dfply.transform

Module Contents

dfply.transform.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

dfply.transform.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)

dfply.transform.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