#3 Excel Data Cleaning in Python- Full Tutorial

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Data Cleaning of Excel workbook with Python: Clean and Process GDP data using Pandas library in jupyter notebook

In this video, I demonstrate how to clean and process an Excel file containing GDP per capita data from the OECD dataset. Using Python and Pandas, I walk you through the essential steps for transforming messy data into a structured and ready-to-analyze format.

What’s Covered in This Video:
1. Loading Excel Data:
• Import data from the OECD.Stat export sheet in the excel file.
• Skip unnecessary rows and focus only on relevant columns
2. Data Cleaning Steps:
• Rename columns dynamically for better readability.
• Identify and remove leading and trailing spaces in text columns.
• Trim extra spaces in text data for consistency.
• Convert numerical columns to proper numeric formats using to_numeric.
Timestamps:

00:00 Intro
00:43 Launch Jupyter lab/jupyter notebook from mac terminal
01:04 What is an environment in programming
01:30 How to check environments in conda
01:46 How to activate an environment in conda
02: 11 Launch Jupyter lab
02:38 Inspect the excel workbook
03:38 Import libraries
03:53 Read Excel workbook into a pandas dataframe
03:58 Define optional parameters while reading excel workbook
05:58 Data Exploration
06:13 Data Cleaning
06:16 How to rename a column in dataframe
06:49 Check unique datatypes in a column
07:19 Check leading and trailing whitespaces in a column
07:41 Remove the whitespaces in a (str datatype) column
08:31 Change datatype of a column(to numeric) and rename it using for loop
10:55 Check statistical info of the dataframe
11:15 Check the null values in the dataframe
11:30 Drop the rows with null values (condition -null values in all the columns of that row)
12:18 Split a column in dataframe based on a delimiter
13:20 Reorder or Rearrange the columns in a dataframe
14:25 Drop a column in the dataframe
15:15 Select few columns in a dataframe
15:50 Check the shape of the dataframe
Why Watch This Video?
• Gain hands-on experience with cleaning Excel files in Python
• Master key techniques like trimming text, renaming columns, and converting data types.
• Learn a practical workflow for processing large datasets like GDP per capita data.

Who Is This Video For?
• Data enthusiasts working with Excel files in Python.
• Beginners learning Pandas for data cleaning.
• Analysts and professionals handling large datasets for analysis.

Видео #3 Excel Data Cleaning in Python- Full Tutorial канала Raghu Veer Tech