Pandas How To Apply Multiple Functions To Dataframe

Is there a way to apply a list of functions to each column in a DataFrame like the DataFrameGroupBy.agg function does? I found an ugly way to do it like this: df=pd.DataFrame(dict

Solution 1:

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  5.1474714.964100
std   2.9711062.753578

For older versions, you could use

In [61]: df.groupby(lambda idx: 0).agg(['mean','std'])
Out[61]: 
        one               two          
       mean       std    mean       std
05.1474712.9711064.96412.753578

Another way would be:

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

Solution 2:

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()

         meanstdone5.3663032.612738two4.8586912.986567

Solution 3:

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

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

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

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

Solution 4:

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