I would like to apply a specific function (in this case a logit model) to a dataframe which can be grouped (by the variable "model"). I know the task can be performed through a loop, however I believe this to be inefficient at best. Example code below:
import pandas as pd
import numpy as np
import statsmodels.api as sm
df1=pd.DataFrame(np.random.randint(0,100,size=(100,10)),columns=list('abcdefghij'))
df2=pd.DataFrame(np.random.randint(0,100,size=(100,10)),columns=list('abcdefghij'))
df1['model']=1
df1['target']=np.random.randint(2,size=100)
df2['model']=2
df2['target']=np.random.randint(2,size=100)
data=pd.concat([df1,df2])
### Clunky, but works...
for i in range(1,2+1):
lm=sm.Logit(data[data['model']==i]['target'],
sm.add_constant(data[data['model']==i].drop(['target'],axis=1))).fit(disp=0)
print(lm.summary2())
### Can this work?
def elegant(self):
lm=sm.Logit(data['target'],
sm.add_constant(data.drop(['target'],axis=1))).fit(disp=0)
better=data.groupby(['model']).apply(elegant)
If the above groupby can work, is this a more efficient way to perform than looping?
This could work:
def elegant(df):
lm = sm.Logit(df['target'],
sm.add_constant(df.drop(['target'],axis=1))).fit(disp=0)
return lm
better = data.groupby('model').apply(elegant)
Using .apply
you passe the dataframe groups to the function elegant
so elegant
has to take a dataframe as the first argument here. Also your function needs to return the result of your calculation lm
.
For more complexe functions the following structure can be used:
def some_fun(df, kw_param=1):
# some calculations to df using kw_param
return df
better = data.groupby('model').apply(lambda group: some_func(group, kw_param=99))
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