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Data Cleaning with Python: From Raw to Ready Data
New
192 students

Data Cleaning with Python: From Raw to Ready Data

Clean real-world datasets in Python using pandas step by step
Created byMousa Al-Salahi
Last updated 8/2026
English

What you'll learn

  • Clean and prepare raw datasets in Python using pandas.
  • Handle missing and null values with appropriate techniques.
  • Detect and remove duplicate records safely.
  • Clean and standardize text data (lower/upper case, spaces, categories, types).
  • Build a simple, reusable data cleaning workflow for future projects.

Course content

1 section • 16 lectures • 1h 54m total length

  • Explore & Select Data from DataFrame7:36

    Explore and select data in the superstore data frame using head, info, shape, and describe to understand columns, rows, and data types; display single or multiple columns with proper capitalization.

  • Filter Rows using ( Loc & iLoc )7:19
  • Filtering RowUsing Conditions5:52

    Learn to filter rows by condition in a dataframe, using boolean masks for quantities greater than 10 or cities like Atlanta, and combine conditions with and/or to extract matching rows.

  • Advanced Filtering ( Range , isin , str.contain )10:17
  • Finding & Handling missing Values11:48

    Learn to identify and handle missing values in a dataframe by discovering, dropping, or filling them, using dropna, isna, and selective column operations.

  • Filling Missing Values ( Fillna )9:10

    Learn to fill missing values with mean or median for numeric columns, and replace with the most frequent value (mode) or 'unknown' for categoricals, using fillna and in-place updates.

  • Course Review Request0:12
  • Finding & Dropping Duplicates7:40
  • Cleaning Strings & Text Columns7:20

    Learn to clean strings in text columns using value counts, strip, replace, and case transformations to standardize categories. Save results in category_clean and verify with value counts.

  • Feature Engineering using Where3:26
  • Feature Engineering using Bins8:28
  • Date Handling11:19

    learn to convert non-date data to dates in pandas, extract year, month, day, and day of week, and compute date differences to analyze orders by year and month.

  • Sort Columns7:56

    Sort data frames using df.sort_values by selecting columns and choosing ascending or descending orders. Manage multiple columns, null value positions, and ignore index options.

  • Rename Columns7:11
  • Group by9:10

    Learn how to use groupby in Python to analyze data by state, city, and category, performing sums, means, max, min, and multiple aggregations.

  • Course Review Request0:12

Requirements

  • There are no strict requirements for this course.
  • Basic understanding of tables (rows and columns) from Excel or any spreadsheet.
  • Very basic Python knowledge (running code cells, simple variables and lists).
  • A computer with internet access and the ability to install Python, or use Google Colab in the browser.

Description

This course teaches you how to clean and prepare real-world data using Python and pandas in a simple, practical way. You will start from raw, messy datasets and learn step by step how to handle missing values, remove duplicates, fix inconsistent text, and correct common data quality issues. By the end of the course, you will have a reusable data cleaning workflow that you can apply to any dataset before analysis or reporting.

Lessons / sections (bullet points)

  • Introduction and setup: Install Python or use Google Colab, load datasets, and overview of pandas.

  • Data inspection and quality check: Explore data structure, types, and detect basic issues.

  • Handling missing and null values: Identify missing data and apply different strategies to deal with it.

  • Removing duplicate records: Find and safely remove duplicates based on full rows or key columns.

  • Removing duplicate records: Find and safely remove duplicates based on full rows or key columns.

  • Cleaning text and categorical data: Standardize lower/upper case, trim spaces, fix inconsistent categories.

  • Fixing data types and formats: Convert columns to numbers, dates, and categories correctly.

  • Handling dates like years months days and seasons by extracting part of dates

  • Handling and cleaning columns names

  • Prepare cleaned data for analysis in the upcoming steps


Who this course is for:

  • Beginners in data analysis who work with messy Excel/CSV files.
  • Junior data analysts who want to clean data using Python and pandas.
  • Learners with basic Python skills who need to handle missing values and duplicates.
  • Anyone who wants a simple, practical introduction to data cleaning.

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