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
  • 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
    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 the current dataframe
  • Create custom visualizations through the plot creator
  • Create pivot table
    Create a pivot table