Python - Data Analytics - Real World Hands-on Projects

Start learning data analysis with these beginners friendly projects

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Last Updated: 2023-03-17 04:03:55 English Course Type: Self Learning

Python Libraries - Pandas, Matplotlib, Numpy
Python Programming Language
Basic Data Science
Data Analytics Projects solved with Python
All projects are Solved, and available with Python Source Codes files & dataset files
These projects can be used in Resume/CV, college submission

In this course, we have uploaded 8 Data Analytics Projects, solved with Python.

These projects can used if you are looking for a starting level job as a Data Analyst.

If you are a student, you can use these projects to submit in college/institute.

The source codes and datasets files are available to download.

All the projects are created with a very easy explanation.

We have mainly used the popular Python Pandas Library, along with Matplotlib to solve these projects.

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To buy our Data Analyst Study Material , you can mail us at datasciencelovers@gmail.com

The projects are :

Project 1 - Weather Data Analysis
Project 2 - Cars Data Analysis
Project 3 - Police Data Analysis
Project 4 - Covid Data Analysis
Project 5 - London Housing Data Analysis
Project 6 - Census Data Analysis
Project 7 - Udemy Data Analysis
Project 8 - Netflix Data Analysis

Some basic examples of commands used in these projects are :

* 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

* index - This attribute provides the index of the dataframe

* columns - It shows the name of each column

* dtypes - It shows the data-type of each column

* unique() - In a column, it shows all the unique values. It can be applied on a single column only, not on the whole dataframe.

* nunique() - It shows the total no. of unique values in each column. It can be applied on a single column as well as on the whole dataframe.

* count - It shows the total no. of non-null values in each column. It can be applied on a single column as well as on the whole dataframe.

* value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only.

* info() - Provides basic information about the dataframe.* size - To show No. of total values(elements) in the dataset.

* duplicated( ) - To check row wise and detect the Duplicate rows.

* isnull( ) - To show where Null value is present.

* dropna( ) - It drops the rows that contains all missing values.

* isin( ) - To show all records including particular elements.

* str.contains( ) - To get all records that contains a given string.

* str.split( ) - It splits a column's string into different columns.

* to_datetime( ) - Converts the data-type of Date-Time Column into datetime[ns] datatype.

* dt.year.value_counts( ) - It counts the occurrence of all individual years in Time column.

* groupby( ) - Groupby is used to split the data into groups based on some criteria.

* sns.countplot(df['Col_name']) - To show the count of all unique values of any column in the form of bar graph.

* max( ), min( ) - It shows the maximum/minimum value of the series.

* mean( ) - It shows the mean value of the series.

Beginners Friendly Projects - Required only basic Python language knowledge
You can use Jupyter notebook, Google Colab etc to run the python code
All datasets & source codes are available to download with this course
This course is for beginners as well as intermediate level ... You will enjoy it

Beginners looking for job as a Data Analyst
Students searching for projects to submit in college/institute
Anyone interested in Data Science and Data Analytics

Section 1 : Python Data Analytics Projects 16 Lectures 00:00:12
Lecture 1 :
Project 1 - Weather Data Analysis Preview
In this lecture, 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. The commands that we used in this project : * 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 * index - This attribute provides the index of the dataframe * columns - It shows the name of each column * dtypes - It shows the data-type of each column * unique() - In a column, it shows all the unique values. It can be applied on a single column only, not on the whole dataframe. * nunique() - It shows the total no. of unique values in each column. It can be applied on a single column as well as on the whole dataframe. * count - It shows the total no. of non-null values in each column. It can be applied on a single column as well as on the whole dataframe. * value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only. * info() - Provides basic information about the dataframe. -------------------------------------------- Q. 1) Find all the unique 'Wind Speed' values in the data. Q. 2) Find the number of times when the 'Weather is exactly Clear'. Q. 3) Find the number of times when the 'Wind Speed was exactly 4 km/h'. Q. 4) Find out all the Null Values in the data. Q. 5) Rename the column name 'Weather' of the dataframe to 'Weather Condition'. Q. 6) What is the mean 'Visibility' ? Q. 7) What is the Standard Deviation of 'Pressure' in this data? Q. 8) What is the Variance of 'Relative Humidity' in this data ? Q. 9) Find all instances when 'Snow' was recorded. Q. 10) Find all instances when 'Wind Speed is above 24' and 'Visibility is 25'. Q. 11) What is the Mean value of each column against each 'Weather Condition ? Q. 12) What is the Minimum & Maximum value of each column against each 'Weather Condition ? Q. 13) Show all the Records where Weather Condition is Fog. Q. 14) Find all instances when 'Weather is Clear' or 'Visibility is above 40'. Q. 15) Find all instances when : A. 'Weather is Clear' and 'Relative Humidity is greater than 50' or B. 'Visibility is above 40'
Lecture 2 :
Project 2 - Cars Data Analysis
In this lecture, 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. 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 ------------------------------------------------------ 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.
Lecture 3 :
Project 3 - Police Data Analysis
In this lecture, 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. 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) * df.isnull( ).sum( ) - It detects the missing values from each column of the dataframe. * df.drop(‘Col_name’ ) - To drop a column from dataframe. * value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only. * df.groupby(‘Col_1’)[‘Col_2’] .sum( ) - To create groups - Two Keys – Apply on Col_2 grouped by Col_1. * df['Column_name'].map( { old1:new1 , old2:new2} ) – Change the all values of a column from old to new. We have to write for all values of column otherwise Nan will appear. * df['Column_name'].mean() - To show Mean value of a column. * df.groupby('Column_1').Column_2.describe() - To create groups based on Column1 and show statistics summary based on Column2. ....................................................................... Q. 1) Instruction ( For Data Cleaning ) - Remove the column that only contains missing values. Q. 2) Question ( Based on Filtering + Value Counts ) - For Speeding , were Men or Women stopped more often ? Q. 3) Question ( Groupby ) - Does gender affect who gets searched during a stop ? Question ( mapping + data-type casting ) Q. 4) Question ( mapping + data-type casting ) - What is the mean stop_duration ? Q. 5) Question ( Groupby , Describe ) - Compare the age distributions for each violation.
Lecture 4 :
Project 4 - Covid-19 Data Analysis
In this lecture, a tiny data set related to the Covid-19 pandemic is taken and analysed in a very Easy To Understand (ETU) language. Here, 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. 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 * df.count() - It counts the no. of non-null values of each column. * df.isnull().sum() - It detects the missing values from the dataframe. * import seaborn as sns - To import the Seaborn library. * import matplotlib.pyplot as plt - To import the Matplotlib library. * sns.heatmap(df.isnull()) - It will show the all columns & missing values in them in heat map form. * plt.show() - To show the plot. * df.groupby(‘Col_name’) - To form groups of all unique values of the column. * df.sort_values(by= ['Col_name'] ) - Sort the entire dataframe by the values of the given column. * df[df.Col_1 = = ‘Element1’] - Filtering – We are accessing all records with Element1 only of Col_1. ....................................................................... Q. 1) Show the number of Confirmed, Deaths and Recovered cases in each Region. Q. 2) Remove all the records where the Confirmed Cases is Less Than 10. Q. 3) In which Region, maximum number of Confirmed cases were recorded ? Q. 4) In which Region, minimum number of Deaths cases were recorded ? Q. 5) How many Confirmed, Deaths & Recovered cases were reported from India till 29 April 2020 ? Q. 6-A ) Sort the entire data wrt No. of Confirmed cases in ascending order. Q. 6-B ) Sort the entire data wrt No. of Recovered cases in descending order.
Lecture 5 :
Project 5 - London Housing Data Analysis
In this lecture, the London Housing Data is analyzed in a very Easy To Understand (ETU) language. Here, 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. 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 * df.count() - It counts the no. of non-null values of each column. * df.isnull().sum() - It detects the missing values from the dataframe. * import seaborn as sns - To import the Seaborn library. * import matplotlib.pyplot as plt - To import the Matplotlib library. * sns.heatmap(df.isnull()) - It will show the all columns & missing values in them in heat map form. * plt.show() - To show the plot. * df.dtypes - To show the datatype of each column. * pd.to_datetime(df.Date_Time_Col) - Converts the data-type of Date-Time Column into datetime[ns] datatype. * df.Time_Col.dt.year - Creating a new column with only year values * df.Time_Col.dt.month - Creating a new column with only month values. * df.insert( index , ‘new_column_name’, new_column_values) - To insert a New column at a particular position with values in it. * df.drop(['Col_name'] , axis=1 , inplace = True) - To drop a column from the dataframe permanently. * df.groupby(‘Col_name’) - To form groups of all unique values of the column. * df[df.Col_1 = = ‘Element1’] - Filtering – We are accessing all records with Element1 only of Col_1. * df.groupby(‘Col_1’)[‘Col_2’] .mean( ) - To create groups - Two Keys – Apply on Col_2 grouped by Col_1. ----------------------------------------------------- Q. 1) Convert the Datatype of 'Date' column to Date-Time format. Q. 2) Add a new column ''year'' in the dataframe, which contains years only. Q. 2-B) Add a new column ''month'' as 2nd column in the dataframe, which contains month only. Q. 3) Remove the columns 'year' and 'month' from the dataframe. Q. 4) Show all the records where 'No. of Crimes' is Zero. And, how many such records are there ? Q. 5) What is the maximum & minimum 'average_price' per year in england ? Q. 6) What is the Maximum & Minimum No. of Crimes recorded per area ? Q. 7) Show the total count of records of each area, where average price is less than 100000.
Lecture 6 :
Project 6 - Census Data Analysis
In this lecture, India Census 2011 data is analyzed in a very Easy To Understand (ETU) language. Here, you will learn about the python commands & 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 commands. 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 * style.hide_index( ) - To hide the index of the dataframe. * style.set_caption('Description of the dataframe') - To give a caption to the dataframe. * isin( ) - To show all records including particular elements. * groupby(‘Col_1’)[‘Col_2’] .sum( )[‘value’] - GroupBy – Two Keys – Apply on Col_2 grouped by Col_1. * df[df.Col_1 == 'Element1']['Col_2'] - Filtering - Filter the records of the dataframe wrt to Element1 of Col1 and then showing results of Col2 only. * set_index( ‘Col_Name’ ) - To set any column of a DF as an index. * add_prefix(‘value_’) - To add prefix to the column name. * add_suffix(‘_value’) - To add suffix to the column name. ....................................................................... Q. 1) How will you hide the indexes of the dataframe. Q. 2) How can we set the caption / heading on the dataframe. Q. 3) Show the records related with the districts - New Delhi , Lucknow , Jaipur. Q. 4) Calculate state-wise : A. Total number of population. B. Total no. of the population with different religions. Q. 5) How many Male Workers were there in Maharashtra state ? Q. 6) How to set a column as index of the dataframe ? Q. 7-A) Add a Suffix to the column names. Q. 7-B) Add a Prefix to the column names.
Lecture 7 :
Project 7 - Udemy Data Analysis
In this video lecture, Udemy Courses Dataset is analyzed in a very Easy To Understand (ETU) language. Here, you will learn about the python commands & 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 functions/commands. 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). * unique( ) - It shows the all unique values of the column. * value_counts - In a column, it shows all the unique values with their count. It can be applied to a single column only. * df[df.Col_1 = = ‘Element1’] - Filtering – We are accessing all records with Element1 only of Col_1. * df.sort_values(‘Col_name' , ascending=False ) - To sort the dataframe wrt any column values in descending order. * df[ (df.Col1 = = ‘Element1’) & (df.Col2 == ‘Element2’) ] - Multilevel filtering - And Filter – Filtering the data with two & more items. * str.contains('Value_to_match’) - To find the records that contains a particular string. * dtypes - To show datatypes of each column. * pd.to_datetime(df.Date_Time_Col) - To convert the data-type of Date-Time Column into datetime[ns] datatype. * dt.year - Creating a new column with only year values. * df.groupby(‘Col_1’)['Col_2'].max() - Using groupby with two different columns. ....................................................................... Q. 1) What are all different subjects for which Udemy is offering courses ? Q. 2) Which subject has the maximum number of courses. Q. 3) Show all the courses which are Free of Cost. Q. 4) Show all the courses which are Paid. Q. 5) Which are Top Selling Courses ? Q. 6) Which are Least Selling Courses ? Q. 7) Show all courses of Graphic Design where the price is below 100 ? Q. 8) List out all the courses that are related to 'Python'. Q. 9) What are courses that were published in the year 2015 ? Q. 10) What is the Max. Number of Subscribers for Each Level of courses ?
Lecture 8 :
Project 8 - Netflix Data Analysis
In this lecture, 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. The commands that we used in this project : * head() - It shows the first N rows in the data (by default, N=5). * tail () - It shows the last N rows in the data (by default, N=5). * shape - It shows the total no. of rows and no. of columns of the dataframe. * size - To show No. of total values(elements) in the dataset. * columns - To show each Column Name. * dtypes - To show the data-type of each column. * info() - To show indexes, columns, data-types of each column, memory at once. * value_counts - In a column, it shows all the unique values with their count. It can be applied on a single column only. * unique() - It shows the all unique values of the series. * nunique() - It shows the total no. of unique values in the series. * duplicated( ) - To check row wise and detect the Duplicate rows. * isnull( ) - To show where Null value is present. * dropna( ) - It drops the rows that contains all missing values. * isin( ) - To show all records including particular elements. * str.contains( ) - To get all records that contains a given string. * str.split( ) - It splits a column's string into different columns. * to_datetime( ) - Converts the data-type of Date-Time Column into datetime[ns] datatype. * dt.year.value_counts( ) - It counts the occurrence of all individual years in Time column. * groupby( ) - Groupby is used to split the data into groups based on some criteria. * sns.countplot(df['Col_name']) - To show the count of all unique values of any column in the form of bar graph. * max( ), min( ) - It shows the maximum/minimum value of the series. * mean( ) - It shows the mean value of the series. You will learn these things also: - Creating New Columns & Dataframe - Filtering (Single Column & Multiple Columns) - Filtering with And and OR - Seaborn Library - Bar Graphs ....................................................................... Task. 1) Is there any Duplicate Record in this dataset ? If yes, then remove the duplicate records. Task. 2) Is there any Null Value present in any column ? Show with Heat-map. Q. 1) For 'House of Cards', what is the Show Id and Who is the Director of this show ? Q. 2) In which year the highest number of the TV Shows & Movies were released ? Show with Bar Graph. Q. 3) How many Movies & TV Shows are in the dataset ? Show with Bar Graph. Q. 4) Show all the Movies that were released in year 2000. Q. 5) Show only the Titles of all TV Shows that were released in India only. Q. 6) Show Top 10 Directors, who gave the highest number of TV Shows & Movies to Netflix ? Q. 7) Show all the Records, where "Category is Movie and Type is Comedies" or "Country is United Kingdom". Q. 8) In how many movies/shows, Tom Cruise was cast ? Q. 9) What are the different Ratings defined by Netflix ? Q. 9.1) How many Movies got the 'TV-14' rating, in Canada ? Q. 9.2) How many TV Shows got the 'R' rating, after year 2018 ? Q. 10) What is the maximum duration of a Movie/Show on Netflix ? Q. 11) Which individual country has the Highest No. of TV Shows ? Q. 12) How can we sort the dataset by Year ? Q. 13) Find all the instances where: Category is 'Movie' and Type is 'Dramas' or Category is 'TV Show' & Type is 'Kids' TV'.
Lecture 9 :
Quiz : Project 1 - Weather Data Analysis
Project 1 - 2 Questions
Lecture 10 :
Quiz : Project 2 - Cars Data Analysis
Project 2 - 2 Questions
Lecture 11 :
Quiz : Project 3 - Police Data Analysis
Project 3 - 2 Questions
Lecture 12 :
Quiz : Project 4 - Covid-19 Data Analysis
Project 4 - 2 Questions
Lecture 13 :
Quiz : Project 5 - London Housing Data Analysis
Project 5 - 2 Questions
Lecture 14 :
Quiz : Project 6 - Census Data Analysis
Project 6 - 2 Questions
Lecture 15 :
Quiz : Project 7 - Udemy Data Analysis
Project 7 - 2 Questions
Lecture 16 :
Quiz : Project 8 - Netflix Data Analysis
Project 8 - 2 Questions

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Hi, I am a Data Analyst, engaged with YouTube channel 'Data Science Lovers'. I have worked on various data analytics projects with Python. I try to explain all the concepts in a very easy way so that even a beginner can understand and learn without any doubt. Keep learning with us, you will enjoy the lectures. Thank You so much

Python - Data Analytics - Real World Hands-on Projects

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Section 1 : Python Data Analytics Projects 16 Lectures 00:00:12

How do i access the course after purchase?

Are these video based online self-learning courses?

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Do i get any certificate for the courses?

For how long can i access my course on the platform?

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