Profile Data with Python#

This session covers the use of the pandas_profiling library for generating comprehensive data reports in Python:

• Library Installation and Import: Learn how to install and import the pandas_profiling library.
• Profile Report Generation: Generate an HTML report with a single line of code using ProfileReport.
• Descriptive Statistics: View detailed descriptive statistics such as variance, standard deviation, and kurtosis.
• Outlier Detection: Identify and analyze outliers within the dataset.
• Correlation Analysis: Understand how variables are correlated with each other using visual representations.
• Handling Missing Values: Get insights on missing data and decide on imputation or removal strategies.
• Initial Data Insights: Use the report to gather early warnings and insights before starting the data cleaning and modeling process.

Here are links used in the video:

• Jupyter Notebook
• Pandas Profiling output
• Learn about the pandas_profiling package. Video
• Learn about the google.colab package