Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Numpy -Data Science Library Part 1\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Creating Numpy arrays"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[0 1 2 3]\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "a=np.array([0,1,2,3])\n",
- "print(a)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Dimensions of array"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#print dimensions\n",
- "\n",
- "a.ndim"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(4,)"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#shape\n",
- "\n",
- "a.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "4"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len(a)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[0, 1, 2],\n",
- " [3, 4, 5]])"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# 2-D, 3-D....\n",
- "\n",
- "b = np.array([[0, 1, 2], [3, 4, 5]])\n",
- "\n",
- "b"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "2"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len(b)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(2, 3)"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "b.shape"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Functions for creating arrays"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "###### Arange function is similar to range in python"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.arange(10)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Creates an array from 0 to 9"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0, 2, 4, 6, 8])"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "b=np.arange(0,10,2)\n",
- "b"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Function signature is arange(start,end, step_size)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "###### Linspace"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0. , 0.2, 0.4, 0.6, 0.8, 1. ])"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a=np.linspace(0,1,6)\n",
- "a"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- " Function signature is np.linspace(start,end,number_of_points_required)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Zeros"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0., 0., 0., 0., 0.])"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.zeros(5)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[0., 0., 0.],\n",
- " [0., 0., 0.]])"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.zeros((2,3))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Ones"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1., 1., 1.],\n",
- " [1., 1., 1.],\n",
- " [1., 1., 1.]])"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.ones((3,3))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Identity Matrix"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1., 0., 0.],\n",
- " [0., 1., 0.],\n",
- " [0., 0., 1.]])"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.eye(3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1., 0.],\n",
- " [0., 1.],\n",
- " [0., 0.]])"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "d = np.eye(3, 2) #3 is number of rows, 2 is number of columns, index of diagonal start with 0\n",
- "\n",
- "d"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Diagonal Matrix"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1, 0, 0, 0],\n",
- " [0, 2, 0, 0],\n",
- " [0, 0, 3, 0],\n",
- " [0, 0, 0, 4]])"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a=np.diag([1,2,3,4])\n",
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([1, 2, 3, 4])"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.diag(a)#extract the diagonal matrix"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Random Arrays"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0.38095827, 0.05454193, 0.5649424 , 0.35600484])"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#create array using random\n",
- "\n",
- "#Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1).\n",
- "a = np.random.rand(4) \n",
- "\n",
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 0.288684 , -0.32566062, 1.09628212, -0.30523579])"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = np.random.randn(4)#Return a sample (or samples) from the “standard normal” distribution. ***Gausian***\n",
- "\n",
- "a"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Note: Numpy arrays are faster than normal python lists"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "277 µs ± 6.15 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
- ]
- }
- ],
- "source": [
- "L = range(1000)\n",
- "%timeit [i**2 for i in L]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "916 ns ± 14.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
- ]
- }
- ],
- "source": [
- "a = np.arange(1000)\n",
- "%timeit a**2"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Data Types"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[0 1 2 3 4 5 6 7 8 9]\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "dtype('int64')"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a=np.arange(10)\n",
- "print(a)\n",
- "a.dtype"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "dtype('float64')"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#You can explicitly specify which data-type you want:\n",
- "\n",
- "a = np.arange(10, dtype='float64')\n",
- "print(a)\n",
- "a.dtype"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[[0. 0. 0.]\n",
- " [0. 0. 0.]\n",
- " [0. 0. 0.]]\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "dtype('float64')"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#The default data type is float for zeros and ones function\n",
- "\n",
- "a = np.zeros((3, 3))\n",
- "\n",
- "print(a)\n",
- "\n",
- "a.dtype"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Each built-in data type has a character code that uniquely identifies it.**\n",
- "\n",
- "'b' − boolean\n",
- "\n",
- "'i' − (signed) integer\n",
- "\n",
- "'u' − unsigned integer\n",
- "\n",
- "'f' − floating-point\n",
- "\n",
- "'c' − complex-floating point\n",
- "\n",
- "'m' − timedelta\n",
- "\n",
- "'M' − datetime\n",
- "\n",
- "'O' − (Python) objects\n",
- "\n",
- "'S', 'a' − (byte-)string\n",
- "\n",
- "'U' − Unicode\n",
- "\n",
- "'V' − raw data (void)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "complex128\n"
- ]
- }
- ],
- "source": [
- "d = np.array([1+2j, 2+4j]) #Complex datatype\n",
- "\n",
- "print(d.dtype)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "bool\n"
- ]
- }
- ],
- "source": [
- "b = np.array([True, False, True, False]) #Boolean datatype\n",
- "\n",
- "print(b.dtype)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "dtype('<U6')"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "s = np.array(['Ram', 'Robert', 'Rahim'])\n",
- "\n",
- "s.dtype"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Indexing and slicing an array"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[0 1 2 3 4 5 6 7 8 9]\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "5"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a=np.arange(10)\n",
- "print(a)\n",
- "a[5]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "indices begin at 0, like other python arrays"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "3\n"
- ]
- }
- ],
- "source": [
- "# For multidimensional arrays, indexes are tuples of integers:\n",
- "\n",
- "a = np.diag([1, 2, 3])\n",
- "\n",
- "print(a[2, 2])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[1, 0, 0],\n",
- " [0, 2, 0],\n",
- " [0, 5, 3]])"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a[2, 1] = 5 #assigning value\n",
- "\n",
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([1, 3, 5, 7])"
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# slicing\n",
- "a = np.arange(10)\n",
- "\n",
- "a[1:8:2]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "It is kind of like syaing get me all values from index 1 to 7 at intervals of 2..which gets the odd numbers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 0, 1, 2, 3, 4, 10, 10, 10, 10, 10])"
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#we can also combine assignment and slicing:\n",
- "\n",
- "a = np.arange(10)\n",
- "a[5:] = 10\n",
- "a"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Sharing memory"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
- ]
- },
- "execution_count": 49,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a=np.arange(10)\n",
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0, 2, 4, 6, 8])"
- ]
- },
- "execution_count": 62,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "b=a[::2]\n",
- "b\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 63,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "False"
- ]
- },
- "execution_count": 63,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "id(a)==id(b)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "4548358352"
- ]
- },
- "execution_count": 64,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "id(a)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "4548359632"
- ]
- },
- "execution_count": 65,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "id(b)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 66,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.shares_memory(a,b)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 67,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([10, 2, 4, 6, 8])"
- ]
- },
- "execution_count": 67,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "b[0]=10\n",
- "b"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 68,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[10 1 2 3 4 5 6 7 8 9]\n"
- ]
- }
- ],
- "source": [
- "print(a)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Change in b causes change in a even though we modified only b**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 69,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0, 2, 4, 6, 8])"
- ]
- },
- "execution_count": 69,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = np.arange(10)\n",
- "\n",
- "c = a[::2].copy() #force a copy\n",
- "c"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 70,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "False"
- ]
- },
- "execution_count": 70,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "np.shares_memory(a, c)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 71,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
- ]
- },
- "execution_count": 71,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "c[0] = 10\n",
- "\n",
- "a"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Indexing using masks"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 72,
- "metadata": {},
- "outputs": [],
- "source": [
- "a=np.random.randint(0,20,15)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 73,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 2, 1, 4, 0, 15, 10, 16, 9, 7, 7, 7, 2, 3, 14, 4])"
- ]
- },
- "execution_count": 73,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 75,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ True, False, True, True, False, True, True, False, False,\n",
- " False, False, True, False, True, True])"
- ]
- },
- "execution_count": 75,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mask=(a%2==0)\n",
- "mask"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 77,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 2, 4, 0, 10, 16, 2, 14, 4])"
- ]
- },
- "execution_count": 77,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "extract_from_a=a[mask]\n",
- "extract_from_a"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Indexing with an array of Integers"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 78,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
- ]
- },
- "execution_count": 78,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "a = np.arange(0, 100, 10)\n",
- "\n",
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 79,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([20, 30, 20, 40, 20])"
- ]
- },
- "execution_count": 79,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#Indexing can be done with an array of integers, where the same index is repeated several time:\n",
- "\n",
- "a[[2, 3, 2, 4, 2]]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 80,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 0, 10, 20, 30, 40, 50, 60, -200, 80, -200])"
- ]
- },
- "execution_count": 80,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# New values can be assigned \n",
- "\n",
- "a[[9, 7]] = -200\n",
- "\n",
- "a"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.0"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
- }
Add Comment
Please, Sign In to add comment