Data Transformation

For the most recent list of transformation functions offered by bamboolib, we recommend installing the latest version of bamboolib, opening it on your computer and clicking on the "Search transformation" dropdown. All available transformations are listed there.
List of Transformations - Aug 28th, 2020
Select or drop columns
Select/delete one or multiple columns
Filter
Select/delete rows based on a condition
Sort
Sort rows beased on values in one or more columns
Group by and aggregate (default)
Group rows by columns and calculate MULTIPLE aggregations (no renaming possible)
Group by and aggregate (with renaming)
Group rows by columns and calculate a SINGLE aggregation that can be named
Join / Merge dataframes
Add columns from another dataframe based on keys
Change data type
Change the data type of a column
To Integer
Convert a column to integer
To Unsigned Integer
Convert a column to unsigned integer
To Float
Convert a column to float
To String
Convert a column to string
To Object
Convert a column to dtype Object
To Datetime
Convert a column to datetime
To Timedelta
Convert a column to timedelta
To Category
Convert a column to category
To Bool
Convert a column to boolean
Rename columns
Rename one or more columns
Replace value
Replace cell values in one or all columns
Set or update values
Replace cell values based on column condition
​String manipulations​
Manipulate string values
Change datetime frequency
EITHER expand timeseries column and fill it with values OR group by and calculate aggregations (also known as: resample or expand grid)
​Extract datetime attributes​
Extract datetime features from a datetime column
Move columns / Change column order
Move / Re-order one or multiple columns
Bin column
Form discrete categories from a numeric column
Concatenate
Concatenate (union / stack) multiple dataframes vertically or horizontally
Pivot/Spread
Reshape the dataframe from long to wide format
Unpivot/Melt
Reshape the dataframe from wide to long format
OneHotEncoder
Create a column for each unique value indicating its presence or absence
LabelEncoder
Turn a categoric column into numeric integer codes (factorize)
Drop missing values
Remove rows with missing values (NAs) in one or more columns
Drop/Remove duplicates
Remove duplicated rows in a dataframe, i.e. only keep distinct rows
Replace missing values
Fill / Impute missing values (NAs) in one or more columns
New column formula
Create a new column from a formula
Add Python Code
Add custom Python code as a transformation
​Explore and visualize dataframe​
Explore and visualize the current dataframe
​Plot dataframe​
Create custom visualizations through the plot creator
Create pivot table
Create a pivot table

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Reference

String manipulations

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List of Transformations - Aug 28th, 2020