With your data cleaned and transformed, it’s time to dive into analysis. This hub covers everything you need to start analyzing, summarizing, and visualizing data using Pandas. From descriptive statistics to plotting and exploring trends, these tutorials help you turn raw numbers into insights.
📊 Core Topics
- Descriptive Statistics – Use mean, median, mode, std, var, and describe() to understand data distributions.
- Data Visualization with Pandas – Quickly generate plots using built-in plotting tools or link with Matplotlib.
- Handling Missing Data – Identify and fill NaNs using ffill, bfill, interpolate, and impute methods.
- Working with Dates and Times – Parse, index, and filter time-series data.
- Merging and Joining DataFrames – Combine multiple datasets with merge(), join(), and concat().
📈 Analysis Tutorials
- How to Calculate Entropy
- Calculate Standard Deviation
- Calculate Standard Error
- Calculate Skewness
- Calculate Kurtosis
- Calculate Median
- Calculate Business Days Between Dates
- Calculate Cumulative Sum
- Calculating Correlations
📊 Visualization Tutorials
🧯 Common Pitfalls
- Outliers: Detection and Removal
- Missing Data During Import
- Troubles with Resampling Time-Series
- Plotting Engine Compatibility Issues
📚 Real-World Use Cases
Use Case 1: You’re working with customer satisfaction scores and want to understand trends. Start with median, boxplots, and outlier detection.
Use Case 2: You’re tracking marketing campaigns over time. Use cumulative sum and time-series plots to visualize engagement growth.
Use Case 3: Working with finance? Combine multiple CSVs with joins and run correlation analysis.