Add data series content in another data series

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Is there a way to concatenate the information of two data series in pandas? Not append or merge the information of two data frames, but actually combine the content of each data series in a new data series.

Example:

ColumnA (Item type) Row 2 = 1 (float64)
ColumnB (Item number) Row 2 = 1 (float64)
ColumnC (Registration Date) Row 2 = 04/07/2018 (43285) (datetime64[ns])

In excel I would concatenate the rows in Column A, B, C and have the number in each column combined, using the formula =concat(A2, B2, C2) The result would be 1143285 in another cell D2, for example.

Is there a way for me to do that in Pandas? I could only find ways to join, combined or append the series in the data frame but not in the series itself.

pandas concatenation python
1 Answers

You can use that

df['D'] = df.apply(lambda row : str(row['A'])+
                     str(row['B']) + str(row['C']), axis = 1)

Following your example it would be

import pandas as pd

d = {'A': [1],'B':[1],'C':[43285]}
df = pd.DataFrame(data=d)
df['D'] = df.apply(lambda row : str(row['A'])+
                     str(row['B']) + str(row['C']), axis = 1)

Output:

   A  B      C        D
0  1  1  43285  1143285
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