.0, 14.0, 12.0, 0.0, 13.0, 17.0, 0.0, 0.0, 0.0, 20.0, 11.0, 4.0, 8.0, 0.0, 5.0, 2.0, 9.0, 0.0, 13.0, 0.0, 0.0, 0.0, 11.0, 20.0],\n",
    "[24.0, 22.0, 0.0, 4.0, 3.0, 0.0, 9.0, 0.0, 16.0, 16.0, 0.0, 22.0, 7.0, 22.0, 24.0, 2.0, 19.0, 22.0, 17.0, 8.0, 9.0, 33.0, 3.0, 11.0, 17.0, 22.0, 11.0, 11.0, 8.0, 9.0, 15.0, 11.0, 8.0, 17.0, 3.0, 6.0, 0.0, 11.0, 18.0, 2.0, 22.0, 9.0, 5.0, 15.0, 4.0, 26.0, 3.0, 14.0, 16.0, 7.0, 30.0, 10.0, 9.0, 17.0, 5.0, 0.0, 0.0, 7.0, 2.0, 10.0, 10.0, 0.0, 6.0, 25.0, 0.0, 22.0, 12.0, 0.0, 14.0, 11.0, 0.0, 14.0, 14.0, 9.0, 11.0, 11.0, 15.0, 23.0, 20.0, 3.0, 0.0, 1.0, 0.0, 8.0, 13.0, 14.0, 18.0, 15.0, 25.0, 8.0, 6.0, 2.0, 21.0, 12.0, 32.0, 0.0, 22.0, 0.0, 10.0, 15.0],\n",
    "[10.0, 19.0, 10.0, 8.0, 31.0, 9.0, 9.0, 25.0, 0.0, 7.0, 0.0, 10.0, 0.0, 12.0, 14.0, 10.0, 30.0, 13.0, 6.0, 3.0, 13.0, 19.0, 11.0, 27.0, 23.0, 10.0, 3.0, 19.0, 5.0, 21.0, 7.0, 8.0, 19.0, 0.0, 10.0, 7.0, 8.0, 20.0, 7.0, 31.0, 23.0, 13.0, 8.0, 0.0, 5.0, 16.0, 18.0, 13.0, 20.0, 15.0, 17.0, 3.0, 12.0, 1.0, 25.0, 16.0, 7.0, 21.0, 12.0, 16.0, 17.0, 6.0, 0.0, 9.0, 10.0, 1.0, 14.0, 22.0, 1.0, 5.0, 14.0, 6.0, 25.0, 3.0, 26.0, 12.0, 1.0, 0.0, 9.0, 16.0, 12.0, 14.0, 14.0, 5.0, 21.0, 11.0, 16.0, 22.0, 22.0, 24.0, 6.0, 1.0, 11.0, 0.0, 20.0, 20.0, 18.0, 25.0, 6.0, 0.0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_means = my_arr.sum(axis=0)/my_arr.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(column_means, bins=20) # you must define column_means as a np.array of column means for my_arr\n",
    "plt.xlabel(\"Value\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q5) Use ```my_arr``` from the previous question. Define a variable ```count``` equal to zero. Write a for-loop to iterate through the values of ```my_arr```. For each item, if it is greater than the column mean for its respective column, add 1 to count. What is the value of count? This should provide a total count of the number of values in ```my_arr``` that are greater than their column mean. How can we represent this as a percentage instead of a count?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bonus: how can this be done with one line of code?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Necessary material: section 1.2.2, 1.4**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2309\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "for r in np.arange(my_arr.shape[0]):\n",
    "    for c in np.arange(my_arr.shape[1]):\n",
    "        if my_arr[r, c] > column_means[c]:\n",
    "            count+=1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46.18 %\n"
     ]
    }
   ],
   "source": [
    "print(count/(my_arr.shape[0] * my_arr.shape[1])*100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2309"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum((my_arr>column_means).flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Topics**\n",
    "* **Pandas package: https://pandas.pydata.org/docs/**\n",
    "* **Reading/writing pandas data frames from csv files: https://pandas.pydata.org/pandas-docs/stable/reference/io.html**\n",
    "* **DataFrame manipulation and indexing (loc, iloc, ix): https://pandas.pydata.org/pandas-docs/stable/reference/frame.html**\n",
    "* **DataFrame Sorting (sort_index, sort_values): https://pandas.pydata.org/pandas-docs/stable/reference/frame.html**\n",
    "* **Manipulations (mean, sum, groupby, count): https://pandas.pydata.org/pandas-docs/stable/reference/groupby.html**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We will now introduce pandas data frames. Use the new link below, to the pandas documentation, to answer the remaining questions. We have converted ```my_arr``` into a pandas data frame and defined names for the columns and rows. The ability to define the row and column names is one of the advantages for pandas data frames compared to numpy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pandas.pydata.org/docs/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(my_arr).T # T transforms the data frame, i.e. flips the rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = [\"Cell\"+str(i) for i in range(0, df.shape[0])]\n",
    "df.columns = [\"Gene\"+str(i) for i in range(0, df.shape[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene0</th>\n",
       "      <th>Gene1</th>\n",
       "      <th>Gene2</th>\n",
       "      <th>Gene3</th>\n",
       "      <th>Gene4</th>\n",
       "      <th>Gene5</th>\n",
       "      <th>Gene6</th>\n",
       "      <th>Gene7</th>\n",
       "      <th>Gene8</th>\n",
       "      <th>Gene9</th>\n",
       "      <th>...</th>\n",
       "      <th>Gene40</th>\n",
       "      <th>Gene41</th>\n",
       "      <th>Gene42</th>\n",
       "      <th>Gene43</th>\n",
       "      <th>Gene44</th>\n",
       "      <th>Gene45</th>\n",
       "      <th>Gene46</th>\n",
       "      <th>Gene47</th>\n",
       "      <th>Gene48</th>\n",
       "      <th>Gene49</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cell0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell3</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell4</th>\n",
       "      <td>27.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell95</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell96</th>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell97</th>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell98</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell99</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gene0  Gene1  Gene2  Gene3  Gene4  Gene5  Gene6  Gene7  Gene8  Gene9  \\\n",
       "Cell0     1.0   21.0   12.0   11.0   23.0    6.0   17.0   11.0    0.0    7.0   \n",
       "Cell1    22.0   13.0   10.0   12.0   12.0   10.0   17.0    0.0   22.0   22.0   \n",
       "Cell2    12.0   27.0    9.0    2.0   12.0   12.0    5.0    0.0   29.0   16.0   \n",
       "Cell3    22.0    8.0   11.0    7.0   24.0    5.0    0.0   13.0    1.0    2.0   \n",
       "Cell4    27.0   25.0   16.0    0.0    1.0   32.0    0.0   23.0   11.0    8.0   \n",
       "...       ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "Cell95   25.0    0.0    7.0    5.0   17.0    8.0   12.0    1.0    0.0    9.0   \n",
       "Cell96   18.0    7.0    1.0   10.0    0.0   15.0   13.0   11.0    7.0   16.0   \n",
       "Cell97   18.0   19.0    4.0    8.0    0.0    7.0    1.0   20.0    0.0    3.0   \n",
       "Cell98    6.0   20.0   16.0    7.0    9.0    4.0    7.0   36.0   24.0    9.0   \n",
       "Cell99    0.0   16.0    2.0    0.0   10.0    0.0    4.0   33.0   13.0    9.0   \n",
       "\n",
       "        ...  Gene40  Gene41  Gene42  Gene43  Gene44  Gene45  Gene46  Gene47  \\\n",
       "Cell0   ...    15.0    13.0     0.0     0.0    17.0    15.0     0.0     5.0   \n",
       "Cell1   ...    19.0    15.0    21.0     0.0    11.0     0.0    15.0    17.0   \n",
       "Cell2   ...     5.0    16.0     0.0     0.0     0.0    21.0     7.0    25.0   \n",
       "Cell3   ...    15.0    22.0    14.0     0.0     6.0    23.0    16.0     0.0   \n",
       "Cell4   ...    12.0    19.0     4.0     0.0    17.0    31.0    15.0     5.0   \n",
       "...     ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "Cell95  ...    20.0    27.0    15.0    26.0    31.0     0.0     2.0     0.0   \n",
       "Cell96  ...    13.0    23.0     3.0     2.0    20.0     5.0    16.0     0.0   \n",
       "Cell97  ...    26.0     0.0     9.0    27.0     2.0     0.0    12.0     0.0   \n",
       "Cell98  ...     3.0    12.0    16.0    13.0     6.0    15.0    18.0    11.0   \n",
       "Cell99  ...     8.0     8.0     6.0     5.0     0.0    22.0     7.0    20.0   \n",
       "\n",
       "        Gene48  Gene49  \n",
       "Cell0     24.0    10.0  \n",
       "Cell1     22.0    19.0  \n",
       "Cell2      0.0    10.0  \n",
       "Cell3      4.0     8.0  \n",
       "Cell4      3.0    31.0  \n",
       "...        ...     ...  \n",
       "Cell95     0.0    20.0  \n",
       "Cell96    22.0    18.0  \n",
       "Cell97     0.0    25.0  \n",
       "Cell98    10.0     6.0  \n",
       "Cell99    15.0     0.0  \n",
       "\n",
       "[100 rows x 50 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 100)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q6) Print the sum of Gene15 and Gene24 for Cell2. Is this greater than the sum of Gene15 and Gene24 for Cell3?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"Cell2\"][\"Gene15\"] + df.loc[\"Cell2\"][\"Gene24\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.loc[\"Cell2\"][\"Gene15\"] + df.loc[\"Cell2\"][\"Gene24\"]) > (df.loc[\"Cell3\"][\"Gene15\"] + df.loc[\"Cell3\"][\"Gene24\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q7) Create a new \"Gene50\" column. Assign each cell a random value from a normal distribution with mean 20 and standard deviation 10 for \"Gene50\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Necessary material: https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html\n",
    "        https://numpy.org/doc/1.16/reference/routines.random.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Gene50\"] = np.random.normal(20, 10, size=df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q8) Now we want to know the mean gene expression for each cell type. Compute this by using pandas groupby to aggregate all columns by their cell type, and take the mean for each gene. Hint: this can be done in a single line!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Necessary material: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene0</th>\n",
       "      <th>Gene1</th>\n",
       "      <th>Gene2</th>\n",
       "      <th>Gene3</th>\n",
       "      <th>Gene4</th>\n",
       "      <th>Gene5</th>\n",
       "      <th>Gene6</th>\n",
       "      <th>Gene7</th>\n",
       "      <th>Gene8</th>\n",
       "      <th>Gene9</th>\n",
       "      <th>...</th>\n",
       "      <th>Gene41</th>\n",
       "      <th>Gene42</th>\n",
       "      <th>Gene43</th>\n",
       "      <th>Gene44</th>\n",
       "      <th>Gene45</th>\n",
       "      <th>Gene46</th>\n",
       "      <th>Gene47</th>\n",
       "      <th>Gene48</th>\n",
       "      <th>Gene49</th>\n",
       "      <th>Gene50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Retina</th>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.878480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brain</th>\n",
       "      <td>22.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>28.145908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart</th>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>28.836561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart</th>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-7.082442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kidney</th>\n",
       "      <td>27.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>29.054162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retina</th>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.676351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brain</th>\n",
       "      <td>18.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.544907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retina</th>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.806614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lung</th>\n",
       "      <td>6.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>32.396720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heart</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.244372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gene0  Gene1  Gene2  Gene3  Gene4  Gene5  Gene6  Gene7  Gene8  Gene9  \\\n",
       "Retina    1.0   21.0   12.0   11.0   23.0    6.0   17.0   11.0    0.0    7.0   \n",
       "Brain    22.0   13.0   10.0   12.0   12.0   10.0   17.0    0.0   22.0   22.0   \n",
       "Heart    12.0   27.0    9.0    2.0   12.0   12.0    5.0    0.0   29.0   16.0   \n",
       "Heart    22.0    8.0   11.0    7.0   24.0    5.0    0.0   13.0    1.0    2.0   \n",
       "Kidney   27.0   25.0   16.0    0.0    1.0   32.0    0.0   23.0   11.0    8.0   \n",
       "...       ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "Retina   25.0    0.0    7.0    5.0   17.0    8.0   12.0    1.0    0.0    9.0   \n",
       "Brain    18.0    7.0    1.0   10.0    0.0   15.0   13.0   11.0    7.0   16.0   \n",
       "Retina   18.0   19.0    4.0    8.0    0.0    7.0    1.0   20.0    0.0    3.0   \n",
       "Lung      6.0   20.0   16.0    7.0    9.0    4.0    7.0   36.0   24.0    9.0   \n",
       "Heart     0.0   16.0    2.0    0.0   10.0    0.0    4.0   33.0   13.0    9.0   \n",
       "\n",
       "        ...  Gene41  Gene42  Gene43  Gene44  Gene45  Gene46  Gene47  Gene48  \\\n",
       "Retina  ...    13.0     0.0     0.0    17.0    15.0     0.0     5.0    24.0   \n",
       "Brain   ...    15.0    21.0     0.0    11.0     0.0    15.0    17.0    22.0   \n",
       "Heart   ...    16.0     0.0     0.0     0.0    21.0     7.0    25.0     0.0   \n",
       "Heart   ...    22.0    14.0     0.0     6.0    23.0    16.0     0.0     4.0   \n",
       "Kidney  ...    19.0     4.0     0.0    17.0    31.0    15.0     5.0     3.0   \n",
       "...     ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "Retina  ...    27.0    15.0    26.0    31.0     0.0     2.0     0.0     0.0   \n",
       "Brain   ...    23.0     3.0     2.0    20.0     5.0    16.0     0.0    22.0   \n",
       "Retina  ...     0.0     9.0    27.0     2.0     0.0    12.0     0.0     0.0   \n",
       "Lung    ...    12.0    16.0    13.0     6.0    15.0    18.0    11.0    10.0   \n",
       "Heart   ...     8.0     6.0     5.0     0.0    22.0     7.0    20.0    15.0   \n",
       "\n",
       "        Gene49     Gene50  \n",
       "Retina    10.0  25.878480  \n",
       "Brain     19.0  28.145908  \n",
       "Heart     10.0  28.836561  \n",
       "Heart      8.0  -7.082442  \n",
       "Kidney    31.0  29.054162  \n",
       "...        ...        ...  \n",
       "Retina    20.0  16.676351  \n",
       "Brain     18.0  14.544907  \n",
       "Retina    25.0  24.806614  \n",
       "Lung       6.0  32.396720  \n",
       "Heart      0.0  -1.244372  \n",
       "\n",
       "[100 rows x 51 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = np.array(['Retina', 'Brain', 'Heart', 'Heart', 'Kidney', 'Brain', 'Brain',\n",
    "       'Heart', 'Heart', 'Kidney', 'Brain', 'Kidney', 'Heart', 'Kidney',\n",
    "       'Heart', 'Brain', 'Brain', 'Retina', 'Brain', 'Heart', 'Retina',\n",
    "       'Retina', 'Kidney', 'Heart', 'Lung', 'Kidney', 'Heart', 'Kidney',\n",
    "       'Lung', 'Kidney', 'Lung', 'Kidney', 'Retina', 'Kidney', 'Heart',\n",
    "       'Retina', 'Brain', 'Lung', 'Heart', 'Heart', 'Heart', 'Retina',\n",
    "       'Lung', 'Lung', 'Retina', 'Brain', 'Kidney', 'Retina', 'Kidney',\n",
    "       'Lung', 'Heart', 'Brain', 'Retina', 'Lung', 'Retina', 'Brain',\n",
    "       'Kidney', 'Heart', 'Kidney', 'Heart', 'Kidney', 'Brain', 'Kidney',\n",
    "       'Kidney', 'Heart', 'Lung', 'Kidney', 'Kidney', 'Retina', 'Lung',\n",
    "       'Retina', 'Heart', 'Brain', 'Lung', 'Retina', 'Retina', 'Brain',\n",
    "       'Lung', 'Lung', 'Brain', 'Brain', 'Brain', 'Brain', 'Kidney',\n",
    "       'Retina', 'Retina', 'Retina', 'Brain', 'Retina', 'Heart', 'Lung',\n",
    "       'Retina', 'Heart', 'Kidney', 'Brain', 'Retina', 'Brain', 'Retina',\n",
    "       'Lung', 'Heart'])\n",
    "df.columns = [\"Gene\"+str(i) for i in np.arange(0, df.shape[1])]\n",
    "df.head(df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q9) We now want to find out which cell types have the maximum and minimum expression of each gene. Use the idxmax and idxmin functions to determine this for each gene."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.idxmax.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gene0       Lung\n",
       "Gene1     Kidney\n",
       "Gene2     Retina\n",
       "Gene3      Brain\n",
       "Gene4     Kidney\n",
       "Gene5      Heart\n",
       "Gene6     Retina\n",
       "Gene7       Lung\n",
       "Gene8     Retina\n",
       "Gene9      Brain\n",
       "Gene10      Lung\n",
       "Gene11      Lung\n",
       "Gene12     Brain\n",
       "Gene13    Kidney\n",
       "Gene14      Lung\n",
       "Gene15    Kidney\n",
       "Gene16     Heart\n",
       "Gene17     Heart\n",
       "Gene18    Kidney\n",
       "Gene19     Brain\n",
       "Gene20    Kidney\n",
       "Gene21     Heart\n",
       "Gene22     Brain\n",
       "Gene23     Heart\n",
       "Gene24      Lung\n",
       "Gene25     Brain\n",
       "Gene26      Lung\n",
       "Gene27      Lung\n",
       "Gene28     Heart\n",
       "Gene29    Kidney\n",
       "Gene30    Retina\n",
       "Gene31     Heart\n",
       "Gene32    Retina\n",
       "Gene33     Brain\n",
       "Gene34    Retina\n",
       "Gene35    Kidney\n",
       "Gene36    Kidney\n",
       "Gene37    Kidney\n",
       "Gene38      Lung\n",
       "Gene39    Retina\n",
       "Gene40    Retina\n",
       "Gene41     Brain\n",
       "Gene42      Lung\n",
       "Gene43      Lung\n",
       "Gene44      Lung\n",
       "Gene45     Heart\n",
       "Gene46     Heart\n",
       "Gene47     Heart\n",
       "Gene48      Lung\n",
       "Gene49    Retina\n",
       "Gene50     Brain\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avgs = df.groupby(axis=0, level=0).mean()\n",
    "avgs.idxmax(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gene0      Heart\n",
       "Gene1      Heart\n",
       "Gene2       Lung\n",
       "Gene3       Lung\n",
       "Gene4       Lung\n",
       "Gene5     Retina\n",
       "Gene6      Brain\n",
       "Gene7      Brain\n",
       "Gene8       Lung\n",
       "Gene9       Lung\n",
       "Gene10    Kidney\n",
       "Gene11    Kidney\n",
       "Gene12     Heart\n",
       "Gene13     Heart\n",
       "Gene14     Brain\n",
       "Gene15      Lung\n",
       "Gene16     Brain\n",
       "Gene17    Kidney\n",
       "Gene18    Retina\n",
       "Gene19     Heart\n",
       "Gene20    Retina\n",
       "Gene21     Brain\n",
       "Gene22      Lung\n",
       "Gene23      Lung\n",
       "Gene24    Kidney\n",
       "Gene25     Heart\n",
       "Gene26     Brain\n",
       "Gene27    Kidney\n",
       "Gene28    Retina\n",
       "Gene29    Retina\n",
       "Gene30    Kidney\n",
       "Gene31      Lung\n",
       "Gene32    Kidney\n",
       "Gene33    Kidney\n",
       "Gene34     Heart\n",
       "Gene35     Heart\n",
       "Gene36    Retina\n",
       "Gene37     Brain\n",
       "Gene38     Heart\n",
       "Gene39     Heart\n",
       "Gene40    Kidney\n",
       "Gene41    Kidney\n",
       "Gene42     Heart\n",
       "Gene43     Brain\n",
       "Gene44     Heart\n",
       "Gene45      Lung\n",
       "Gene46    Kidney\n",
       "Gene47    Kidney\n",
       "Gene48     Brain\n",
       "Gene49      Lung\n",
       "Gene50      Lung\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avgs.idxmin(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q10) Often, it is useful to compare the average expression of each gene between two groups of cells, in order to determine which genes are more highly or more lowly expressed in a particular cell type. Using your answer from Q8, in which you computed the mean for each gene by cell type, make a scatter plot using the matplotlib library (i.e. ```matplotlib.pyplot.scatter```) in which each point is a gene, the x-axis corresponds to the average expression in the \"Brain\", and the y-axis corresponds to the average expression in the \"Lung\". Don't forget to label your axes with ```plt.xlabel``` and ```plt.ylabel```!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "q8_result = df.groupby(axis=0, level=0).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(q8_result.loc[\"Brain\"], q8_result.loc[\"Lung\"])\n",
    "plt.xlabel(\"Mean Gene Expression Brain\")\n",
    "plt.ylabel(\"Mean Gene Expression Lung\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great job, you are at the end of the tutorial! Now you have all the basic ingredients to start working with some real single cell RNA-sequencing datasets!"
   ]
  }
 ],
 "metadata": {
  "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}