CODEWITHSUNDEEP
pythonpandasfilteringslice
yoshiserry
yoshiserry

Reputation: 21365

how to apply a function to multiple columns in a pandas dataframe at one time

I frequently deal with data which is poorly formatted (I.e. number fields are not consistent etc)

There may be other ways, which I am not aware of but the way I format a single column in a dataframe is by using a function and mapping the column to that function.

format = df.column_name.map(format_number)

Question: 1 - what if I have a dataframe with 50 columns, and want to apply that formatting to multiple columns, etc column 1, 3, 5, 7, 9,

Can you go:

format = df.1,3,5,9.map(format_number)

.. This way I could format all my number columns in one line?

Upvotes: 17

Views: 21856

Answers (3)

Kostynha
Kostynha

Reputation: 145

try:

df.apply(lambda row: [...], axis=1)

to:

df.transpose().apply(lambda col: [...]).transpose()

I kinda just found out by accident.

Upvotes: 0

EdChum
EdChum

Reputation: 394319

You could use apply like this:

df.apply(lambda row: format_number(row), axis=1)

You would need to specify the columns though in your format_number function:

def format_number(row):
    row['Col1'] = doSomething(row['Col1']
    row['Col2'] = doSomething(row['Col2'])
    row['Col3'] = doSomething(row['Col3'])

This is not as elegant as @BrenBarn's answer but it has an advantage that the dataframe is modified in place so you don't need to assign the columns back again

Upvotes: 3

BrenBarn
BrenBarn

Reputation: 251548

You can do df[['Col1', 'Col2', 'Col3']].applymap(format_number). Note, though that this will return new columns; it won't modify the existing DataFrame. If you want to put the values back in the original, you'll have to do df[['Col1', 'Col2', 'Col3']] = df[['Col1', 'Col2', 'Col3']].applymap(format_number).

Upvotes: 21

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