Instead of appending value as a new column on the same row, pandas adds a new column AND new row

Question

What I have below is an example of the type of the type of concatenation that I am trying to do.

df = pd.DataFrame(np.array([1, 2, 3]).reshape((1, 3)), columns = ['col1', 'col2', 'col3'], index = ['a']) df2 = pd.DataFrame() # already exists elsewhere in code df2 = df2.append([df, pd.Series(1, name = 'label')])

The result I am hoping for is:

 col1 col2 col3 label a 1.0 2.0 3.0 1

but I get is

 col1 col2 col3 0 a 1.0 2.0 3.0 NaN 0 NaN NaN NaN 1.0

I know that I'm joining these wrong, but I cannot seem to figure out how its done. Any advice?

Brandon · Accepted Answer · 2019-11-26 00:50:26Z

This is because the series you are adding has an incompatible index. The original dataframe has ['a'] as the specified index and there is no index specified in the series. If you want to add a new column without specifying an index, the following will give you what you want:

df = pd.DataFrame(np.array([1, 2, 3]).reshape((1, 3)), columns = ['col1', 'col2', 'col3'], index = ['a']) df2 = pd.DataFrame() # already exists elsewhere in code df2 = df2.append([df]) # append the desired dataframe df2['label'] = 1 # add a new column with the value 1 across all rows

print(df2.to_string()) col1 col2 col3 label a 1 2 3 1

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