Pandas how to apply multiple functions to dataframe

For Pandas 0.20.0 or newer, use df.agg (thanks to ayhan for pointing this out):

In [11]: df.agg(['mean', 'std'])
Out[11]: 
           one       two
mean  5.147471  4.964100
std   2.971106  2.753578

For older versions, you could use

In [61]: df.groupby(lambda idx: 0).agg(['mean','std'])
Out[61]: 
        one               two          
       mean       std    mean       std
0  5.147471  2.971106  4.9641  2.753578

Another way would be:

In [68]: pd.DataFrame({col: [getattr(df[col], func)() for func in ('mean', 'std')] for col in df}, index=('mean', 'std'))
Out[68]: 
           one       two
mean  5.147471  4.964100
std   2.971106  2.753578

In the general case where you have arbitrary functions and column names, you could do this:

df.apply(lambda r: pd.Series({'mean': r.mean(), 'std': r.std()})).transpose()

         mean       std
one  5.366303  2.612738
two  4.858691  2.986567

I am using pandas to analyze Chilean legislation drafts. In my dataframe, the list of authors are stored as a string. The answer above did not work for me (using pandas 0.20.3). So I used my own logic and came up with this:

df.authors.apply(eval).apply(len).sum()

Concatenated applies! A pipeline!! The first apply transforms

"['Barros Montero: Ramón', 'Bellolio Avaria: Jaime', 'Gahona Salazar: Sergio']"

into the obvious list, the second apply counts the number of lawmakers involved in the project. I want the size of every pair (lawmaker, project number) (so I can presize an array where I will study which parties work on what).

Interestingly, this works! Even more interestingly, that last call fails if one gets too ambitious and does this instead:

df.autores.apply(eval).apply(len).apply(sum)

with an error:

TypeError: 'int' object is not iterable

coming from deep within /site-packages/pandas/core/series.py in apply

I tried to apply three functions into a column and it works

#removing new line character
rem_newline = lambda x : re.sub('\n',' ',x).strip()

#character lower and removing spaces
lower_strip = lambda x : x.lower().strip()

df = df['users_name'].apply(lower_strip).apply(rem_newline).str.split('(',n=1,expand=True)