Documents/40-pandas-libs.ipynb

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    "# Daily-Dose-of-Data-Science\n",
    "\n",
    "[Daily Dose of Data Science](https://avichawla.substack.com) is a publication on Substack that brings together intriguing frameworks, libraries, technologies, and tips that make the life cycle of a Data Science project effortless. \n",
    "\n",
    "Author: Avi Chawla\n",
    "\n",
    "[Medium](https://medium.com/@avi_chawla) | [LinkedIn](https://www.linkedin.com/in/avi-chawla/)\n",
    "\n",
    "# 40 Open-Source Tools to Supercharge Your Pandas Workflow\n",
    "\n",
    "Post Link: [Substack](https://avichawla.substack.com/p/37-open-source-tools-to-supercharge-pandas)\n",
    "\n",
    "LinkedIn Post: [LinkedIn](https://www.linkedin.com/posts/avi-chawla_pandas-receives-over-3m-downloads-per-day-activity-7030484085068210176-XApG?utm_source=share&utm_medium=member_desktop)"
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    "Jupyter-Datatables: Enrich the default preview of a DataFrame in jupyter notebook.\n",
    "\n",
    "Link: https://bit.ly/jupy-dtables\n",
    "\n",
    "SummaryTools: Supercharge the describe() method in Pandas.\n",
    "\n",
    "Link: https://bit.ly/summarytools\n",
    "\n",
    "Sidetable: Supercharge the value_counts() method in Pandas.\n",
    "\n",
    "Link: https://bit.ly/py-sidetable\n",
    "\n",
    "Sketch: Generate code/insights about data by asking questions in natural language.\n",
    "\n",
    "Link: https://bit.ly/py-sketch\n",
    "\n",
    "Deepchecks: Generate a comprehensive validation report of your data.\n",
    "\n",
    "Link: https://bit.ly/deepchks\n",
    "\n",
    "Pandas Flavor: Extend Pandas to attach methods to the dataframe object.\n",
    "\n",
    "Link: https://bit.ly/pd-flavor\n",
    "\n",
    "Pandarallel: Parallelize Pandas across multiple CPU cores.\n",
    "\n",
    "Link: https://bit.ly/pandarallel\n",
    "\n",
    "PandasML: Pandas, sklearn and matplotlib integrated.\n",
    "\n",
    "Link: https://bit.ly/pandasml\n",
    "\n",
    "Geopandas: Work with Geospatial data in Pandas.\n",
    "\n",
    "Link: https://bit.ly/geo-pd\n",
    "\n",
    "DuckDB: Run SQL queries on dataframes.\n",
    "\n",
    "Link: https://bit.ly/duckdb\n",
    "\n",
    "Modin: Boost Pandas' performance up to 70x by modifying the import.\n",
    "\n",
    "Link: https://bit.ly/modin-guide\n",
    "\n",
    "PivotTableJS: Create pivot tables by using drag and drop functionality.\n",
    "\n",
    "Link: https://bit.ly/pivottablejs\n",
    "\n",
    "Missingno: Visualize missing values in your dataset.\n",
    "\n",
    "Link: https://bit.ly/py-missingno\n",
    "\n",
    "Pandas Alive: Create animated charts for pandas dataframes.\n",
    "\n",
    "Link: https://bit.ly/pd-alive\n",
    "\n",
    "Skimpy: Supercharge the describe() method in Pandas.\n",
    "\n",
    "Link: https://bit.ly/py-skimpy\n",
    "\n",
    "Pandas-log: Debug pandas pipeline using step-by-step logging.\n",
    "\n",
    "Link: https://bit.ly/py-log\n",
    "\n",
    "tsflex: Process time series and perform feature extraction.\n",
    "\n",
    "Link: https://bit.ly/tsflex\n",
    "\n",
    "pandas-profiling: Generate EDA report of data in one-line of code.\n",
    "\n",
    "Link: https://bit.ly/pd-profiling\n",
    "\n",
    "Mars: A tensor-based framework for scaling numpy, pandas, scikit-learn, and Python functions.\n",
    "\n",
    "Link: https://bit.ly/py-mars\n",
    "\n",
    "nptyping: Apply type hints for Pandas data frames.\n",
    "\n",
    "Link: https://bit.ly/nptyping\n",
    "\n",
    "\n",
    "popmon: Profile your data to determine its stability.\n",
    "\n",
    "Link: https://bit.ly/py-popmon\n",
    "\n",
    "Gspread-pandas: Interact with Google sheets through pandas dataframes.\n",
    "\n",
    "Link: https://bit.ly/pd-gsheets\n",
    "\n",
    "pdpipe: Create pandas pipeline easily and intuitively.\n",
    "\n",
    "Link: https://bit.ly/py-pdpipe\n",
    "\n",
    "PrettyPandas: Prettify the dataframe when printed.\n",
    "\n",
    "Link: https://bit.ly/PrettyPandas\n",
    "\n",
    "Dora: An intuitive API for data cleaning, processing, feature selection, visualization, etc.\n",
    "\n",
    "Link: https://bit.ly/py-dora\n",
    "\n",
    "Pandapy: The speed of NumPy combined with Pandas' elegance.\n",
    "\n",
    "Link: https://bit.ly/pandapy\n",
    "\n",
    "PyJanitor: A clean API for cleaning data.\n",
    "\n",
    "Link: https://bit.ly/pyjanitor\n",
    "\n",
    "swifter: Speed-up the apply() method in Pandas.\n",
    "\n",
    "Link: https://bit.ly/py-swifter\n",
    "\n",
    "Mito: Analyze data in Jupyter by editing a spreadsheet.\n",
    "\n",
    "Link: https://bit.ly/mito-ds\n",
    "\n",
    "Visual Python: GUI-based Python code generator for data science\n",
    "\n",
    "Link: https://bit.ly/visual-py\n",
    "\n",
    "tqdm: Add progress bars to Pandas methods.\n",
    "\n",
    "Link: https://bit.ly/tqdm-pd\n",
    "\n",
    "Lux: Automatic data visualization.\n",
    "\n",
    "Link: https://bit.ly/pd-lux\n",
    "\n",
    "D-Tale: Visualizer for pandas dataframe.\n",
    "\n",
    "Link: https://bit.ly/py-dtale\n",
    "\n",
    "AutoClean: Automated data preprocessing & cleaning.\n",
    "\n",
    "Link: https://bit.ly/py-autoclean\n",
    "\n",
    "pytablewriter: Write a dataframe in various formats: AsciiDoc / CSV / HTML / JSON / LaTeX / Markdown / Excel / TOML / TSV / YAML, etc.\n",
    "\n",
    "Link: https://bit.ly/pytablewriter\n",
    "\n",
    "itables: Pandas dataframes as interactive datatables.\n",
    "\n",
    "Link: https://bit.ly/itables\n",
    "\n",
    "PandasGUI: A GUI for Pandas dataframes.\n",
    "\n",
    "Link: https://bit.ly/PandasGUI\n",
    "\n",
    "tabula-py: Extract table from PDF into Pandas dataframe.\n",
    "\n",
    "Link: https://bit.ly/tabulapy\n",
    "\n",
    "Pingouin: Perform statistical testing on Pandas dataframe.\n",
    "\n",
    "Link: https://bit.ly/pypingouin\n",
    "\n",
    "Dexplot: Create many types of beautiful data visualizations with a simple, consistent, and intuitive API.\n",
    "\n",
    "Link: https://bit.ly/dexplot"
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