16/08/2026
Exploratory Data Analysis (EDA) is where you stop treating a dataset as a spreadsheet and start understanding what is actually inside it.
A practical EDA workflow usually moves from basic inspection to deeper investigation:
1. Load & preview the data
Start with df.head() to understand the structure and get an initial look at the records.
2. Check data types
Use df.dtypes to identify numerical, categorical, date, and incorrectly typed columns.
3. Find missing values
df.isna().sum() quickly shows where data is incomplete.
4. Check duplicates
Use df.duplicated() to identify repeated rows, then decide whether they should actually be removed.
5. Review summary statistics
df.describe() helps reveal typical values, spread, ranges, and potentially unusual observations.
6. Analyze distributions
Histograms and density plots show how numerical variables are distributed—something averages alone cannot tell you.
7. Investigate outliers
Box plots make unusually high or low observations easier to spot. An outlier is a signal to investigate, not automatically a value to delete.
8. Explore correlations
A correlation matrix can reveal relationships among numerical variables, but correlation should not be interpreted as causation.
9. Examine class distribution
For classification datasets, value_counts() helps identify whether the target classes are balanced or heavily skewed.
10. Turn exploration into findings
Summarize the missing-data issues, distributions, unusual values, relationships, class balance, and other patterns that should influence the next stage of analysis.
EDA is not just about running Python commands. The important part is understanding what each result tells you about the data and what you should investigate next.
