Exploratory Data Analysis (EDA) with Pandas
EDA is the first step in data analysis where we explore datasets to understand their structure, quality, patterns, and relationships. It is flexible, visual, and often interactive.
1. Initial Exploration
Load your dataset:
Understand its structure:
2. Univariate Analysis (One Variable)
Look at distributions and unique values:
Summary statistics:
3. Bivariate/Multivariate Analysis (Two or More Variables)
Relationships and trends between variables:
Use crosstabs:
Pivot tables:
Confirmatory Data Analysis (CDA) with Pandas
CDA is about validating assumptions and testing hypotheses using statistical techniques. While Pandas doesn’t perform deep statistical tests directly, it provides the structure to prepare data for analysis.
1. Formulate Hypotheses
Example:
- “Do employees in the Sales department earn more than in HR?”
- “Is there a difference in salary distribution between male and female employees?”
2. Group & Compare
Use grouping for analysis:
Comparing statistics:
You can then export this data for statistical testing (e.g., using SciPy):
Pandas helps structure the data, clean it, filter it, and segment it before passing to statistical libraries.
Summary
| Concept | Purpose | Pandas Use |
|---|---|---|
| EDA | Explore data, find patterns, detect issues | df.head(), describe(), value_counts(), groupby() |
| CDA | Validate assumptions, test hypotheses | groupby(), pivot_table(), prepare for stats libraries |
