How to add an empty column to a dataframe?

Solution 1:

If I understand correctly, assignment should fill:

>>> import numpy as np
>>> import pandas as pd
>>> df = pd.DataFrame({"A": [1,2,3], "B": [2,3,4]})
>>> df
   A  B
0  1  2
1  2  3
2  3  4
>>> df["C"] = ""
>>> df["D"] = np.nan
>>> df
   A  B C   D
0  1  2   NaN
1  2  3   NaN
2  3  4   NaN

Solution 2:

To add to DSM's answer and building on this associated question, I'd split the approach into two cases:

• Adding a single column: Just assign empty values to the new columns, e.g. df['C'] = np.nan

• Adding multiple columns: I'd suggest using the .reindex(columns=[...]) method of pandas to add the new columns to the dataframe's column index. This also works for adding multiple new rows with .reindex(rows=[...]). Note that newer versions of Pandas (v>0.20) allow you to specify an axis keyword rather than explicitly assigning to columns or rows.

Here is an example adding multiple columns:

mydf = mydf.reindex(columns = mydf.columns.tolist() + ['newcol1','newcol2'])

or

mydf = mydf.reindex(mydf.columns.tolist() + ['newcol1','newcol2'], axis=1)  # version > 0.20.0

You can also always concatenate a new (empty) dataframe to the existing dataframe, but that doesn't feel as pythonic to me :)

Solution 3:

an even simpler solution is:

df = df.reindex(columns = header_list)                

where "header_list" is a list of the headers you want to appear.

any header included in the list that is not found already in the dataframe will be added with blank cells below.

so if

header_list = ['a','b','c', 'd']

then c and d will be added as columns with blank cells

Solution 4:

I like:

df['new'] = pd.Series(dtype='int')

# or use other dtypes like 'float', 'object', ...

If you have an empty dataframe, this solution makes sure that no new row containing only NaN is added.

Specifying dtype is not strictly necessary, however newer Pandas versions produce a DeprecationWarning if not specified.

Solution 5:

Starting with v0.16.0, DF.assign() could be used to assign new columns (single/multiple) to a DF. These columns get inserted in alphabetical order at the end of the DF.

This becomes advantageous compared to simple assignment in cases wherein you want to perform a series of chained operations directly on the returned dataframe.

Consider the same DF sample demonstrated by @DSM:

df = pd.DataFrame({"A": [1,2,3], "B": [2,3,4]})
df
Out[18]:
   A  B
0  1  2
1  2  3
2  3  4

df.assign(C="",D=np.nan)
Out[21]:
   A  B C   D
0  1  2   NaN
1  2  3   NaN
2  3  4   NaN

Note that this returns a copy with all the previous columns along with the newly created ones. In order for the original DF to be modified accordingly, use it like : df = df.assign(...) as it does not support inplace operation currently.