Table of Contents
Creating Multiple New Columns from a Function Using Pandas
Original Approach: Assigning to Index Range
Iterable Solution
Using zip()
Improved DataFrame Methods
Home Backend Development Python Tutorial How to Efficiently Create Multiple New Columns from a Function in Pandas?

How to Efficiently Create Multiple New Columns from a Function in Pandas?

Oct 28, 2024 pm 08:58 PM

How to Efficiently Create Multiple New Columns from a Function in Pandas?

Creating Multiple New Columns from a Function Using Pandas

In Pandas, you can encounter situations where you need to create multiple new columns based on a custom function applied to an existing column. The task may seem straightforward, but unexpected challenges can arise due to the expected return type of the function.

Original Approach: Assigning to Index Range

Initially, you might attempt to assign the output of a function directly to a range of indices in a DataFrame using the df.ix[: ,10:16] = df.textcol.map(extract_text_features) syntax. However, this approach can often result in errors due to the incompatible return type of the function.

Iterable Solution

One potential solution is to iterate over each row of the DataFrame using df.iterrows(). This method allows you to apply the function to each row individually and capture the results as a tuple. However, this approach can be significantly slower than other options.

Using zip()

A more efficient and flexible approach is to use the zip() function in conjunction with map() to create the new columns. The zip() function combines the output of the function into a tuple, which can then be unpacked into individual columns. For instance, the following code demonstrates how to create six new columns using the zip() method:

<code class="python">df['p1'], df['p2'], df['p3'], df['p4'], df['p5'], df['p6'] = zip(*df['num'].map(powers))</code>

Improved DataFrame Methods

Recent updates to Pandas have introduced more convenient methods for applying functions to columns and creating new columns. For instance, the df.apply() method allows you to specify the output format (DataFrame, Series, or list) and handle additional parameters. Additionally, the df.assign() method enables you to create new columns directly without explicitly assigning the output. These newer methods provide more flexibility and efficiency in creating multiple new columns based on a function.

The above is the detailed content of How to Efficiently Create Multiple New Columns from a Function in Pandas?. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undress AI Tool

Undress AI Tool

Undress images for free

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Hot Topics

PHP Tutorial
1503
276
How to handle API authentication in Python How to handle API authentication in Python Jul 13, 2025 am 02:22 AM

The key to dealing with API authentication is to understand and use the authentication method correctly. 1. APIKey is the simplest authentication method, usually placed in the request header or URL parameters; 2. BasicAuth uses username and password for Base64 encoding transmission, which is suitable for internal systems; 3. OAuth2 needs to obtain the token first through client_id and client_secret, and then bring the BearerToken in the request header; 4. In order to deal with the token expiration, the token management class can be encapsulated and automatically refreshed the token; in short, selecting the appropriate method according to the document and safely storing the key information is the key.

How to iterate over two lists at once Python How to iterate over two lists at once Python Jul 09, 2025 am 01:13 AM

A common method to traverse two lists simultaneously in Python is to use the zip() function, which will pair multiple lists in order and be the shortest; if the list length is inconsistent, you can use itertools.zip_longest() to be the longest and fill in the missing values; combined with enumerate(), you can get the index at the same time. 1.zip() is concise and practical, suitable for paired data iteration; 2.zip_longest() can fill in the default value when dealing with inconsistent lengths; 3.enumerate(zip()) can obtain indexes during traversal, meeting the needs of a variety of complex scenarios.

Python FastAPI tutorial Python FastAPI tutorial Jul 12, 2025 am 02:42 AM

To create modern and efficient APIs using Python, FastAPI is recommended; it is based on standard Python type prompts and can automatically generate documents, with excellent performance. After installing FastAPI and ASGI server uvicorn, you can write interface code. By defining routes, writing processing functions, and returning data, APIs can be quickly built. FastAPI supports a variety of HTTP methods and provides automatically generated SwaggerUI and ReDoc documentation systems. URL parameters can be captured through path definition, while query parameters can be implemented by setting default values ​​for function parameters. The rational use of Pydantic models can help improve development efficiency and accuracy.

How to test an API with Python How to test an API with Python Jul 12, 2025 am 02:47 AM

To test the API, you need to use Python's Requests library. The steps are to install the library, send requests, verify responses, set timeouts and retry. First, install the library through pipinstallrequests; then use requests.get() or requests.post() and other methods to send GET or POST requests; then check response.status_code and response.json() to ensure that the return result is in compliance with expectations; finally, add timeout parameters to set the timeout time, and combine the retrying library to achieve automatic retry to enhance stability.

Python variable scope in functions Python variable scope in functions Jul 12, 2025 am 02:49 AM

In Python, variables defined inside a function are local variables and are only valid within the function; externally defined are global variables that can be read anywhere. 1. Local variables are destroyed as the function is executed; 2. The function can access global variables but cannot be modified directly, so the global keyword is required; 3. If you want to modify outer function variables in nested functions, you need to use the nonlocal keyword; 4. Variables with the same name do not affect each other in different scopes; 5. Global must be declared when modifying global variables, otherwise UnboundLocalError error will be raised. Understanding these rules helps avoid bugs and write more reliable functions.

How to parse an HTML table with Python and Pandas How to parse an HTML table with Python and Pandas Jul 10, 2025 pm 01:39 PM

Yes, you can parse HTML tables using Python and Pandas. First, use the pandas.read_html() function to extract the table, which can parse HTML elements in a web page or string into a DataFrame list; then, if the table has no clear column title, it can be fixed by specifying the header parameters or manually setting the .columns attribute; for complex pages, you can combine the requests library to obtain HTML content or use BeautifulSoup to locate specific tables; pay attention to common pitfalls such as JavaScript rendering, encoding problems, and multi-table recognition.

Access nested JSON object in Python Access nested JSON object in Python Jul 11, 2025 am 02:36 AM

The way to access nested JSON objects in Python is to first clarify the structure and then index layer by layer. First, confirm the hierarchical relationship of JSON, such as a dictionary nested dictionary or list; then use dictionary keys and list index to access layer by layer, such as data "details"["zip"] to obtain zip encoding, data "details"[0] to obtain the first hobby; to avoid KeyError and IndexError, the default value can be set by the .get() method, or the encapsulation function safe_get can be used to achieve secure access; for complex structures, recursively search or use third-party libraries such as jmespath to handle.

Python def vs lambda deep dive Python def vs lambda deep dive Jul 10, 2025 pm 01:45 PM

def is suitable for complex functions, supports multiple lines, document strings and nesting; lambda is suitable for simple anonymous functions and is often used in scenarios where functions are passed by parameters. The situation of selecting def: ① The function body has multiple lines; ② Document description is required; ③ Called multiple places. When choosing a lambda: ① One-time use; ② No name or document required; ③ Simple logic. Note that lambda delay binding variables may throw errors and do not support default parameters, generators, or asynchronous. In actual applications, flexibly choose according to needs and give priority to clarity.

See all articles