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
- Calculate Standard Deviation
- Calculate Standard Error
- Calculate Business Days Between Dates
- Calculate Cumulative Sum
- Calculating Correlations
- Calculate Median
- Time Series Analysis
- Data Distribution Analysis
- Advanced Filtering Techniques
📊 Visualization Tutorials
🧯 Common Pitfalls
- Outliers: Detection and Removal
- Missing Data During Import
- Troubleshooting Query Operations
- Fix KeyError While Analyzing
📚 Real-World Use Cases
Use Case 1: Customer Satisfaction Analysis
You’re working with customer satisfaction scores and want to understand trends. Start with descriptive statistics, boxplots, and outlier detection.
You’re working with customer satisfaction scores and want to understand trends. Start with descriptive statistics, boxplots, and outlier detection.
Use Case 2: Marketing Campaign Tracking
You’re tracking marketing campaigns over time. Use cumulative sum and time-series plots to visualize engagement growth.
You’re tracking marketing campaigns over time. Use cumulative sum and time-series plots to visualize engagement growth.
Use Case 3: Financial Data Analysis
Working with finance? Combine multiple CSVs with joins and run correlation analysis.
Working with finance? Combine multiple CSVs with joins and run correlation analysis.
📌 Continue Learning
- Explore best practices for efficient exploration
- Analyze time-series, optimize performance, or explore pivot tables
- Bring in external datasets with Data Input and Output
🔢 Statistical Analysis Methods
📊 Descriptive Stats
📈 Distribution
⏱️ Time Series
🔗 Relationships
✅ Data Quality & Handling
- Handle and Interpolate Missing Data – Fill gaps using multiple strategies
- Remove Duplicates – Ensure data integrity
- Filter and Query Data – Extract relevant subsets
- Check Data Membership – Validate data consistency
🎓 Ready to Advance? Explore Intermediate Pandas techniques, dive into scientific computing, or integrate with machine learning pipelines.