


How to Efficiently Delete Rows from a Pandas DataFrame Based on a Column Value?
Deleting DataFrame Row in Pandas Based on Column Value
Problem:
Consider a Pandas DataFrame with a column named line_race. The task is to remove all rows where the value in the line_race column is equal to 0.
Efficient Solution:
To efficiently remove rows based on a specific column value, use the following steps:
-
Import the Pandas library:
import pandas as pd
Copy after login -
Create the DataFrame with the given data:
data = { "line_race": [11, 11, 9, 10, 10, 9, 8, 9, 11, 8, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "rating": [56, 67, 66, 83, 88, 52, 66, 70, 68, 72, 65, 70, 64, 70, 70, -1, -1, -1, -1, -1, 69, -1, -1, -1, -1], "rw": [1.000000, 1.000000, 1.000000, 0.880678, 0.793033, 0.636655, 0.581946, 0.518825, 0.486226, 0.446667, 0.164591, 0.142409, 0.134800, 0.117803, 0.113758, 0.109852, 0.098919, 0.093168, 0.083063, 0.075171, 0.048690, 0.045404, 0.039679, 0.034160, 0.030915], "wrating": [56.000000, 67.000000, 66.000000, 73.096278, 69.786942, 33.106077, 38.408408, 36.317752, 33.063381, 32.160051, 10.698423, 9.968634, 8.627219, 8.246238, 7.963072, -0.109852, -0.098919, -0.093168, -0.083063, -0.075171, 3.359623, -0.045404, -0.039679, -0.034160, -0.030915], "line_date": ["2007-03-31", "2007-03-10", "2007-02-10", "2007-01-13", "2006-12-23", "2006-11-09", "2006-10-22", "2006-09-29", "2006-09-16", "2006-08-30", "2006-02-11", "2006-01-13", "2006-01-02", "2005-12-06", "2005-11-29", "2005-11-22", "2005-11-01", "2005-10-20", "2005-09-27", "2005-09-07", "2005-06-12", "2005-05-29", "2005-05-02", "2005-04-02", "2005-03-13", "2004-11-09"] } df = pd.DataFrame(data)
Copy after login -
Filter the DataFrame using the query() method, which is faster than using boolean indexing:
df_filtered = df.query("line_race != 0")
Copy after login -
Alternatively, you can use the drop() method with the inplace parameter set to True:
df.drop(df.index[df['line_race'] == 0], inplace=True)
Copy after login - The filtered DataFrame can then be assigned to the original DataFrame variable or assigned to a new variable.
The updated DataFrame will no longer contain rows where the line_race column is equal to 0.
The above is the detailed content of How to Efficiently Delete Rows from a Pandas DataFrame Based on a Column Value?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

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

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Pythonlistsarepartofthestandardlibrary,whilearraysarenot.Listsarebuilt-in,versatile,andusedforstoringcollections,whereasarraysareprovidedbythearraymoduleandlesscommonlyusedduetolimitedfunctionality.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.
