英文:
Pandas "Consecutive"/Rolling Percent Rank
问题
You can achieve the desired consecutive percent rank using the expanding window in pandas. Here's the modified code:
df['rank'] = df['value'].expanding().apply(lambda x: (x.rank(ascending=False, pct=True)).iloc[-1])
This code will calculate the consecutive percent rank for each row in the DataFrame as the window grows with the expanding function.
英文:
How can I create a "consecutive"/rolling percent rank on a pandas df series with a rolling window that grows as the data frame row count grows- as opposed to being a fixed window. rolling() requires an integer for the window size.
I basically want the ranking to be consecutively calculated as opposed to running the ranking function across the entire series and outputting the results. First row would have a rank just on that one row. By the end of the data frame, the rank would be calculated across the entire series.
Desired dataframe output:
| Index | Value | Rank (descending) | description |
|---|---|---|---|
| 0 | 6 | 1 | 6 is the first row so rank on 6 is 1 |
| 1 | 3 | 2 | 3 is the second largest value between 6 and 3 so rank is 2 |
| 2 | 4 | 2 | 4 is the second largest value between 6,3, and 4 so rank is 2 |
| 3 | 100 | 1 | 100 is the largest value in the series of 6,3,4,100 so rank is 1 |
| 4 | 1 | 5 | 1 is the smallest value in series of 6,3,4,100,1 so rank is last as 5 |
My thinking:
df['len']=range(len(df))
df['rank']=df['value'].rolling(df['len']).rank(pct=True)
答案1
得分: 0
df是一个包含值的DataFrame：
value
0 6
1 3
2 4
3 100
4 1
以下是代码的输出：
使用以下代码，创建了一个新的列"rank"，表示值的排名：
value rank
0 6 1
1 3 2
2 4 2
3 100 1
4 1 5
如果你想要百分位排名，可以使用以下代码，创建了一个新的列"rank_pct"：
value rank_pct
0 6 1.000000
1 3 1.000000
2 4 0.666667
3 100 0.250000
4 1 1.000000
英文:
Example
df = pd.DataFrame([6, 3, 4, 100, 1], columns=['value'])
df
value
0 6
1 3
2 4
3 100
4 1
Code
df.assign(rank=df['value'].expanding().rank(ascending=False).astype('int'))
output:
value rank
0 6 1
1 3 2
2 4 2
3 100 1
4 1 5
if you want pct rank use following code
df.assign(rank_pct=df['value'].expanding().rank(ascending=False, pct=True))
output:
value rank_pct
0 6 1.000000
1 3 1.000000
2 4 0.666667
3 100 0.250000
4 1 1.000000
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
评论