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Pandas is a well-known open-source Python library for processing and analysing data. It can support relational or labelled data both easily and intuitively. Furthermore, it makes it simple to load, manipulate, and analyse the data because it includes capabilities for working with tabular, time series, and heterogeneous data. Built on top of the NumPy library, it offers data structures such as Series and DataFrame that are tailored for data analysis jobs and are built on top of the NumPy library.
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Here are 15 open-source tools that can enhance your experience while using Pandas.
1. Sketch
Sketch is an AI-based code-writing assistant for the panda framework that recognises the context of your data, boosting the value of ideas. Sketch can be used instantly and doesn’t need to be added as a plugin to your integrated development environment software (IDE).
2. Pandarallel
By changing just one line of code, Pandarallel enables you to parallelise its processes to multiple CPU cores. Codes such as apply(), applymap(), groupby(), map(), and rolling() are all supported here.
3. Modin
Much like Pandarallel, Modin can also be used to enhance your pandas workflow by altering a single line of code.
4. Jupyter-Datatables
The aim of Jupyter-Datatables is to improve DataFrame’s default preview. It comprises sorting, filtering, exporting, displaying column distribution, printing several data types, and pagination.
5. DuckDB
With DuckDB, you can analyse a Pandas DataFrame in Jupyter with SQL syntax without noticing any significant run-time differences.
6. Swifter
swifter package quickly and effectively applies any function to a pandas dataframe or series to make it faster.
7. Dora
Dora, a python module, was created to automate the long processes involved in exploratory data analysis. Convenience functions for data cleansing, feature selection and extraction, visualisation, data partitioning for model validation, and data versioning are all included in the package. In addition, the library employs popular Python data analysis tools such as pandas, scikit-learn, and matplotlib.
8. Tabula-py
tabula-py is a Python wrapper for tabula-java, a programme that can read tables from PDFs. Tables from a PDF can be read and then changed into a pandas DataFrame. A PDF file can also be transformed into a CSV, TSV, or JSON file using tabula-py.
9. Pandas Alive
Similar to Pandas’ existing Visualisation function, Pandas Alive can generate animated matplotlib charts for Pandas DataFrames.
10. Visual Python
A GUI-based Python code generator, Visual Python was created to serve as an add-on for JupyterLab, Jupyter Notebook, and Google Colab. An open-source project called ‘Visual Python’ was released by students who had trouble coding in their data science Python classes.
11. Mars
Mars is a tensor-based unified framework that integrates numpy, pandas, scikit-learn, and other libraries for large-scale data computation.
12. Dexplot
Dexplot is a Python module that provides users with a simple interface for data visualisations. Dexplot’s objectives are to keep an intuitive, consistent API with the fewest methods required to create the appropriate statistical plots and to give the user immense power without requiring matplotlib.
13. D-Tale
D-Tale integrates a React front-end with a Flask backend to simplify exploring and analysing Pandas data structures and act as a visualiser. It supports python/ipython terminals and ipython notebooks. Pandas objects, such as DataFrame, Series, MultiIndex, DatetimeIndex, and RangeIndex, can work.
14. Pandas-log
Pandas-log provides feedback on the fundamental pandas’ activities. It offers wrapper functions for the most used ones, including. query,.apply, .merge, .group_by, and others.
15. Skimpy
Using the terminal or your interactive Python window, the lightweight tool Skimpy generates summary statistics on variables in data frames.
