将日期的python列表解析为pandas数据帧

样品:

L = ['',
 '20180715:1700-20180716:1600',
 '20180716:1700-20180717:1600',
 '20180717:1700-20180718:1600',
 '20180718:1700-20180719:1600',
 '20180719:1700-20180720:1600',
 '20180721:CLOSED',
 '20180722:1700-20180723:1600',
 '20180723:1700-20180724:1600',
 '20180724:1700-20180725:1600',
 '20180725:1700-20180726:1600',
 '20180726:1700-20180727:1600',
 '20180728:CLOSED']

我认为这里最好的方法是使用按分隔符拆分的列表理解和不使用分隔符筛选出值:

df = pd.DataFrame([x.split('-') for x in L if '-' in x], columns=['start','end'])
print (df)
           start            end
0  20180715:1700  20180716:1600
1  20180716:1700  20180717:1600
2  20180717:1700  20180718:1600
3  20180718:1700  20180719:1600
4  20180719:1700  20180720:1600
5  20180722:1700  20180723:1600
6  20180723:1700  20180724:1600
7  20180724:1700  20180725:1600
8  20180725:1700  20180726:1600
9  20180726:1700  20180727:1600

熊猫解决方案也是可能的,特别是在需要的时候 Series -这是用过的 split 和 dropna :

s = pd.Series(L)

df = s.str.split('-', expand=True).dropna(subset=[1])
df.columns = ['start','end']
print (df)
            start            end
1   20180715:1700  20180716:1600
2   20180716:1700  20180717:1600
3   20180717:1700  20180718:1600
4   20180718:1700  20180719:1600
5   20180719:1700  20180720:1600
7   20180722:1700  20180723:1600
8   20180723:1700  20180724:1600
9   20180724:1700  20180725:1600
10  20180725:1700  20180726:1600
11  20180726:1700  20180727:1600