Reading in Date / Time Values Correctly-CodePudding

Any ideas on how I can manipulate my current date-time data to make it suitable for use when converting the datatype to time? For example:

df1['Date/Time'] = pd.to_datetime(df1['Date/Time'])

The current format for the data is mm/dd 00:00:00

an example of the column in the dataframe can be seen below.

          Date/Time  Dry_Temp[C]  Wet_Temp[C]  Solar_Diffuse_Rate[[W/m2]]  \
0   01/01  00:10:00         8.45     8.237306                         0.0   
1   01/01  00:20:00         7.30     6.968360                         0.0   
2   01/01  00:30:00         6.15     5.710239                         0.0   
3   01/01  00:40:00         5.00     4.462898                         0.0   
4   01/01  00:50:00         3.85     3.226244                         0.0

CodePudding user response：

To access the values of the datetime (namely the time), you can use:

# These are now in a usable format
seconds = df1['Date/Time'].dt.second
minutes = df1['Date/Time'].dt.minute
hours = df1['Date/Time'].dt.hours

And if need be, you can create its own independent time series with:

df1['Dat/Time'].dt.time

CodePudding user response：

For the condition where the hour is denoted as 24, you have two choices. First you can simply reset the hour to 00 and second you can reset the hour to 00 and also add 1 to the date.
In either case the first step is detecting the condition which can be done with a simple find statement t.find(' 24:') Having detected the condition in the first case it is a simple matter of reseting the hour to 00 and proceeding with the process of formatting the field. In the second case, however, adding 1 to the day is a little more complicated because of the fact you can roll over to next month.

Here is the approach I would use:

Given a df of form:

    Date Time
0   01/01 00:00:00
1   01/01 00:24:00
2   01/01 24:00:00
3   01/31 24:00:00

The First Case

def parseDate2(tx):
    ti = tx.find('  24:')
    if ti >= 0:
        tk = pd.to_datetime(tx[:5] '  00:' tx[10:], format= '%m/%d  %H:%M:%S') 
        return tk   du.relativedelta.relativedelta(hours= 24)
    return pd.to_datetime(tx, format= '%m/%d  %H:%M:%S')        

df['Date Time'] = df['Date Time'].apply(lambda x: parseDate(x))

Produces the following:

    Date Time
0   1900-01-01 00:00:00
1   1900-01-01 00:24:00
2   1900-01-01 00:00:00
3   1900-01-31 00:00:00

For the second case, I employed the dateutil relativedelta library and slightly modified my parseDate funstion as shown below:

import dateutil as du
def parseDate2(tx):
    ti = tx.find('  24:')
    if ti >= 0:
        tk = pd.to_datetime(tx[:5] '  00:' tx[10:], format= '%m/%d  %H:%M:%S') 
        return tk   du.relativedelta.relativedelta(hours= 24)
    return pd.to_datetime(tx, format= '%m/%d  %H:%M:%S')        

df['Date Time'] = df['Date Time'].apply(lambda x: parseDate2(x))

Yields:

    Date Time
0   1900-01-01 00:00:00
1   1900-01-01 00:24:00
2   1900-01-02 00:00:00
3   1900-02-01 00:00:00