Top 50 matplotlib Visualizations [https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/]
Pandas open-source gems that will immensely supercharge your Pandas workflow (the moment you start using them).
Please find the full list here: https://bit.ly/pd-list.
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Jupyter-Datatables: Enrich the default preview of a DataFrame. Link: https://bit.ly/jupy-dtable
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SummaryTools: Supercharge the describe() method. Link: https://bit.ly/summ-tools
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Sidetable: Supercharge the value_counts() method. Link: https://lnkd.in/dSqfbg-5
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Sketch: Generate code/insights by asking questions in natural language. Link: https://bit.ly/py-sketch
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Deepchecks: Generate a comprehensive data validation report. Link: https://bit.ly/deepchks
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Pandas Flavor: Extend Pandas to attach methods to the dataframe object. Link: https://bit.ly/py-pdflavor
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Pandarallel: Parallelize Pandas across all CPU cores. Link: https://bit.ly/pd-parallel
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PandasML: Pandas, sklearn and matplotlib integrated. Link: https://bit.ly/pandasml
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Geopandas: Work with Geospatial data in Pandas. Link: https://bit.ly/geo-pd
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DuckDB: Run SQL queries on dataframes. Link: https://bit.ly/pd-sql
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Modin: Boost Pandas' performance up to 70x by modifying the import. Link: https://bit.ly/py-modin
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PivotTableJS: Create pivot tables by using drag and drop functionality. Link: https://bit.ly/PivotJS
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Missingno: Visualize missing values in your dataset. Link: https://bit.ly/py-missing
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Pandas Alive: Create animated charts for pandas dataframes. Link: https://bit.ly/pd-alive
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Skimpy: Supercharge the describe() method. Link: https://bit.ly/py-skim
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Pandas-log: Debug Pandas pipeline with step-by-step logging. Link: https://bit.ly/py-log
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tsflex: Process time series and perform feature extraction. Link: https://bit.ly/tsflex
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pandas-profiling: Generate EDA report of data in one-line. Link: https://lnkd.in/dQrS8KTA
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Mars: A tensor-based framework for scaling numpy, pandas, scikit-learn, etc. Link: https://bit.ly/py-mars
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nptyping: Apply type hints for Pandas dataframes. Link: https://bit.ly/nptyping
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popmon: Profile your data to determine its stability. Link: https://bit.ly/py-popmon
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Gspread-pandas: Interact with Google sheets using dataframes. Link: https://bit.ly/pd-gsheets
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pdpipe: Create pandas pipeline easily and intuitively. Link: https://bit.ly/py-pdpipe
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PrettyPandas: Prettify the dataframe when printed. Link: https://lnkd.in/deGXBryJ
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Dora: An intuitive API for data cleaning, processing, feature selection, visualization, etc. Link: https://bit.ly/py-dora
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Pandapy: The speed of NumPy combined with Pandas' elegance. Link: https://bit.ly/pandapy
Linear DP Link: https://lnkd.in/dNJFUBcW
DP on Strings Link: https://lnkd.in/dpetSA_s
Knapsack Dp Link: https://lnkd.in/d5Dc4j4N
DP with Tree & Graph Link: https://lnkd.in/dbdAW_x3
Dp on math problems Link : https://lnkd.in/d_-jKvmM
Dp with bits manipulation Link:https://lnkd.in/dfQCrQim
Grid-based DP Link:https://lnkd.in/d5DJc6Cy
Multidimensional DP Link: https://lnkd.in/dGgXsjK2
Digit Problem DP Link: https://lnkd.in/dW5RKihx
Classical DP problem Link: htps://lnkd.in/d_iVkeUV
✅ Writing Python Efficient Code: Measuring Python Code Efficiency https://lnkd.in/dQSUpQsQ
✅ Python Code Optimization for Data Scientists https://lnkd.in/dGS7DWbX
✅ How To Eliminate Loops From Your Python Code https://lnkd.in/dV5xDhyV
✅ Stop Looping Through Pandas DataFrames https://lnkd.in/dsf7zWFs
✅ How To Use .groupby() Effectively As A Data Scient https://lnkd.in/d8UX2zr6
✅ Selecting & Replacing Values In Pandas Effectively https://lnkd.in/dcw6c36z
✅ Make Your Pandas Code 1000 Times Faster https://lnkd.in/dvawCSGv
✅ 20 Pandas Functions for 80% of Data Science Tasks https://lnkd.in/dvC4pJ9E
✅ Top 10 Pandas Mistakes to Avoid https://lnkd.in/dvPZdis6