:mod:`dfply.transform`
======================

.. py:module:: dfply.transform







Module Contents
---------------






.. function:: mutate(df, **kwargs)

   
   Creates new variables (columns) in the DataFrame specified by keyword
   argument pairs, where the key is the column name and the value is the
   new column value(s).

   Args:
       df (pandas.DataFrame): data passed in through the pipe.

   Kwargs:
       **kwargs: keys are the names of the new columns, values indicate
           what the new column values will be.

   Example:
       diamonds >> mutate(x_plus_y=X.x + X.y) >> select_from('x') >> head(3)

             x     y     z  x_plus_y
       0  3.95  3.98  2.43      7.93
       1  3.89  3.84  2.31      7.73
       2  4.05  4.07  2.31      8.12

   









.. function:: mutate_if(df, predicate, fun)

   
   Modifies columns in place if the specified predicate is true.
   Args:
       df (pandas.DataFrame): data passed in through the pipe.
       predicate: a function applied to columns that returns a boolean value
       fun: a function that will be applied to columns where predicate returns True

   Example:
       diamonds >> mutate_if(lambda col: min(col) < 1 and mean(col) < 4, lambda row: 2 * row) >> head(3)
          carat      cut color clarity  depth  table  price     x     y     z
       0   0.46    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  4.86
       1   0.42  Premium     E     SI1   59.8   61.0    326  3.89  3.84  4.62
       2   0.46     Good     E     VS1   56.9   65.0    327  4.05  4.07  4.62
       (columns 'carat' and 'z', both having a min < 1 and mean < 4, are doubled, while the
       other rows remain as they were)

   









.. function:: transmute(df, *keep_columns, **kwargs)

   
   Creates columns and then returns those new columns and optionally specified
   original columns from the DataFrame.

   This works like `mutate`, but designed to discard the original columns used
   to create the new ones.

   Args:
       *keep_columns: Column labels to keep. Can be string, symbolic, or
           integer position.

   Kwargs:
       **kwargs: keys are the names of the new columns, values indicate
           what the new column values will be.

   Example:
       diamonds >> transmute(x_plus_y=X.x + X.y, y_div_z=(X.y / X.z)) >> head(3)

           y_div_z  x_plus_y
       0  1.637860      7.93
       1  1.662338      7.73
       2  1.761905      8.12