Understanding the Data Distribution Quickly

What if a library can help reduce the lines of code?

Photo by Hannes Egler on Unsplash

For all python data analyzers, some of our famous lines of code when getting started with the data and understanding the data are

df.info() 
df.describe() 
df.isna().mean()

The first line of code helps to visualize the columns, data types, and row counts. The second line of code helps to quickly check the data statistics and the third helps to check the proportion of the null value if any

• QuickDa (short for Quick Exploratory Data Analysis) compresses the above steps into one line of code by pip-installing the library and running the code below

explore(df)

Here is a sample demo below

As you can see above, this library also gets the min/max of categorical variables in the same table and finds the cardinalities of columns (i.e df.nunique())

Remember to pip install first using

!pip install quickda

This library has so many other features. Read more about it in the documentation [here](https://github.com/sid-the-coder/QuickDA)

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