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