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We can add an empty column to a DataFrame in Pandas using the reindex(), , assign()and insert()methods of the DataFrame object. We can also directly assign a null value to the column of the DataFrame to create an empty column in Pandas.

Creating Empty Pandas Columns with Simple Assignment

We can directly assign a column of DataFrame to an empty string, NaNvalue or empty Pandas Seriesto create an empty column in Pandas.

import pandas as pd
import numpy as np

dates = ["April-20", "April-21", "April-22", "April-23", "April-24", "April-25"]
income = [10, 20, 10, 15, 10, 12]
expenses = [3, 8, 4, 5, 6, 10]

df = pd.DataFrame({"Date": dates, "Income": income, "Expenses": expenses})

df["Empty_1"] = ""
df["Empty_2"] = np.nan
df["Empty_3"] = pd.Series()

print(df)

Output:

       Date  Income  Expenses Empty_1  Empty_2  Empty_3
0  April-20      10         3              NaN      NaN
1  April-21      20         8              NaN      NaN
2  April-22      10         4              NaN      NaN
3  April-23      15         5              NaN      NaN
4  April-24      10         6              NaN      NaN
5  April-25      12        10              NaN      NaN

It creates three empty columns in df. Empty_1The column is assigned an empty string, is Empty_2assigned a NaN value, and is Empty_3assigned an empty Pandas Serieswhich will also cause the entire Empty_3to be NaN.

pandas.DataFrame.reindex()Adding an empty column in Pandas using the

We can add multiple empty columns to the DataFrame in Pandas using pandas.DataFrame.reindex() method.

import pandas as pd
import numpy as np

dates = ["April-20", "April-21", "April-22", "April-23", "April-24", "April-25"]
income = [10, 20, 10, 15, 10, 12]
expenses = [3, 8, 4, 5, 6, 10]

df = pd.DataFrame({"Date": dates, "Income": income, "Expenses": expenses})

column_names = ["Empty_1", "Empty_2", "Empty_3"]

df = df.reindex(columns=column_names)
print(df)

Output:

   Empty_1  Empty_2  Empty_3
0      NaN      NaN      NaN
1      NaN      NaN      NaN
2      NaN      NaN      NaN
3      NaN      NaN      NaN
4      NaN      NaN      NaN
5      NaN      NaN      NaN

Empty_1This code creates new columns , Empty_2, , with all NaN values ​​in df, Empty_3and all the old information is lost.

To add multiple new columns while preserving the initial columns, we can write code like this:

import pandas as pd
import numpy as np

dates = ["April-20", "April-21", "April-22", "April-23", "April-24", "April-25"]
income = [10, 20, 10, 15, 10, 12]
expenses = [3, 8, 4, 5, 6, 10]

df = pd.DataFrame({"Date": dates, "Income": income, "Expenses": expenses})

df = df.reindex(columns=df.columns.tolist() + ["Empty_1", "Empty_2", "Empty_3"])
print(df)

Output:

       Date  Income  Expenses  Empty_1  Empty_2  Empty_3
0  April-20      10         3      NaN      NaN      NaN
1  April-21      20         8      NaN      NaN      NaN
2  April-22      10         4      NaN      NaN      NaN
3  April-23      15         5      NaN      NaN      NaN
4  April-24      10         6      NaN      NaN      NaN
5  April-25      12        10      NaN      NaN      NaN

Empty_1This will add empty columns , Empty_2and, to df while preserving the initial information Empty_3.

pandas.DataFrame.assign()Adding an Empty Column to a Pandas DataFrame

We can add an empty column to the DataFrame in Pandas using pandas.DataFrame.assign() method.

import pandas as pd
import numpy as np

dates = ["April-20", "April-21", "April-22", "April-23", "April-24", "April-25"]
income = [10, 20, 10, 15, 10, 12]
expenses = [3, 8, 4, 5, 6, 10]

df = pd.DataFrame({"Date": dates, "Income": income, "Expenses": expenses})

df = df.assign(Empty_1="", Empty_2=np.nan)
print(df)

Output:

       Date  Income  Expenses Empty_1  Empty_2
0  April-20      10         3              NaN
1  April-21      20         8              NaN
2  April-22      10         4              NaN
3  April-23      15         5              NaN
4  April-24      10         6              NaN
5  April-25      12        10              NaN

It will create an empty column named and containing only Empty_1NaN values ​​in .Empty_2df

pandas.DataFrame.insert()Adding empty columns to a DataFrame

pandas.DataFrame.insert() allows us to insert a column into a DataFrame at a specified position. We can use this method to DataFrameadd an empty column to .

grammar:

DataFrame.insert(loc, column, value, allow_duplicates=False)

It creates a new column locwith name at position with default value . It ensures that there is only one column with name in . We can add an empty column to DataFrame if we pass an empty string or value as value argument.columnvalueallow_duplicates = FalseDataFramecolumnNaN

import pandas as pd
import numpy as np

dates = ["April-20", "April-21", "April-22", "April-23", "April-24", "April-25"]
income = [10, 20, 10, 15, 10, 12]
expenses = [3, 8, 4, 5, 6, 10]

df = pd.DataFrame({"Date": dates, "Income": income, "Expenses": expenses})
df.insert(3, "Empty_1", "")
df.insert(4, "Empty_2", np.nan)
print(df)

Output:

       Date  Income  Expenses Empty_1  Empty_2
0  April-20      10         3              NaN
1  April-21      20         8              NaN
2  April-22      10         4              NaN
3  April-23      15         5              NaN
4  April-24      10         6              NaN
5  April-25      12        10              NaN

It dfcreates Empty_1a column in 3with all null values ​​at index and 4creates a with all NaNvalues ​​at index Empty_2.