python - 列值的 pct_change

Question

Using Pandas documentation

http://pandas.pydata.org/pandas-docs/version/0.13.1/generated/pandas.DataFrame.pct_change.html

我正在尝试创建此函数来计算 percentage_change 。我将两个参数传递给它

 def PCT(df,n):
        d = df['Close'].pct_change(n)

即使以不同的方式重写相同的代码也会出现同样的错误

 P = pd.Series(df['Close'].pct_change(n), name = 'PCT_' + str(n))

1. 数据框
2. 我希望百分比变化的窗口

它抛出错误

  File "D:\Python Scripts\TA_Liabrary.py", line 15, in PCT
    d = df['Close'].pct_change(n)
TypeError: 'NoneType' object has no attribute '__getitem__'

谁能帮帮我

示例数据

Index   open    high    low close   volume  adj.
1/01/2014   54.97   54.97   54.97   54.97   0   49.31993
2/01/2014   55.1    55.95   54.86   55.08   216100  49.41862
3/01/2014   54.5    55  54.16   55  392600  49.34685
6/01/2014   54.82   55.47   54.62   55.14   344500  49.47245
7/01/2014   55.06   55.17   54.27   54.35   677400  48.76365
8/01/2014   54.64   54.88   53.87   54.38   587500  48.79057
9/01/2014   54.57   54.8    54.05   54.48   466800  48.88029

Answer 1

将pct_change应用于单/多列，在数据框中可以如下完成

df = pd.DataFrame({
    'open': [54.97,55.1,54.5,54.82],
    'high': [54.97,55.95,55,55.47],
    'low': [54.97,54.86,54.16,54.62],
    'close': [54.97,53.08,55,55.14]},
    index=['2014-01-01', '2014-02-01', '2014-03-01','2014-04-01'])

            open    high    low     close
2014-01-01  54.97   54.97   54.97   54.97
2014-02-01  55.10   55.95   54.86   53.08
2014-03-01  54.50   55.00   54.16   55.00
2014-04-01  54.82   55.47   54.62   55.14

将 pct_change 应用于单列(关闭)

df.close = df.close.pct_change(periods = 1)

            open    high    low     close
2014-01-01  54.97   54.97   54.97   NaN
2014-02-01  55.10   55.95   54.86   -0.034382
2014-03-01  54.50   55.00   54.16   0.036172
2014-04-01  54.82   55.47   54.62   0.002545

如下应用于多列

# apply pct_change to 'open' and 'close'
df[['open','close']] = df[['open','close']].pct_change(periods = 1)

            open        high    low     close
2014-01-01  NaN         54.97   54.97   NaN
2014-02-01  0.002365    55.95   54.86   -0.034382
2014-03-01  -0.010889   55.00   54.16   0.036172
2014-04-01  0.005872    55.47   54.62   0.002545