How to Create an Empty Column in Pandas DataFrame
- Create Empty Column Pandas With the Simple Assignment
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pandas.DataFrame.reindex()Method to Add an Empty Column in Pandas -
pandas.DataFrame.assign()to Add an Empty Column in Pandas DataFrame -
pandas.DataFrame.insert()to Add an Empty Column to a DataFrame
We could use reindex(), assign() and insert() methods of DataFrame object to add an empty column to DataFrame in Pandas. We can also directly assign an empty value to the column of DataFrame to create an empty column in Pandas.
Create Empty Column Pandas With the Simple Assignment
We can directly assign columns of DataFrame to empty string, NaN value, or empty Pandas Series to 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. The column Empty_1 is assigned with the empty string, Empty_2 is assigned with NaN values, and Empty_3 is assigned with an empty Pandas Series which also results in NaN values of the entire Empty_3 column.
pandas.DataFrame.reindex() Method to Add an Empty Column in Pandas
We can use pandas.DataFrame.reindex() method to add multiple empty columns to a DataFrame 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})
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
The code creates new columns Empty_1, Empty_2, Empty_3 in df with all NaN values while all the old information is lost.
To add multiple new columns while preserving the initial, we could write code as follows:
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
This adds empty columns Empty_1, Empty_2, and Empty_3 to the df with preserving the initial information.
pandas.DataFrame.assign() to Add an Empty Column in Pandas DataFrame
We can use pandas.DataFrame.assign() method to add an empty column to DataFrame 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 = 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 creates an empty column named Empty_1 and Empty_2 containing only NaN values in the df.
pandas.DataFrame.insert() to Add an Empty Column to a DataFrame
pandas.DataFrame.insert() allows us to insert a column in a DataFrame at specified location. We can use this method to add an empty column to a DataFrame.
Syntax:
DataFrame.insert(loc, column, value, allow_duplicates=False)
It creates a new column with the name column at location loc with default value value. allow_duplicates=False ensures there is only one column with the name column in the dataFrame. If we pass an empty string or NaN value as a value parameter, we can add an empty column to the DataFrame.
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 creates Empty_1 column in df with all empty values at index 3 and Empty_2 at index 4 with all NaN values.
Suraj Joshi is a backend software engineer at Matrice.ai.
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