i have data like:
00052600150.00942615
00052601000.01014910
00052601050.02709672
00052601100.11454732
00052601150.23151254
00052601200.36262522
00052601250.66432348
00052601301.07723763
00052601351.26019487
00052601401.20568581
First 10 digits represent the timestep YYMMDDhhmm followed by a number
Its supposed to be 0005260010,0.00799872 where the first block is a timestep and the second a measurement.
Ive tried to read the data with pandas and convert it to a str but then i loose the leading zeros? Is there a way to separate a float by digit?
greetings
You can read the column as str and split your value by positions
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
Out:
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