I have a dictionary of pandas dataframes, each frame contains timestamps and market caps corresponding to the timestamps, the keys of which are:
coins = ['dashcoin','litecoin','dogecoin','nxt']
I would like to create a new key in the dictionary 'merge' and using the pd.merge method merge the 4 existing dataframes according to their timestamp (I want completed rows so using 'inner' join method will be appropriate.
Sample of one of the 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 currently getting a result using this code:
data2['merged'] = data2['dogecoin']
for coin in coins:
data2['merged'] = pd.merge(left=data2['merged'],right=data2[coin], left_on='timestamp', right_on='timestamp')
but this repeats 'dogecoin' in 'merged', however if data2['merged']
is not = data2['dogecoin']
(or some similar data) then the merge function won't work as the values are non existent in 'merge'
EDIT: my desired result is create one merged dataframe seen in a new element in dictionary 'data2' (data2['merged']), containing the merged data frames from the other elements in data2