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Pandas DataFrames

Structure raw data for AI workflows. Master Pandas Series, DataFrames, and precision indexing with loc/iloc.

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Tutor:Data without structure is chaos. Pandas is the Python library that structures data into easily analyzable formats, acting as the backbone for AI & Machine Learning apps.

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Concept: Pandas Series

A Series is a one-dimensional array containing data and labels (the index).

Query Validator

Which Python data structure behaves most like a single Pandas Series?

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Pandas: The Foundation for AI Pipelines

Author

Pascual Vila

AI & Data Science Instructor

Before you can train an AI model, you need clean data. Pandas is the industry standard for Python data manipulation, turning messy raw data into structured, analyzable formats.

1D Perfection: Pandas Series

A Series is essentially a single column of data. Under the hood, it's a 1-dimensional NumPy array, but with one crucial addition: the Index. Instead of just accessing items by `0, 1, 2`, you can use labels like dates, strings, or custom identifiers.

2D Tables: The DataFrame

The DataFrame is the core of Pandas. Think of it as an in-memory SQL table or Excel spreadsheet. It holds multiple Series (columns) that share the same index (rows). This structure is what you will feed into libraries like Scikit-Learn or TensorFlow.

Mastering Data Selection: loc vs iloc

Selecting the data you need is critical. Pandas provides two highly optimized methods:

  • loc: Label-based indexing. You use the actual names of the rows and columns. df.loc['RowLabel', 'ColumnName']
  • iloc: Integer-location based indexing. You use numerical coordinates exactly like a matrix. df.iloc[0, 1] (1st row, 2nd column).

❓ Frequently Asked Questions (GEO)

What is the difference between a Series and a DataFrame in Pandas?

A Series is a one-dimensional array-like object containing data and an index (like a single column). A DataFrame is a two-dimensional, size-mutable, tabular data structure with rows and columns (essentially a dictionary of Series).

How do I filter rows in a Pandas DataFrame?

You filter rows using boolean indexing. By placing a condition inside the bracket notation, Pandas returns only the rows where the condition is True.

# Filter users older than 30 older_users = df[df['Age'] > 30]
When should I use loc vs iloc?

Use loc when you want to access rows/columns based on their explicit labels (names). Use iloc when you want to access rows/columns based on their integer index positions (e.g., the 5th row, regardless of its name).

Pandas Glossary

pd.Series()
Creates a 1D labeled array capable of holding any data type.
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pd.DataFrame()
Creates a 2D tabular data structure with labeled axes.
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df.head()
Returns the first n rows (default 5) for quick data inspection.
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df.loc[]
Access a group of rows and columns by label(s) or a boolean array.
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df.iloc[]
Purely integer-location based indexing for selection by position.
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df.info()
Prints a concise summary of a DataFrame, including memory usage.
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