Do not assign values to linked indexes! Through this pit, you can really step into the ranks of pandas masters

Tianyuan prodigal son 2020-11-13 09:06:18
assign values linked indexes pit


To be honest , I've never liked Pandas, Because it's so powerful , Want to master it skillfully , For middle-aged and old people like me , The cost of learning is high . however , For young people with a strong receptivity ,Pandas It is indeed an indispensable tool in data processing , Many of my nephews and nephews like to use . It's because they are Pandas In the course of using , Keep asking me questions , In the process of helping them solve their problems , I'm getting familiar with it Pandas. see , Another question came up at noon today , This is a very classic question , Almost everyone meets . so to speak , Learned to solve this problem , To really understand Pandas. I put the background of this problem 、 Causes and solutions , Write it down here in plain text as much as possible , I hope this article can become Pandas Introduction to . A more detailed tutorial , Please refer to my other blog 《Pandas A concise tutorial 》.

1. The background of the problem

1.1 Construct a test DataFrame

DataFrame yes Pandas The core and most commonly used data structure , Can be understood as a two-dimensional heterogeneous table . Heterogeneous , Refer to DataFrame Each column of can have its own data type , It doesn't have to be like Numpy Multidimensional array of (ndarray) Then all elements must be of the same data type .DataFrame Each column of has a column name , Each row has an index .

There are many ways to create DataFrame object , Convert dictionary data to DataFrame Object is the most common creation method , The key of the dictionary corresponds to DataFrame The column of , Key names are automatically called column names . If no index is specified , The default index is used ( from 0 The starting consecutive integer ).

>>> import pandas as pd
>>> data = {

' East China science and technology ': [1.91, 1.90, 1.86, 1.84],
' Changan automobile ': [11.27, 11.14, 11.28, 11.71],
' Tibet Mining ': [7.89, 7.79, 7.61, 7.50],
' Chongqing beer ': [50.46, 50.17, 50.28, 50.28]
}
>>> df = pd.DataFrame(data)
>>>> df
East China science and technology Changan automobile Tibet Mining Chongqing beer
0 1.91 11.27 7.89 50.46
1 1.90 11.14 7.79 50.17
2 1.86 11.28 7.61 50.28
3 1.84 11.71 7.50 50.28

1.2 Conditional search

Pandas Conditional search is very flexible , The following code demonstrates the most common ways .

>>> df[df. Changan automobile > 11.2] # The stock price of Changan Automobile is more than 11.2 All of the line 
East China science and technology Changan automobile Tibet Mining Chongqing beer
0 1.91 11.27 7.89 50.46
2 1.86 11.28 7.61 50.28
3 1.84 11.71 7.50 50.28
>>> df[(df. Changan automobile > 11.2) & (df. East China science and technology < 1.9)] # Search satisfies “ And ” All the lines of the condition 
East China science and technology Changan automobile Tibet Mining Chongqing beer
2 1.86 11.28 7.61 50.28
3 1.84 11.71 7.50 50.28
>>> df[df. Tibet Mining .isin([7.61, 7.89])] # Search all the lines where the stock price of Tibet Mining is equal to multiple specified values 
East China science and technology Changan automobile Tibet Mining Chongqing beer
0 1.91 11.27 7.89 50.46
2 1.86 11.28 7.61 50.28
>>> df[df.index.isin([1,3])] # Retrieve all lines with index number equal to the specified value 
East China science and technology Changan automobile Tibet Mining Chongqing beer
1 1.90 11.14 7.79 50.17
3 1.84 11.71 7.50 50.28

Pandas Conditional search for , In essence, Numpy It's the same , It returns a boolean result , We use this boolean result to index , The search results are obtained .

>>> df. Changan automobile > 11.2
0 True
1 False
2 True
3 True
Name: Changan automobile , dtype: bool

Again , Use Numpy The negative sign of (~) You can reverse the search results .

>>> df[~df.index.isin([1,3])] # Take the opposite 
East China science and technology Changan automobile Tibet Mining Chongqing beer
0 1.91 11.27 7.89 50.46
2 1.86 11.28 7.61 50.28

1.3 Modify the retrieved data

For the retrieved result data , If you want to change it , For example, change to invalid value nan( Need to import in advance Numpy), It's usually written like this :

>>> import numpy as np
>>> df[~df.index.isin([1,3])].iloc[:,:] = np.nan

However , It doesn't work . Interestingly , It's not a mistake , It's a warning . Come here , congratulations , classical SettingWithCopyWarning The problem finally came up ! Solved it , You can walk into Pandas It's one of the best .

Warning (from warnings module):
File "C:\Users\xufive\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\indexing.py", line 671
self._setitem_with_indexer(indexer, value)
SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Warning (from warnings module):
File "__main__", line 1
SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

2. Cause analysis

It's using loc Wrong way to select data ? Obviously not , Because with loc Select directly df There is no problem in modifying the data of .

>>> df.iloc[:,:] = np.nan
>>> df
East China science and technology Changan automobile Tibet Mining Chongqing beer
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN

Although the search results also look like a DataFrame, But use the search results again loc Select and modify data , There's a problem . The original DataFrame And as a result of the search DataFrame What's the difference ?

original , This is a Pandas For chain assignment (Chained Assignment) As a result of the protection mechanism . So called chain assignment , Is to assign a value to the index result of the index . When we use conditional retrieval , It's like an index , I'm working on this result loc selection , It's a secondary index , That's chain index , And the chain index is in Pandas Assignment is forbidden in the system . Simple understanding , That is, we can't assign values to the data selected through two square brackets .

3. Solution

Understand the cause of the problem , It's easy to find a solution : Use the search condition as loc Row parameters of , Turn two indexes into one , Naturally, there is no chained index , Assignment is no longer limited . Here is the complete code .

>>> import pandas as pd
>>>> import numpy as np
>>> data = {

' East China science and technology ': [1.91, 1.90, 1.86, 1.84],
' Changan automobile ': [11.27, 11.14, 11.28, 11.71],
' Tibet Mining ': [7.89, 7.79, 7.61, 7.50],
' Chongqing beer ': [50.46, 50.17, 50.28, 50.28]
}
>>> df = pd.DataFrame(data)
>>> df
East China science and technology Changan automobile Tibet Mining Chongqing beer
0 1.91 11.27 7.89 50.46
1 1.90 11.14 7.79 50.17
2 1.86 11.28 7.61 50.28
3 1.84 11.71 7.50 50.28
>>> df.loc[~df.index.isin([1,3]), :] = np.nan
>>> df
East China science and technology Changan automobile Tibet Mining Chongqing beer
0 NaN NaN NaN NaN
1 1.90 11.14 7.79 50.17
2 NaN NaN NaN NaN
3 1.84 11.71 7.50 50.28

loc Column parameters of , Except for the colon (:) Specify all columns , You can also specify a single column with a column name , Or use tuples to specify multiple columns .

>>> df = pd.DataFrame(data)
>>> df
East China science and technology Changan automobile Tibet Mining Chongqing beer
0 1.91 11.27 7.89 50.46
1 1.90 11.14 7.79 50.17
2 1.86 11.28 7.61 50.28
3 1.84 11.71 7.50 50.28
>>> df.loc[df. Changan automobile > 11.2, (' East China science and technology ', ' Tibet Mining ', ' Chongqing beer ')] = np.nan
>>> df
East China science and technology Changan automobile Tibet Mining Chongqing beer
0 NaN 11.27 NaN NaN
1 1.9 11.14 7.79 50.17
2 NaN 11.28 NaN NaN
3 NaN 11.71 NaN NaN

Get along well with Pandas It's that simple , you get Have we arrived ?

版权声明
本文为[Tianyuan prodigal son]所创,转载请带上原文链接,感谢

  1. 利用Python爬虫获取招聘网站职位信息
  2. Using Python crawler to obtain job information of recruitment website
  3. Several highly rated Python libraries arrow, jsonpath, psutil and tenacity are recommended
  4. Python装饰器
  5. Python实现LDAP认证
  6. Python decorator
  7. Implementing LDAP authentication with Python
  8. Vscode configures Python development environment!
  9. In Python, how dare you say you can't log module? ️
  10. 我收藏的有关Python的电子书和资料
  11. python 中 lambda的一些tips
  12. python中字典的一些tips
  13. python 用生成器生成斐波那契数列
  14. python脚本转pyc踩了个坑。。。
  15. My collection of e-books and materials about Python
  16. Some tips of lambda in Python
  17. Some tips of dictionary in Python
  18. Using Python generator to generate Fibonacci sequence
  19. The conversion of Python script to PyC stepped on a pit...
  20. Python游戏开发,pygame模块,Python实现扫雷小游戏
  21. Python game development, pyGame module, python implementation of minesweeping games
  22. Python实用工具,email模块,Python实现邮件远程控制自己电脑
  23. Python utility, email module, python realizes mail remote control of its own computer
  24. 毫无头绪的自学Python,你可能连门槛都摸不到!【最佳学习路线】
  25. Python读取二进制文件代码方法解析
  26. Python字典的实现原理
  27. Without a clue, you may not even touch the threshold【 Best learning route]
  28. Parsing method of Python reading binary file code
  29. Implementation principle of Python dictionary
  30. You must know the function of pandas to parse JSON data - JSON_ normalize()
  31. Python实用案例,私人定制,Python自动化生成爱豆专属2021日历
  32. Python practical case, private customization, python automatic generation of Adu exclusive 2021 calendar
  33. 《Python实例》震惊了,用Python这么简单实现了聊天系统的脏话,广告检测
  34. "Python instance" was shocked and realized the dirty words and advertisement detection of the chat system in Python
  35. Convolutional neural network processing sequence for Python deep learning
  36. Python data structure and algorithm (1) -- enum type enum
  37. 超全大厂算法岗百问百答(推荐系统/机器学习/深度学习/C++/Spark/python)
  38. 【Python进阶】你真的明白NumPy中的ndarray吗?
  39. All questions and answers for algorithm posts of super large factories (recommended system / machine learning / deep learning / C + + / spark / Python)
  40. [advanced Python] do you really understand ndarray in numpy?
  41. 【Python进阶】Python进阶专栏栏主自述:不忘初心,砥砺前行
  42. [advanced Python] Python advanced column main readme: never forget the original intention and forge ahead
  43. python垃圾回收和缓存管理
  44. java调用Python程序
  45. java调用Python程序
  46. Python常用函数有哪些?Python基础入门课程
  47. Python garbage collection and cache management
  48. Java calling Python program
  49. Java calling Python program
  50. What functions are commonly used in Python? Introduction to Python Basics
  51. Python basic knowledge
  52. Anaconda5.2 安装 Python 库(MySQLdb)的方法
  53. Python实现对脑电数据情绪分析
  54. Anaconda 5.2 method of installing Python Library (mysqldb)
  55. Python implements emotion analysis of EEG data
  56. Master some advanced usage of Python in 30 seconds, which makes others envy it
  57. python爬取百度图片并对图片做一系列处理
  58. Python crawls Baidu pictures and does a series of processing on them
  59. python链接mysql数据库
  60. Python link MySQL database