[英]How to Group By and Count total in that group Pandas
嗨,我有以下 DataFrame:
# Import pandas library
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
# initialize list of lists
data = [['tom', 10,1], ['nick', 15,0], ['tom', 14,1], ['jason', 15,0], ['nick', 18,1], ['jason', 15,0], ['jason', 17,1]
, ['tom', 14,0], ['nick',16 ,1], ['tom', 22,1]]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns = ['Name', 'Attempts','Target'])
# print dataframe.
df
Name Attempts Target
0 tom 10 1
1 nick 15 0
2 tom 14 1
3 jason 15 0
4 nick 18 1
5 jason 15 0
6 jason 17 1
7 tom 14 0
8 nick 16 1
9 tom 22 1
我希望简单地在每个名字旁边得到一个总数,这样它就变成了:
Name Attempts Target totalentries
0 tom 10 1 4
1 nick 15 0 3
2 tom 14 1 4
3 jason 15 0 3
4 nick 18 1 3
5 jason 15 0 3
6 jason 17 1 3
7 tom 14 0 4
8 nick 16 1 3
9 tom 22 1 4
试过:
df['totalentries'] = df.groupby('Name').nunique()
但得到一个ValueError: Wrong number of items passed 8, placement implies 1
有任何想法吗? 非常感谢!
将GroupBy.transform
与groupby
之后的指定列一起使用,并聚合 function:
df['totalentries'] = df.groupby('Name')['Target'].transform('nunique')
如果需要计算值:
df['totalentries'] = df.groupby('Name')['Target'].transform('size')
你应该试试这个:
df["totalentries"] = [df.groupby("Name")["Name"].count()[i] for i in df["Name"].values]
这将为您提供所需的 output。
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