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
- Import Library
- Load or create a dataframe
- Add column with constant value to dataframe
To understand these above mentioned steps, lets discuss some examples :
Example 1: (By using Pandas Series)
Python3
# import packagesimport pandas as pdimport numpy as np # create dataframedf = 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 dataframeprint("Initial dataframe")display(df) # adding column with constant valuedf['Power 0'] = pd.Series([1 for x in range(len(df.index))]) # view dataframeprint("Final dataframe")display(df) |
Output :
Example 2: (As static value)
Python3
# import packagesimport pandas as pdimport numpy as np # create dataframedf = 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 dataframeprint("Initial dataframe")display(df) # adding column with constant valuedf['Pass'] = True # view dataframeprint("Final dataframe")display(df) |
Output :
