How to Create A New Column In Pandas Using Special Condition?

To create a new column in pandas using a special condition, you can use the np.where() function along with the apply() method. First, define the condition that you want to apply to the DataFrame. Then, use the np.where() function to apply the condition to each row in the DataFrame and create the new column based on the condition. Finally, assign the result to a new column in the DataFrame using the apply() method. This will create a new column in the DataFrame with values based on the special condition you specified.

Best Python Books of November 2024

1

Rating is 5 out of 5

Learning Python, 5th Edition

Get Book

2

Rating is 4.9 out of 5

Head First Python: A Brain-Friendly Guide

Get Book

3

Rating is 4.8 out of 5

Python for Beginners: 2 Books in 1: Python Programming for Beginners, Python Workbook

Get Book

4

Rating is 4.7 out of 5

Python All-in-One For Dummies (For Dummies (Computer/Tech))

Get Book

5

Rating is 4.6 out of 5

Python for Everybody: Exploring Data in Python 3

Get Book

6

Rating is 4.5 out of 5

Learn Python Programming: The no-nonsense, beginner's guide to programming, data science, and web development with Python 3.7, 2nd Edition

Get Book

7

Rating is 4.4 out of 5

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

Get Book

What is the limitation of using lambda functions in creating a new column in pandas?

One limitation of using lambda functions in creating a new column in pandas is that lambda functions are limited in terms of complexity and flexibility compared to defining a regular function. Lambda functions are typically used for simple operations and can become difficult to read and understand for more complex operations. Additionally, lambda functions do not support multiple expressions or statements, making them less suitable for more intricate manipulations of DataFrame columns.

How to create a new column using an existing column in pandas?

You can create a new column in a pandas DataFrame by accessing the existing column and performing operations on it. Here's an example:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10

import pandas as pd

# Create a sample DataFrame
data = {'A': [1, 2, 3, 4, 5]}
df = pd.DataFrame(data)

# Create a new column 'B' by adding 10 to column 'A'
df['B'] = df['A'] + 10

print(df)

This will output:

1
2
3
4
5
6

   A   B
0  1  11
1  2  12
2  3  13
3  4  14
4  5  15

In this example, we create a new column 'B' by adding 10 to each value in column 'A'. You can perform different operations on the existing column to create the new column as per your requirements.

How to create a new column with datetime values in pandas?

You can create a new column with datetime values in pandas by using the following steps:

1. Import the pandas library:

1

import pandas as pd

1. Create a DataFrame with your desired data:

1
2

data = {'date': ['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04']}
df = pd.DataFrame(data)

1. Convert the 'date' column to datetime format:

1

df['date'] = pd.to_datetime(df['date'])

1. Create a new column with datetime values:

1

df['new_date'] = pd.to_datetime('2021-01-01') + pd.to_timedelta(df.index, unit='D')

Now you have a new column named 'new_date' with datetime values in your pandas DataFrame.