Photo by the author
# Entry
As a data scientist, you’re probably already familiar with libraries like NumPy, pandas, scikit-learnAND Matplotlib. However, the Python ecosystem is enormous and there are many lesser-known libraries that can assist make your data science tasks easier.
In this article, we’ll look at ten such libraries, divided into four key areas that data scientists work with every day:
- Automated EDA and profiling for faster exploratory analysis
- Vast-scale data processing to handle data sets that do not fit in memory
- Data quality and validation to maintain spotless and reliable pipelines
- Expert data analysis for domain-specific tasks such as geospatial and time series work
We’ll also provide you with educational resources to assist you get started quickly. I hope you find some libraries to add to your data science toolkit!
# 1. Pandera
Data validation is indispensable in any data analysis pipeline, but is often done manually or through custom scripts. Pandera is a statistical data checking library that enables type suggestion and schema validation in pandas DataFrames.
Here is a list of features that make Pandera useful:
- Lets you define schemas for data frames, specifying expected data types, value ranges, and statistical properties for each column
- Integrates with pandas and displays informative error messages when validation fails, making debugging much easier.
- Supports hypothesis testing within the schema definition, allowing you to check the statistical properties of your data during pipeline execution.
How to use Pandas with Pandera for data validation in Python by Arjan Codes provides clear examples of how to get started with schema definitions and validation patterns.
# 2.Vaex
Working with data sets that do not fit in memory is a common challenge. Vaex is a high-performance, indolent, out-of-core DataFrame Python library that can handle billions of rows on a laptop.
Key features that make Vaex worth knowing:
- It uses memory mapping and indolent evaluation to work with data sets larger than RAM without loading everything into memory
- It provides swift aggregation and filtering operations using effective C++ implementations
- It offers a familiar pandas-like API, making the transition seamless for existing pandas users who need to scale up
Getting started with Vaex in 11 minutes is a quick introduction to working with gigantic data sets using Vaex.
# 3. Pyjanitor
Data cleansing code can become sloppy and challenging to read quickly. Pyanitor is a library that provides a limpid API for combining methods for pandas DataFrames. This makes data cleansing workflows more readable and easier to maintain.
Here’s what Pyjanitor offers:
- Extends pandas with additional methods for common cleanup tasks, such as removing empty columns, renaming columns to Snake_case, and handling missing values.
- Enables combining methods for data cleansing operations, making preprocessing steps readable like a clear pipeline
- Includes functions for common but tedious tasks such as flagging missing values, filtering by time ranges, and creating conditional columns
To watch Pyjanitor: Clean Data Cleansing APIs talk to Eric Ma and check Easily Clean Data in Python with PyJanitor – Full Step-by-Step Tutorial start.
# 4. D-Story
Exploring and visualizing data frames often requires switching between multiple tools and writing a lot of code. D-Story is a Python library that provides an interactive GUI for visualizing and analyzing pandas data frames using a spreadsheet-like interface.
Here’s what makes D-Tale useful:
- Launches an interactive web interface where you can sort, filter, and explore the DataFrame without writing additional code
- Provides built-in charting capabilities including histograms, correlations, and custom charts available via a point-and-click interface
- Includes features such as data cleansing, outlier detection, code export, and the ability to create custom columns via a graphical user interface
How to quickly explore data in Python using the D-Tale library provides a comprehensive guide.
# 5.Sweetviz
Generating benchmarking reports between datasets is tedious using 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 target analysis reports showing how features relate to the target variable for classification or regression tasks
- Perfect for comparing datasets, allowing you to compare training and test sets or before and after transformation using side-by-side visualizations
- Creates reports in seconds and includes linkage analysis, showing correlations and relationships between all features
How to quickly perform exploratory data analysis (EDA) in Python using Sweetviz the tutorial is a great resource to get started.
# 6. cuDF
When working with gigantic datasets, CPU-based processing can become a bottleneck. cuDF is NVIDIA’s GPU DataFrame library that provides a pandas-like API but runs operations on GPUs for massive speedups.
Features that make cuDF helpful:
- Delivers 50-100x acceleration of common operations such as grouping, combining and filtering on compatible hardware
- It offers an API that closely mirrors Pandas, requiring minimal code changes to take advantage of GPU acceleration
- Integrates with the broader RAPIDS ecosystem to provide end-to-end GPU-accelerated data analytics workflows
NVIDIA RAPIDS cuDF Pandas – large data preprocessing in cuDF pandas accelerator mode by Krish Naik is a useful resource to start with.
# 7. IT tables
Exploring data frames in Jupyter notebooks can be unwieldy for gigantic data sets. IT tables (Interactive Tables) provides interactive data tables in Jupyter, allowing you to search, sort, and paginate data frames directly in your notebook.
What makes ITables helpful:
- Converts pandas DataFrames into interactive tables with built-in search, sorting and pagination functionality
- Efficiently handles gigantic DataFrames by rendering only evident rows, keeping your notebooks responsive
- Requires minimal code; often a single import statement is enough to transform all DataFrame displays in a notebook.
Quick start to interactive tables contains clear examples of operate.
# 8. GeoPand
Spatial data analysis is becoming increasingly significant in various industries. However, many data scientists avoid it due to its complexity. GeoPand extends pandas to support spatial operations by providing analysis of geographic data.
Here’s what GeoPandas offers:
- It provides spatial operations such as intersections, unions, and buffers using a familiar pandas-like interface
- It supports various geospatial data formats, including shapefiles, GeoJSON and PostGIS databases
- Integrates with matplotlib and other visualization libraries to create maps and spatial visualizations
Geospatial analysis Kaggle’s micro-course covers the basics of GeoPanda.
# 9. teaspoon of fresh
Features that make tsfresh useful:
- Automatically computes time series features, including statistical properties, frequency domain features, and entropy measures
- It includes feature selection methods that identify which features are actually relevant to a particular forecasting task
# 10. Ydata profiling (pandas profiling)
Exploratory data analysis can be repetitive and time-consuming. data profiling (formerly pandas-profiling) generates comprehensive HTML reports for a DataFrame with statistics, correlations, missing values and distributions in seconds.
What makes ydata profiling useful:
- Automatically produces comprehensive EDA reports, including univariate analysis, correlations, interactions, and missing data patterns
- Identifies potential data quality issues such as high cardinality, skewness, and duplicate rows
- Provides an interactive report in HTML format that can be shared with Wittsfresh stakeholders or used in documentation
Profiling Pandas (ydata profiling) in Python: a beginner’s guide from DataCamp provides detailed examples.
# Summary
You don’t have to learn it all at once. Start by determining which category is your current bottleneck.
- If you spend too much time on manual EDA, try Sweetviz or ydata profiling.
- If memory is your limit, experiment with Vaex.
- If data quality issues consistently disrupt your pipeline, look into Pandera.
Have fun exploring!
Bala Priya C is a software developer and technical writer from India. He likes working at the intersection of mathematics, programming, data analytics and content creation. Her areas of interest and specialization include DevOps, data analytics and natural language processing. She enjoys reading, writing, coding and coffee! He is currently working on learning and sharing his knowledge with the developer community by writing tutorials, guides, reviews, and more. Bala also creates captivating resource overviews and coding tutorials.