diff --git a/Aditya_210066_week_1/Anaconda.ipynb b/Aditya_210066_week_1/Anaconda.ipynb new file mode 100644 index 0000000..6343dab --- /dev/null +++ b/Aditya_210066_week_1/Anaconda.ipynb @@ -0,0 +1,71 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2322030c", + "metadata": {}, + "source": [ + " # Anaconda\n", + " \n", + " \n", + " ### What is Anaconda?\n", + " Conda is an open-source package and environment management system that runs on Windows, macOS, and Linux.\n", + " Conda quickly installs, runs, and updates packages and their dependencies. It also easily creates, saves,\n", + " loads, and switches between environments on your local computer. It was created for Python programs, but\n", + " it can package and distribute software for any language.\n", + " \n", + "\n", + "### Installing Anaconda-\n", + "I reffered to https://youtu.be/5mDYijMfSzs this video to download Anaconda on my laptop(windows 10).\n", + "Or go through the following steps to download it on your laptop(windows 10)\n", + "1. Search Anaconda.com on Google.\n", + "2. Click on download button(Python 3.9 version).\n", + "3. Open the downloaded file. Setup will start.\n", + "4. Wait till setup is completed.\n", + "5. Few moments later the installation will be completed.\n", + "\n", + "---\n", + "\n", + "## Environment in Anaconda\n", + "\n", + "### What is conda environment?\n", + "Conda environment is a directory which contains different types of packages which you have installed.\n", + "Anaconda comes with a already installed base environment which contains various pacakages. It is \n", + "preffered to use different environments for different projects.\n", + "\n", + "### How to create environment in Anaconda?\n", + "1. Open Anaconda Prompt.\n", + "2. Type \" conda create name --'type name of environment'\".\n", + "3. Type \"y\".\n", + "4. This will create a new environment.\n", + "\n", + "### How to install packages in environment?\n", + "1. Click on 'Environments'.\n", + "2. Select desired environment.\n", + "3. Above 'Name' column select 'Not installed'.\n", + "4. In top right corner enter the name of package you want to download in the block saying 'Search packages'." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Aditya_210066_week_1/Jupyter.ipynb b/Aditya_210066_week_1/Jupyter.ipynb new file mode 100644 index 0000000..84129b9 --- /dev/null +++ b/Aditya_210066_week_1/Jupyter.ipynb @@ -0,0 +1,59 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "00581b96", + "metadata": {}, + "source": [ + "# Jupyter Notebook\n", + "\n", + "## Introduction\n", + "The Jupyter Notebook is an open source web application that you can use to create and share\n", + "documents that contain live code, equations, visualizations, and text. Jupyter Notebook is \n", + "maintained by the people at Project Jupyter.Jupyter notebooks can also be converted to a \n", + "number of standard output formats (HTML, Powerpoint, LaTeX, PDF, ReStructuredText, Markdown,\n", + "Python) through the web interface. This flexibility makes it easy for data scientists to share\n", + "their work with others.\n", + "\n", + "\n", + "## What are Jupyter notebooks used for?\n", + "Jupyter notebooks are especially useful for \"showing the work\" that your data team has done \n", + "through a combination of code, markdown, links, and images. They are easy to use and can be \n", + "run cell by cell to better understand what the code does.Jupyter notebooks are used for all \n", + "sorts of data science tasks such as exploratory data analysis (EDA), data cleaning and \n", + "transformation, data visualization, statistical modeling, machine learning, and deep learning.\n", + "\n", + "## Cells \n", + "A cell is a multiline text input field, and its contents can be executed by using **Shift-Enter**\n", + ", or by clicking either the “Play” button the toolbar, or Cell, Run in the menu bar. The \n", + "execution behavior of a cell is determined by the cell's type. Basically they are used to write \n", + "code and run it.\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Aditya_210066_week_1/Matplotlib.ipynb b/Aditya_210066_week_1/Matplotlib.ipynb new file mode 100644 index 0000000..9f88129 --- /dev/null +++ b/Aditya_210066_week_1/Matplotlib.ipynb @@ -0,0 +1,283 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a89f75ec", + "metadata": {}, + "source": [ + "# Matplotlib\n", + "\n", + "Matplotlib is a comprehensive library for creating static, animated, and interactive\n", + "visualizations in Python. It offers a viable open source alternative to MATLAB. Most \n", + "of the Matplotlib utilities lies under the pyplot submodule, and are usually imported \n", + "under the plt alias. It is a cross-platform library for making 2D plots from data in \n", + "arrays.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9d219e0e", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import pyplot as plt \n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "4d7fab9f", + "metadata": {}, + "source": [ + "## Plotting graphs and using different functions in matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ee8c283c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = [1,4,7,9]\n", + "y = [4,7,30,23]\n", + "plt.xlabel('X-axis') # labelling x coordinate\n", + "plt.ylabel('Y-axis') # labelling y coordinate\n", + "plt.plot(x,y)\n", + "plt.title('x vs y') # title of graph\n", + "plt.show() # Shows the graph" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "023bfaff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 2 4 6 8 10 12 14] [ 0 4 16 36 64 100 144 196]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "v=np.arange(0,15,2)\n", + "u=v**2\n", + "plt.plot(v,u)\n", + "print(v,u) # prints the values of v & u in the form of array\n", + "plt.plot(v,u,marker=\"o\") # to marks the points on graph\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "424fa1a9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = [1,4,7,9,14]\n", + "y = [4,7,30,23,12]\n", + "plt.xlabel('X-axis')\n", + "plt.ylabel('Y-axis')\n", + "plt.plot(x,y,marker=\"x\",ms=\"15\")\n", + "plt.plot(v,u,marker=\"o\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "430e6758", + "metadata": {}, + "source": [ + "### Using arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cb27f007", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 2 4 6 8 10 12 14] [ 0 4 16 36 64 100 144 196]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i=np.arange(0,15,2)\n", + "j=np.arange(4,40,5)\n", + "plt.scatter(i,j,label='1')\n", + "v=np.arange(0,15,2)\n", + "u=v**2\n", + "print(v,u)\n", + "plt.scatter(v,u,label='2')\n", + "plt.title(\"Sample\")\n", + "plt.legend()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "6fb0d651", + "metadata": {}, + "source": [ + "### To change the style of graph" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "acb28c2b", + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib import style" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6c3f6ef3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 2 4 6 8 10 12 14] [ 0 4 16 36 64 100 144 196]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "v=np.arange(0,15,2)\n", + "u=v**2\n", + "plt.plot(v,u)\n", + "print(v,u) \n", + "plt.plot(v,u,marker=\"o\") \n", + "style.use('fivethirtyeight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a3ce9645", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = [1,4,7,9,14]\n", + "y = [4,7,30,23,12]\n", + "plt.xlabel('X-axis')\n", + "plt.ylabel('Y-axis')\n", + "plt.plot(x,y,marker=\"x\",ms=\"15\")\n", + "style.use('dark_background')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "939db6bb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Aditya_210066_week_1/Numpy.ipynb b/Aditya_210066_week_1/Numpy.ipynb new file mode 100644 index 0000000..c76881e --- /dev/null +++ b/Aditya_210066_week_1/Numpy.ipynb @@ -0,0 +1,548 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "399d0657", + "metadata": {}, + "source": [ + "# Numpy\n", + "\n", + "1. Numpy provides a lot of functions to work in the domain of Linear Algebra, Matrices, Fourier transform \n", + " and more.\n", + "2. It is a Python library used for working with arrays and functions related to arrays.\n", + "3. Most of the part of the Numpy library is created using C & C++.\n", + "\n", + "## Working with Numpy\n", + "\n", + "### Creating an array" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d8d27c5e", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np # imports Numpy " + ] + }, + { + "cell_type": "markdown", + "id": "ecb6c3c3", + "metadata": {}, + "source": [ + "#### 0 dimensional array-" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5166a205", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21\n", + "0\n" + ] + }, + { + "data": { + "text/plain": [ + "()" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a=np.array(21)\n", + "print(a)\n", + "print(a.ndim)# printing dimension of array\n", + "a.shape #gives size of array" + ] + }, + { + "cell_type": "markdown", + "id": "fbe015a9", + "metadata": {}, + "source": [ + "#### 1 dimensional array-" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9a0c945b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3]\n", + "1\n" + ] + }, + { + "data": { + "text/plain": [ + "(3,)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b=np.array([1,2,3])\n", + "print(b)\n", + "print(b.ndim)\n", + "b.shape" + ] + }, + { + "cell_type": "markdown", + "id": "eb91a756", + "metadata": {}, + "source": [ + "#### 2 dimensional array-" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "573851f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2 3]\n", + " [4 5 6]]\n", + "2\n" + ] + }, + { + "data": { + "text/plain": [ + "(2, 3)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c=np.array([[1,2,3],[4,5,6]])\n", + "print(c)\n", + "print(c.ndim)\n", + "c.shape" + ] + }, + { + "cell_type": "markdown", + "id": "b6104b3f", + "metadata": {}, + "source": [ + "#### 3 dimensional array-" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e11db9d5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[1 2]\n", + " [3 4]\n", + " [1 8]]\n", + "\n", + " [[2 8]\n", + " [5 6]\n", + " [6 7]]]\n", + "3\n" + ] + }, + { + "data": { + "text/plain": [ + "(2, 3, 2)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d=np.array([[[1,2],[3,4],[1,8]],[[2,8],[5,6],[6,7]]])\n", + "print(d)\n", + "print(d.ndim)\n", + "d.shape" + ] + }, + { + "cell_type": "markdown", + "id": "a1710d47", + "metadata": {}, + "source": [ + "#### Function to access specific element \n", + "\n", + "**Index start from 0 and not 1.**" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "47360502", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b[2]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "25ad4f41", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c[0,0]" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "97e3a0b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d[0,1,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c10a894c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 6])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c[:,2] #prints 3rd column of matrix\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "3c8a787f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4, 5, 6])" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c[1,:] #prints 2nd row of given matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "1198d14b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2, 8],\n", + " [5, 6],\n", + " [6, 7]])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d[1,:,:] " + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "dd2e69fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([6, 7])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d[1,2,:]" + ] + }, + { + "cell_type": "markdown", + "id": "39f0bf28", + "metadata": {}, + "source": [ + "#### Arrays with numerical range\n", + " Format - np.arrange(start index,stop index,step size,dtype)\n", + " \n", + "**Stop index is not included in array.**\n", + "\n", + "**If data type is not specified then default data type(int) is taken.**" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "688e0435", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 2 4 6 8]\n", + "[ 5 6 7 8 9 10]\n" + ] + } + ], + "source": [ + "q=np.arange(0,10,2)\n", + "p=np.arange(5,11,1,dtype=\"int\")\n", + "print(q)\n", + "print(p)" + ] + }, + { + "cell_type": "markdown", + "id": "c917ff7b", + "metadata": {}, + "source": [ + "#### Matrix operations" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "fcea9678", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 5 7 17]\n", + "[26 28 38]\n" + ] + } + ], + "source": [ + "w=np.array([1,2,8])\n", + "r=np.array([4,5,9])\n", + "print(w+r) #prints sum of matrices\n", + "print(a+w+r)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "b4664164", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "86\n" + ] + } + ], + "source": [ + "t=np.matmul(w,r) #matrix multiplication between w & r\n", + "print(t)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "8b36dc5f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "1\n", + "8\n" + ] + } + ], + "source": [ + "e=np.min(b) #gives minimum value in a matrix\n", + "y=np.min(d)\n", + "g=np.max(d) # gives maximum value in a matrix\n", + "print(e)\n", + "print(y)\n", + "print(g)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "0afb0fcc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1, 4],\n", + " [2, 5],\n", + " [3, 6]])" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c.T # gives transpose of a matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "17de7db4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[2 3]\n", + " [4 5]\n", + " [2 9]]\n", + "\n", + " [[3 9]\n", + " [6 7]\n", + " [7 8]]]\n", + "[-1 0 1]\n", + "[0.5 1. 1.5]\n", + "[2 4 6]\n" + ] + } + ], + "source": [ + "print(d+1)# adds 1 to all elements of given matrix\n", + "print(b-2)\n", + "print(b/2)#divides every element of given matrix\n", + "print(b*2)#multiplies every element of given matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e32ca90b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Aditya_210066_week_1/Pandas.ipynb b/Aditya_210066_week_1/Pandas.ipynb new file mode 100644 index 0000000..bd4575d --- /dev/null +++ b/Aditya_210066_week_1/Pandas.ipynb @@ -0,0 +1,634 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d3c0eb19", + "metadata": {}, + "source": [ + "# Pandas\n", + "\n", + "1. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool.\n", + "2. It is built on top of the Python programming language.\n", + "3. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python\n", + "\n", + "It has three types of data structure\n", + "1. Series(1 Dimensional)\n", + "2. DataFrame(2 Dimensional)\n", + "3. Panel(Multi dimensional)\n", + "\n", + "## Working" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d87f18a0", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "ed441118", + "metadata": {}, + "source": [ + "#### *Working with series*" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8b0e7526", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 2\n", + "2 3\n", + "3 5\n", + "dtype: int64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a=[1,2,3,5]\n", + "pd.Series(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "23c24b2e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "a 1\n", + "b 2\n", + "c 3\n", + "d 5\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#To change index \n", + "pd.Series(a,index=['a','b','c','d'])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3109bfed", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "339dc83a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 2\n", + "2 5\n", + "dtype: int32" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#To create series using array\n", + "a=np.array([1,2,5])\n", + "pd.Series(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bed1b62d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Name Rahul\n", + "Marks 12\n", + "Result Fail\n", + "dtype: object" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d={'Name':'Rahul','Marks':12,'Result':'Fail'}\n", + "pd.Series(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c9b7bd16", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1\n", + "1 2\n", + "dtype: int32" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(a).head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e037b1c1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 2\n", + "2 5\n", + "dtype: int32" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(a).tail(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8cde201c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 2, 5])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(a).values" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "a689fcbc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "[1, 2, 5]\n", + "Length: 3, dtype: int32" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Series(a).array" + ] + }, + { + "cell_type": "markdown", + "id": "387a9066", + "metadata": {}, + "source": [ + "#### *Working with Dataframe*" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "37c83604", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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