python - Pandas - Split dataframe into multiple dataframes based on dates?

Question

I have a dataframe with multiple columns along with a date column. The date format is 12/31/15 and I have set it as a datetime object.

I set the datetime column as the index and want to perform a regression calculation for each month of the dataframe.

I believe the methodology to do this would be to split the dataframe into multiple dataframes based on month, store into a list of dataframes, then perform regression on each dataframe in the list.

I have used groupby which successfully split the dataframe by month, but am unsure how to correctly convert each group in the groupby object into a dataframe to be able to run my regression function on it.

Does anyone know how to split a dataframe into multiple dataframes based on date, or a better approach to my problem?

Here is my code I've written so far

import pandas as pd
import numpy as np
import statsmodels.api as sm
from patsy import dmatrices

df = pd.read_csv('data.csv')
df['date'] = pd.to_datetime(df['date'], format='%Y%m%d')
df = df.set_index('date')

# Group dataframe on index by month and year 
# Groupby works, but dmatrices does not 
for df_group in df.groupby(pd.TimeGrouper("M")):
    y,X = dmatrices('value1 ~ value2 + value3', data=df_group,      
    return_type='dataframe')

Answer 1

If you must loop, you need to unpack the key and the dataframe when you iterate over a groupby object:

import pandas as pd
import numpy as np
import statsmodels.api as sm
from patsy import dmatrices

df = pd.read_csv('data.csv')
df['date'] = pd.to_datetime(df['date'], format='%Y%m%d')
df = df.set_index('date')

Note the use of group_name here:

for group_name, df_group in df.groupby(pd.Grouper(freq='M')):
    y,X = dmatrices('value1 ~ value2 + value3', data=df_group,      
    return_type='dataframe')

If you want to avoid iteration, do have a look at the notebook in Paul H's gist (see his comment), but a simple example of using apply would be:

def do_regression(df_group, ret='outcome'):
    """Apply the function to each group in the data and return one result."""
    y,X = dmatrices('value1 ~ value2 + value3',
                    data=df_group,      
                    return_type='dataframe')
    if ret == 'outcome':
        return y
    else:
        return X

outcome = df.groupby(pd.Grouper(freq='M')).apply(do_regression, ret='outcome')