Cookbook
Data wrangling
Prepare and preprocess your data. You will find here code recipes for data joining, spliting, filtering and more!
DataFrame info
Check information about DataFrame columns, non-nulls, types and memory usage
DataFrame describe
Display statistical description for numeric columns in DataFrame
Display DataFrame
Display rows from Pandas DataFrame
Select X,y
Select training attributes and target for Machine Learning model training
Select Columns
Select columns from dataframe
Filter rows
Filter rows in Pandas DataFrame based on condition
Split to train/test
Split data into train and test subsets
Check missing values
Check and count missing values in Pandas DataFrame
Fill missing values
Fill missing values in Pandas DataFrame
Use imputer on new data
Use previously fitted imputer on new data
Categorical to integer
Convert features with non-numeric values (categoricals) to integers
Use encoder on new data
Use previously fitted categoricals encoder on new data
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