Setting values with pandas.DataFrame

Question

Having this DataFrame:

import pandas

dates = pandas.date_range('2016-01-01', periods=5, freq='H')
s = pandas.Series([0, 1, 2, 3, 4], index=dates)
df = pandas.DataFrame([(1, 2, s, 8)], columns=['a', 'b', 'foo', 'bar'])
df.set_index(['a', 'b'], inplace=True)

df

I would like to replace the Series in there with a new one that is simply the old one, but resampled to a day period (i.e. x.resample('D').sum().dropna()).

When I try:

df['foo'][0] = df['foo'][0].resample('D').sum().dropna()

That seems to work well:

However, I get a warning:

SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

The question is, how should I do this instead?

Notes

Things I have tried but do not work (resampling or not, the assignment raises an exception):

df.iloc[0].loc['foo'] = df.iloc[0].loc['foo']
df.loc[(1, 2), 'foo'] = df.loc[(1, 2), 'foo']
df.loc[df.index[0], 'foo'] = df.loc[df.index[0], 'foo']

A bit more information about the data (in case it is relevant):

• The real DataFrame has more columns in the multi-index. Not all of them necessarily integers, but more generally numerical and categorical. The index is unique (i.e.: there is only one row with a given index value).
• The real DataFrame has, of course, many more rows in it (thousands).
• There are not necessarily only two columns in the DataFrame and there may be more than 1 columns containing a Series type. Columns usually contain series, categorical data and numerical data as well. Any single column is always single-typed (either numerical, or categorical, or series).
• The series contained in each cell usually have a variable length (i.e.: two series/cells in the DataFrame do not, unless pure coincidence, have the same length, and will probably never have the same index anyway, as dates vary as well between series).

Using Python 3.5.1 and Pandas 0.18.1.

Answer 1

This should work:

df.iat[0, df.columns.get_loc('foo')] = df['foo'][0].resample('D').sum().dropna()

Pandas is complaining about chained indexing but when you don't do it that way it's facing problems assigning whole series to a cell. With iat you can force something like that. I don't think it would be a preferable thing to do, but seems like a working solution.

Answer 2

Simply set df.is_copy = False before asignment of new value.