Data Cleaning Project in Python (Part 2): Visual Insights + Export to CSV & PDF Report
Learn how to take your cleaned dataset to the next level!
In Part 2 of our Python Data Cleaning series, you’ll uncover real insights through advanced Seaborn and Matplotlib charts and learn how to export your visuals into a polished PDF report.
In this tutorial, you’ll learn:
• How to create box plots, heatmaps, histograms & scatter plots in Python
• How to analyze trends like age vs. spending
• How to export both your cleaned CSV and a full visual report to PDF
This is the perfect follow-up to Part 1 where we cleaned raw customer data using pandas and Jupyter Notebook.
✅ Tools used: pandas, seaborn, matplotlib
📈 Bonus: PDF report ready to use for business or portfolio projects!
📂 Watch Part 1 here : • Clean Messy Data in Python (Beginner Panda...
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How to download Anaconda to use Jupyter Notebook for Python coding: • How to Download Anaconda for Jupyter Noteb...
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Hands On Machine Learning with Scikit-Learn & TensorFlow: https://amzn.to/4h8IxRS
Python Machine Learning by Sebastian Raschka: https://amzn.to/401eIMU
Modern Python Cookbook: updated: https://amzn.to/3BV6sE0
Continue your learning with Python: https://learnpython.com/?ref=mgzmzjn
Get the dataset here : https://docs.google.com/spreadsheets/...
⏳ Timestamps ⏳
00:00 Intro & what we will cover in this video
00:43 Overview of Part 1 (data cleaning with python)
01:43 Overview of all steps for this video
03:03 Step 1: Saving the cleaned dataset in Python (Pandas) Jupyter Notebook
03:52 Looking for the saved dataset in Jupyter Notebook
04:08 How to save the dataset csv file created and saved in Python Jupyter Notebook
04:30 Step 2: Load the cleaned dataset back into Jupyter Notebook with Python (Pandas)
05:22 How to review or view the head of the dataset after upload
06:18 Step 3: Create a Box Plot: Purchase Amount by Age Group
08:36 Step 4: Heatmap: Purchase Frequency by Age Group and Season
09:56 Step 5: Histogram: Distribution of Purchase Amount
11:17 Step 6: Scatter Plot: Age vs Purchase Amount by Gender (with Hue)
12:43 Step 7: Save the cleaned and analyzed dataset
13:36 Save the visualizations to a PDF in Python using Jupyter Notebook using matplotlib and Pdfpages.
Thank you for watching.
#Python #DataCleaning #JupyterNotebook #PDFReport #DataVisualization #Pandas #Seaborn #Matplotlib #CSVExport
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