Python：如何修复 Python 中的 AttributeError？

如何解决Python：如何修复 Python 中的 AttributeError？

我有代码：

df_mean_woman = df_mean_woman.rename(index = {"Less than 1 year":0},inplace = True)
df_mean_woman

当我运行它时，我收到错误

AttributeError                            Traceback (most recent call last)
<ipython-input-136-94a5cc6acf63> in <module>
----> 1 df_woman = df_woman.rename(index = {"Less than 1 year":0},2                                   #"More than 50 years":int(51)},3                                   inplace = True)
      4 df_woman

AttributeError: 'nonetype' object has no attribute 'rename'

虽然当我简单地输入 df_mean_woman.rename(index = {"Less than 1 year":0},inplace = True) 时错误就会消失 但是我不能简单地这样做，因为我需要稍后再次调用 df 。我已经尝试做很多事情来解决这个问题，但似乎没有任何效果。我不认为这是因为“不到 1 年”拼写不正确。我的主要问题似乎是当我打印出 df_mean_woman （重命名之前）时，据说 df 不存在。 当我重新运行 Juptyr 时，我可以打印出 df 但所有打印出来的都是“无”。

我的完整代码是

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('data.csv') 
%matplotlib inline
df_new = df.copy()
df_new = df_new.drop(['Age1stCode','CompTotal','Respondent','MainBranch','Hobbyist','Age','CompFreq','Country','CurrencyDesc','CurrencySymbol','DatabaseDesirenextYear','DatabaseWorkedWith','DevType','EdLevel','Employment','Ethnicity','JobFactors','JobSat','JobSeek','LanguageDesirenextYear','LanguageWorkedWith','MiscTechDesirenextYear','MiscTechWorkedWith','NEWCollabToolsDesirenextYear','NEWCollabToolsWorkedWith','NEWDevOps','NEWDevOpsImpt','NEWEdImpt','NEWJobHunt','NEWJobHuntResearch','NEWLearn','NEWOffTopic','NEWOnboardGood','NEWOtherComms','NEWOvertime','NEWPurchaseResearch','NEWPurpleLink','NEWSOSites','NEWStuck','OpSys','OrgSize','PlatformDesirenextYear','PlatformWorkedWith','PurchaseWhat','Sexuality','SOAccount','SOComm','SOPartFreq','SOVisitFreq','SurveyEase','SurveyLength','Trans','UndergradMajor','WebframeDesirenextYear','WebframeWorkedWith','WelcomeChange','WorkWeekHrs','YearsCodePro'],axis = 'columns')
df_new = df_new.dropna()
df_new    
df_woman = df_new.drop(index=df_new[df_new['Gender'] != 'Woman'].index,inplace=True)
df_woman = df_new
df_woman = df_woman.drop(['Gender'],axis ='columns')
df_news = df_new.copy()

df_woman = df_woman.rename(index = {"Less than 1 year":int(0)},#"More than 50 years":int(51)},inplace = True)
df_woman['YearsCode'] = df_woman['YearsCode'].apply(lambda x: '{0:0>2}'.format(x))
df_mean_woman = df_woman.groupby('YearsCode')['ConvertedComp'].mean().sort_index()

df_mean_woman

解决方法

df_woman = df_new.drop(index=df_new[df_new['Gender'] != 'Woman'].index,inplace=True)

.drop() 在 None 时返回 inplace=True。

看这里https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.drop.html

,

看起来您排除的列多于您要包含的列，因此制作您想要的列的列表而不是更长的要删除的列的列表会更容易。

总的来说，我不会使用 drop，而是将 loc 用于大多数这些操作。也不清楚您为什么要尝试操作索引而不是列值。

# looks like stackoverflow survey data
df = pd.read_csv('survey_results_public.csv')

unwanted = {'Age1stCode','CompTotal','Respondent','MainBranch','Hobbyist','Age','CompFreq','Country','CurrencyDesc','CurrencySymbol','DatabaseDesireNextYear','DatabaseWorkedWith','DevType','EdLevel','Employment','Ethnicity','JobFactors','JobSat','JobSeek','LanguageDesireNextYear','LanguageWorkedWith','MiscTechDesireNextYear','MiscTechWorkedWith','NEWCollabToolsDesireNextYear','NEWCollabToolsWorkedWith','NEWDevOps','NEWDevOpsImpt','NEWEdImpt','NEWJobHunt','NEWJobHuntResearch','NEWLearn','NEWOffTopic','NEWOnboardGood','NEWOtherComms','NEWOvertime','NEWPurchaseResearch','NEWPurpleLink','NEWSOSites','NEWStuck','OpSys','OrgSize','PlatformDesireNextYear','PlatformWorkedWith','PurchaseWhat','Sexuality','SOAccount','SOComm','SOPartFreq','SOVisitFreq','SurveyEase','SurveyLength','Trans','UndergradMajor','WebframeDesireNextYear','WebframeWorkedWith','WelcomeChange','WorkWeekHrs','YearsCodePro'}

# no need to copy dataframe before selecting columns
df_new = df.loc[:,list(set(df.columns) - unwanted)]

# use .loc to make df_woman
df_woman = df_new.loc[df_new['Gender'] != 'Woman',df_new.columns.drop('Gender')]

# convert strings to numeric values
df_woman['YearsCode'] = df_woman['YearsCode'].str.replace('Less than 1 year','0')
df_woman['YearsCode'] = df_woman['YearsCode'].str.replace('More than 50 years','51')
df_woman['YearsCode'] = pd.to_numeric(df_woman['YearsCode'],errors='coerce').fillna(0).astype(int)

# now groupby and analyze
df_woman.groupby('YearsCode')['ConvertedComp'].mean()