Pandas

7. Pandas#

Pandas is an open-source Python library used for manipulating, cleaning, exploring, and analyzing data. Pandas provides easy-to-use tools and special data structures that make handling data fast and efficient. Pandas is especially good for table-like data, such as spreadsheets or data from SQL databases.

Pandas objects are like advanced versions of NumPy structured arrays, but with one key difference: rows and columns are labeled, allowing you to identify data using meaningful names instead of just integer indices.

It’s very popular in data science because it works well with other important Python libraries. Pandas is built on top of NumPy, which makes it easier to manipulate and analyze data. It also connects nicely with other libraries, including: Matplotlib for creating charts and graphs, SciPy, for statistical calculations, and Scikit-learn for machine learning.

Some common tasks you can perform using Pandas:

  • Clean, Merge, and Combine Data: Fix messy data, remove duplicates, and join multiple datasets together.

  • Handle Missing Data: Identify and fill in missing values (NaN) or remove them if needed.

  • Manage Columns: Easily add, delete, or update columns in your data.

  • Group Data: Group rows to calculate totals, averages, and other summaries.

  • Visualize Data: Create basic graphs and charts for initial data exploration and quick visualizations.

Pandas makes working with data simple, powerful, and efficient — making it one of the most important tools for anyone learning data science or analytics.

Pandas is good for data analysis. Some say that Pandas is an extremely powerful version of Excel, with a lot more features. We will through the following topics:

  • Introduction to Pandas

  • Series

  • DataFrames

  • Missing Data

  • GroupBy

  • Merging, Joining, and Concatenating

  • Operations

  • Data Input and Output

Data Types

Pandas has two data structures (classes) for handling data:

  • Series: a one-dimensional labeled array holding data of any type such as integers, strings, Python objects, etc.

  • DataFrame: a two-dimensional data structure that holds data like a two-dimension array or a table with rows and columns.

Key advantages of Series over NumPy arrays:

Since Pandas Series builds upon NumPy arrays while adding powerful labeling capabilities, it offers several distinct advantages for data manipulation and analysis:

  • Explicit indexing: Labels make data access more intuitive

  • Mixed data types: Can handle heterogeneous data (NumPy arrays are homogeneous)

  • Better handling of missing data: Native support for NaN values

  • Label-based alignment: Operations align by index labels, not position

Performance note: Converting large NumPy arrays to Series has minimal overhead since Series is built on top of NumPy arrays internally.

Key advantages of DataFrames:

Just as Series extends NumPy arrays with labeled indices, DataFrames extend NumPy matrices by adding both row and column labels, representing the cornerstone of Pandas functionality and providing a powerful and flexible way to work with structured data.

  • Two-dimensional structure: Combines multiple Series with aligned indices, perfect for tabular data

  • Heterogeneous columns: Each column can contain different data types (strings, numbers, dates, etc.)

  • Powerful indexing: Both row and column labels enable intuitive data selection and manipulation

  • Built-in data operations: Native support for filtering, grouping, merging, and aggregating data

  • Missing data handling: Sophisticated tools for detecting, filling, and removing missing values

  • Integration capabilities: Seamless reading from and writing to various file formats (CSV, Excel, JSON, SQL databases)

You can think of a DataFrame as a supercharged Excel spreadsheet where each column can have different data types, and you can perform complex operations programmatically rather than manually.