06 - Week/6 - NumPy and Pandas with Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical: NumPy and Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro to NumPy\n",
    "\n",
    "NumPy is a Python library for scientific computing. It provides high-performance multidimensional data structure called\n",
    "arrays. NumPy is usually imported like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arrays\n",
    "\n",
    "A NumPy array is a tensor of values all of the same type. The number of dimensions is the rank of the array, the shape\n",
    "of an array is a tuple of integers giving the size of the array along each dimension.\n",
    "\n",
    "We can create a NumPy array (aka a vector) from Python lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([1, 2, 3, 4])"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4])\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous code cell has created a NumPy array of rank 1. To check that this is a NumPy array we can use the\n",
    "function `type`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "numpy.ndarray"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we are given an array and we need to know its shape, we can use its attribute `shape`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(4,)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What shape returns is a tuple containing the length of each dimension. In this case, we have only one dimension of\n",
    "size 4.\n",
    "\n",
    "To access the values of a one-dimensional array we can do like we do when using Python lists:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "1"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now create a 2-dimensional array (aka a matrix in math):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1, 2, 3, 4],\n       [5, 6, 7, 8]])"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
    "a2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use shape to get the length of the dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(2, 4)"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a matrix (with rank 2) with 2 rows and 4 columns.\n",
    "\n",
    "To access the individual values of this matrix we can do like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "1"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2[0, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will access the value positioned at the first row and first column. Try to access other locations until you get\n",
    "and error.\n",
    "\n",
    "NumPy also provides some handy functions to create commonly used tensors when developing scientific applications.\n",
    "To use these functions we just need to give a tuple as a parameter defining the size of each dimensions.\n",
    "\n",
    "For example, use zeros to create a tensor filled with zeros:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0., 0., 0., 0.],\n       [0., 0., 0., 0.],\n       [0., 0., 0., 0.],\n       [0., 0., 0., 0.]])"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((4, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use ones to create a tensor filled with ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1., 1., 1., 1.],\n       [1., 1., 1., 1.],\n       [1., 1., 1., 1.],\n       [1., 1., 1., 1.]])"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((4, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use random.random to create a tensor filled with random values $\\in [0,1]$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0.94647877, 0.57113212, 0.79166982, 0.64481641],\n       [0.76992908, 0.40182176, 0.37303434, 0.80260845],\n       [0.76415036, 0.51619216, 0.70856672, 0.39991868],\n       [0.55102859, 0.57135672, 0.68962596, 0.25676674]])"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((4, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a matrix containing a given value, we can either use the method full and provide the given value,\n",
    "or create a tensor of ones and multiplying it by the given value, or create a tensor of zeros and sum the given value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[3, 3, 3, 3],\n       [3, 3, 3, 3],\n       [3, 3, 3, 3],\n       [3, 3, 3, 3]])"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full((4, 4), 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[3., 3., 3., 3.],\n       [3., 3., 3., 3.],\n       [3., 3., 3., 3.],\n       [3., 3., 3., 3.]])"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((4, 4)) * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[3., 3., 3., 3.],\n       [3., 3., 3., 3.],\n       [3., 3., 3., 3.],\n       [3., 3., 3., 3.]])"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((4, 4)) + 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two code cells above also demonstrate that in NumPy we can easily do arithmetic operations between tensors and\n",
    "scalars.\n",
    "\n",
    "To create a matrix with the diagonal full with ones (aka the identity matrix), we can use the method eye like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1., 0., 0., 0.],\n       [0., 1., 0., 0.],\n       [0., 0., 1., 0.],\n       [0., 0., 0., 1.]])"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array Selection\n",
    "\n",
    "We can select a part of a tensor using the slicing operator of Python. However, in NumPy the range operator can be\n",
    "used with multi-dimensional tensors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 1,  2,  3,  4],\n       [ 5,  6,  7,  8],\n       [ 9, 10, 11, 12]])"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, to select only the 2x2 matrix on the top right, we can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[3, 4],\n       [7, 8]])"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:2, 2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to select the last row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 9, 10, 11, 12]])"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to select the last two columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 3,  4],\n       [ 7,  8],\n       [11, 12]])"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:,2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use a list of integers to extract only the coordinates we want, for example if we want to extract only the\n",
    "values of the 4 corners of the matrix `a`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([ 1,  4,  9, 12])"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[[0, 0, 2, 2],[0, 3, 0, 3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arithmetic Operators with Arrays\n",
    "\n",
    "To sum a scalar to a matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[4., 3., 3., 3.],\n       [3., 4., 3., 3.],\n       [3., 3., 4., 3.],\n       [3., 3., 3., 4.]])"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.eye(4) \n",
    "a + 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To subtract a scalar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[-4., -5., -5., -5.],\n       [-5., -4., -5., -5.],\n       [-5., -5., -4., -5.],\n       [-5., -5., -5., -4.]])"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a - 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To multiply a scalar to a matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[3., 0., 0., 0.],\n       [0., 3., 0., 0.],\n       [0., 0., 3., 0.],\n       [0., 0., 0., 3.]])"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To divide by a scalar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0.33333333, 0.        , 0.        , 0.        ],\n       [0.        , 0.33333333, 0.        , 0.        ],\n       [0.        , 0.        , 0.33333333, 0.        ],\n       [0.        , 0.        , 0.        , 0.33333333]])"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a / 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To exponentiate by a value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[8., 1., 1., 1.],\n       [1., 8., 1., 1.],\n       [1., 1., 8., 1.],\n       [1., 1., 1., 8.]])"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a + 1) ** 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also do matrix operations like the dot product like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([13, 20])"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 = np.array([4, 3])\n",
    "a2 = np.array([[1, 2],[3, 4]])\n",
    "\n",
    "a1 @ a2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can transpose a matrix like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1, 3],\n       [2, 4]])"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison Operators with Arrays\n",
    "\n",
    "We can use the Python comparison operators with arrays. However, this will not return a Boolean value but an array o\n",
    "f Boolean values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([False, False,  True, False])"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4])\n",
    "a == 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, greater or equal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([False, False,  True,  True])"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a >= 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extended Datatypes\n",
    "\n",
    "NumPy is implemented in C. This allows us to use C data-types like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([1, 2, 3, 4], dtype=int32)"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4], dtype=np.int32)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can find details about C datatypes [here](https://docs.scipy.org/doc/numpy/user/basics.types.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 27\n",
    "\n",
    "Compute the following sequence when n is equal to 5:\n",
    "\n",
    "$\n",
    "a_0 = \n",
    "\\begin{bmatrix}\n",
    "1&0\\\\\n",
    "0&1\\\\\n",
    "\\end{bmatrix}\n",
    "$\n",
    "\n",
    "$\n",
    "a_n = \n",
    "\\begin{bmatrix}\n",
    "1\\\\\n",
    "2\\\\\n",
    "\\end{bmatrix} \\cdot \n",
    "\\begin{bmatrix}\n",
    "2&1\\\\\n",
    "\\end{bmatrix} -\n",
    "a_{n-1}$\n",
    "\n",
    "\n",
    "The result should be:\n",
    "\n",
    "$a_5 = \\begin{bmatrix}\n",
    "1&1\\\\\n",
    "4&1\\\\\n",
    "\\end{bmatrix}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1., 1.],\n       [4., 1.]])"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = 5\n",
    "\n",
    "a = np.eye(2)\n",
    "for _ in range(n):\n",
    "    a = np.array([[1], [2]]) @ np.array([[2, 1]]) - a\n",
    "\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro to Pandas\n",
    "\n",
    "Pandas is an open source package providing high-performance easy-to-use data structures and data analysis tools for\n",
    "the Python programming language.\n",
    "\n",
    "Pandas is usually imported using the following import statement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas defines two data structures called DataFrame and Series. \n",
    "\n",
    "### Read a CSV\n",
    "\n",
    "To read a CSV file we can use the read_csv method. This will create a DataFrame. To read a CSV you can pass to this\n",
    "method either a file path to a CSV file stored locally, or an URL to a CSV file which pandas will download and parse\n",
    "automatically.\n",
    "\n",
    "For this tutorial we will work with a famous dataset called IRIS. This dataset describes the features of 3 different\n",
    "species of flowers. Each row is a flower and each column is a different measurement made on the flowers with the\n",
    "exception of the last column which represents the species of the flower:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_length  sepal_width  petal_length  petal_width    species\n0             5.1          3.5           1.4          0.2     setosa\n1             4.9          3.0           1.4          0.2     setosa\n2             4.7          3.2           1.3          0.2     setosa\n3             4.6          3.1           1.5          0.2     setosa\n4             5.0          3.6           1.4          0.2     setosa\n..            ...          ...           ...          ...        ...\n145           6.7          3.0           5.2          2.3  virginica\n146           6.3          2.5           5.0          1.9  virginica\n147           6.5          3.0           5.2          2.0  virginica\n148           6.2          3.4           5.4          2.3  virginica\n149           5.9          3.0           5.1          1.8  virginica\n\n[150 rows x 5 columns]",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>6.7</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>6.3</td>\n      <td>2.5</td>\n      <td>5.0</td>\n      <td>1.9</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>6.5</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.0</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>6.2</td>\n      <td>3.4</td>\n      <td>5.4</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>5.9</td>\n      <td>3.0</td>\n      <td>5.1</td>\n      <td>1.8</td>\n      <td>virginica</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the first 5 rows of the DataFrame by using head:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   sepal_length  sepal_width  petal_length  petal_width species\n0           5.1          3.5           1.4          0.2  setosa\n1           4.9          3.0           1.4          0.2  setosa\n2           4.7          3.2           1.3          0.2  setosa\n3           4.6          3.1           1.5          0.2  setosa\n4           5.0          3.6           1.4          0.2  setosa",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or tail:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_length  sepal_width  petal_length  petal_width    species\n145           6.7          3.0           5.2          2.3  virginica\n146           6.3          2.5           5.0          1.9  virginica\n147           6.5          3.0           5.2          2.0  virginica\n148           6.2          3.4           5.4          2.3  virginica\n149           5.9          3.0           5.1          1.8  virginica",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>145</th>\n      <td>6.7</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>6.3</td>\n      <td>2.5</td>\n      <td>5.0</td>\n      <td>1.9</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>6.5</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.0</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>6.2</td>\n      <td>3.4</td>\n      <td>5.4</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>5.9</td>\n      <td>3.0</td>\n      <td>5.1</td>\n      <td>1.8</td>\n      <td>virginica</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like NumPy, we can get the size of the dimensions of the DataFrame using shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(150, 5)"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can analyze the content of the DataFrame using describe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "       sepal_length  sepal_width  petal_length  petal_width\ncount    150.000000   150.000000    150.000000   150.000000\nmean       5.843333     3.057333      3.758000     1.199333\nstd        0.828066     0.435866      1.765298     0.762238\nmin        4.300000     2.000000      1.000000     0.100000\n25%        5.100000     2.800000      1.600000     0.300000\n50%        5.800000     3.000000      4.350000     1.300000\n75%        6.400000     3.300000      5.100000     1.800000\nmax        7.900000     4.400000      6.900000     2.500000",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>150.000000</td>\n      <td>150.000000</td>\n      <td>150.000000</td>\n      <td>150.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.843333</td>\n      <td>3.057333</td>\n      <td>3.758000</td>\n      <td>1.199333</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.828066</td>\n      <td>0.435866</td>\n      <td>1.765298</td>\n      <td>0.762238</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>4.300000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>0.100000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>5.100000</td>\n      <td>2.800000</td>\n      <td>1.600000</td>\n      <td>0.300000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>5.800000</td>\n      <td>3.000000</td>\n      <td>4.350000</td>\n      <td>1.300000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>6.400000</td>\n      <td>3.300000</td>\n      <td>5.100000</td>\n      <td>1.800000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>7.900000</td>\n      <td>4.400000</td>\n      <td>6.900000</td>\n      <td>2.500000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the type of each column (Series) we can use dtypes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "sepal_length    float64\nsepal_width     float64\npetal_length    float64\npetal_width     float64\nspecies          object\ndtype: object"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Columns (Series)\n",
    "\n",
    "To access a column we can either use the name of the column as a method or use the squared brackets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0      5.1\n1      4.9\n2      4.7\n3      4.6\n4      5.0\n      ... \n145    6.7\n146    6.3\n147    6.5\n148    6.2\n149    5.9\nName: sepal_length, Length: 150, dtype: float64"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.sepal_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0      5.1\n1      4.9\n2      4.7\n3      4.6\n4      5.0\n      ... \n145    6.7\n146    6.3\n147    6.5\n148    6.2\n149    5.9\nName: sepal_length, Length: 150, dtype: float64"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris['sepal_length']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a DataFrame back we need to put the name of the column in a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_length\n0             5.1\n1             4.9\n2             4.7\n3             4.6\n4             5.0\n..            ...\n145           6.7\n146           6.3\n147           6.5\n148           6.2\n149           5.9\n\n[150 rows x 1 columns]",
      "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>sepal_length</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>6.7</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>6.3</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>6.5</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>6.2</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>5.9</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[['sepal_length']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also select two columns using a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_length  sepal_width\n0             5.1          3.5\n1             4.9          3.0\n2             4.7          3.2\n3             4.6          3.1\n4             5.0          3.6\n..            ...          ...\n145           6.7          3.0\n146           6.3          2.5\n147           6.5          3.0\n148           6.2          3.4\n149           5.9          3.0\n\n[150 rows x 2 columns]",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>6.7</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>6.3</td>\n      <td>2.5</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>6.5</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>6.2</td>\n      <td>3.4</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>5.9</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[['sepal_length', 'sepal_width']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select the first row we need to use the iloc like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "sepal_length       5.1\nsepal_width        3.5\npetal_length       1.4\npetal_width        0.2\nspecies         setosa\nName: 0, dtype: object"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Rows\n",
    "\n",
    "To select rows based on a condition we can use boolean operators. For example, to select all the flowers that have a\n",
    "sepal length greater or equal than 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_length  sepal_width  petal_length  petal_width    species\n0             5.1          3.5           1.4          0.2     setosa\n4             5.0          3.6           1.4          0.2     setosa\n5             5.4          3.9           1.7          0.4     setosa\n7             5.0          3.4           1.5          0.2     setosa\n10            5.4          3.7           1.5          0.2     setosa\n..            ...          ...           ...          ...        ...\n145           6.7          3.0           5.2          2.3  virginica\n146           6.3          2.5           5.0          1.9  virginica\n147           6.5          3.0           5.2          2.0  virginica\n148           6.2          3.4           5.4          2.3  virginica\n149           5.9          3.0           5.1          1.8  virginica\n\n[128 rows x 5 columns]",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5.4</td>\n      <td>3.9</td>\n      <td>1.7</td>\n      <td>0.4</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>5.0</td>\n      <td>3.4</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>5.4</td>\n      <td>3.7</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>6.7</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>6.3</td>\n      <td>2.5</td>\n      <td>5.0</td>\n      <td>1.9</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>6.5</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.0</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>6.2</td>\n      <td>3.4</td>\n      <td>5.4</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>5.9</td>\n      <td>3.0</td>\n      <td>5.1</td>\n      <td>1.8</td>\n      <td>virginica</td>\n    </tr>\n  </tbody>\n</table>\n<p>128 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[iris.sepal_length >= 5.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing Values\n",
    "\n",
    "We can change the value of a column similarly to the way we access the DataFrame.\n",
    "\n",
    "For example to change all the values of a column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_length  sepal_width  petal_length  petal_width    species\n0               0          3.5           1.4          0.2     setosa\n1               0          3.0           1.4          0.2     setosa\n2               0          3.2           1.3          0.2     setosa\n3               0          3.1           1.5          0.2     setosa\n4               0          3.6           1.4          0.2     setosa\n..            ...          ...           ...          ...        ...\n145             0          3.0           5.2          2.3  virginica\n146             0          2.5           5.0          1.9  virginica\n147             0          3.0           5.2          2.0  virginica\n148             0          3.4           5.4          2.3  virginica\n149             0          3.0           5.1          1.8  virginica\n\n[150 rows x 5 columns]",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>0</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>0</td>\n      <td>2.5</td>\n      <td>5.0</td>\n      <td>1.9</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>0</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.0</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>0</td>\n      <td>3.4</td>\n      <td>5.4</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>0</td>\n      <td>3.0</td>\n      <td>5.1</td>\n      <td>1.8</td>\n      <td>virginica</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.sepal_length = 0\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To change the values of a row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_length  sepal_width  petal_length  petal_width    species\n0               0          0.0           0.0          0.0         ok\n1               0          3.0           1.4          0.2     setosa\n2               0          3.2           1.3          0.2     setosa\n3               0          3.1           1.5          0.2     setosa\n4               0          3.6           1.4          0.2     setosa\n..            ...          ...           ...          ...        ...\n145             0          3.0           5.2          2.3  virginica\n146             0          2.5           5.0          1.9  virginica\n147             0          3.0           5.2          2.0  virginica\n148             0          3.4           5.4          2.3  virginica\n149             0          3.0           5.1          1.8  virginica\n\n[150 rows x 5 columns]",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>ok</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>0</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>0</td>\n      <td>2.5</td>\n      <td>5.0</td>\n      <td>1.9</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>0</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.0</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>0</td>\n      <td>3.4</td>\n      <td>5.4</td>\n      <td>2.3</td>\n      <td>virginica</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>0</td>\n      <td>3.0</td>\n      <td>5.1</td>\n      <td>1.8</td>\n      <td>virginica</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.iloc[0] = [0, 0, 0, 0, 'ok']\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Functions\n",
    "\n",
    "We can use the method mean, std, mode, max, min, etc. To compute any basic statistics we need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "3.0340000000000003"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.sepal_width.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0    3.0\ndtype: float64"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.sepal_width.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.5008489437240835"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.sepal_width.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting with Pandas\n",
    "\n",
    "Pandas uses matplotlib to plot series. For example we can plot a column using the plot method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.sepal_width.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the histogram of the column like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.sepal_width.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 28\n",
    "\n",
    "Reload the IRIS dataset and compute the standard error of each numerical column per flower species, and store them into\n",
    "the DataFrame.\n",
    "\n",
    "The standard error for a normal distribution is computed like this:\n",
    "\n",
    "$$\\mu \\pm 1.96 \\sqrt{\\frac{\\sigma^2}{n}}$$\n",
    "\n",
    "where, $\\mu$ is the mean, $\\sigma^2$ is the variance, 1.96 is the approximate value of the 97.5\n",
    "percentile point of the normal distribution, and $n$ is the sample size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "     sepal_length  sepal_width  petal_length  petal_width    species  \\\n0             5.1          3.5           1.4          0.2     setosa   \n1             4.9          3.0           1.4          0.2     setosa   \n2             4.7          3.2           1.3          0.2     setosa   \n3             4.6          3.1           1.5          0.2     setosa   \n4             5.0          3.6           1.4          0.2     setosa   \n..            ...          ...           ...          ...        ...   \n145           6.7          3.0           5.2          2.3  virginica   \n146           6.3          2.5           5.0          1.9  virginica   \n147           6.5          3.0           5.2          2.0  virginica   \n148           6.2          3.4           5.4          2.3  virginica   \n149           5.9          3.0           5.1          1.8  virginica   \n\n     sepal_length_se  sepal_width_se  petal_length_se  petal_width_se  \\\n0                  0               0                0               0   \n1                  0               0                0               0   \n2                  0               0                0               0   \n3                  0               0                0               0   \n4                  0               0                0               0   \n..               ...             ...              ...             ...   \n145                0               0                0               0   \n146                0               0                0               0   \n147                0               0                0               0   \n148                0               0                0               0   \n149                0               0                0               0   \n\n     sepal_length_se_p  sepal_length_se_n  sepal_width_se_p  sepal_width_se_n  \\\n0             5.103705           4.908295          3.533071          3.322929   \n1             5.103705           4.908295          3.533071          3.322929   \n2             5.103705           4.908295          3.533071          3.322929   \n3             5.103705           4.908295          3.533071          3.322929   \n4             5.103705           4.908295          3.533071          3.322929   \n..                 ...                ...               ...               ...   \n145           6.764257           6.411743          3.063392          2.884608   \n146           6.764257           6.411743          3.063392          2.884608   \n147           6.764257           6.411743          3.063392          2.884608   \n148           6.764257           6.411743          3.063392          2.884608   \n149           6.764257           6.411743          3.063392          2.884608   \n\n     petal_length_se_p  petal_length_se_n  petal_width_se_p  petal_width_se_n  \n0             1.510137           1.413863          0.275211          0.216789  \n1             1.510137           1.413863          0.275211          0.216789  \n2             1.510137           1.413863          0.275211          0.216789  \n3             1.510137           1.413863          0.275211          0.216789  \n4             1.510137           1.413863          0.275211          0.216789  \n..                 ...                ...               ...               ...  \n145           5.704977           5.399023          2.102129          1.949871  \n146           5.704977           5.399023          2.102129          1.949871  \n147           5.704977           5.399023          2.102129          1.949871  \n148           5.704977           5.399023          2.102129          1.949871  \n149           5.704977           5.399023          2.102129          1.949871  \n\n[150 rows x 17 columns]",
      "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>sepal_length</th>\n      <th>sepal_width</th>\n      <th>petal_length</th>\n      <th>petal_width</th>\n      <th>species</th>\n      <th>sepal_length_se</th>\n      <th>sepal_width_se</th>\n      <th>petal_length_se</th>\n      <th>petal_width_se</th>\n      <th>sepal_length_se_p</th>\n      <th>sepal_length_se_n</th>\n      <th>sepal_width_se_p</th>\n      <th>sepal_width_se_n</th>\n      <th>petal_length_se_p</th>\n      <th>petal_length_se_n</th>\n      <th>petal_width_se_p</th>\n      <th>petal_width_se_n</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5.1</td>\n      <td>3.5</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5.103705</td>\n      <td>4.908295</td>\n      <td>3.533071</td>\n      <td>3.322929</td>\n      <td>1.510137</td>\n      <td>1.413863</td>\n      <td>0.275211</td>\n      <td>0.216789</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.9</td>\n      <td>3.0</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5.103705</td>\n      <td>4.908295</td>\n      <td>3.533071</td>\n      <td>3.322929</td>\n      <td>1.510137</td>\n      <td>1.413863</td>\n      <td>0.275211</td>\n      <td>0.216789</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.7</td>\n      <td>3.2</td>\n      <td>1.3</td>\n      <td>0.2</td>\n      <td>setosa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5.103705</td>\n      <td>4.908295</td>\n      <td>3.533071</td>\n      <td>3.322929</td>\n      <td>1.510137</td>\n      <td>1.413863</td>\n      <td>0.275211</td>\n      <td>0.216789</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.6</td>\n      <td>3.1</td>\n      <td>1.5</td>\n      <td>0.2</td>\n      <td>setosa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5.103705</td>\n      <td>4.908295</td>\n      <td>3.533071</td>\n      <td>3.322929</td>\n      <td>1.510137</td>\n      <td>1.413863</td>\n      <td>0.275211</td>\n      <td>0.216789</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3.6</td>\n      <td>1.4</td>\n      <td>0.2</td>\n      <td>setosa</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5.103705</td>\n      <td>4.908295</td>\n      <td>3.533071</td>\n      <td>3.322929</td>\n      <td>1.510137</td>\n      <td>1.413863</td>\n      <td>0.275211</td>\n      <td>0.216789</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    </tr>\n    <tr>\n      <th>145</th>\n      <td>6.7</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.3</td>\n      <td>virginica</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6.764257</td>\n      <td>6.411743</td>\n      <td>3.063392</td>\n      <td>2.884608</td>\n      <td>5.704977</td>\n      <td>5.399023</td>\n      <td>2.102129</td>\n      <td>1.949871</td>\n    </tr>\n    <tr>\n      <th>146</th>\n      <td>6.3</td>\n      <td>2.5</td>\n      <td>5.0</td>\n      <td>1.9</td>\n      <td>virginica</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6.764257</td>\n      <td>6.411743</td>\n      <td>3.063392</td>\n      <td>2.884608</td>\n      <td>5.704977</td>\n      <td>5.399023</td>\n      <td>2.102129</td>\n      <td>1.949871</td>\n    </tr>\n    <tr>\n      <th>147</th>\n      <td>6.5</td>\n      <td>3.0</td>\n      <td>5.2</td>\n      <td>2.0</td>\n      <td>virginica</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6.764257</td>\n      <td>6.411743</td>\n      <td>3.063392</td>\n      <td>2.884608</td>\n      <td>5.704977</td>\n      <td>5.399023</td>\n      <td>2.102129</td>\n      <td>1.949871</td>\n    </tr>\n    <tr>\n      <th>148</th>\n      <td>6.2</td>\n      <td>3.4</td>\n      <td>5.4</td>\n      <td>2.3</td>\n      <td>virginica</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6.764257</td>\n      <td>6.411743</td>\n      <td>3.063392</td>\n      <td>2.884608</td>\n      <td>5.704977</td>\n      <td>5.399023</td>\n      <td>2.102129</td>\n      <td>1.949871</td>\n    </tr>\n    <tr>\n      <th>149</th>\n      <td>5.9</td>\n      <td>3.0</td>\n      <td>5.1</td>\n      <td>1.8</td>\n      <td>virginica</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>6.764257</td>\n      <td>6.411743</td>\n      <td>3.063392</td>\n      <td>2.884608</td>\n      <td>5.704977</td>\n      <td>5.399023</td>\n      <td>2.102129</td>\n      <td>1.949871</td>\n    </tr>\n  </tbody>\n</table>\n<p>150 rows × 17 columns</p>\n</div>"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')\n",
    "\n",
    "numerical_columns = []\n",
    "for column in iris.columns:\n",
    "    if iris[column].dtypes == 'float64':\n",
    "        numerical_columns.append(column)\n",
    "\n",
    "for column in numerical_columns:\n",
    "    iris[column + '_se'] = 0\n",
    "\n",
    "for species in iris.species.unique():\n",
    "    species_iris = iris[iris.species == species]\n",
    "    for column in numerical_columns:\n",
    "        mean = species_iris[column].mean()\n",
    "        var = species_iris[column].var()\n",
    "        n = species_iris[column].count()\n",
    "        se = 1.96 * (var/n) ** (1/2)\n",
    "        iris.loc[iris.species == species, column + '_se_p'] = mean + se\n",
    "        iris.loc[iris.species == species, column + '_se_n'] = mean - se\n",
    "\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 29\n",
    "\n",
    "Imagine you are given a NumPy matrix and you want to transform this matrix into a pandas DataFrame,\n",
    "how can you do this? (Use the Internet to answer this question).\n",
    "\n",
    "What about when you have a pandas DataFrame and you need a NumPy matrix?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "          0         1         2         3         4         5         6  \\\n0  0.579083  0.694758  0.573545  0.524707  0.585135  0.431660  0.367137   \n1  0.289609  0.053947  0.130013  0.467681  0.450228  0.708497  0.169948   \n2  0.631659  0.794416  0.352122  0.426539  0.202774  0.964784  0.904256   \n3  0.978710  0.259280  0.612281  0.420177  0.800728  0.501438  0.683369   \n4  0.458848  0.636557  0.993729  0.478225  0.471178  0.952678  0.775802   \n5  0.528220  0.769952  0.070714  0.949891  0.129877  0.907519  0.365001   \n6  0.037361  0.206318  0.932849  0.506185  0.833432  0.754523  0.634697   \n7  0.528502  0.010251  0.200502  0.415917  0.824039  0.010315  0.555365   \n8  0.438679  0.468975  0.641468  0.445515  0.119906  0.062446  0.774696   \n9  0.729948  0.278724  0.903275  0.948534  0.369752  0.496910  0.317727   \n\n          7         8         9  \n0  0.823766  0.645151  0.780487  \n1  0.384288  0.276012  0.610076  \n2  0.692890  0.177241  0.260601  \n3  0.049500  0.684635  0.679648  \n4  0.402458  0.273864  0.118550  \n5  0.643519  0.140800  0.261055  \n6  0.908472  0.897050  0.995559  \n7  0.076018  0.426106  0.942384  \n8  0.863375  0.995159  0.838232  \n9  0.239956  0.186325  0.032723  ",
      "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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.579083</td>\n      <td>0.694758</td>\n      <td>0.573545</td>\n      <td>0.524707</td>\n      <td>0.585135</td>\n      <td>0.431660</td>\n      <td>0.367137</td>\n      <td>0.823766</td>\n      <td>0.645151</td>\n      <td>0.780487</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.289609</td>\n      <td>0.053947</td>\n      <td>0.130013</td>\n      <td>0.467681</td>\n      <td>0.450228</td>\n      <td>0.708497</td>\n      <td>0.169948</td>\n      <td>0.384288</td>\n      <td>0.276012</td>\n      <td>0.610076</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.631659</td>\n      <td>0.794416</td>\n      <td>0.352122</td>\n      <td>0.426539</td>\n      <td>0.202774</td>\n      <td>0.964784</td>\n      <td>0.904256</td>\n      <td>0.692890</td>\n      <td>0.177241</td>\n      <td>0.260601</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.978710</td>\n      <td>0.259280</td>\n      <td>0.612281</td>\n      <td>0.420177</td>\n      <td>0.800728</td>\n      <td>0.501438</td>\n      <td>0.683369</td>\n      <td>0.049500</td>\n      <td>0.684635</td>\n      <td>0.679648</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.458848</td>\n      <td>0.636557</td>\n      <td>0.993729</td>\n      <td>0.478225</td>\n      <td>0.471178</td>\n      <td>0.952678</td>\n      <td>0.775802</td>\n      <td>0.402458</td>\n      <td>0.273864</td>\n      <td>0.118550</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.528220</td>\n      <td>0.769952</td>\n      <td>0.070714</td>\n      <td>0.949891</td>\n      <td>0.129877</td>\n      <td>0.907519</td>\n      <td>0.365001</td>\n      <td>0.643519</td>\n      <td>0.140800</td>\n      <td>0.261055</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.037361</td>\n      <td>0.206318</td>\n      <td>0.932849</td>\n      <td>0.506185</td>\n      <td>0.833432</td>\n      <td>0.754523</td>\n      <td>0.634697</td>\n      <td>0.908472</td>\n      <td>0.897050</td>\n      <td>0.995559</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0.528502</td>\n      <td>0.010251</td>\n      <td>0.200502</td>\n      <td>0.415917</td>\n      <td>0.824039</td>\n      <td>0.010315</td>\n      <td>0.555365</td>\n      <td>0.076018</td>\n      <td>0.426106</td>\n      <td>0.942384</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.438679</td>\n      <td>0.468975</td>\n      <td>0.641468</td>\n      <td>0.445515</td>\n      <td>0.119906</td>\n      <td>0.062446</td>\n      <td>0.774696</td>\n      <td>0.863375</td>\n      <td>0.995159</td>\n      <td>0.838232</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0.729948</td>\n      <td>0.278724</td>\n      <td>0.903275</td>\n      <td>0.948534</td>\n      <td>0.369752</td>\n      <td>0.496910</td>\n      <td>0.317727</td>\n      <td>0.239956</td>\n      <td>0.186325</td>\n      <td>0.032723</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = np.random.random((10, 10))\n",
    "\n",
    "pd_matrix = pd.DataFrame(data=matrix)\n",
    "\n",
    "pd_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0.57908326, 0.69475761, 0.57354547, 0.52470743, 0.58513463,\n        0.43165994, 0.36713708, 0.8237664 , 0.64515053, 0.78048657],\n       [0.28960888, 0.05394666, 0.13001258, 0.46768054, 0.45022815,\n        0.70849676, 0.16994797, 0.38428791, 0.27601156, 0.61007596],\n       [0.63165924, 0.7944158 , 0.35212245, 0.426539  , 0.20277403,\n        0.96478448, 0.90425569, 0.69288963, 0.17724124, 0.26060112],\n       [0.97871031, 0.25927956, 0.61228147, 0.42017659, 0.80072816,\n        0.50143793, 0.68336852, 0.04949961, 0.68463468, 0.67964782],\n       [0.45884791, 0.63655668, 0.99372886, 0.47822484, 0.47117756,\n        0.95267817, 0.77580243, 0.40245828, 0.27386432, 0.11855047],\n       [0.5282204 , 0.76995226, 0.07071444, 0.94989099, 0.12987682,\n        0.9075194 , 0.36500059, 0.64351933, 0.14079968, 0.26105511],\n       [0.03736057, 0.2063182 , 0.9328492 , 0.5061853 , 0.8334316 ,\n        0.75452329, 0.634697  , 0.90847211, 0.89704981, 0.99555868],\n       [0.52850157, 0.01025081, 0.20050236, 0.41591698, 0.8240395 ,\n        0.01031457, 0.55536527, 0.0760176 , 0.4261062 , 0.94238391],\n       [0.43867851, 0.46897484, 0.64146785, 0.4455153 , 0.11990623,\n        0.06244647, 0.77469606, 0.86337511, 0.99515851, 0.8382322 ],\n       [0.72994791, 0.27872418, 0.90327451, 0.94853436, 0.36975187,\n        0.49690986, 0.31772695, 0.23995565, 0.1863249 , 0.03272347]])"
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix = pd_matrix.values\n",
    "\n",
    "matrix"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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