import pandas as pd
my_data = {
"names": ["Shivam", "John", "Prateek", "Gaurav"],
"profession": ["Engineer", "Carpenter", "Writer", "marketer"]
}
df = pd.DataFrame(my_data)
# Add new column with rows data
df['address'] = ['India', 'New York', 'India', 'India']
print(df)
Output
names profession address
0 Shivam Engineer India
1 John Carpenter New York
2 Prateek Writer India
3 Gaurav marketer India
Syntax
df['COLUMN_NAME'] = ['Value 1', 'Value 2', ...]
In the above code example,
1. Import pandas library in your Python project file.
import pandas as pd
2. Created a Pandas Dataframe df with columns and rows.
3. Add a new column 'address' to this Dataframe df and print the new Dataframe.
df['address'] = ['India', 'New York', 'India', 'India']
We can use the insert() function of Pandas Dataframe to add a new column to an existing Dataframe. We can define position column names, values and allow duplicated while adding the new column with values.
Syntax
DataFrame.insert(location, column_name, values, allow_duplicates = False)
Code Example
import pandas as pd
my_data = {
"names": ["Shivam", "John", "Prateek", "Gaurav"],
"profession": ["Engineer", "Carpenter", "Writer", "marketer"]
}
df = pd.DataFrame(my_data)
# Add new column with rows data
df.insert(1, 'address', ['India', 'New York', 'India', 'India'], True)
print(df)
Output
names address profession
0 Shivam India Engineer
1 John New York Carpenter
2 Prateek India Writer
3 Gaurav India marketer
Added new column 'address' using DataFrame.insert() function at position 1(locations starts from 0).
import pandas as pd
my_data = {
"names": ["Shivam", "John", "Prateek", "Gaurav"],
"profession": ["Engineer", "Carpenter", "Writer", "marketer"]
}
# Create dataframe
df = pd.DataFrame(my_data)
# Add new column - department_id
df = df.assign(department_id = ['E01', 'C02', 'W03', 'M04'])
print(df)
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