Module 3: Exploring & Understanding Data

Once you load data, the first step is always to look at it. Pandas provides powerful tools to summarize and inspect your dataset before you start processing it.

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Lesson 5: Data Inspection

Rapid Overview

• df.head(n): View the first n rows (default 5).
• df.tail(n): View the last n rows.
• df.sample(n): Pick n random rows (great for checking bias).
• df.shape: Returns (rows, columns).

Structural Analysis

• df.info(): Shows column names, non-null counts, and data types (Dtypes). Critical for finding missing data.
• df.describe(): Shows statistics (mean, std, min, max) for numerical columns.

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Lesson 6: Indexing & Selection

Accessing specific data in Pandas is done via [], .loc[], and .iloc[].

1. Column Selection

• df['ColumnName'] returns a Series.
• df[['Col1', 'Col2']] returns a subset DataFrame.

2. .iloc[] (Integer Location)

Selects by position (like NumPy arrays).

• df.iloc[0] (First row)
• df.iloc[:5, 0] (First 5 rows, first column)

3. .loc[] (Label Location)

Selects by name/label.

• df.loc[0, 'Score'] (Row with index 0, column 'Score')
• df.loc[:, ['ID', 'Score']] (All rows, specific columns)

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Practice: Data Inspector

Challenge:

1. Create a DataFrame with 10 rows of random numbers.
2. Print the shape of the dataframe.
3. Use .iloc to print the value in the last row and first column.

Key Takeaways

✅ df.info() is your first diagnostic toll.
✅ df.describe() gives you instant statistical insight.
✅ Use .iloc for index-based access and .loc for label-based access.