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Python-Tutorials

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

DP, Problem List

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

9 Kaggle Notebooks that will help you to write Efficient Python Code:

✅ 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