我尝试按列进行分组,找到该组中的最小日期值,并将其插入该列中所有值的新列中。
以下内容:
d = {'one' : pd.Series(np.random.randn(6), index=pd.date_range('1/1/2011', periods=6, freq='H')),
'two' : pd.Series(["A", "B", "C"] * 2, index=pd.date_range('1/1/2011', periods=6, freq='H'))}
df = pd.DataFrame(d)
df['ts'] = df.index
df['min_date'] = df.groupby('two')['ts'].min()
df
给了我这个输出:
Out[7]:
one two ts min_date
2011-01-01 00:00:00 1.676829 A 2011-01-01 00:00:00 NaT
2011-01-01 01:00:00 -0.490976 B 2011-01-01 01:00:00 NaT
2011-01-01 02:00:00 -1.934902 C 2011-01-01 02:00:00 NaT
2011-01-01 03:00:00 -0.625931 A 2011-01-01 03:00:00 NaT
2011-01-01 04:00:00 1.534645 B 2011-01-01 04:00:00 NaT
2011-01-01 05:00:00 0.123045 C 2011-01-01 05:00:00 NaT
[6 rows x 4 columns]
我想要的地方:
Out[7]:
one two ts min_date
2011-01-01 00:00:00 1.676829 A 2011-01-01 00:00:00 2011-01-01 00:00:00
2011-01-01 01:00:00 -0.490976 B 2011-01-01 01:00:00 2011-01-01 01:00:00
2011-01-01 02:00:00 -1.934902 C 2011-01-01 02:00:00 2011-01-01 02:00:00
2011-01-01 03:00:00 -0.625931 A 2011-01-01 03:00:00 2011-01-01 00:00:00
2011-01-01 04:00:00 1.534645 B 2011-01-01 04:00:00 2011-01-01 01:00:00
2011-01-01 05:00:00 0.123045 C 2011-01-01 05:00:00 2011-01-01 02:00:00
[6 rows x 4 columns]
对列two
进行分组,以便在min_date中为所有A条目等设置A的第一次出现。
答案 0 :(得分:2)
我想你想要transform
方法:
>>> df['min_date'] = df.groupby('two')['ts'].transform("min")
>>> df
one two ts min_date
2011-01-01 00:00:00 0.574285 A 2011-01-01 00:00:00 2011-01-01 00:00:00
2011-01-01 01:00:00 -0.200439 B 2011-01-01 01:00:00 2011-01-01 01:00:00
2011-01-01 02:00:00 0.549725 C 2011-01-01 02:00:00 2011-01-01 02:00:00
2011-01-01 03:00:00 1.187299 A 2011-01-01 03:00:00 2011-01-01 00:00:00
2011-01-01 04:00:00 0.770180 B 2011-01-01 04:00:00 2011-01-01 01:00:00
2011-01-01 05:00:00 -0.448781 C 2011-01-01 05:00:00 2011-01-01 02:00:00
[6 rows x 4 columns]
用于执行聚合操作,然后将结果广播到整个组。