Pandas 值的累积排名

Question

我正在尝试找到一种方法来计算 Pandas 中的关系的累计总数。

让我们从田径比赛中获取假设数据，其中有人员、比赛、预赛和时间。

每个人的排名如下：

对于给定的 race/heat 组合：

时间最少的人排在第一位
时间倒数第二的人排名第二

等等...

这将是相当简单的代码，但一方面..

如果两个人有相同的时间，他们都得到相同的位置，然后下一次大于他们的时间将以该值 + 1 作为位置。

下面的table中，100码冲刺，热身1，RUNNER1第一，RUNNER2/RUNNER3获得第二名，RUNNER3获得第三名（下一次在RUNNER2/RUNNER3之后）

所以基本上，逻辑如下：

If race <> race.shift() or heat <> heat.shift() then place =1

If race = race.shift() and heat = heat.shift() and time>time.shift then place =place.shift()+1

If race = race.shift() and heat = heat.shift() and time>time.shift then place =place.shift()

让我困惑的部分是如何处理领带。否则我可以做类似

的事情

df['Place']=np.where(
              (df['race']==df['race'].shift())
              &
              (df['heat']==df['heat'].shift()),
              df['Place'].shift()+1,
              1)

谢谢！

示例数据如下：

Person,Race,Heat,Time
RUNNER1,100 Yard Dash,1,9.87
RUNNER2,100 Yard Dash,1,9.92
RUNNER3,100 Yard Dash,1,9.92
RUNNER4,100 Yard Dash,1,9.96
RUNNER5,100 Yard Dash,1,9.97
RUNNER6,100 Yard Dash,1,10.01
RUNNER7,100 Yard Dash,2,9.88
RUNNER8,100 Yard Dash,2,9.93
RUNNER9,100 Yard Dash,2,9.93
RUNNER10,100 Yard Dash,2,10.03
RUNNER11,100 Yard Dash,2,10.26
RUNNER7,200 Yard Dash,1,19.63
RUNNER8,200 Yard Dash,1,19.67
RUNNER9,200 Yard Dash,1,19.72
RUNNER10,200 Yard Dash,1,19.72
RUNNER11,200 Yard Dash,1,19.86
RUNNER12,200 Yard Dash,1,19.92

最后我想要的是

Person,Race,Heat,Time,Place
RUNNER1,100 Yard Dash,1,9.87,1
RUNNER2,100 Yard Dash,1,9.92,2
RUNNER3,100 Yard Dash,1,9.92,2
RUNNER4,100 Yard Dash,1,9.96,3
RUNNER5,100 Yard Dash,1,9.97,4
RUNNER6,100 Yard Dash,1,10.01,5
RUNNER7,100 Yard Dash,2,9.88,1
RUNNER8,100 Yard Dash,2,9.93,2
RUNNER9,100 Yard Dash,2,9.93,2
RUNNER10,100 Yard Dash,2,10.03,3
RUNNER11,100 Yard Dash,2,10.26,4
RUNNER7,200 Yard Dash,1,19.63,1
RUNNER8,200 Yard Dash,1,19.67,2
RUNNER9,200 Yard Dash,1,19.72,3
RUNNER10,200 Yard Dash,1,19.72,3
RUNNER11,200 Yard Dash,1,19.86,4
RUNNER12,200 Yard Dash,1,19.92,4

[edit] 现在，更进一步..

假设一旦我留下一组唯一值，下次出现该组时，这些值将重置为 1..

因此，例如，- 请注意，它进入 "heat 1"，然后进入 "heat 2"，然后返回 "heat 1" - 我不希望排名从之前的 "heat 1"，我希望他们重新设置。

Person,Race,Heat,Time,Place
RUNNER1,100 Yard Dash,1,9.87,1
RUNNER2,100 Yard Dash,1,9.92,2
RUNNER3,100 Yard Dash,1,9.92,2
RUNNER4,100 Yard Dash,2,9.96,1
RUNNER5,100 Yard Dash,2,9.97,2
RUNNER6,100 Yard Dash,2,10.01,3
RUNNER7,100 Yard Dash,1,9.88,1
RUNNER8,100 Yard Dash,1,9.93,2
RUNNER9,100 Yard Dash,1,9.93,2

Answer 1

您可以使用：

grouped =  df.groupby(['Race','Heat'])
df['Place'] = grouped['Time'].transform(lambda x: pd.factorize(x, sort=True)[0]+1)

import pandas as pd
df = pd.DataFrame({'Heat': [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1], 'Person': ['RUNNER1', 'RUNNER2', 'RUNNER3', 'RUNNER4', 'RUNNER5', 'RUNNER6', 'RUNNER7', 'RUNNER8', 'RUNNER9', 'RUNNER10', 'RUNNER11', 'RUNNER7', 'RUNNER8', 'RUNNER9', 'RUNNER10', 'RUNNER11', 'RUNNER12'], 'Race': ['100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '200 Yard Dash', '200 Yard Dash', '200 Yard Dash', '200 Yard Dash', '200 Yard Dash', '200 Yard Dash'], 'Time': [9.8699999999999992, 9.9199999999999999, 9.9199999999999999, 9.9600000000000009, 9.9700000000000006, 10.01, 9.8800000000000008, 9.9299999999999997, 9.9299999999999997, 10.029999999999999, 10.26, 19.629999999999999, 19.670000000000002, 19.719999999999999, 19.719999999999999, 19.859999999999999, 19.920000000000002]})

grouped =  df.groupby(['Race','Heat'])
df['Place'] = grouped['Time'].transform(lambda x: pd.factorize(x, sort=True)[0]+1)
df['Rank'] = grouped['Time'].rank(method='min')
print(df)

产量

    Heat    Person           Race   Time  Place  Rank
0      1   RUNNER1  100 Yard Dash   9.87    1.0   1.0
1      1   RUNNER2  100 Yard Dash   9.92    2.0   2.0
2      1   RUNNER3  100 Yard Dash   9.92    2.0   2.0
3      1   RUNNER4  100 Yard Dash   9.96    3.0   4.0
4      1   RUNNER5  100 Yard Dash   9.97    4.0   5.0
5      1   RUNNER6  100 Yard Dash  10.01    5.0   6.0
6      2   RUNNER7  100 Yard Dash   9.88    1.0   1.0
7      2   RUNNER8  100 Yard Dash   9.93    2.0   2.0
8      2   RUNNER9  100 Yard Dash   9.93    2.0   2.0
9      2  RUNNER10  100 Yard Dash  10.03    3.0   4.0
10     2  RUNNER11  100 Yard Dash  10.26    4.0   5.0
11     1   RUNNER7  200 Yard Dash  19.63    1.0   1.0
12     1   RUNNER8  200 Yard Dash  19.67    2.0   2.0
13     1   RUNNER9  200 Yard Dash  19.72    3.0   3.0
14     1  RUNNER10  200 Yard Dash  19.72    3.0   3.0
15     1  RUNNER11  200 Yard Dash  19.86    4.0   5.0
16     1  RUNNER12  200 Yard Dash  19.92    5.0   6.0

请注意 Pandas 有一个 Groupby.rank 方法可以计算许多常见的排名形式 -- 但不是您描述的那种。请注意，例如在第 3 行，在第二名和第三名之间的平局之后 Rank 是 4，而 Place 是 3.

关于编辑：使用

(df['Heat'] != df['Heat'].shift()).cumsum()

消除预赛的歧义：

import pandas as pd
df = pd.DataFrame({'Heat': [1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1], 'Person': ['RUNNER1', 'RUNNER2', 'RUNNER3', 'RUNNER4', 'RUNNER5', 'RUNNER6', 'RUNNER7', 'RUNNER8', 'RUNNER9', 'RUNNER10', 'RUNNER11', 'RUNNER7', 'RUNNER8', 'RUNNER9', 'RUNNER10', 'RUNNER11', 'RUNNER12'], 'Race': ['100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash', '100 Yard Dash'], 'Time': [9.8699999999999992, 9.9199999999999999, 9.9199999999999999, 9.9600000000000009, 9.9700000000000006, 10.01, 9.8800000000000008, 9.9299999999999997, 9.9299999999999997, 10.029999999999999, 10.26, 19.629999999999999, 19.670000000000002, 19.719999999999999, 19.719999999999999, 19.859999999999999, 19.920000000000002]})

df['HeatGroup'] = (df['Heat'] != df['Heat'].shift()).cumsum()
grouped =  df.groupby(['Race','HeatGroup'])
df['Place'] = grouped['Time'].transform(lambda x: pd.factorize(x, sort=True)[0]+1)
df['Rank'] = grouped['Time'].rank(method='min')
print(df)

产量

    Heat    Person           Race   Time  HeatGroup  Place  Rank
0      1   RUNNER1  100 Yard Dash   9.87          1    1.0   1.0
1      1   RUNNER2  100 Yard Dash   9.92          1    2.0   2.0
2      1   RUNNER3  100 Yard Dash   9.92          1    2.0   2.0
3      1   RUNNER4  100 Yard Dash   9.96          1    3.0   4.0
4      1   RUNNER5  100 Yard Dash   9.97          1    4.0   5.0
5      1   RUNNER6  100 Yard Dash  10.01          1    5.0   6.0
6      2   RUNNER7  100 Yard Dash   9.88          2    1.0   1.0
7      2   RUNNER8  100 Yard Dash   9.93          2    2.0   2.0
8      2   RUNNER9  100 Yard Dash   9.93          2    2.0   2.0
9      2  RUNNER10  100 Yard Dash  10.03          2    3.0   4.0
10     2  RUNNER11  100 Yard Dash  10.26          2    4.0   5.0
11     1   RUNNER7  100 Yard Dash  19.63          3    1.0   1.0
12     1   RUNNER8  100 Yard Dash  19.67          3    2.0   2.0
13     1   RUNNER9  100 Yard Dash  19.72          3    3.0   3.0
14     1  RUNNER10  100 Yard Dash  19.72          3    3.0   3.0
15     1  RUNNER11  100 Yard Dash  19.86          3    4.0   5.0
16     1  RUNNER12  100 Yard Dash  19.92          3    5.0   6.0

Pandas 值的累积排名

Cumulative Ranking of Values in Pandas with Ties

python

pandas

dataframe

cumulative-sum