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nandahkrishnanandahkrishna
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Completion of Modules
* Added questions to modules * Revised README * Other minor fixes
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‎Data/stress_detect.pkl renamed to ‎Data/Stress.pkl

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‎Lookups/NumPyBasics.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# NumPy Basics"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:23.903252Z",
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"start_time": "2018-05-23T15:35:23.605538Z"
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}
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},
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"outputs": [],
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"source": [
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"# Importing\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Creation of np.array"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:23.917148Z",
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"start_time": "2018-05-23T15:35:23.906370Z"
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}
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},
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"outputs": [],
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"source": [
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"# Creation of np.array from list\n",
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"list_a = [1, 2, 3]\n",
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"a = np.array(list_a)\n",
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"print(a)\n",
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"print(type(a))\n",
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"print(a.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:23.935386Z",
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"start_time": "2018-05-23T15:35:23.922430Z"
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}
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},
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"outputs": [],
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"source": [
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"# 2D array\n",
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"list_b = [[1, 0, 0],\n",
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" [0, 1, 0],\n",
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" [0, 0, 1]]\n",
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"b = np.array(list_b)\n",
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"print(b)\n",
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"print(type(b))\n",
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"print(b.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:23.954285Z",
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"start_time": "2018-05-23T15:35:23.939383Z"
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}
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},
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"outputs": [],
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"source": [
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"# To create arrays of 0s and 1s\n",
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"zero_array = np.zeros((2,3))\n",
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"one_array = np.ones((3,4))\n",
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"print(zero_array)\n",
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"print(one_array)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:23.976733Z",
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"start_time": "2018-05-23T15:35:23.958331Z"
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}
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},
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"outputs": [],
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"source": [
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"# Generating random numbers\n",
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"c = np.random.randint(low=1, high=10, size=(3,3))\n",
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"d = np.random.rand(8, 3)\n",
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"e = np.random.random_sample((3, 4))\n",
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"print(c)\n",
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"print(d)\n",
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"print(e)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Reshaping arrays"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:23.999537Z",
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"start_time": "2018-05-23T15:35:23.980468Z"
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}
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},
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"outputs": [],
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"source": [
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"# Reshaping arrays into required shape\n",
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"d_1 = np.reshape(d, newshape=(6, 4))\n",
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"# In order to reshape from 8x3 to 6x4, size can also be mentioned as (6, -1)\n",
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"# The remaining factor (in place of -1) is automatically computed\n",
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"d_2 = d.reshape((6, -1))\n",
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"print(d_1)\n",
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"print(d_2)\n",
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"print(d_1 == d_2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:24.013137Z",
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"start_time": "2018-05-23T15:35:24.005781Z"
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}
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},
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"outputs": [],
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"source": [
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"# Task: Try to reshape the array into a 3D array of shape 2x3x4"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Deletion of elements"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:24.030355Z",
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"start_time": "2018-05-23T15:35:24.020446Z"
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}
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},
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"outputs": [],
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"source": [
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"# Deletion of rows/columns\n",
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"d_col_delete = np.delete(d, 1, axis=1)\n",
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"d_row_delete = np.delete(d, 2, axis=0)\n",
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"print(d_col_delete.shape)\n",
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"print(d_row_delete.shape)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:24.043052Z",
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"start_time": "2018-05-23T15:35:24.036309Z"
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}
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},
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"outputs": [],
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"source": [
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"# Learn the concepts of axis properly\n",
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"# Check StackOverflow or NumPy documentation for more details"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:24.054460Z",
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"start_time": "2018-05-23T15:35:24.048648Z"
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}
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},
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"outputs": [],
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"source": [
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"# Task: Delete a particular element of array `c`"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Merging Rows or Columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:24.065854Z",
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"start_time": "2018-05-23T15:35:24.059225Z"
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}
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},
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"outputs": [],
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"source": [
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"# Merging Rows/Columns\n",
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"merge_rows = np.vstack((c, d))\n",
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"merge_cols = np.hstack((c, e))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:24.081593Z",
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"start_time": "2018-05-23T15:35:24.071818Z"
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}
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},
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"outputs": [],
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"source": [
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"print(mergeRows)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:24.095793Z",
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"start_time": "2018-05-23T15:35:24.087851Z"
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}
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},
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"outputs": [],
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"source": [
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"print(mergeCols)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:24.106243Z",
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"start_time": "2018-05-23T15:35:24.100140Z"
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}
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},
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"outputs": [],
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"source": [
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"# Task: Try using np.concatenate to perform the above operations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Aggregate Functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2018-05-23T15:35:24.115888Z",
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"start_time": "2018-05-23T15:35:24.110775Z"
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}
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},
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"outputs": [],
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"source": [
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"# Task: Find the row-wise minimum of the array `c`\n",
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"# Task: find the column-wise mean of array `d`\n",
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"# Task: Find the row-wise mean of array `e`\n",
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"# Task: Find the maximum of all values in the array `c`"
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]
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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