Python Pandas-KeyError:列不在索引中

KeyError:“['Adj.Open''Adj.High''Adj.Low''Adj.Close''Adj.Volume']不在索引中”

---

列名称中需要空格-在

之后添加一个空格：

import pandas as pd
import quandl

df=quandl.get('WIKI/GOOGL')

#print(df.head())

df=df[['Adj.Open','Adj.High','Adj.Low','Adj.Close','Adj.Volume']]

df['HL_PCT'] =(df['Adj.High']-df['Adj.Close'])/df['Adj.Close'] * 100

df['PCT_change'] =(df['Adj.Close']-df['Adj.Open'])/df['Adj.Open'] * 100

df=df[['Adj.Close','HT_PCT','PCT_change','Adj.Volume']]


如果上述解决方案不起作用，您可以尝试：

import pandas as pd
import quandl

df=quandl.get('WIKI/GOOGL')

#print(df.head())
#print (df.columns.tolist())

df=df[['Adj. Open','Adj. High','Adj. Low','Adj. Close','Adj. Volume']]

df['HL_PCT'] =(df['Adj. High']-df['Adj. Close'])/df['Adj. Close'] * 100

df['PCT_change'] =(df['Adj. Close']-df['Adj. Open'])/df['Adj. Open'] * 100

df=df[['Adj. Close','HL_PCT','PCT_change','Adj. Volume']]
print (df.head())
            Adj. Close    HL_PCT  PCT_change  Adj. Volume
Date                                                     
2004-08-19   50.322842  3.712563    0.324968   44659000.0
2004-08-20   54.322689  0.710922    7.227007   22834300.0
2004-08-23   54.869377  3.729433   -1.227880   18256100.0
2004-08-24   52.597363  6.417469   -5.726357   15247300.0
2004-08-25   53.164113  1.886792    1.183658    9188600.0

什么是打印（df.columns.tolist（））

？按名称选择列似乎有一些错误。错误在哪里？在问题中包含完整的错误回溯。给出错误属性error:“Index”对象没有属性“to_list”@boardtc-它是

tolist（）

不是

to_list

我的错，我想编辑该评论，谢谢。顺便说一句，上面的内容不起作用，应该是df.columns.values.tolist（）@boardtc-添加到答案中。

import pandas as pd
import quandl

df=quandl.get('WIKI/GOOGL')

#print(df.head())
#print (df.columns.tolist())

df=df[['Adj. Open','Adj. High','Adj. Low','Adj. Close','Adj. Volume']]

df['HL_PCT'] =(df['Adj. High']-df['Adj. Close'])/df['Adj. Close'] * 100

df['PCT_change'] =(df['Adj. Close']-df['Adj. Open'])/df['Adj. Open'] * 100

df=df[['Adj. Close','HL_PCT','PCT_change','Adj. Volume']]
print (df.head())
            Adj. Close    HL_PCT  PCT_change  Adj. Volume
Date                                                     
2004-08-19   50.322842  3.712563    0.324968   44659000.0
2004-08-20   54.322689  0.710922    7.227007   22834300.0
2004-08-23   54.869377  3.729433   -1.227880   18256100.0
2004-08-24   52.597363  6.417469   -5.726357   15247300.0
2004-08-25   53.164113  1.886792    1.183658    9188600.0

#pandas below 0.24+
print (df.columns.values.tolist())
#pandas above 0.24+
print (df.columns.to_numpy().tolist())