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# Introduction
As a data scientist, you are probably already familiar with libraries such as NumPy, the pandas, scikit-learnagain Matplotlib. But the Python ecosystem is huge, and there are plenty of lesser-known libraries that can help you simplify your data science tasks.
In this article, we'll explore ten libraries organized into four key areas that data scientists work with every day:
- Automated EDA and profiling for rapid test analysis
- Big data processing for handling data sets that do not fit in memory
- Data quality and assurance to maintain clean, reliable pipelines
- Specialized data analysis for domain-specific functions such as geospatial and time series functions
We'll also provide you with learning resources to help you hit the ground running. I hope you find a few libraries to add to your data science toolkit!
# 1. Pandera
Data validation is essential to any data science pipeline, yet it is often done manually or with custom scripts. Pandera is a statistical data validation library that brings type inference and schema validation to pandas DataFrames.
Here is a list of features that make Pandera useful:
- It allows you to define schemas for DataFrames, specifying expected data types, value ranges, and statistical properties for each column.
- It integrates with pandas and provides informative error messages when validation fails, making debugging much easier.
- It supports hypothesis testing within your schema definitions, allowing you to verify statistical properties of your data during pipeline execution.
How to Use Pandas with Pandara to Validate Your Data in Python by Arjan Codes provides clear examples to get started with schema definitions and validation patterns.
# 2. Vax
Working with out-of-memory datasets is a common challenge. Vax is a high-performance Python library for lazy, out-of-the-core DataFrames that can handle billions of rows on a laptop.
Key features that make Vaex worth checking out:
- It uses memory mapping and lazy checking to work with data sets larger than RAM without loading everything into memory.
- It provides fast compilation and sorting functionality using efficient C++ implementations
- Provides a standard API similar to pandas, making the transition smooth for existing pandas users who need to upgrade
Introduction to Vaex in 11 minutes is a quick introduction to working with large datasets using Vaex.
# 3. Pyjanitor
Data cleaning code can be ugly and hard to read quickly. Pyjanitor is a library that provides a clean API, which includes methods for pandas DataFrames. This makes the data cleaning workflow more readable and maintainable.
Here's what Pyjanitor offers:
- Extends pandas with additional methods for common cleanup operations such as removing empty columns, renaming columns to snake_case, and handling missing values.
- It enables a way to integrate data cleaning operations, making your preprocessing steps read like a clear pipeline
- Includes common but tedious tasks such as flagging missing values, sorting by time range, and conditional column creation
Look Pyjanitor: Clean APIs for cleaning data talk to Eric Ma and check Easy Data Cleaning in Python with PyJanitor – Full Step by Step Tutorial to get started.
# 4. D-Tale
Examining and visualizing DataFrames often requires switching between multiple tools and writing multiple codes. D-Tale is a Python library that provides an interactive GUI for viewing and analyzing pandas DataFrames in a spreadsheet-like interface.
Here's what makes D-Tale useful:
- Opens a web interface where you can sort, sort, and inspect your DataFrame without writing additional code
- It provides built-in charting capabilities including histograms, correlations, and custom plots accessible through a point-and-click interface.
- Includes features such as data cleaning, external detection, code export, and the ability to create custom columns through the GUI
How to quickly analyze data in Python using the D-Tale library it offers a complete journey.
# 5. Sweetviz
Generating analytical reports for comparisons between data sets is tedious with standard EDA tools. Sweetviz is an automated EDA library that creates useful visualizations and provides detailed comparisons between datasets.
What makes Sweetviz useful:
- Generates comprehensive HTML reports with targeted analysis, showing how factors relate to your target variable for segmentation or regression functions
- It is ideal for comparing data sets, allowing you to compare training against test sets or before comparing changes with side-by-side visibility.
- It generates reports in seconds and includes correlation analysis, showing correlations and relationships between all factors
How to Quickly Perform Experimental Data Analysis (EDA) in Python using Sweetviz The tutorial is a great source for getting started.
# 6. cuDF
When working with large data sets, CPU-based processing can be a bottleneck. to DF is a GPU DataFrame library from NVIDIA that provides a pandas-API similar to the API but runs operations on GPUs with greater acceleration.
Features that make cuDF useful:
- Provides 50-100x speedup for common operations like group, join, and sort on compatible hardware
- It provides an API that closely resembles pandas, requiring minimal code changes to improve GPU acceleration
- It also includes a comprehensive RAPIDS ecosystem for GPU-accelerated data science workflows
NVIDIA RAPIDS cuDF Pandas – Big Data Processing with cuDF pandas acceleration mode by Krish Naik is a useful starting point.
# 7. Tables
Examining DataFrames in Jupyter notebooks can be difficult with large data sets. IT Tables (Interactive Tables) brings interactive DataTables to Jupyter, allowing you to search, sort, and paginate your DataFrames right in your notebook.
What makes ITables useful:
- Converts pandas DataFrames into interactive tables with built-in search, sorting, and paging functionality
- Handles large DataFrames efficiently by rendering only visible rows, keeping your bookmarks responsive
- It requires little code; usually a single import statement to modify all DataFrame displays in your notebook.
Quick Start to Interactive Tables includes clear examples of use.
# 8. GeoPandas
Location data analysis is very important in all industries. Yet many data scientists avoid it because of the complexity. GeoPandas extends pandas to support spatial functionality, making spatial data analysis accessible.
Here's what GeoPandas offers:
- Provides local functions such as intersections, unions, and buffers using a pandas-like interface
- Handles various geospatial data formats including shapefiles, GeoJSON, and PostGIS databases
- It integrates with matplotlib and other visualization libraries to create maps and visualizations
Geospatial analysis A short tutorial from Kaggle covers the basics of GeoPandas.
# 9. tsfresh
Manually extracting meaningful features from time series data is time-consuming and requires domain expertise. tsfresh it automatically extracts hundreds of time series features and selects the most suitable ones for your prediction task.
Features that make tsfresh useful:
- Calculates time series properties automatically, including statistical properties, frequency domain properties, and entropy measures
- It includes feature selection methods that identify which features are best suited for your forecasting task
Introduction to tsfresh covers what tsfresh is and how it is useful for time series engineering applications.
# 10. ydata-profiling (pandas-profiling)
Analyzing test data can be repetitive and time consuming. ydata-profiling (formerly pandas-profiling) generates comprehensive HTML reports for your DataFrame with statistics, correlations, missing values, and distributions in seconds.
What makes the ydata profile useful:
- Automatically creates comprehensive EDA reports, including analysis of variables, correlations, correlations, and missing data patterns
- Identifies potential data quality problems such as high cardinality, skewness, and duplicate rows
- It provides an interactive HTML report that you can share with wittsfresh participants or use for documentation
Pandas Profiling (ydata-profiling) in Python: A Beginner's Guide from DataCamp includes detailed examples.
# Wrapping up
These ten libraries address the real challenges you'll face in a data science career. To summarize, we have compiled useful libraries to work with data sets that are too large to store in memory, we need to quickly profile new data, we want to ensure data quality in production pipelines, or work with special formats such as geospatial or time series data.
You don't need to read this all at once. Start by identifying which section addresses your current problem.
- If you spend a lot of time on manual EDA, try Sweetviz or ydata-profiling.
- If memory is your concern, try Vaex.
- If data quality issues keep breaking your pipelines, look to Pandera.
Enjoy exploring!
Count Priya C is an engineer and technical writer from India. He loves working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, he works to learn and share his knowledge with the engineering community by authoring tutorials, how-to guides, ideas, and more. Bala also creates engaging resource overviews and code tutorials.
