DS01_01_Numpy package.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "StTsCgKpgZ7D"
   },
   "source": [
    "# Data Analyst Role\n",
    "------------------\n",
    "- Level 3 we will learn the skill set required for **<font color=\"red\">Data Analyst Role</font>**. We will learn mainly three things (Read /Transform/present)\n",
    "    - `Read` from any where (Big data,rdbms,nosql databases,files,api calls,graph databases(tigergraph,neo4J) ..etc\n",
    "    - `Transformation/Manipulation` of data from one format to other format\n",
    "    - Present data in `Graphical` format\n",
    "- Resources who are working on BI tools like Power BI,OBI,Tableau,Cognos,SAPBO ... etc are also called as data analysts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x3JynJmpgZ7J"
   },
   "source": [
    "# Numpy\n",
    "-----\n",
    "- https://www.numpy.org/\n",
    "- Numerical Python, or \"Numpy\" for short, is a `foundational package` on which many of the most common `data science packages are built`.\n",
    "- Numpy provides us with `high performance multi-dimensional arrays` which we can use as vectors or matrices.\n",
    "- https://numpy.org/devdocs/user/quickstart.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fum3pVmQgaEn",
    "toc-hr-collapsed": true
   },
   "source": [
    "## Working with arrays\n",
    "- Generally we will use numpy to store numbers.\n",
    "- However it can be used to store any collection of `single datatype` values\n",
    "- NumPy is a powerful **N-dimensional array object**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mdvPKg6igaEp",
    "toc-hr-collapsed": true
   },
   "source": [
    "### Create 1d array\n",
    "- 1d array is nothing but `One Column/ Vector`\n",
    "- In core python `range` function is equal to numpy `arange` function\n",
    "- Using range ,we will get `list` as a output\n",
    "- Using np.arange, we will get `array` as a output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GeZtzfEZgaEq",
    "outputId": "3bb1d327-24ff-43a7-b23f-7c946a9fed6d"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#a_1d_array = np.arange?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LV7LMfzrgaE2"
   },
   "outputs": [],
   "source": [
    "#range?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LQeuKwlQgaE5"
   },
   "outputs": [],
   "source": [
    "a_1d_array = np.arange(0,12,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CpUmn1FrgaE9",
    "outputId": "8b6d9617-c16a-4c36-929a-bc744875805e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_1d_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J251zwoMgaFG",
    "outputId": "ca508fb6-297a-4941-8b93-58604601f40d",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_1d_array.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ERhNkXZqgaFQ",
    "outputId": "597665c9-9e94-414a-877e-62cb32e86641"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_1d_array.shape # in 1d array how many items available ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O1Pwur9KgaFU"
   },
   "source": [
    "#### Reading numpy 1d array items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "97NlYyqmgaFW",
    "outputId": "1009c7f6-c52e-44e8-bfb7-e1cbebd4e5c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_1d_array[0] # index method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "olAyBK4NgaFl",
    "outputId": "bba74bad-96c6-4375-8cb4-3433f3fb53cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_1d_array[0:3] # Slice Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I3qsqs52gaF3",
    "outputId": "dccd4941-5cd9-4cba-d11a-20b566f24266"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 6])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_1d_array[[0,3]] # fancy Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5Lk_rE__gaF9"
   },
   "source": [
    "#### numpy array is mutable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fX-ZRalPgaGC"
   },
   "outputs": [],
   "source": [
    "a_1d_array[1]=200 # update number 2 with 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rjuG6PhPgaGG",
    "outputId": "3661919a-5795-41ba-f787-0a5c40c83a95"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0, 200,   4,   6,   8,  10])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_1d_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create 1d array from column/Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>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>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"iris.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "s=df.species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=s.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "279jiqi6gaGV",
    "toc-hr-collapsed": true
   },
   "source": [
    "### Create 2D array\n",
    "- 2D array examples are \n",
    "    - Table\n",
    "    - 2 or more than 2 columns data\n",
    "    - Matrix\n",
    "    - Black and white image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "99hsQcB6gaGW"
   },
   "outputs": [],
   "source": [
    "# Create 2d array with 3 samples and 4 features\n",
    "a_np_2d_array = np.arange(0,24,2).reshape(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r3jl0hZcgaHk",
    "outputId": "c85b48c8-aede-4bef-9463-261d0c4f0e77"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  4,  6],\n",
       "       [ 8, 10, 12, 14],\n",
       "       [16, 18, 20, 22]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_2d_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g2WfEPlBgaHw",
    "outputId": "1c54d408-bbed-433c-9f58-ab1d99d71516"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_2d_array.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4B2aZI2IgaH4",
    "outputId": "8c496652-c84f-4e55-8029-f77df5e0823e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_2d_array.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MQGp0yYYgaIB",
    "outputId": "7c25f14b-f6e1-4398-92a9-73497ef018b3"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 13 into shape (3,4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-37-6d4a9d9804f4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma_np_2d_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m13\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: cannot reshape array of size 13 into shape (3,4)"
     ]
    }
   ],
   "source": [
    "a_np_2d_array = np.arange(13).reshape(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9T-R-JgFgaIN",
    "outputId": "f39e5430-0b63-47b4-b483-fa0083c07c97"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 11 into shape (3,4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-6417df2a93bf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma_np_2d_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: cannot reshape array of size 11 into shape (3,4)"
     ]
    }
   ],
   "source": [
    "a_np_2d_array = np.arange(11).reshape(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "snxsnnorgaIV"
   },
   "source": [
    "#### Reading 2D np array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kof4qhr6gaIW",
    "outputId": "c829508a-d717-431f-88c7-de589acea4f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6])"
      ]
     },
     "execution_count": 44,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_2d_array[0] # 1st row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0Ab_pRXagaIa",
    "outputId": "25de5715-123a-401b-8fa9-6471e1335bc5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_2d_array[0,0] # 1st row #1st column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ue_AZ2E7gaIe",
    "outputId": "3976702c-6dea-4994-ca26-3ab318cac513"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_2d_array[0][2] # 1st row # 3rd column value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HCc2rMoMgaIl",
    "outputId": "0bd09dc6-5eea-4ebb-8153-a94cc0606176"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2],\n",
       "       [ 8, 10],\n",
       "       [16, 18]])"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_2d_array[:,0:2] # all rows #first and secon columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jmwSe-z1gaIp",
    "outputId": "c0a66c3f-93e2-4b88-90e6-98f947275b7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 10, 12, 14])"
      ]
     },
     "execution_count": 48,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_2d_array[1,:] # all columns # second row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AfScrgAfgaIu"
   },
   "source": [
    "> In Machine learning we will use \n",
    "    - input(X) --> 2D np array\n",
    "    - output(y) --> 1D np array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dLdHyvQYgaIv"
   },
   "source": [
    "#### Create 2D array from table/data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[df.columns[:-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=X.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape #(n_samples,n_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dLdHyvQYgaIv"
   },
   "source": [
    "#### Create 2D array from Black and white image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=183x275 at 0x1F17C859940>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "temp=Image.open('images/MylaRamReddy.jpg')\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=1 size=183x275 at 0x1F17C79C630>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert color image to black and white\n",
    "temp=temp.convert('1')\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False,  True, False, ..., False, False,  True],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False,  True],\n",
       "       [False, False,  True, ...,  True, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get 2d array from black and white image \n",
    "import numpy as np\n",
    "A = np.array(temp)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_A=emtyp((A.shape[0],A.shape[1]),None)\n",
    "#create array with 0(flase) and 1(True)\n",
    "for i in range(len(A)):\n",
    "    for j in range(len(A[i])):\n",
    "        if A[i][j]==True:\n",
    "            new_A[i][j]=0\n",
    "        else:\n",
    "            new_A[i][j]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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.imshow(new_A,cmap=\"Greys\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dLdHyvQYgaIv",
    "toc-hr-collapsed": true
   },
   "source": [
    "### Create 3D array\n",
    "- Color image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3KWuoKk8gaIw"
   },
   "outputs": [],
   "source": [
    "a_np_3d_array = np.arange(24).reshape(2,3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XvU6kWFjgaI0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_3d_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vdIHGwPkgaI8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_3d_array.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wyja5jgBgaI_"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_3d_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O1IOOJyFgaJJ"
   },
   "source": [
    "#### Create 3D array from color image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=183x275 at 0x26DBCAE0470>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "temp=Image.open('MylaRamReddy.jpg')\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.array(temp)\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275, 183, 3)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O1IOOJyFgaJJ"
   },
   "source": [
    "### Create 4D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vlceXpikgaJL"
   },
   "outputs": [],
   "source": [
    "a_np_4d_array = np.arange(48).reshape(2,2,3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QKYZCWxrgaJb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[ 0,  1,  2,  3],\n",
       "         [ 4,  5,  6,  7],\n",
       "         [ 8,  9, 10, 11]],\n",
       "\n",
       "        [[12, 13, 14, 15],\n",
       "         [16, 17, 18, 19],\n",
       "         [20, 21, 22, 23]]],\n",
       "\n",
       "\n",
       "       [[[24, 25, 26, 27],\n",
       "         [28, 29, 30, 31],\n",
       "         [32, 33, 34, 35]],\n",
       "\n",
       "        [[36, 37, 38, 39],\n",
       "         [40, 41, 42, 43],\n",
       "         [44, 45, 46, 47]]]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_4d_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "B_6__HXhgZ7b",
    "toc-hr-collapsed": true
   },
   "source": [
    " ## Why we need to learn `Numpy array` ? though we have `python list` ?\n",
    " - Let us observe below `BMI` calculation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q180lXlSgZ7v"
   },
   "source": [
    "$BMI = \\frac {weight\\ in\\ kgs}{height\\ in\\ meters^2} $\n",
    "- It's one way to see if you're at a healthy weight.\n",
    "- `Underweight`: Your BMI is **less than 18.5**. \n",
    "- `Healthy weight`: Your BMI is **18.5 to 24.9**.\n",
    "- `Overweight`: Your BMI is **25 to 29.9**. \n",
    "- `Obese`: Your BMI is **30 or higher**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### can we divide a list with int?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for /: 'list' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-96-f5fa06834386>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'list' and 'int'"
     ]
    }
   ],
   "source": [
    "[10,20]/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GEqCPEnOgZ8C"
   },
   "source": [
    "### Using normal list to calculate BMI of 4 members? is it possible ?\n",
    "- Normal list we learned in level 1: core python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HHv8-V3pgZ8M"
   },
   "outputs": [],
   "source": [
    "weight = [60,65,70,75] # in kgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XIH7GeabgZ84",
    "outputId": "4414e484-8330-4689-9d19-723108327990"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "24iSBxKwgZ90"
   },
   "outputs": [],
   "source": [
    "height =[1.60,1.65,1.70,1.75] # in meters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CeS4gU7IgZ-Z",
    "outputId": "2e49d749-d53b-4dad-bd4d-5814d3e845b2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fe4ls7IZgZ-u",
    "outputId": "ee5572bd-4ace-4419-9904-f02d1bbcb827"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for ** or pow(): 'list' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-dbd925cc794d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mBMI\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mheight\u001b[0m \u001b[1;33m**\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for ** or pow(): 'list' and 'int'"
     ]
    }
   ],
   "source": [
    "BMI = weight/height ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1dHKZ0dQgZ_d"
   },
   "source": [
    "### Let us do above problem with numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OvZxDuEugZ_g"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TSqC0xnhgaA0"
   },
   "outputs": [],
   "source": [
    "np_height = np.array([1.60,1.65,1.70,1.75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_u10AhDPgaBK",
    "outputId": "7673c339-4be1-48c4-fefb-ed28e20cc9d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np_height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MGebHP72gaBm",
    "outputId": "6be1b108-21b6-4f75-cf9c-602cc5c32a0d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_height.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Zy3EsN80gaB0",
    "outputId": "faf4615d-685e-4a57-a105-4d331160ca8e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_height.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3tgyWyGVgaB-",
    "outputId": "bf00a206-c619-4c1a-c8ed-33daa2c0e6cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_height.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bCLpNAObgaCG",
    "outputId": "177ef2d2-59e6-403f-8edf-d85d0be9dcf1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_height[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pQqvKISJgaCV"
   },
   "outputs": [],
   "source": [
    "np_weight = np.array([60,65,70,75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "v0hVk1QbgaCb",
    "outputId": "bf3a62ad-3757-43bc-b485-0ad8308ea473"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tQaEzfQNgaCo",
    "outputId": "1fc7e65e-33c3-4d85-bb50-5369b45cb1b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_weight.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MB-rh2ofgaCy",
    "outputId": "f64c8c90-bb63-4546-c66a-46aeaf02b9da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_weight.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GdHmKHmjgaDM"
   },
   "outputs": [],
   "source": [
    "BMI = (np_weight/np_height ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uTJ1iZaPgaDV",
    "outputId": "fc87a779-ee0b-4900-9b62-6cea6659e286"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.4375    , 23.87511478, 24.22145329, 24.48979592])"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BMI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "25akuriygaDg"
   },
   "source": [
    "below 18.5 – you're in the underweight range. \n",
    "\n",
    "between 18.5 and 24.9 – you're in the healthy weight range. \n",
    "\n",
    "between 25 and 29.9 – you're in the overweight range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6EOumiyogaDi"
   },
   "source": [
    "## NumPy array is a homogeneous multidimensional array\n",
    "- It is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jIL9blYqgaDk"
   },
   "outputs": [],
   "source": [
    "a_list = [10,10.33,\"python\",1+2J,True,[10,30]] # normal list is a hetrogeneous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "is7Zm7jOgaDt",
    "outputId": "6bd508e7-814f-4c69-eb74-53d65d3b2a46"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 10.33, 'python', (1+2j), True, [10, 30]]"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T3rnlRsOgaD4"
   },
   "outputs": [],
   "source": [
    "a_np_list = np.array([10,10.33]) # i given input 10 it is int 10.33 it is float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "x7TXGLzdgaEK",
    "outputId": "c65774bd-e7a0-4d4e-ccab-59ae245a1f76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.  , 10.33])"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np_list # it converted integer into float # it is a homogeneous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fMWy1TG-gaJi"
   },
   "source": [
    "## NumPy array occupies less memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9EOn8F63gaJn"
   },
   "outputs": [],
   "source": [
    "py_list = list(range(200)) # python list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "epRN3wVigaJx",
    "outputId": "adddfee9-220f-4f31-8fe6-4e8a42ca4156"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 199, 200)"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(py_list),max(py_list),len(py_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AkiNJKLTgaJ3"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np_list = np.array(list(range(200))) # numpy list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jVyj9F-BgaJ6",
    "outputId": "fff3b890-473b-4171-fea5-1a7fdbabc7d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 199, 200)"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(np_list),max(np_list),len(np_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3KxOcQXVgaKI",
    "outputId": "d6fa8a43-45f1-4960-9b63-bb93a500ab34"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1912\n",
      "896\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(sys.getsizeof(py_list))\n",
    "print(sys.getsizeof(np_list))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "DS01_01_Numpy package.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 4
}