Update README.md

Author: gulabpatel

README.md

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 ## Vizualizations
 #### 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.
+
+1) Jupyter-Datatables: Enrich the default preview of a DataFrame.
+Link: https://bit.ly/jupy-dtable
+
+2) SummaryTools: Supercharge the describe() method.
+Link: https://bit.ly/summ-tools
+
+3) Sidetable: Supercharge the value_counts() method.
+Link: https://lnkd.in/dSqfbg-5
+
+4) Sketch: Generate code/insights by asking questions in natural language.
+Link: https://bit.ly/py-sketch
+
+5) Deepchecks: Generate a comprehensive data validation report.
+Link: https://bit.ly/deepchks
+
+6) Pandas Flavor: Extend Pandas to attach methods to the dataframe object.
+Link: https://bit.ly/py-pdflavor
+
+7) Pandarallel: Parallelize Pandas across all CPU cores.
+Link: https://bit.ly/pd-parallel
+
+8) PandasML: Pandas, sklearn and matplotlib integrated.
+Link: https://bit.ly/pandasml
+
+9) Geopandas: Work with Geospatial data in Pandas.
+Link: https://bit.ly/geo-pd
+
+10) DuckDB: Run SQL queries on dataframes.
+Link: https://bit.ly/pd-sql
+
+11) Modin: Boost Pandas' performance up to 70x by modifying the import.
+Link: https://bit.ly/py-modin
+
+12) PivotTableJS: Create pivot tables by using drag and drop functionality.
+Link: https://bit.ly/PivotJS
+
+13) Missingno: Visualize missing values in your dataset. 
+Link: https://bit.ly/py-missing
+
+14) Pandas Alive: Create animated charts for pandas dataframes.
+Link: https://bit.ly/pd-alive
+
+15) Skimpy: Supercharge the describe() method.
+Link: https://bit.ly/py-skim
+
+16) Pandas-log: Debug Pandas pipeline with step-by-step logging.
+Link: https://bit.ly/py-log
+
+17) tsflex: Process time series and perform feature extraction.
+Link: https://bit.ly/tsflex
+
+18) pandas-profiling: Generate EDA report of data in one-line.
+Link: https://lnkd.in/dQrS8KTA
+
+19) Mars: A tensor-based framework for scaling numpy, pandas, scikit-learn, etc.
+Link: https://bit.ly/py-mars
+
+20) nptyping: Apply type hints for Pandas dataframes.
+Link: https://bit.ly/nptyping
+
+21) popmon: Profile your data to determine its stability.
+Link: https://bit.ly/py-popmon
+
+22) Gspread-pandas: Interact with Google sheets using dataframes.
+Link: https://bit.ly/pd-gsheets
+
+23) pdpipe: Create pandas pipeline easily and intuitively.
+Link: https://bit.ly/py-pdpipe
+
+24) PrettyPandas: Prettify the dataframe when printed. 
+Link: https://lnkd.in/deGXBryJ
+
+25) Dora: An intuitive API for data cleaning, processing, feature selection, visualization, etc.
+Link: https://bit.ly/py-dora
+
+26) Pandapy: The speed of NumPy combined with Pandas' elegance.
+Link: https://bit.ly/pandapy