Python Pandas Create Multiple dataframes from list

To answer your question literally, globals()['dfA'] = dfNew would define dfA in the global namespace:

for i, row in enumerate(dfList):
    dfName = dfNames[i]
    dfNew = df[df['variable'] == row]
    globals()[dfName] = dfNew   

However, there is never a good reason to define dynamically-named variables.

• If the names are not known until runtime -- that is, if the names are truly dynamic -- then you you can't use the names in your code since your code has to be written before runtime. So what's the point of creating a variable named dfA if you can't refer to it in your code?

• If, on the other hand, you know before hand that you will have a variable named dfA, then your code isn't really dynamic. You have static variable names. The only reason to use the loop is to cut down on boiler-plate code. However, even in this case, there is a better alternative. The solution is to use a dict (see below) or list¹.

• Adding dynamically-named variables pollutes the global namespace.

• It does not generalize well. If you had 100 dynamically named variables, how would you access them? How would you loop over them?

• To "manage" dynamically named variables you would need to keep a list of their names as strings: e.g. ['dfA', 'dfB', 'dfC',...] and then accessed the newly minted global variables via the globals() dict: e.g. globals()['dfA']. That is awkward.

So the conclusion programmers reach through bitter experience is that dynamically-named variables are somewhere between awkward and useless and it is much more pleasant, powerful, practical to store key/value pairs in a dict. The name of the variable becomes a key in the dict, and the value of the variable becomes the value associated with the key. So, instead of having a bare name dfA you would have a dict dfs and you would access the dfA DataFrame via dfs['dfA']:

dfs = dict()
for i, row in enumerate(dfList):
    dfName = dfNames[i]
    dfNew = df[df['variable'] == row]
    dfs[dfName] = dfNew   

or, as Jianxun Li shows,

dfs = {k: g for k, g in df.groupby('variable')}

This is why Jon Clements and Jianxun Li answered your question by showing alternatives to defining dynamically-named variables. It's because we all believe it is a terrible idea.

Using Jianxun Li's solution, to loop over a dict's key/value pairs you could then use:

dfs = {k: g for k, g in df.groupby('variable')}
for key, df in dfs.items():
    ...

or using Jon Clements' solution, to iterate through groups you could use:

grouped = df.groupby('variable')
for key, df in grouped:
    ...

¹If the names are numbered or ordered you could use a list instead of a dict.