Add a New Column to Existing DataFrame With Default Value in Pandas
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pandas.DataFrame.assign()to Add a New Column in Pandas DataFrame - Access the New Column to Set It With a Default Value
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pandas.DataFrame.insert()to Add a New Column in Pandas DataFrame
We could use assign() and insert() methods of DataFrame objects to add a new column to the existing DataFrame with default values. We can also directly assign a default value to the column of DataFrame to be created.
We will use the below dataframe as an example in the following sections.
import pandas as pd
dates=['April-10', 'April-11', 'April-12', 'April-13']
fruits=['Apple', 'Papaya', 'Banana', 'Mango']
prices=[3, 1, 2, 4]
df = pd.DataFrame({'Date':dates ,
'Fruit':fruits ,
'Price': prices})
print(df)
Output:
Date Fruit Price
0 April-10 Apple 3
1 April-11 Papaya 1
2 April-12 Banana 2
3 April-13 Mango 4
pandas.DataFrame.assign() to Add a New Column in Pandas DataFrame
We can use pandas.DataFrame.assign() method to add a new column to the existing DataFrame and assign the newly created DataFrame column with default values.
import pandas as pd
dates=['April-10', 'April-11', 'April-12', 'April-13']
fruits=['Apple', 'Papaya', 'Banana', 'Mango']
prices=[3, 1, 2, 4]
df = pd.DataFrame({'Date':dates ,
'Fruit':fruits ,
'Price': prices})
new_df=df.assign(Profit=6)
print(new_df)
Output:
Date Fruit Price Profit
0 April-10 Apple 3 6
1 April-11 Papaya 1 6
2 April-12 Banana 2 6
3 April-13 Mango 4 6
The code creates a new column Profit in the DataFrame and sets the entire column’s values to be 6.
Access the New Column to Set It With a Default Value
We can use DataFrame indexing to create a new column in DataFrame and set it to default values.
Syntax:
df[col_name]=value
It creates a new column col_name in DataFrame df and sets the default value for the entire column to value.
import pandas as pd
dates=['April-10', 'April-11', 'April-12', 'April-13']
fruits=['Apple', 'Papaya', 'Banana', 'Mango']
prices=[3, 1, 2, 4]
df = pd.DataFrame({'Date':dates ,
'Fruit':fruits ,
'Price': prices})
df['Profit']=5
print(df)
Output:
Date Fruit Price Profit
0 April-10 Apple 3 5
1 April-11 Papaya 1 5
2 April-12 Banana 2 5
3 April-13 Mango 4 5
pandas.DataFrame.insert() to Add a New Column in Pandas DataFrame
pandas.DataFrame.insert() allows us to insert a column in a DataFrame at specified location.
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.
import pandas as pd
dates=['April-10', 'April-11', 'April-12', 'April-13']
fruits=['Apple', 'Papaya', 'Banana', 'Mango']
prices=[3, 1, 2, 4]
df = pd.DataFrame({'Date':dates ,
'Fruit':fruits ,
'Price': prices})
df.insert(2, "profit", 4, allow_duplicates=False)
print(df
Output:
Date Fruit profit Price
0 April-10 Apple 4 3
1 April-11 Papaya 4 1
2 April-12 Banana 4 2
3 April-13 Mango 4 4
Here, a column with the name profit is inserted at index 2 with the default value 4.
Suraj Joshi is a backend software engineer at Matrice.ai.
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