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
    Select/delete rows based on a condition
    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
    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 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 (union / stack) multiple dataframes vertically or horizontally
    Reshape the dataframe from long to wide format
    Reshape the dataframe from wide to long format
    Create a column for each unique value indicating its presence or absence
    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 the current dataframe
    ​Plot dataframe​
    Create custom visualizations through the plot creator
    Create pivot table
    Create a pivot table
Last modified 1yr ago