In pandas you can add a new constant column with a literal value to DataFrame using assign() method, this method returns a new Dataframe after adding a column. insert() is also used to update the existing DataFrame with a new constant column. In this article, I will explain several ways of how to add a new column with a constant value to pandas DataFrame with examples.
1. Quick Examples of Add Constant Column to Pandas DataFrame
If you are in a hurry, below are some quick examples of how to add a constant column value to pandas DataFrame.
# Below are quick example
# Adding new column with a constant value
df["Discount_Percentage"] = 10
# Using DataFrame.insert() to add column constant value
df.insert(1, 'Discount_Percentage', '10')
# Add a constant number to each column elements
df['Discount'] = df['Discount'] + 150
# Using DataFrame.apply() and lambda function
df['Discount_Percentage'] = df.apply(lambda x: 10, axis=1)
# Using DataFrame.assign() to add constant column
df2 = df.assign(Discount_Percentage=10)
# Add multiple constant columns
data = {'Discount_Percentage': 10, 'Advance': 1000}
df2 = df.assign(**data)
# Using pandas series
df['Discount_Percentage'] = pd.Series([10 for x in range(len(df.index))])
Now, let’s create a DataFrame with a few rows and columns, execute these examples and validate results. Our DataFrame contains column names Courses, Fee, Duration, and Discount.
import pandas as pd
technologies = {
'Courses':["Spark","PySpark","Python","pandas"],
'Fee' :[20000,25000,22000,30000],
'Duration':['30days','40days','35days','50days'],
'Discount':[1000,2300,1200,2000]
}
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount
r1 Spark 20000 30days 1000
r2 PySpark 25000 40days 2300
r3 Python 22000 35days 1200
r4 pandas 30000 50days 2000
2. Pandas Add Column with Constant Value to DataFrame
You have an existing DataFrame where you need to add an additional column with the same constant value for every row. df["Discount_Percentage"]=10 will add the “Discount_Percentage” column and set every row with a constant value 10.
# Adding new column with a constant value
df["Discount_Percentage"] = 10
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount Discount_Percentage
0 Spark 20000 30days 1000 10
1 PySpark 25000 40days 2300 10
2 Python 22000 35days 1200 10
3 pandas 30000 50days 2000 10
3. Using DataFrame.insert() to Add Column Constant Value
DataFrame.insert() method is used to add a new column to DataFrame at any position of the existing DataFrame. Using this you can specify the index where you would like to add a column. The below example adds a constant column at the second position (Index 1). Note that in pandas, the Index starts from zero.
insert() method updates the existing DataFrame object with the new column.
# Using DataFrame.insert() to add column constant value
df = pd.DataFrame(technologies,index=index_labels)
df.insert(1, 'Discount_Percentage', '10')
print(df)
Yields below output.
# Output:
Courses Discount_Percentage Fee Duration Discount
0 Spark 10 20000 30days 1000
1 PySpark 10 25000 40days 2300
2 Python 10 22000 35days 1200
3 pandas 10 30000 50days 2000
4. Add a Constant Number to Each Column Elements
You can use df['Discount']=df['Discount']+150 method to add 150 to Discount column. Using this approach you can add a value to inter columns or append a constant string to String columns.
# Add a constant number to each column elements
df = pd.DataFrame(technologies,index=index_labels)
df['Discount'] = df['Discount'] + 150
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount
r1 Spark 20000 30days 1150
r2 PySpark 25000 40days 2450
r3 Python 22000 35days 1350
r4 pandas 30000 50days 2150
5. Using DataFrame.apply() and Lambda Function to Add Column Constant Value
Use DataFrame.apply() and lambda to create Discount_Percentage column with constant value 10.
# Using DataFrame.apply() and lambda function
df = pd.DataFrame(technologies,index=index_labels)
df['Discount_Percentage'] = df.apply(lambda x: 10, axis=1)
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount Discount_Percentage
0 Spark 20000 30days 1000 10
1 PySpark 25000 40days 2300 10
2 Python 22000 35days 1200 10
3 pandas 30000 50days 2000 10
6. Using DataFrame.assign() to Add Constant Column
DataFrame.assign() is also used to add a constant column to the pandas DataFrame, this method returns a new DataFrame after adding a "Discount_Percentage" column to the existing DataFrame.
# Using DataFrame.assign() to add constant column
df = pd.DataFrame(technologies,index=index_labels)
df2 = df.assign(Discount_Percentage=10)
print(df2)
Yields same output as above.
7. Add Multiple Constant Columns Using DataFrame.assign()
You can also use DataFrame.assign() method to add multiple constant columns to the pandas DataFrame. If you need to assign multiple columns with different values, you should use assign with a dictionary.
# Add multiple constant columns
data = {'Discount_Percentage': 10, 'Advance': 1000}
df2 = df.assign(**data)
print(df2)
Yields below output.
# Output:
Courses Fee Duration Discount Discount_Percentage Advance
0 Spark 20000 30days 1000 10 1000
1 PySpark 25000 40days 2300 10 1000
2 Python 22000 35days 1200 10 1000
3 pandas 30000 50days 2000 10 1000
8. Using Pandas Series
# Using pandas series
df = pd.DataFrame(technologies)
df['Discount_Percentage'] = pd.Series([10 for x in range(len(df.index))])
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount Discount_Percentage
0 Spark 20000 30days 1000 10
1 PySpark 25000 40days 2300 10
2 Python 22000 35days 1200 10
3 pandas 30000 50days 2000 10
9. Complete Example For Add Constant Column to DataFrame
import pandas as pd
technologies = {
'Courses':["Spark","PySpark","Python","pandas"],
'Fee' :[20000,25000,22000,30000],
'Duration':['30days','40days','35days','50days'],
'Discount':[1000,2300,1200,2000]
}
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print(df)
# Adding new column with a constant value
df["Discount_Percentage"] = 10
print(df)
# Using DataFrame.insert() to add column constant value
df = pd.DataFrame(technologies,index=index_labels)
df.insert(1, 'Discount_Percentage', '10')
print(df)
# Add a constant number to each column elements
df = pd.DataFrame(technologies,index=index_labels)
df['Discount'] = df['Discount'] + 150
print(df)
# Using DataFrame.apply() and lambda function
df = pd.DataFrame(technologies,index=index_labels)
df['Discount_Percentage'] = df.apply(lambda x: 10, axis=1)
print(df)
# Using DataFrame.assign() to add constant column
df = pd.DataFrame(technologies,index=index_labels)
df2 = df.assign(Discount_Percentage=10)
print(df2)
# Add multiple constant columns
df = pd.DataFrame(technologies,index=index_labels)
data = {'Discount_Percentage': 10, 'Advance': 1000}
df2 = df.assign(**data)
print(df2)
# Using pandas series
df['Discount_Percentage'] = pd.Series([10 for x in range(len(df.index))])
print(df)
Conclusion
In this article, you have learned how to add a constant column to pandas DataFrame by using DataFrame.assing(), DataFrame.insert(), pandas.Series(), DataFrame.apply() and Lambda function with examples.
Happy Learning !!
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