Python 3.x 大熊猫的百分比变化

我有如下数据帧

      Date         ACH   BABA   BIDU    CEA    CHA   CTRP    EDU    HNP  
0     2000-06-30  $1.00  $3.00  $1.00  $0.00  $0.00  $0.00  $0.00  $0.00   
1     2000-07-03  $3.00  $2.00  $6.20  $1.50  $0.00  $0.00  $0.00 $-0.48   
2     2000-07-04  $5.00  $6.00  $3.00  $0.00  $0.00  $0.00  $0.00  $0.00 

我正在尝试使用以下公式计算每种方法的百分比变化：

df_vals = df[[ticker for ticker in tickers]].pct_change()

但是，我得到以下错误

TypeError: unsupported operand type(s) for /: 'str' and 'str'

我假设我得到这个错误，因为我有列标题，所以它不能计算字符串。然后我尝试添加shift（可能也错了）

---

这将返回相同的错误。谢谢您的帮助。

您需要先删除

$

并将其强制转换为

浮动

s：

import pandas as pd

s = '''\
Date        ACH   BABA   BIDU    CEA    CHA   CTRP    EDU    HNP  
2000-06-30  $1.00  $3.00  $1.00  $0.00  $0.00  $0.00  $0.00  $0.00   
2000-07-03  $3.00  $2.00  $6.20  $1.50  $0.00  $0.00  $0.00 $-0.48   
2000-07-04  $5.00  $6.00  $3.00  $0.00  $0.00  $0.00  $0.00  $0.00'''

# Recreate sample dataframe
df = pd.read_csv(pd.compat.StringIO(s),sep='\s+')

# Set index date (to not include) and remove all $
df = df.set_index('Date').replace('\$', '', regex=True).astype(float)

# Apply pct change and reset index
df = df.pct_change().reset_index()

print(df)

返回：

         Date       ACH      BABA      BIDU       CEA  CHA  CTRP  EDU  \
0  2000-06-30       NaN       NaN       NaN       NaN  NaN   NaN  NaN   
1  2000-07-03  2.000000 -0.333333  5.200000       inf  NaN   NaN  NaN   
2  2000-07-04  0.666667  2.000000 -0.516129 -1.000000  NaN   NaN  NaN   

        HNP  
0       NaN  
1      -inf  
2 -1.000000  

---

当OP要求所有栏目时，我删除了标记。谢谢你的帮助

df[[ticker for ticker in tickers]]

必须在那里，因为代码是更大类的一部分，这是阅读的一部分。我还忘了我在前面的类中有一个代码

pd.options.display.float_format='${:，.2f}.

format，该类将$符号添加到我的所有输出中。不管怎样，现在一切似乎都好了，谢谢你！

         Date       ACH      BABA      BIDU       CEA  CHA  CTRP  EDU  \
0  2000-06-30       NaN       NaN       NaN       NaN  NaN   NaN  NaN   
1  2000-07-03  2.000000 -0.333333  5.200000       inf  NaN   NaN  NaN   
2  2000-07-04  0.666667  2.000000 -0.516129 -1.000000  NaN   NaN  NaN   

        HNP  
0       NaN  
1      -inf  
2 -1.000000