FREE Python – Data Analytics – Real World Hands-on Projects

Last Updated on March 1, 2023 by GeeksGod

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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The projects are :

Project 1 – Weather Data AnalysisProject 2 – Cars Data AnalysisProject 3 – Police Data AnalysisProject 4 – Covid Data AnalysisProject 5 – London Housing Data AnalysisProject 6 – Census Data AnalysisProject 7 – Udemy Data AnalysisProject 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.