我已经从雅虎财经下载了一些财务数据，并使用Pandas在Python中加载。现在我正在尝试使用pct_change（），但它给了我一个错误
使用的代码
sp = pd.read_csv('SP500.csv')
sp=sp.set_index('Date')
print(sp.head())
ret=sp.pct_change()
数据框头
Open High Low Close Adj Close Volume
Date
03-01-1950 16.66 16.66 16.66 16.66 16.66 1260000
04-01-1950 16.85 16.85 16.85 16.85 16.85 1890000
05-01-1950 16.93 16.93 16.93 16.93 16.93 2550000
06-01-1950 16.98 16.98 16.98 16.98 16.98 2010000
09-01-1950 17.09 17.09 17.08 17.08 17.08 3850000
pct_change（）
后出错TypeError: unsupported operand type(s) for /: 'str' and 'str'
CSV文件的顶行
Date,Open,High,Low,Close,Adj Close,Volume
03-01-1950,16.66,16.66,16.66,16.66,16.66,1260000
04-01-1950,16.85,16.85,16.85,16.85,16.85,1890000
05-01-1950,16.93,16.93,16.93,16.93,16.93,2550000
06-01-1950,16.98,16.98,16.98,16.98,16.98,2010000
09-01-1950,17.09,17.09,17.08,17.08,17.08,3850000
10-01-1950,17.030001,17.030001,17.030001,17.030001,17.030001,2160000
11-01-1950,17.09,17.09,17.09,17.09,17.09,2630000
12-01-1950,16.76,16.76,16.76,16.76,16.76,2970000
13-01-1950,16.67,16.67,16.67,16.67,16.67,3330000
16-01-1950,16.65,16.719999,16.65,16.719999,16.719999,2640000
17-01-1950,16.860001,16.860001,16.860001,16.860001,16.860001,1790000
18-01-1950,16.85,16.85,16.85,16.85,16.85,1570000
19-01-1950,16.870001,16.870001,16.870001,16.870001,16.870001,1170000
20-01-1950,16.9,16.9,16.9,16.9,16.9,1440000
答案 0 :(得分：0)
您需要to_numeric才能将非数字转换为NaN：
import pandas as pd
from pandas.compat import StringIO
temp=u"""Date,Open,High,Low,Close Adj,Close,Volume
03-01-1950,16.66,16.66,16.66,16.66,16.66,1260000
04-01-1950,16.85,16.85,16.85,16.85,16.85,1890000
05-01-1950,16.93,16.93,16.93,16.93,16.93,2550000
06-01-1950,16.98,16.98,16.98,16.98,16.98,2010000
09-01-1950,qqq,17.09,17.08,17.08,17.08,3850000"""
#after testing replace 'StringIO(temp)' to 'SP500.csv'
sp = pd.read_csv(StringIO(temp), index_col=['Date'], parse_dates=True)
print(sp.head())
Open High Low Close Adj Close Volume
Date
1950-03-01 16.66 16.66 16.66 16.66 16.66 1260000
1950-04-01 16.85 16.85 16.85 16.85 16.85 1890000
1950-05-01 16.93 16.93 16.93 16.93 16.93 2550000
1950-06-01 16.98 16.98 16.98 16.98 16.98 2010000
1950-09-01 qqq 17.09 17.08 17.08 17.08 3850000 <- add bad value qqq
ret = sp.apply(pd.to_numeric, errors='coerce').pct_change()
print (ret)
Open High Low Close Adj Close Volume
Date
1950-03-01 NaN NaN NaN NaN NaN NaN
1950-04-01 0.011405 0.011405 0.011405 0.011405 0.011405 0.500000
1950-05-01 0.004748 0.004748 0.004748 0.004748 0.004748 0.349206
1950-06-01 0.002953 0.002953 0.002953 0.002953 0.002953 -0.211765
1950-09-01 NaN 0.006478 0.005889 0.005889 0.005889 0.915423 <- replaced to NaN
答案 1 :(得分：0)
df['column_name'].pct_change()
这将有助于获取特定列的百分比变化。