Add column with constant value to pandas dataframe

Prerequisite: Pandas 

In this article, we will learn how to add a new column with constant value to a Pandas DataFrame. Before that one must be familiar with the following concepts:

• Pandas DataFrame : Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular arrangement with labeled axes (rows and columns). A Data frame may be a two-dimensional arrangement , i.e., data is aligned during a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns.
• Column in DataFrame : In Order to pick a column in Pandas DataFrame, we will either access the columns by calling them by their columns name. Column Addition: so as to feature a column in Pandas DataFrame, we will declare a replacement list as a column and increase a existing Dataframe.
• Constant : A fixed value. In Algebra, a continuing may be a number on its own, or sometimes a letter like a, b or c to face for a hard and fast number. Example: in "x + 5 = 9", 5 and 9 are constants.

Approach

1. Import Library
2. Load or create a dataframe
3. Add column with constant value to dataframe

To understand these above mentioned steps, lets discuss some examples :

Example 1: (By using Pandas Series)

# import packages
import pandas as pd
import numpy as np

# create dataframe
df = pd.DataFrame({'Number': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5},
                   'Power 2': {0: 1, 1: 4, 2: 9, 3: 16, 4: 25},
                   'Power 3': {0: 1, 1: 8, 2: 27, 3: 64, 4: 125}})

# view dataframe
print("Initial dataframe")
display(df)


# adding column with constant value
df['Power 0'] = pd.Series([1 for x in range(len(df.index))])

# view dataframe
print("Final dataframe")
display(df)

Output :

Example 2: (As static value)

# import packages
import pandas as pd
import numpy as np

# create dataframe
df = pd.DataFrame({'Name': {0: 'Ram', 1: 'Deep', 2: 'Yash', 3: 'Aman', 4: 'Akash'},
                   'Marks': {0: 68, 1: 87, 2: 45, 3: 78, 4: 56}})


# view dataframe
print("Initial dataframe")
display(df)


# adding column with constant value
df['Pass'] = True

# view dataframe
print("Final dataframe")
display(df)

Output :