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.
- Select or drop columnsSelect/delete one or multiple columns
- FilterSelect/delete rows based on a condition
- SortSort 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 dataframesAdd columns from another dataframe based on keys
- Change data typeChange the data type of a column
- To IntegerConvert a column to integer
- To Unsigned IntegerConvert a column to unsigned integer
- To FloatConvert a column to float
- To StringConvert a column to string
- To ObjectConvert a column to dtype Object
- To DatetimeConvert a column to datetime
- To TimedeltaConvert a column to timedelta
- To CategoryConvert a column to category
- To BoolConvert a column to boolean
- Rename columnsRename one or more columns
- Replace valueReplace cell values in one or all columns
- Set or update valuesReplace cell values based on column condition
- Manipulate string values
- Change datetime frequencyEITHER expand timeseries column and fill it with values OR group by and calculate aggregations (also known as: resample or expand grid)
- Extract datetime features from a datetime column
- Move columns / Change column orderMove / Re-order one or multiple columns
- Bin columnForm discrete categories from a numeric column
- ConcatenateConcatenate (union / stack) multiple dataframes vertically or horizontally
- Pivot/SpreadReshape the dataframe from long to wide format
- Unpivot/MeltReshape the dataframe from wide to long format
- OneHotEncoderCreate a column for each unique value indicating its presence or absence
- LabelEncoderTurn a categoric column into numeric integer codes (factorize)
- Drop missing valuesRemove rows with missing values (NAs) in one or more columns
- Drop/Remove duplicatesRemove duplicated rows in a dataframe, i.e. only keep distinct rows
- Replace missing valuesFill / Impute missing values (NAs) in one or more columns
- New column formulaCreate a new column from a formula
- Add Python CodeAdd custom Python code as a transformation
- Explore and visualize the current dataframe
- Create custom visualizations through the plot creator
- Create pivot tableCreate a pivot table
Last modified 2yr ago