5 Python Libraries For Data Science Beginners | Pandas | NumPy | Matplotlib | Seaborn | SciPy

Get - Python Data Analytics Course Notes and Projects Source Codes ( Rs.450 ) - https://rzp.io/l/dslstudymaterial Buy our "Self Study Material", which includes all the Projects Source Codes and Notes of the complete Data Analytics course, which contain all commands of Core Python, Numpy, Pandas, Matplotlib, SQL that we use for Big-Data Analytics ( cost @ Rs.450 or $6 or €6 ) Contact Mail Id : datasciencelovers@gmail.com ------------------------------- Complete Course - 'Data Analysis with Python' - https://www.youtube.com/watch?v=77jgzVGlSyA&list=PLy3lFw0OTluuf6PhQRxpF-MW4GlEhxroh Join our Telegram Channel - https://t.me/dsl99900 Join our Facebook Group - https://www.facebook.com/groups/2637191416382018 Like our Facebook Page - https://www.facebook.com/LoveDataScience In this video, we are telling about the most useful & common Python Libraries for Data Science, with their use and some basic commands. 1. Pandas Pandas is the most famous library for Data Analysis. Pandas is an open-source library that allows us to perform data manipulation in Python. Pandas provides an easy way to create, manipulate and wrangle the data. Some Basic Pandas Commands : import pandas as pd - To Import the Pandas Library. pd.read_csv(“filename”) – To read the CSV file. df.head( ) / df.tail ( ) - To check first/last 5 rows of the dataframe(table). df.describe( ) – To get the summary statistics of all numeric columns. df.isnull( ) – To detect the missing values from the dataframe. df.dropna( ) – To delete the rows that contains all or any missing values. df.to_excel(“filename”) – To save a file in Excel format. 2. Numpy NumPy – Numerical Python Numpy is an open-source library that is used for performing mathematical operations on arrays. Numpy is a general-purpose array processing package. Numpy provides a high-performance multidimensional array object and tools for working with these arrays. Numpy contains a multi-dimensional array and matrix data structures. Some Basic Numpy Commands : import numpy as np - To Import the Numpy Library. np.array( [1,2,3,4,5] ) – To create an One-dimensional array. A.reshape ( 3,4 ) – To reshape an array in 3x4 size. np.random.random() – To create an array with random values. np.ones((2,4)) – To create an array of size 2x4 with all 1. A[1:2,1:2,1:2] – Array indexing 3. Matplotlib Matplotlib is a powerful library for creating graphs and charts. Matplotlib in Python is used for visualization purposes. Matplotlib allows us to draw many different types of plots like : Line Plot Bar Plot Scatter Plot Pie Chart Heat Map Some Basic Matplotlib Commands : import matplotlib.pyplot as plt – To import matplotlib library. plt.title(‘Title_Name’, fontsize=24) – To give the title on the graph. plt.plot( df[‘Year’] , df[‘Sales’] ) – To draw a plot with Year & Sales column. plt.scatter( x-elements, y-elements , color = ‘r’) - To draw a scatter plot with red color. plt.pcolor(df, cmap=‘RdBu’) – To draw a heatmap. Select & Convert the Cell to MarkDown -- Edit Tab -- Insert Image -- Run - To insert an image in Jupyter Notebook. 4. Seaborn Seaborn is an open-source library based on matplotlib. Seaborn provides a variety of visualization patterns by using fewer syntax. Seaborn is preferred to draw interactive and informative graphics. Some Basic Seaborn Commands : import seaborn as sns – To import the Seaborn library. sns.regplot(x=df.Col_x , y=df.Col_y) – To draw Linear Regression graph. sns.distplot(df.Col_y , color= ‘r’) – To draw a Distribution plot. sns.relplot(x=‘Col_1’ , y=‘Col_2’ , data=df_name ) – To check the relationship between two columns. sns.heatmap(df.corr( ) , vmin=-1, vmax=1, center=0) – Pearson Correlation Heatmap. sns.catplot(x = ‘Col_1’ , y = 'Col_2’ , data = df_name) – To draw a plot with Categorical data. 5. SciPy SciPy – Scientific Python SciPy is an open-source library based on Numpy. SciPy is used to perform Scientific & Mathematical operations. SciPy is used in the fields of mathematics, science, and engineering. There are many sub-packages also in SciPy like : Cluster, Integrate, Optimize etc. import scipy - To import SciPy library. Data Analytics Projects for beginners : Project 7 - https://youtu.be/AO5uhxa1R84 Project 6 - https://youtu.be/e1zKFSrKeLs Project 5 - https://youtu.be/q-Omt6LgRLc Project 4 - https://youtu.be/89eYAAPyRfo Project 3 - https://youtu.be/GyUbo45mVSE Project 2 - https://www.youtube.com/watch?v=fhiUl7f5DnI Project 1 - https://www.youtube.com/watch?v=4hYOkHijtNw #datascience #python #bigdata #pandas #numpy #matplotlib #seaborn #scipy #pythondataanalytics #datasciencecourse #pythoncourse #learnpython #learndatascience #learndataanalysis#freedatasciencetutorials #freepythontutorials #freedataanalyticscourse #dataanalysiswithpython #dataanalytics #dataanalyticscourse #machinelearning #machinelearningtutorials #machinelearningcourse #examples