python pandas - Editing multiple DataFrames with a for loop

Question

Considering the following 2 lists of 3 dicts and 3 empty DataFrames

dict0={\'actual\': {\'2013-02-20 13:30:00\': 0.93}}
dict1={\'actual\': {\'2013-02-20 13:3         


        

Answer 1

This will get it done in place!!!
Please note the 3 exclamations

one liner

[dfs[i].set_value(r, c, v)
 for i, dn in enumerate(dicts)
 for r, dr in dn.items()
 for c, v in dr.items()]; 

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somewhat more intuitive

for d, df in zip(dicts, dfs):
    temp = pd.DataFrame(d).stack()
    for (r, c), v in temp.iteritems():
        df.set_value(r, c, v)

df0

                     actual
2013-02-20 13:30:00    0.93

---

equivalent alternative
without the pd.DataFrame construction

for i, dn in enumerate(dicts):
    for r, dr in dn.items():
        for c, v in dr.items():
            dfs[i].set_value(r, c, v)

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Why is this different?
All the other answers, so far, reassign a new dataframe to the requisite position in the list of dataframes. They clobber the dataframe that was there. The original dataframe is left empty while a new non-empty one rests in the list.

This solution edits the dataframe in place ensuring the original dataframe is updated with new information.

Per OP:

However, when trying to retrieve for instance 1 of the df outside of the loop, it is still empty

---

timing
It's also considerably faster

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setup

dict0={'actual': {'2013-02-20 13:30:00': 0.93}}
dict1={'actual': {'2013-02-20 13:30:00': 0.85}}
dict2={'actual': {'2013-02-20 13:30:00': 0.98}}
dicts=[dict0, dict1, dict2]

df0=pd.DataFrame()
df1=pd.DataFrame()
df2=pd.DataFrame()
dfs=[df0, df1, df2]