python pandas groupby计算变化 [英] python pandas groupby calculate change

问题描述

我想按组计算值变化.

这是我拥有的python pandas dataframe df:

This is the python pandas dataframe df I have:

Group |   Date      | Value
  A     01-02-2016     16 
  A     01-03-2016     15 
  A     01-04-2016     14 
  A     01-05-2016     17 
  A     01-06-2016     19 
  A     01-07-2016     20 
  B     01-02-2016     16 
  B     01-03-2016     13 
  B     01-04-2016     13 
  C     01-02-2016     16 
  C     01-03-2016     16 

我要计算的是，对于A组，这些值在上升，对于B组，它们在下降，对于C组，它们没有变化.

I want to calculate that for Group A, the values are going up, for Group B they are going down and for Group C they are not changing.

我不确定该如何处理，因为在A组中，该值先减小然后增大.那么，我应该看看平均变化还是最近的变化?

I am not sure how to approach it, since in Group A the values initially decrease and then increase. So should I look at the average change or most recent change?

我应该使用pct_change吗? http://pandas.pydata.org/pandas- docs/stable/generated/pandas.DataFrame.pct_change.html 我不确定如何指定时间范围.

Should I use pct_change? http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pct_change.html I was not sure how to specify the timeframe fot that.

df.groupby.pct_change

如果我也能形象地看到它，那将是很棒的.任何建议或提示，不胜感激！谢谢

It would be great if I could visualize it too. Any advice or hint is greatly appreciated! Thank you

推荐答案

在groupby

d1 = df.set_index(['Date', 'Group']).Value
d2 = d1.groupby(level='Group').pct_change()
print(d2)

Date        Group
2016-01-02  A             NaN
2016-01-03  A       -0.062500
2016-01-04  A       -0.066667
2016-01-05  A        0.214286
2016-01-06  A        0.117647
2016-01-07  A        0.052632
2016-01-02  B             NaN
2016-01-03  B       -0.187500
2016-01-04  B        0.000000
2016-01-02  C             NaN
2016-01-03  C        0.000000
Name: Value, dtype: float64

---

可视化和比较的许多方法之一是查看它们的增长方式.在这种情况下，我会

---

One of many ways to visualize and compare is to see how they grow. In this case, I'd

• fillna(0)
• add(1)
• cumprod()

• fillna(0)
• add(1)
• cumprod()

d2.fillna(0).add(1).cumprod().unstack().plot()

设置

setup

from io import StringIO
import pandas as pd

txt = """Group   Date       Value
  A     01-02-2016     16 
  A     01-03-2016     15 
  A     01-04-2016     14 
  A     01-05-2016     17 
  A     01-06-2016     19 
  A     01-07-2016     20 
  B     01-02-2016     16 
  B     01-03-2016     13 
  B     01-04-2016     13 
  C     01-02-2016     16 
  C     01-03-2016     16 """

df = pd.read_clipboard(parse_dates=[1])

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