Day 1 Numpy array.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy (or Numpy) is a Linear Algebra Library for Python, the reason it is so important for Data Science with Python is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks.\n",
    "\n",
    "Numpy is also incredibly fast, as it has bindings to C libraries.\n",
    "\n",
    "We will only learn the basics of NumPy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**install**\n",
    "\n",
    "    !pip install numpy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### from python lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1= [1,2,3]\n",
    "list1_np= np.array(list1)\n",
    "type(list1_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2_D arrays -> matrix\n",
    "matrix1= [[1,2],[3,4]]\n",
    "np.array(matrix1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Built-in methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# arange() -> (start, end-not included, step)\n",
    "# Returns evenly spaced values in an interval\n",
    "np.arange(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,10,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### zeros, ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### eye"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#identity matrix\n",
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### rand\n",
    "It creates array of a given shape and populates it with random values from an uniform distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9644203307125712"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.56373702, 0.5968386 ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.35541639, 0.36915485, 0.00824921],\n",
       "       [0.76669309, 0.72633326, 0.31226496],\n",
       "       [0.95993215, 0.49515244, 0.1119133 ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(3,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.hist(np.random.rand(20000))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### randn\n",
    "returns values from standard normal distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.22599158,  0.01264132,  0.09098834],\n",
       "       [-0.62689363,  0.33117328, -1.09699203],\n",
       "       [ 0.2314655 , -0.52813472, -0.65476896]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.randn(20000))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (0.10.1)\n",
      "Requirement already satisfied: numpy>=1.13.3 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from seaborn) (1.16.2)\n",
      "Requirement already satisfied: scipy>=1.0.1 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from seaborn) (1.3.0)\n",
      "Requirement already satisfied: matplotlib>=2.1.2 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from seaborn) (3.1.1)\n",
      "Requirement already satisfied: pandas>=0.22.0 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from seaborn) (0.24.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from matplotlib>=2.1.2->seaborn) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from matplotlib>=2.1.2->seaborn) (2.4.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from matplotlib>=2.1.2->seaborn) (2.8.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from matplotlib>=2.1.2->seaborn) (1.1.0)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pandas>=0.22.0->seaborn) (2019.1)\n",
      "Requirement already satisfied: six in c:\\users\\lenovo\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from cycler>=0.10->matplotlib>=2.1.2->seaborn) (1.12.0)\n",
      "Requirement already satisfied: setuptools in c:\\users\\lenovo\\appdata\\roaming\\python\\python37\\site-packages (from kiwisolver>=1.0.1->matplotlib>=2.1.2->seaborn) (41.0.1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: You are using pip version 19.1.1, however version 20.2.1 is available.\n",
      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip3 install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24310cb1a58>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 seaborn as sns\n",
    "sns.distplot(np.random.randn(20000), bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array attributes and methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr= np.arange(25)\n",
    "ranarr= np.random.randint(0,50,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25, 21, 20, 26, 47,  3, 42, 35, 15, 43])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(5,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25],\n",
       "       [21],\n",
       "       [20],\n",
       "       [26],\n",
       "       [47],\n",
       "       [ 3],\n",
       "       [42],\n",
       "       [35],\n",
       "       [15],\n",
       "       [43]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dimention mismatch error -> reshape(1,-1)\n",
    "ranarr.reshape(10,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25, 21, 20, 26, 47,  3, 42, 35, 15, 43]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr.reshape(1,10) #-> 2-D ---- [[1,2,3,4,]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([25, 21, 20, 26, 47,  3, 42, 35, 15, 43])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr # 1-D ------ [1,2,3,4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### max, min, argmax, argmin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranarr.argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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