[英]How to groupby with certain condition in pandas dataframe
I have dataframe like this 我有这样的数据帧
A B
0 1 a
1 2 a
2 3 b
3 4 b
4 5 a
I want to get result below(1row*4column dataframe), 我想得到下面的结果(1row * 4列数据帧),
A_count_all means the number of rows in dataframe df.A.count()
A_sum_all means the df.A.sum()
A_count_a is df.loc[df.B==a,"A"].count()
A_sum_a is df.loc[df.B==a,"A"].sum()
A_count_all A_sum_all A_count_a A_sum_a
0 5 15 3 8
how can I get this result dataframe? 我怎样才能得到这个结果数据帧?
You can use DataFrame
constructor: 您可以使用
DataFrame
构造函数:
A_count_all = df.A.count()
A_sum_all = df.A.sum()
A_count_a = df.loc[df.B=='a',"A"].count()
A_sum_a = df.loc[df.B=='a',"A"].sum()
print (pd.DataFrame({'A_count_all':A_count_all,
'A_sum_all':A_sum_all,
'A_count_a':A_count_a,
'A_sum_a':A_sum_a},
index=[0],
columns=['A_count_all','A_sum_all','A_count_a','A_sum_a']))
A_count_all A_sum_all A_count_a A_sum_a
0 5 15 3 8
Thank you Kris
for another solution: 谢谢
Kris
的另一个解决方案:
print (pd.DataFrame(data=[[df.A.count(),
df.A.sum(),
df.loc[df.B=='a',"A"].count(),
df.loc[df.B=='a',"A"].sum()]],
columns=['A_count_all','A_sum_all','A_count_a','A_sum_a']))
A_count_all A_sum_all A_count_a A_sum_a
0 5 15 3 8
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