How to merge pandas DataFrame with same column name?

Question

The index is a timestamp and column name, and also the ability to replace NaN to value. It does not seem to be working.

sample:

import pandas as pd

times = pd.to_datetime(pd.Series(['2014-07-4',
'2014-07-15','2014-08-24','2014-08-25','2014-09-10','2014-09-17']))
valuea = [0.01, 0.02, -0.03, 0.4 ,0.5,np.NaN]

times2 = pd.to_datetime(pd.Series(['2014-07-6',
'2014-07-16','2014-08-27','2014-09-5','2014-09-11','2014-09-17']))
valuea2 = [1, 2, 3, 4,5,-6]


df1 = pd.DataFrame({'value A': valuea}, index=times)
df2 = pd.DataFrame({'value A': valuea2}, index=times2)

df3=pd.merge(df1,df2, left_index=True, right_index=True)
df3.head()

Do you get any error(s)? Try replacing np.Nan to np.NaN

harpan
– harpan

2018-06-12 19:42:25 +00:00
Commented Jun 12, 2018 at 19:42 — harpan
– harpan, Commented Jun 12, 2018 at 19:42
sorry mistype...

Rami Ramich
– Rami Ramich

2018-06-12 19:56:44 +00:00
Commented Jun 12, 2018 at 19:56 — Rami Ramich
– Rami Ramich, Commented Jun 12, 2018 at 19:56
May be you need pd.concat([df1, df2]) ?

harpan
– harpan

2018-06-12 20:02:28 +00:00
Commented Jun 12, 2018 at 20:02 — harpan
– harpan, Commented Jun 12, 2018 at 20:02

BENY · Accepted Answer · 2018-06-12 20:02:39Z

1

Assuming you need outer join

pd.concat([df1,df2],axis=1)
Out[321]: 
            value A  value A
2014-07-04     0.01      NaN
2014-07-06      NaN      1.0
2014-07-15     0.02      NaN
2014-07-16      NaN      2.0
2014-08-24    -0.03      NaN
2014-08-25     0.40      NaN
2014-08-27      NaN      3.0
2014-09-05      NaN      4.0
2014-09-10     0.50      NaN
2014-09-11      NaN      5.0
2014-09-17      NaN     -6.0

Update

df1.combine_first(df2)
Out[324]: 
            value A
2014-07-04     0.01
2014-07-06     1.00
2014-07-15     0.02
2014-07-16     2.00
2014-08-24    -0.03
2014-08-25     0.40
2014-08-27     3.00
2014-09-05     4.00
2014-09-10     0.50
2014-09-11     5.00
2014-09-17    -6.00

edited Jun 12, 2018 at 20:02

answered Jun 12, 2018 at 19:43

BENY

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3 Comments

harpan Over a year ago

pd.concat([df1, df2]) would do.

BENY Over a year ago

@RamiRamich that is combine_first

harpan Over a year ago

@Wen, I am not sure if I understand OP's question fully but yes, you are right. pd.concat([df1, df2]).dropna() 's equivavelnt would be df1.combine_first(df2). After all OP did mention about NaN values. It is not clear if OP wants to remove it or keep it and then use .fillna()

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