Get - Projects Source Codes and Data Analytics Course Notes. Download Free Sample Notes (Data Analytics with Python) - https://drive.google.com/file/d/1y5xWgslRARtCsIgMpasud4W0T1p3r5LR/view?usp=sharing If you need all the Projects Source codes and Notes of the complete course, which contain all commands of Core Python, Numpy, Pandas, Matplotlib, SQL that we use for Big-Data Analytics ( cost @ Rs.750 or $25 ), simply drop a mail to us on 'datasciencelovers@gmail.com' and get all the material within 3 hours. Download Dataset File - https://drive.google.com/file/d/1MlrQh-fKcL75ni_swveeiRPW_Vn9Q3xw/view?usp=sharing 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, you will learn how to work on a real project of Data Analysis with Python. Questions are given in the project and then solved with the help of Python. It is a project of Data Analysis with Python or you can say, Data Science with Python. Q. 1) Instruction ( For Data Cleaning ) - Find all Null Values in the dataset. If there is any null value in any column, then fill it with the mean of that column. Q. 2) Question ( Based on Value Counts )- Check what are the different types of Make are there in our dataset. And, what is the count (occurrence) of each Make in the data ? Q. 3) Instruction ( Filtering ) - Show all the records where Origin is Asia or Europe. Q. 4) Instruction ( Removing unwanted records ) - Remove all the records (rows) where Weight is above 4000. Q. 5) Instruction ( Applying function on a column ) - Increase all the values of 'MPG_City' column by 3. ----------------------------------------------------------------------- The commands that we used in this project : * import pandas as pd -- To import Pandas library * pd.read_csv - To import the CSV file in Jupyter notebook * head() - It shows the first N rows in the data (by default, N=5) * shape - It shows the total no. of rows and no. of columns of the dataframe * df.isnull( ).sum( ) - It detects the missing values from each column of the dataframe. * fillna() - To fill the null values of a column with some particular value * value_counts - In a column, it shows all the unique values with their count. It can be applied to a single column only. * isin() - To show all records including particular elements * apply() - To apply a function along any axis of DF ------------------------------------------------------ You must check our other videos : Project 8 - https://youtu.be/b7Kd0fLwgO4 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 1 - https://www.youtube.com/watch?v=4hYOkHijtNw #datascience #python #bigdata #dataanalytics