Most surveys indicate that data scientists and data analysts spend 70-80% of their time cleaning and preparing data for analysis. For many data workers, the cleaning and preparation of data is also their least favorite part of their job, so they spend the other 20-30% of their time complaining about it . . . or so the joke goes . . .
Unfortunately, data is invariably going to have certain inconsistencies, missing inputs, irrelevant information, duplicate information, or downright errors; there’s no getting around that. Especially when data comes from different sources, each one will have its own set of quirks, challenges, and irregularities. Messy data is useless data, which is why data scientists spend a majority of their time making sense of all the nonsense.
There is no doubt that cleaning and preparing data is as tedious and painstaking as it is important. The cleaner and more organized your data is, the faster, easier, and more efficient everything will be. Here at Dataquest, we know the struggle, so we’re happy to share our top 15 picks for the most helpful Python libraries for data cleaning.
NumPy is a fast and easy-to-use open-source scientific computing Python library. It’s also a fundamental library for the data science ecosystem because many of the most popular Python libraries like Pandas and Matplotlib are built on top of NumPy.
In addition to serving as the foundation for other powerful libraries, NumPy has a number of qualities that make it indispensable for Python for data analysis. Thanks to its speed and versatility, NumPy’s vectorization, indexing, and broadcasting concepts represent the de facto standard for array computing; however, NumPy really shines when working with multi-dimensional arrays. It also offers a comprehensive toolbox of numerical computing tools like linear algebra routines, Fourier transforms, and more.
NumPy can do a lot for many people. Its high-level syntax allows programmers from any background or experience level to use its powerful data processing capabilities. For example, NumPy enabled the Event Horizon Space Telescope to produce the first-ever image of black holes. It also confirmed the existence of gravitational waves, and it’s currently accelerating a variety of scientific studies and sports analytics.
Is it a surprise that a program that covers everything from sports to space can also help you manage and clean your data?
Pandas is one of the libraries powered by NumPy. It’s the #1 most widely used data analysis and manipulation library for Python, and it’s not hard to see why.
Pandas is fast and easy to use, and its syntax is very user-friendly, which, combined with its incredible flexibility for manipulating DataFrames, makes it an indispensable tool for analyzing, manipulating, and cleaning data.
This powerful Python library not only handles numerical data, it also handles text data and dates. It allows you to join, merge, concatenate, or duplicate DataFrames and easily add or remove columns or rows using its drop() function.
In short, pandas combines speed, ease of use, and flexible functionality to create an incredibly powerful tool that makes data manipulation and analysis fast and easy.
Understanding your data is a critical part of the cleaning process. The whole point of cleaning your data is to make it understandable. But before you can have beautifully clean data, you need to understand the problems in your messy data, such as their kind and extent, before you can clean it. A big part of that operation depends on an accurate and intuitive presentation of data.
Matplotlib is famous for its impressive data visualization, which makes it a valuable tool for data cleaning. It’s the go-to library for generating graphs, charts, and other 2D data visualizations using Python.
You can use Matplotlib in data cleaning by generating distribution plots to help you understand where your data falls short. You can let it handle the identifying and visualizing of the problems and irregularities in your data. That means you can concentrate on solving those data problems.
Datacleaner is a third-party library built on pandas’ DataFrame. Datacleaner is fairly new and less popular than pandas because much of what Datacleaner can do is also possible in pandas. However, Datacleaner has a unique method that combines a few typical data cleaning functions and automates them, saving you valuable time and effort.
With Datacleaner, you can easily replace missing values with the mode or median on a column-by-column basis, encode categorical variables, and drop rows with missing values.
The Dora library uses scikit-learn, pandas, and Matplotlib for exploratory analysis, or more specifically, for automating the most undesirable aspects of exploratory analysis. In addition to taking care of feature selection and extraction and visualization, Dora also optimizes and automates data cleaning.
Dora will save you valuable time and effort with a number of data cleansing features like imputing missing values, reading data with missing and poorly scaled values, and scaling values of input variables.
Additionally, Dora sports a simple interface for taking snapshots of your data as you transform it, and it stands apart from other Python packages with its unique data versioning capabilities.
Earlier in this article, we discussed the importance of visualizing data to reveal data deficiencies and inconsistencies. Before you can solve the problems in your data, you need to know what they are and where they are: data visualization is the answer. For many Python users, Matplotlib is the go-to library for data visualization. However, some users found the limitations of Matplotlib regarding the customization of data visualization options frustrating. That is why we now have seaborn.
Seaborn is a data visualization package that builds on Matplotlib and generates attractive and informative statistical graphics while providing customizable data visualizations.
While many users prefer seaborn because of its customization features, it also improves upon one of its predecessor’s functionality issues: namely, seaborn works more smoothly within pandas’ DataFrames, making exploratory analysis and data cleaning much more pleasant.
An important aspect of improving the quality of your data is creating uniformity and consistency throughout your DataFrame. It can be frustrating for Python developers attempting to create that uniformity when dealing with dates and times. Countless hours and lines of code later, and the peculiar difficulties of date and time formatting remain.
Arrow is a Python library built specifically to handle those exact difficulties and create data consistency. Its time-saving features include timezone conversion; automatic string formatting and parsing; support for pytz, dateutil objects, ZoneInfo tzinfo; and generation of ranges, floors, timespans, and ceilings for time frames ranging from microseconds to years.
Arrow is timezone aware (unlike the standard Python library), and it is UTC by default. It grants users more adept command over date and time manipulation with less code and fewer inputs. That means that you can bring greater uniformity to your data while spending less time wrestling with the clock.
A favorite among finance and healthcare data scientists, Scrubadub is a Python library specializing in eliminating personally identifiable information (PII) from free text.
This simple, free, and open-source package makes it easy to remove sensitive personal information from your data and preserve the privacy and security of those who trust you with it.
Scrubadub currently enables users to purge their data of the following information:
- Email Addresses
- Skype usernames
- Phone numbers
- Password/username combinations
- Social security numbers
With just a single function call, Tabulate will use your data to create small and attractive tables that are profoundly readable thanks to a number of features like number formatting, headers, and column alignment by the decimal.
This open-source library also allows users to work with tabular data in other tools and languages by enabling the user to output data in other popular formats like HTML, PHP, or Markdown Extra.
Handling missing values is one of the primary aspects of data cleaning. The Missingno library does just that. It identifies and visualizes missing values in the DataFrame column by column so that the user can see the state their data is in.
Visualizing the problem is the first step to solving the problem, and Missingno is a simple and handy library that gets the job done.
Pandas is already a fast library, as we mentioned above. But Modin takes pandas to a whole new level. Modin enhances pandas’ performance by distributing data and computation speed.
Modin users will benefit from a smooth and unobtrusive integration with pandas’ syntax that can increase pandas’ speed by up to 400%!
Another specialized library, Ftfy is gloriously simple and good at what it does. It’s all in the name, Ftfy, or “Fixes text for you.” Ftfy was born for a simple task: to take bad Unicode and useless characters and turn them into relevant and readable text data.
If you spend a lot of time working with text data, Ftfy is a handy little tool to quickly make sense of the nonsensical.
Unlike the other mentions on this list, SciPy is not just a library; it’s an entire data science ecosystem offering a collection of open source libraries already mentioned on this list, including NumPy, Matplotlib, and pandas.
Additionally, SciPy also makes available a number of specialized tools, one of which is Scikit-learn, whose “Preprocessing” package you can leverage for data cleaning and standardizing of datasets.
One of scikit-learn’s core engineers developed Dabl as a data analysis library to simplify data exploration and preprocessing.
Dabl has an integral process to detect certain data types and quality problems within a dataset and automatically apply the proper pre-processing procedures.
It can handle missing values, convert categorical variables into numerical values, and it even has built-in visualization options to facilitate quick data exploration.
The final library in our countdown is Imbalanced-learn (abbreviated as Imblearn), which relies on Scikit-learn and offers tools for Python users confronted with classification and imbalanced classes.
Using the preprocessing technique known as “undersampling,” Imblearn will comb through your data and remove missing, inconsistent, or otherwise irregular values in your dataset.
When it comes to data science, you get out what you put in. Your data analysis model is only as good as the data you’re feeding into it, and the cleaner your data, the simpler it becomes to process, analyze, and visualize. We’ve dedicated an entire skill path to data cleaning with Python for this very reason.
This list of libraries is by no means exhaustive. There are many powerful tools in the Python ecosystem that can drastically improve the everyday processes of a data scientist. While you probably won’t use all of these tools, we hope that by adopting a few of them, you’ll see a noticeable improvement in daily efficiency, productivity, and enjoyment.
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