pd.concat requires that the indices be unique. To remove rows with duplicate indices, use
df = df.loc[~df.index.duplicated(keep='first')]
import pandas as pd
from pandas import Timestamp
df1 = pd.DataFrame(
{'price': [0.7286, 0.7286, 0.7286, 0.7286],
'side': [2, 2, 2, 2],
'timestamp': [1451865675631331, 1451865675631400,
1451865675631861, 1451865675631866]},
index=pd.DatetimeIndex(['2000-1-1', '2000-1-1', '2001-1-1', '2002-1-1']))
df2 = pd.DataFrame(
{'bid': [0.7284, 0.7284, 0.7284, 0.7285, 0.7285],
'bid_size': [4000000, 4000000, 5000000, 1000000, 4000000],
'offer': [0.7285, 0.729, 0.7286, 0.7286, 0.729],
'offer_size': [1000000, 4000000, 4000000, 4000000, 4000000]},
index=pd.DatetimeIndex(['2000-1-1', '2001-1-1', '2002-1-1', '2003-1-1', '2004-1-1']))
df1 = df1.loc[~df1.index.duplicated(keep='first')]
# price side timestamp
# 2000-01-01 0.7286 2 1451865675631331
# 2001-01-01 0.7286 2 1451865675631861
# 2002-01-01 0.7286 2 1451865675631866
df2 = df2.loc[~df2.index.duplicated(keep='first')]
# bid bid_size offer offer_size
# 2000-01-01 0.7284 4000000 0.7285 1000000
# 2001-01-01 0.7284 4000000 0.7290 4000000
# 2002-01-01 0.7284 5000000 0.7286 4000000
# 2003-01-01 0.7285 1000000 0.7286 4000000
# 2004-01-01 0.7285 4000000 0.7290 4000000
result = pd.concat([df1, df2], axis=0)
print(result)
bid bid_size offer offer_size price side timestamp
2000-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2001-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2002-01-01 NaN NaN NaN NaN 0.7286 2 1.451866e+15
2000-01-01 0.7284 4000000 0.7285 1000000 NaN NaN NaN
2001-01-01 0.7284 4000000 0.7290 4000000 NaN NaN NaN
2002-01-01 0.7284 5000000 0.7286 4000000 NaN NaN NaN
2003-01-01 0.7285 1000000 0.7286 4000000 NaN NaN NaN
2004-01-01 0.7285 4000000 0.7290 4000000 NaN NaN NaN
Note there is also pd.join, which can join DataFrames based on their indices, and handle non-unique indices based on the how parameter. Rows with duplicate index are not removed.
In [94]: df1.join(df2)
Out[94]:
price side timestamp bid bid_size offer \
2000-01-01 0.7286 2 1451865675631331 0.7284 4000000 0.7285
2000-01-01 0.7286 2 1451865675631400 0.7284 4000000 0.7285
2001-01-01 0.7286 2 1451865675631861 0.7284 4000000 0.7290
2002-01-01 0.7286 2 1451865675631866 0.7284 5000000 0.7286
offer_size
2000-01-01 1000000
2000-01-01 1000000
2001-01-01 4000000
2002-01-01 4000000
In [95]: df1.join(df2, how='outer')
Out[95]:
price side timestamp bid bid_size offer offer_size
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2000-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7285 1000000
2001-01-01 0.7286 2 1.451866e+15 0.7284 4000000 0.7290 4000000
2002-01-01 0.7286 2 1.451866e+15 0.7284 5000000 0.7286 4000000
2003-01-01 NaN NaN NaN 0.7285 1000000 0.7286 4000000
2004-01-01 NaN NaN NaN 0.7285 4000000 0.7290 4000000