我有一个问题，要在同一个数据帧（start_end）中将两列合并为一列，还要删除空值。我打算将“起始站”和“结束站”合并为“站”，并根据新列“站”保留“持续时间”我试过pd.merge，pd.concat，pd.append，但是我没办法解决。
起始端数据帧：
Duration End station Start station
14 1407 NaN 14th & V St NW
19 509 NaN 21st & I St NW
20 638 15th & P St NW. NaN
27 1532 NaN Massachusetts Ave & Dupont Circle NW
28 759 NaN Adams Mill & Columbia Rd NW
预期产量：
Duration stations
14 1407 14th & V St NW
19 509 21st & I St NW
20 638 15th & P St NW
27 1532 Massachusetts Ave & Dupont Circle NW
28 759 Adams Mill & Columbia Rd NW
我目前掌握的代码：
#start_end is the dataframe, 'start station', 'end station', 'duration'
start_end = pd.concat([df_start, df_end])
这就是我试图：
station = pd.merge([start_end['Start station'],start_end['End station']])
最佳答案
>>> df
Duration End station Start station
0 1407 NaN 14th & V St NW
1 509 NaN 21st & I St NW
2 638 15th & P St NW. NaN
3 1532 NaN Massachusetts Ave & Dupont Circle NW
4 759 NaN Adams Mill & Columbia Rd NW
给这两列起相同的名字
>>> df.columns = df.columns.str.replace('.*?station', 'station')
>>> df
Duration station station
0 1407 NaN 14th & V St NW
1 509 NaN 21st & I St NW
2 638 15th & P St NW. NaN
3 1532 NaN Massachusetts Ave & Dupont Circle NW
4 759 NaN Adams Mill & Columbia Rd NW
堆叠然后松开。
>>> s = df.stack()
>>> s
0 Duration 1407
station 14th & V St NW
1 Duration 509
station 21st & I St NW
2 Duration 638
station 15th & P St NW.
3 Duration 1532
station Massachusetts Ave & Dupont Circle NW
4 Duration 759
station Adams Mill & Columbia Rd NW
dtype: object
>>> df = s.unstack()
>>> df
Duration station
0 1407 14th & V St NW
1 509 21st & I St NW
2 638 15th & P St NW.
3 1532 Massachusetts Ave & Dupont Circle NW
4 759 Adams Mill & Columbia Rd NW
>>>
这就是我的想法：
.stack创建具有多索引的序列，并为您处理空值。它在列名上对齐第二个级别，因为列名是相同的，所以只有一个级别-取消堆叠只生成一个列。如果不更改列名，这实际上只是根据索引之间的差异进行的猜测。
>>> # without changing column names
>>> s.index
MultiIndex(levels=[[0, 1, 2, 3, 4], ['Duration', 'End station', 'Start station']],
labels=[[0, 0, 1, 1, 2, 2, 3, 3, 4, 4], [0, 2, 0, 2, 0, 1, 0, 2, 0, 2]])
>>> # column names the same
>>> s.index
MultiIndex(levels=[[0, 1, 2, 3, 4], ['Duration', 'station']],
labels=[[0, 0, 1, 1, 2, 2, 3, 3, 4, 4], [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]])
似乎有点棘手，也许有人会对此发表评论。
替代-使用
pd.concat和.dropna>>> stations = pd.concat([df.iloc[:,1],df.iloc[:,2]]).dropna()
>>> stations.name = 'stations'
>>> stations
2 15th & P St NW.
0 14th & V St NW
1 21st & I St NW
3 Massachusetts Ave & Dupont Circle NW
4 Adams Mill & Columbia Rd NW
Name: stations, dtype: object
>>> df2 = pd.concat([df['Duration'], stations], axis=1)
>>> df2
Duration stations
0 1407 14th & V St NW
1 509 21st & I St NW
2 638 15th & P St NW.
3 1532 Massachusetts Ave & Dupont Circle NW
4 759 Adams Mill & Columbia Rd NW
关于python - 在pandas/python的同一数据框中将两列合并为一列，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/50662613/