- 🎯 We will see in this hands-on training notebookhow to effectively diagnose and treat missing data in Python.
- 📊 The majority of data science work often revolves around pre-processing data, and making sure it's ready for analysis. However, we will be covering how transform our raw data into accurate insights. In this notebook, we will see:
- Import data into pandas, and use simple functions to diagnose problems in our data.
- Visualize missing and out of range data using missingnoandseaborn.
- Apply a range of data cleaning tasks that will ensure the delivery of accurate insights.
- Make sure we have a clean dataset ready for data analysis.
 
- Import data into 
- 📫 Feel free to contact me if anything is wrong or if anything needs to be changed 😎! labrijisaad@gmail.com
- 
                Notifications
    You must be signed in to change notification settings 
- Fork 2
labrijisaad/exploratory-data-analysis-in-Python
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
|   |   | |||
|   |   | |||
|   |   | |||
Repository files navigation
About
In this project, we will see in a hands-on training jupyter notebook how to effectively diagnose and deal with missing data in Python.
Topics
Resources
Stars
Watchers
Forks
Releases
No releases published
              Packages 0
        No packages published 
      
              