Credits: LinkedIn
A suite of visualization and diagnostic tools for faster model selection.
Automate ML workflows with this low-code library.
A variety of methods to handle class imbalance.
Boost Pandas' performance up to 70x by modifying the import.
Explain the output of any ML model in few lines of code.
Visualize missing values in your dataset with ease.
Produce high-quality forecasts on time-series data.
Parallelize Pandas across all CPU cores for faster computation.
Automated feature engineering for ML Models.
Train 30 machine learning models in one line of code.
A collection of utility functions for processing, evaluating, visualizing models.
High-performance package for lazy Out-of-Core DataFrames.
In-depth EDA report in two lines of code.
Leverage the power of PyTorch with the elegance of sklearn.
Efficient algorithms for similarity search and clustering dense vectors.
Statistical testing and Data exploration at fingertips.
Generate a high-level EDA report of your data in no time.
Create and host data-based Python web apps in few lines of code.
Over 15 categorical data encoders.
Run SQL queries on DataFrame.
Pandas data wrangling + Sklearn algorithms + Matplotlib visualization.
An elegant testing framework to test your code.
Parallelize NumPy to all CPU cores for 20x speedup.
Explore, query and describe CSV files from terminal.
Drap-n-drop tools to group, pivot, plot dataframe.
Generate fake yet meaningful data in seconds.
Don't debug with print()
. Use icecream.
No need to write imports. Automatic package import.
Profile your code. Track new variables, and their updates.
Supercharge Pandas' value_counts()
method.