fillNa(0) instead producing None - pandas

Question

My question is just below the code snippet, below:
I have raw sensor time series data that .. is getting really close to be usable now :)
locDf = locationDf.copy()
locDf.set_index('date', inplace=True)
locDfs = {}
for user, user_loc_dc in locDf.groupby('user'):
locDfs[user] = user_loc_dc.resample('15T').agg('max').bfill()
aDf = appDf.copy()
aDf.set_index('date', inplace=True)
userLocAppDfs = {}
appDfs = []
for user, a2_df in aDf.groupby('user'):
userDf = a2_df.resample('15T').agg('min')
userDf.reset_index(inplace=True)
userDf = pd.crosstab(index=userDf['date'], columns=userDf['app'], values=userDf['metric'], aggfunc=np.mean).fillna(0, downcast='infer')
userDf['user'] = user
userDf.reset_index(inplace=True)
userDf.set_index('date', inplace=True)
appDfs.append(userDf)
userLocAppDfs[user] = userDf
loDf = locDfs[user]
loDf.reset_index(inplace=True)
loDf = pd.crosstab([loDf.date, loDf.user], loDf.location)
loDf.reset_index(inplace=True)
loDf.set_index('date', inplace=True)
loDf.drop('user', axis=1, inplace=True)
userLocAppDfs[user] = userLocAppDfs[user].join(loDf, how='outer')
userLocAppDfs[user]['user'].fillna(user, inplace=True)
#for app in a2_df['app'].unique():
# userLocAppDfs[user][app] = userLocAppDfs[user][app].fillna(0, inplace=True)
userLocAppDfs['user_1'].head(5)
Question
If I uncomment those last two lines to try to fill the NaN's at the start, I dont' get zeros. I get None. :( Can anyone tell me why?
I'd like to.. you know, get 0's there:
2017-08-28 00:00:00 0 0 user_1 0.0 0.0 0.0 1.0 0.0
2017-08-28 00:15:00 0 0 user_1 0.0 0.0 1.0 0.0 0.0
2017-08-28 00:30:00 0 0 user_1 0.0 0.0 1.0 0.0 0.0
2017-08-28 00:45:00 0 0 user_1 0.0 0.0 1.0 0.0 0.0
2017-08-28 01:00:00 0 0 user_1 0.0 0.0 1.0 0.0 0.0
The last step will be for me to get the rolling average of those app_* numbers, so that I get a curve.

Answer 1

Try
for app in a2_df['app'].unique():
userLocAppDfs[user][app].fillna(0, inplace=True)
# or userLocAppDfs[user][app] = userLocAppDfs[user][app].fillna(0)
So it is because you have specified inplace = True and at the same time you assign it back.
Note that df.fillna(0, inplace=True) will not return a value. Rather it will directly modify the originaldf. Try print(df.fillna(0, inplace=True)), it will give you None. So what you've done above was assigning None to column apps.