用熊猫过滤组

Question

我正在尝试使用熊猫将过滤器添加到组中。 在下面的棒球数据中，我希望计算出归纳栏中从初始“ N”到最终“ Y”所需的平均时间。 本质上，我希望计算的是在归纳列中包括“ Y”并且具有多行的每组的长度。 任何提示都会有所帮助！

   playerID  yearid votedBy  ballots  needed  votes inducted category needed_note
2860  aaronha01    1982   BBWAA      415     312    406        Y   Player         NaN
3743  abbotji01    2005   BBWAA      516     387     13        N   Player         NaN
 146  adamsba01    1937   BBWAA      201     151      8        N   Player         NaN
 259  adamsba01    1938   BBWAA      262     197     11        N   Player         NaN
 384  adamsba01    1939   BBWAA      274     206     11        N   Player         NaN
 497  adamsba01    1942   BBWAA      233     175     11        N   Player         NaN
 574  adamsba01    1945   BBWAA      247     186      7        N   Player         NaN
2108  adamsbo03    1966   BBWAA      302     227      1        N   Player         NaN

Answer 1

我修改了数据集，以便有两个这样的组。 一个从N到Y有2行，另一个从N到Y有8行。 这取决于您是否计算包含y的行。 如果没有，它将分为两组，一组包含1行，另一组包含7行。 看起来您没有时间序列列，因此我想这意味着各行按时间均匀分布。

In [25]:

df=pd.read_clipboard()
print df
       playerID  yearid votedBy  ballots  needed  votes inducted category  needed_note 
3741  abbotji01    2005   BBWAA      516     387     13        N   Player          NaN 
2860  aaronha01    1982   BBWAA      415     312    406        Y   Player          NaN 
3743  abbotji01    2005   BBWAA      516     387     13        N   Player          NaN 
146   adamsba01    1937   BBWAA      201     151      8        N   Player          NaN 
259   adamsba01    1938   BBWAA      262     197     11        N   Player          NaN 
384   adamsba01    1939   BBWAA      274     206     11        N   Player          NaN 
497   adamsba01    1942   BBWAA      233     175     11        N   Player          NaN 
574   adamsba01    1945   BBWAA      247     186      7        N   Player          NaN 
2108  adamsbo03    1966   BBWAA      302     227      1        N   Player          NaN 
2861  aaronha01    1982   BBWAA      415     312    406        Y   Player          NaN 

In [26]:

df['isY']=(df.inducted=='Y')
df['isY']=np.hstack((0,df['isY'].cumsum().values[:-1])).T
In [27]:

print df.groupby('isY').count()
     playerID  yearid  votedBy  ballots  needed  votes  inducted  category  needed_note  isY 
0           2       2        2        2       2      2         2         2            0    2 
1           8       8        8        8       8      8         8         8            0    8 
[2 rows x 10 columns]    

假设您不计算Y ，则均值可以通过以下方式计算：

df2=df.groupby('isY').count().isY-1
df2[df2!=1].mean()

Answer 2

我模拟了自己的数据，以轻松测试您的问题。 我创建了一组名为df_inducted的播放器，其中包括最终被引入的播放器，然后通过使用isin（）函数，我们可以确保仅在分析中考虑它们。 然后，我找到它们的日期的最小值和最大值，并将它们的差平均。

import pandas as pd

df = pd.DataFrame({'player':['Nate','Will','Nate','Will'], 
                   'inducted': ['Y','Y','N','N'],
                   'date':[2014,2000,2011,1999]})

df_inducted = df[df.inducted=='Y']
df_subset = df[df.player.isin(df_inducted.player)]

maxs = df_subset.groupby('player')['date'].max()
mins = df_subset.groupby('player')['date'].min()

maxs = pd.DataFrame(maxs)
maxs.columns = ['max_date']
mins = pd.DataFrame(mins)
mins.columns = ['min_date']

min_and_max = maxs.join(mins)
final = min_and_max['max_date'] - min_and_max['min_date']

print "average time:", final.mean()

Answer 3

类DataFrameGroupBy的过滤方法在组中的每个子帧上运行。 请参阅help(pd.core.groupby.DataFrameGroupBy.filter) 。 数据是：

print df
  inducted playerID
0        Y        a
1        N        a
2        N        a
3        Y        b
4        N        b
5        N        c
6        N        c
7        N        c

示例代码：

import pandas as pd

g = df.groupby('playerID')
madeit = g.filter(
        lambda subframe:
                'Y' in set(subframe.inducted)).groupby('playerID')

# The filter removed player 'c' who never has inducted == 'Y'
print madeit.head()
           inducted playerID
playerID                    
a        0        Y        a
         1        N        a
         2        N        a
b        3        Y        b
         4        N        b

# The 'aggregate' function applies a function to each subframe
print madeit.aggregate(len)
          inducted
playerID          
a                3
b                2