Python Tutorials

In the following Python tutorials, we will explore the different Python libraries that are used in data-science and data-management.

Some of the libraries that we will use include, pandas, sklearn, statsmodels, matplotlib, pyodbc, and more. Each Python tutorial contains a step-by-step guide with the full data-set that you can apply.

Databases & SQL

Connect Python to MS Access
Connect Python to Oracle
Connect Python to SQL Server
Connect Python to MySQL

Create a Database using sqlite3
Use SQL in Python
Insert Values into MySQL

Insert Values into MS Access
Insert Values into SQL Server

Update values in MySQL

GUI – tkinter

Create GUI – tkinter
Yes/No message box
Place charts on GUI

Machine Learning

Linear Regression – statsmodels
Multiple Linear Regression
K-Means Clustering


Calculate stats
Import CSV File into Python
Import CSV with Variable Name
Import Excel File into Python
Create Pandas DataFrame
Export DataFrame to CSV
Export DataFrame to Excel
Export DataFrame to JSON
IF condition – DataFrame
Concatenate Column Values
Convert DataFrame to List
Sort Pandas DataFrame
Create Pivot Table
Remove Duplicates from Pandas DataFrame
Select Row from Pandas DataFrame
Union Pandas DataFrames

System, Installations and Upgrades

Install package – PIP
Install package – Anaconda

Install package – connection error
Uninstall package – PIP
Upgrade PIP – Anaconda
Upgrade Anaconda PIP to Version 18.0
Upgrade PIP – Windows
Batch File to Run Script
System Dates

Current Date
Delete File or Folder
Run Batch File from Python
Execute Python Script using Windows Scheduler
Add Python to Windows Path


Create Charts – Matplotlib
Export Matplotlib Charts to PDF


While Loop
Concatenate and Slice Lists