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What you will learn
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
Description
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 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.
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