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How do I add multiple empty columns to a DataFrame from a list?
I can do:
df["B"] = None df["C"] = None df["D"] = None But I can't do:
df[["B", "C", "D"]] = None
KeyError: "['B' 'C' 'D'] not in index"
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There are multiple ways to add a new empty/blank column (single or multiple columns) to a pandas DataFrame by using assign operator, assign() , insert() and apply() methods. By using these you can add one or multiple empty columns with either NaN , None , Blank or Empty string values to all cells.
Add Empty Column to Pandas You can add an empty column to the pandas dataframe using the = operator and assign null values to the column. What is this? An empty column will be added at the end of the dataframe with the column header Empty_Column. You can also add a column with nan values.
First, create an empty dataframe using pd. Dataframe() . Next, you can append a column to the created dataframe using the insert() method. To know more about other methods available to add columns to the dataframe, refer to add column to dataframe tutorial.
You could use df.reindex to add new columns:
In [18]: df = pd.DataFrame(np.random.randint(10, size=(5,1)), columns=['A']) In [19]: df Out[19]: A 0 4 1 7 2 0 3 7 4 6 In [20]: df.reindex(columns=list('ABCD')) Out[20]: A B C D 0 4 NaN NaN NaN 1 7 NaN NaN NaN 2 0 NaN NaN NaN 3 7 NaN NaN NaN 4 6 NaN NaN NaN reindex will return a new DataFrame, with columns appearing in the order they are listed:
In [31]: df.reindex(columns=list('DCBA')) Out[31]: D C B A 0 NaN NaN NaN 4 1 NaN NaN NaN 7 2 NaN NaN NaN 0 3 NaN NaN NaN 7 4 NaN NaN NaN 6 The reindex method as a fill_value parameter as well:
In [22]: df.reindex(columns=list('ABCD'), fill_value=0) Out[22]: A B C D 0 4 0 0 0 1 7 0 0 0 2 0 0 0 0 3 7 0 0 0 4 6 0 0 0 I'd concat using a DataFrame:
In [23]: df = pd.DataFrame(columns=['A']) df Out[23]: Empty DataFrame Columns: [A] Index: [] In [24]: pd.concat([df,pd.DataFrame(columns=list('BCD'))]) Out[24]: Empty DataFrame Columns: [A, B, C, D] Index: [] So by passing a list containing your original df, and a new one with the columns you wish to add, this will return a new df with the additional columns.
Caveat: See the discussion of performance in the other answers and/or the comment discussions. reindex may be preferable where performance is critical.
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