Question

我的初始数据框是

df = pd.DataFrame({"a":["2020-01-01", "2020-01-06", "2020-01-04", "2020-01-07"],
                   "b":["a", "a", "b", "b"],
                   "c":[1, 2, 3,4]})
print(df)
            a  b  c
0  2020-01-01  a  1
1  2020-01-06  a  2
2  2020-01-04  b  3
3  2020-01-07  b  4

我希望我的数据集像这样

            a  b  c
0  2020-01-01  a  1
1  2020-01-02  a  NaN
2  2020-01-03  a  NaN
3  2020-01-04  a  NaN
4  2020-01-05  a  NaN
5  2020-01-06  a  2
6  2020-01-04  b  3
7  2020-01-05  b  NaN
8  2020-01-06  b  NaN
3  2020-01-07  b  4

我尝试过

d.set_index([d.a, d.b], inplace=True)
d.asfreq("D")

d.set_index([d.a, d.b], inplace=True)
d.resample("D")

但是我遇到了

TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'MultiIndex'
enter code here

我真正的DataFrame的列（在此示例中为“ b”列）具有许多唯一值。

Answer 1

df = pd.DataFrame({"a":["2020-01-01", "2020-01-06", "2020-01-04", "2020-01-07"],
                   "b":["a", "a", "b", "b"],
                   "c":[1, 2, 3,4]})
# make datetime
df['a'] = pd.to_datetime(df['a'])
# create a group
g = df.groupby('b')
# list comprehension with reindex and date_range then concat list of frames
df2 = pd.concat([df.set_index('a').reindex(pd.date_range(df['a'].min(),
                                                         df['a'].max(),freq='D')) for _,df in g])
# ffill column b
df2['b'] = df2['b'].ffill()


            b    c
2020-01-01  a  1.0
2020-01-02  a  NaN
2020-01-03  a  NaN
2020-01-04  a  NaN
2020-01-05  a  NaN
2020-01-06  a  2.0
2020-01-04  b  3.0
2020-01-05  b  NaN
2020-01-06  b  NaN
2020-01-07  b  4.0

Answer 2

使用groupby和asfreq的另一种方法：

(df.set_index('a')
   .groupby('b').apply(lambda x: x.drop('b',axis=1).asfreq('D'))
   .reset_index()
)

输出：

   b          a    c
0  a 2020-01-01  1.0
1  a 2020-01-02  NaN
2  a 2020-01-03  NaN
3  a 2020-01-04  NaN
4  a 2020-01-05  NaN
5  a 2020-01-06  2.0
6  b 2020-01-04  3.0
7  b 2020-01-05  NaN
8  b 2020-01-06  NaN
9  b 2020-01-07  4.0

如何在熊猫DataFrame中填写缺少的日期分组

2 个答案: