Python, pandas concatenate multiple dataframes

Question

I have a list of csv files which I load as data frames using pd.read_csv()

I am currently trying to iterate through the list of csv and using the pd.concat() method and setting the axis parameter to one to add all the dataframes together by columns.

It is working as hoped however I am encountering the issue that since all of the data frames have the same colums names when I concatenated them I get for example ten columns all with the key "Date"

is there anyway that I can give the colums all unique names example London_Date, Berlin_Date? obviously the names being based on the name of the data frames.

Possible duplicate of [Selecting/Excluding sets of columns in Pandas](http://stackoverflow.com/questions/14940743/selecting-excluding-sets-of-columns-in-pandas) — Amey Kumar Samala, May 16 '17 at 16:58

YashTD · Answer 1 · 2017-05-16T17:04:46.467

If you pass a list of keys to concat(), you can then individually index any column you want with the given keys like so:

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                'B': ['B0', 'B1', 'B2', 'B3'],
                'C': ['C0', 'C1', 'C2', 'C3'],
                'D': ['D0', 'D1', 'D2', 'D3']},
                index=[0, 1, 2, 3])
df2 = df1
df3 = df1
add = pd.concat([df1, df2, df3], axis = 1, keys=['Group_1', 'Group_2', 'Group_3'])
print(add.Group_1.A) # or add.Group_2.B etc...

Python, pandas concatenate multiple dataframes

1 Answers1