[英]pandas groupby apply with condition on the first occurrence of a column value
我有一個如下所示的數據框,其中pid
和event_date
是應用groupby
后的索引。 這次我想再次將groupby
應用於pid
,並應用於兩個條件:
如果滿足上述兩個條件,則在 groupby-ed dataframe 中將此 person/pid 分配為 True。
age label
pid event_date
00000001 2000-08-28 76.334247 False
2000-10-17 76.471233 False
2000-10-31 76.509589 True
2000-11-02 76.512329 True
... ... ... ...
00000005 2014-08-15 42.769863 False
2015-04-04 43.476712 False
2015-11-06 44.057534 True
2017-03-06 45.386301 True
到目前為止,我只是為了實現第一個條件:
df = (df.groupby(['pid']).apply(lambda x: sum(x['label'])>1).to_frame('label'))
第二個對我來說很棘手。 如何以某些列值的第一次出現為條件? 非常歡迎任何建議! 非常感謝!
更新示例 dataframe:
a = pd.DataFrame(columns=['pid', 'event_date', 'age', 'label'])
a['pid'] = [1, 1, 1, 1, 5, 5, 5, 5]
a['event_date'] = ['2000-08-28', '2000-08-28', '2000-08-28', '2000-08-28',\
'2000-08-28', '2000-08-28', '2000-08-28', '2000-08-28']
a['event_date'] = pd.to_datetime(a.event_date)
a['age'] = [76.334247, 76.471233, 76.509589, 76.512329, 42.769863, 43.476712, 44.057534, 45.386301]
a['label'] = [False, False, True, True, False, False, True, True]
a = (a.groupby(['pid', 'event_date', 'age']).apply(lambda x: x['label'].any()).to_frame('label'))
a.reset_index(level=['age'], inplace=True)
現在,如果我申請(a.groupby(['pid']).apply(lambda x: sum(x['label'])>1).to_frame('label'))
我會得到
label
pid
1 True
5 True
僅滿足第一個條件(好吧,因為我跳過了第二個條件)。 添加第二個條件應該只有 label pid=5
True 因為當第一個label=True
發生時只有這個人/pid 低於 45。
半(有趣)小時后,我想出了這個:
condition = a.reset_index().groupby('pid')['label'].sum().ge(2) & a.reset_index().groupby('pid').apply(lambda x: x['age'][x['label'].idxmax()] < 45)
Output:
>>> condition
pid
1 False
5 True
dtype: bool
如果索引是正常的,而不是pid
+ event_date
的 MultiIndex ,它可能會縮短一點(刪除兩個.reset_index()
調用)。 如果您從一開始就無法避免這種情況並且您不介意更改a
:
a = a.reset_index()
condition = a.groupby('pid')['label'].sum().ge(2) & a.groupby('pid').apply(lambda x: x['age'][x['label'].idxmax()] < 45)
擴展:
condition = (
a.groupby('pid') # Group by pid
['label'] # Get the label column for each group
.sum() # Compute the sum of the True values
.ge(2) # Are there two or more?
& # Boolean mask. The previous and the next bits of code are the two conditions, and they return a series, where the index is each unique pid, and the value is whether the condition is met for all the rows in that pid
a.groupby('pid') # Group by pid
.apply( # Call a function for each group, passing the group (a dataframe) to the function as its first parameter
lambda x: # Function start
x['age'][ # Get item from the age column at the specified index
x['label'].idxmax() # Get the index of the highest value of the label column (since they're only boolean values, the highest will be the first True value)
] < 45 # Check if it's less than 45
)
)
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