6️⃣ 📊 Pandas Statistical Analysis & Aggregation – Descriptive Stats, Grouping & Correlation
Gain insights from your data using built-in statistics, grouping, and correlation tools
🧲 Introduction – Why Perform Statistical Analysis in Pandas?
Data becomes powerful when you can extract patterns, relationships, and summaries from it. Pandas simplifies statistical analysis with functions for descriptive statistics, aggregation, and correlation. Whether you’re summarizing data, finding trends, or segmenting datasets for deeper insight, this tutorial equips you with the right tools.
🎯 In this guide, you’ll learn:
- How to describe and summarize datasets statistically
- Use
groupby()for segmented analysis - Apply aggregation functions to discover trends
- Analyze relationships using correlation functions
📘 Topics Covered
| 🔢 Topic | 🔎 Description |
|---|---|
| Pandas Descriptive Statistics | Compute count, mean, std, min, max, etc. |
| Pandas Statistical Functions | Apply mathematical stats: median, mode, skew, kurt |
| Pandas Aggregation Techniques | Use .agg() and .aggregate() for custom summaries |
| Pandas Grouping Data (GroupBy) | Segment data for comparison by groups |
| Pandas Correlation Analysis | Discover relationships between columns |
📈 Pandas Descriptive Statistics
Use .describe() to get a summary of numeric columns:
df.describe()
This returns:
- Count, Mean, Std, Min, 25%, 50%, 75%, Max
Get specific measures:
df['sales'].mean()
df['sales'].std()
df['sales'].min()
🧠 Pandas Statistical Functions
Additional statistical tools include:
df['sales'].median() # Median
df['sales'].mode() # Mode
df['sales'].skew() # Skewness
df['sales'].kurt() # Kurtosis
These help in understanding data shape and spread.
🔄 Pandas Aggregation Techniques
Use .agg() to compute multiple stats:
df.agg({
'sales': ['sum', 'mean'],
'profit': ['min', 'max']
})
Or use .aggregate() on specific columns.
🧱 Pandas Grouping Data (GroupBy)
Group data based on a column and apply aggregates:
df.groupby('region')['sales'].sum()
df.groupby('category').agg({'profit': 'mean', 'sales': 'sum'})
Enables comparison between segments (e.g., per region, category).
🔗 Pandas Correlation Analysis
Measure how two columns relate:
df.corr() # Full correlation matrix
df['sales'].corr(df['profit']) # Specific correlation
Correlation values range:
1→ perfect positive0→ no correlation-1→ perfect negative
📌 Summary – Recap & Next Steps
Statistical analysis with Pandas helps you understand data distribution, variance, and relationships. With grouping and correlation tools, Pandas empowers analysts to uncover meaningful insights from raw datasets.
🔍 Key Takeaways:
- Use
.describe()and other functions for summaries - Apply
.agg()and.groupby()to analyze data by segments - Discover relationships using correlation coefficients
⚙️ Real-World Relevance:
Essential for data exploration, reporting, trend analysis, and data-driven decision-making in business, science, and machine learning.
❓ FAQ – Pandas Statistical Analysis & Aggregation
❓ What is .describe() in Pandas?
✅ It returns a summary of count, mean, std deviation, min, quartiles, and max for numeric data columns.
❓ What is the difference between agg() and groupby()?
✅ agg() summarizes columns directly. groupby() segments data and then applies aggregation to each group.
❓ How to find relationships between numeric columns?
✅ Use .corr() to generate a correlation matrix showing linear dependencies.
❓ Can I aggregate non-numeric columns?
✅ Yes, using groupby() with count() or first()/last() on categorical/text data.
❓ What is skewness and kurtosis in Pandas?
✅ skew() shows the asymmetry of the distribution; kurt() shows the tailedness or outlier sensitivity.
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