python - Merging dataframes together in a for loop -


i have dictionary of pandas dataframes, each frame contains timestamps , market caps corresponding timestamps, keys of are: 

coins = ['dashcoin','litecoin','dogecoin','nxt']

i create new key in dictionary 'merge' , using pd.merge method merge 4 existing dataframes according timestamp (i want completed rows using 'inner' join method appropriate.

sample of 1 of data frames: 

data2['nxt'].head() out[214]: timestamp   nxt_cap 0   2013-12-04  15091900 1   2013-12-05  14936300 2   2013-12-06  11237100 3   2013-12-07  7031430 4   2013-12-08  6292640

i'm getting result using code: 

data2['merged'] = data2['dogecoin']  coin in coins:     data2['merged'] = pd.merge(left=data2['merged'],right=data2[coin], left_on='timestamp', right_on='timestamp')

but repeats 'dogecoin' in 'merged', if data2['merged'] not = data2['dogecoin'] (or similar data) merge function won't work values non existent in 'merge'

edit: desired result create 1 merged dataframe seen in new element in dictionary 'data2' (data2['merged']), containing merged data frames other elements in data2

try replacing generalized pd.merge() actual named df must begin dataframe @ least first one:

data2['merged'] = data2['dashcoin']  # leave out first element coin in coins[1:]:        data2['merged'] = data2['merged'].merge(data2[coin], on='timestamp')

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