A DataFrame is a Pandas table-like structure with labeled rows and columns, ideal for analyzing structured data.

Why use vectorized operations?

Vectorized operations apply math to whole arrays at once, making code much faster and cleaner than looping over individual elements.

Intermediate🖼️ 20 slides⏱ 4 minData Science & Analytics

🐼 Pandas & NumPy

Pandas and NumPy are the core Python libraries for data analysis. This visual guide covers NumPy arrays, Pandas DataFrames, loading data, cleaning, filtering, grouping, and the operations every data analyst uses daily.

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Why Pandas and NumPy?

They power most data analysis in Python.

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What Is NumPy?

Fast arrays and math for numerical data.

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NumPy Arrays

Efficient grids of numbers.

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Vectorized Operations

Do math on whole arrays at once.

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What Is Pandas?

Tables and tools for analyzing data.

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The DataFrame

A table with rows, columns, and labels.

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The Series

A single labeled column of data.

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Loading Data

Read CSV, Excel, and SQL easily.

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Inspecting Data

head(), info(), and describe().

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Selecting Data

Pick rows and columns with loc/iloc.

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Filtering Rows

Keep rows that match conditions.

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Handling Missing Data

Fill or drop NaN values.

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Adding Columns

Create new computed columns.

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GroupBy

Aggregate data by category.

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Merging and Joining

Combine multiple datasets.

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Sorting

Order data by columns.

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Aggregations

sum, mean, count, and more.

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Pandas + Matplotlib

Plot data quickly.

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Performance Tips

Vectorize instead of looping.

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Getting Started

Load a CSV and explore it with Pandas.

Frequently Asked Questions

NumPy provides fast numerical arrays and math, while Pandas provides DataFrames and tools for loading, cleaning, and analyzing tabular data in Python.