英文:
Adding Rows to Pandas Dataframe where for all values below the max value of a Column
问题
请注意以下代码的翻译：
df = pd.DataFrame({"names": ["foo", "boo", "coo","coo"],"time": [1,4,2,3],"values": [20,10,15,12]})
我想要为每个名称在1和时间列的最大值之间插入所有可能的时间行。所以期望的数据框如下：
df = pd.DataFrame({"names": ["foo","boo","boo", "boo","boo","coo","coo","coo"],"time": [1,1,2,3,4,1,2,3],"values": [20,NaN,NaN,NaN,10,NaN,15,12]})
如何操作？
英文:
Let's consider the dataframe below -
df = pd.DataFrame({"names": ["foo", "boo", "coo","coo"],"time": [1,4,2,3],"values": [20,10,15,12]})
I want to insert rows for all possible time between 1 and maximum of time column for each name.
So the desired dataframe would be -
df = pd.DataFrame({"names": ["foo","boo","boo", "boo","boo","coo","coo","coo"],"time": [1,1,2,3,4,1,2,3],"values": [20,NaN,NaN,NaN,10,NaN,15,12]})
How to do it?
答案1
得分: 0
使用 GroupBy.apply 中的自定义函数，结合 Series.reindex，通过 range：
out = (df.set_index('time')
.groupby('names', sort=False)['values']
.apply(lambda x: x.reindex(range(1, x.index.max()+1)))
.reset_index())
print(out)
names time values
0 foo 1 20.0
1 boo 1 NaN
2 boo 2 NaN
3 boo 3 NaN
4 boo 4 10.0
5 coo 1 NaN
6 coo 2 15.0
7 coo 3 12.0
英文:
Use custom function in GroupBy.apply with Series.reindex by range:
out = (df.set_index('time')
.groupby('names', sort=False)['values']
.apply(lambda x: x.reindex(range(1, x.index.max()+1)))
.reset_index())
print (out)
names time values
0 foo 1 20.0
1 boo 1 NaN
2 boo 2 NaN
3 boo 3 NaN
4 boo 4 10.0
5 coo 1 NaN
6 coo 2 15.0
7 coo 3 12.0
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