Pandas - Split Dataframe Into Multiple Dataframes Based On Dates?

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 wa

Solution 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:

defdo_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')

Solution 2:

This is a split per year.

import pandas as pd
import dateutil.parser
dfile = 'rg_unificado.csv'
df = pd.read_csv(dfile, sep='|', quotechar='"', encoding='latin-1')
df['FECHA'] = df['FECHA'].apply(lambda x: dateutil.parser.parse(x)) 
#http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases#use to_period
per = df['FECHA'].dt.to_period("Y")
#group by that period
agg = df.groupby([per])
for year, group in agg:
    #this simple save the data
    datep =  str(year).replace('-', '')
    filename = '%s_%s.csv' % (dfile.replace('.csv', ''), datep)
    group.to_csv(filename, sep='|', quotechar='"', encoding='latin-1', index=False, header=True)