Pandas：用缺少的分隔符分隔两列

Question

我有如下数据：

00052600150.00942615
00052601000.01014910
00052601050.02709672
00052601100.11454732
00052601150.23151254
00052601200.36262522
00052601250.66432348
00052601301.07723763
00052601351.26019487
00052601401.20568581

前10位数字表示时间步长YYMMDDhhmm，后跟一个数字

它应该是0005260010,0.00799872，其中第一个块是时间步，第二个块是测量值

我试着用pandas读取数据，并将其转换为str，但我失去了前导零？有没有办法用数字分隔浮点数

问候

Answer 1

您可以将列读取为str，并按位置拆分值

df = pd.read_csv('yourfile.csv', header=None, dtype='str', names=['col1'])
df['time'] = pd.to_datetime(df.col1.str[:10], unit='s')
df['value'] = (df.col1.str[10:]).astype('float')
df

输出：

                   col1                time     value
0  00052600150.00942615 1970-03-02 21:06:55  0.009426
1  00052601000.01014910 1970-03-02 21:08:20  0.010149
2  00052601050.02709672 1970-03-02 21:08:25  0.027097
3  00052601100.11454732 1970-03-02 21:08:30  0.114547
4  00052601150.23151254 1970-03-02 21:08:35  0.231513
5  00052601200.36262522 1970-03-02 21:08:40  0.362625
6  00052601250.66432348 1970-03-02 21:08:45  0.664323
7  00052601301.07723763 1970-03-02 21:08:50  1.077238
8  00052601351.26019487 1970-03-02 21:08:55  1.260195
9  00052601401.20568581 1970-03-02 21:09:00  1.205686

Answer 2

带有熊猫的正则表达式可以在不使用delimeter的情况下拆分列

# sample data
df = pd.DataFrame({'A': [
    '00052600150.00942615',
    '00052601000.01014910',
    '00052601050.02709672',
    '00052601100.11454732',
    '00052601150.23151254',
    '00052601200.36262522',
    '00052601250.66432348',
    '00052601301.07723763',
    '00052601351.26019487',
    '00052601401.20568581',
]})

df3 = df['A'].str.extract(
    r'(\d{2})(\d{2})(\d{2})(\d{2})(\d{2})(\d\.\d*)',
    expand=True)
df3.columns = ['Year', 'Month', 'Day', 'Hour', 'Minute', 'Reading']
print(df3)

输出

  Year Month Day Hour Minute     Reading
0   00    05  26   00     15  0.00942615
1   00    05  26   01     00  0.01014910
2   00    05  26   01     05  0.02709672
3   00    05  26   01     10  0.11454732
4   00    05  26   01     15  0.23151254
5   00    05  26   01     20  0.36262522
6   00    05  26   01     25  0.66432348
7   00    05  26   01     30  1.07723763
8   00    05  26   01     35  1.26019487
9   00    05  26   01     40  1.20568581