how to loop / iterate over multiple dataframes using their names as strings - pandas

Question

i have some dataframes
df_1
df_2
…
df_99
df_100
over which i would like to iterate to perform some operations on a specific column, say Column_A, which exists in each dataframe.
i can create strings with the names of the dataframes using
for i in range (1,101):
’df_’+str(i)
but when i try to use these to access the dataframes like this
for i in range (1,101):
df_x = ’df_’+str(i)
df_x['Column_A’].someoperation(i)
# the operation involves the number of the dataframe
i get a TypeError: „string indices must be integers“.
I searched extensively and the suggested solution to this kind of problem which i found most often was to create a dictionary with the names of the dataframes as keys and the actual dataframes as the associated values.
However i would not like to proceed like this for two or three reasons:
For one, as i am still rather new to pandas, i am not sure about how to address a specific column in a dataframe which is placed as a value in a dictionary.
Additionally, putting the dataframes in a dictionary would create copies of them (if i understand correctly), which is not ideal if there are very many dataframes or if the dataframes are large.
But most importantly, since i do not know how to iterate over the names, putting the dataframes in a dictionary would have to be done manually, so it is still the same problem in a way.
I tried creating a list with the names of the dataframes to loop over
df_list= [ ]
for i in range (1,101):
df_list.append('df_‘+str(i))
for df in df_list:
df['Column_A’].someoperation
but that approach results in the same type error as above - and i cannot conveniently involve the number of the dataframe in "someoperation".
Apparently pandas does take df_1 , df_2 etc as the strings they are and not as the name of the already existing dataframe i would like to access, but i dont know how to tell it to do otherwise.
Any suggestions how this could be solved are much appreciated.

Answer 1

You're defining a list of strings, but you're not giving Python any way of knowing that "df_1" is in some way connected to df_1
To answer your question, you're looking for the eval function, which takes a string, executes it as code, and returns the output. So eval("df_1") will give you the dataframe df_1.
df_list= [ ]
for i in range (1,101): #~ look up list comprehensions for a more elegant way to do this.
df_list.append('df_'+str(i))
for df in df_list:
eval(df)['Column_A'].someoperation
However, you should take the advice you've gotten and use a dictionary or list. Putting the dataframes in a dictionary would definitely not create copies of them. The dictionary is simply a mapping from a set of strings to the corresponding object in memory. This is also a much more elegant solution, keeping all of the relevant dataframes in one place without having to adhere to a strict naming convention that will inevitably get messed up in some way.
If you don't really need names for each dataframe and just want them accessible together, an even simpler solution would be to put them in a list and access each one as dfs[0]-dfs[100].
If you've already got df_1-df_100 loaded the way you're describing, eval will let you organize them all into one place like that: dfs = [eval("df_"+str(i)) for i in range(1,101)] or dfs={i:eval(f"df_{i}") for i in range(1,101)}
Finally, you can access columns and do operations on dataframes accessed through lists and dictionaries in the normal way. E.g.
dfs[0]['column 1'] = 1.
means = dfs[40].groupby('date').mean()
#~ ect.