ValueError: unconverted data remains: 00:00:00: Extract from Netcdf and arrange by location (rows) and values (precipitation) in columns

Question

I want to extract precipitation data from Netcdf files for 10 different locations (10 longitude/ latitude), daily (365), from 2014 to 2017. I want the end product to have: 10 rows (corresponding to 10 locations) and 368 columns (longitude, latitude, year, and 365 columns for precipitation from day 1 to day 365). I tried the following code and it the following chunk gives me error:

ValueError: unconverted data remains:  00:00:00

Here is my code

all_years = []

    for file in glob.glob('*.nc'):
        #print(file)
        data = Dataset(file, 'r')
        time = data.variables['time']
        year = file[0:4]

    all_years.append(year)

    year_start = min(all_years)

    #type(year_start)

    end_year = max(all_years)
    date_range = pd.date_range(start = str(year_start) + '-01-01', end = str(end_year) 
    +'-12-31', freq = 'D')
    df = pd.DataFrame(0.0, columns=['Latitude','Longitude','Precipitation'], index = 
    date_range)
    print(df)

    #Accessing locations to extract data (This has 3 columns: latitude, longitude, year)

    locations = pd.read_csv('LocationsToBeExtracted.csv')

    def datestdtojd (stddate):
    stddate.strip() 
    fmt='%Y-%m-%d'
    sdtdate = datetime.strptime(stddate, fmt)
    sdtdate = sdtdate.timetuple()
    jdate = sdtdate.tm_yday


for index, row in locations.iterrows():

        year = row['Year']
        location_latitude = row['Latitude']
        location_longitude = row['Longitude']
        all_years.sort()
        for yr in all_years:
            data = Dataset(str(yr)+'.nc', 'r')
        
            #storing lon lat data of the netCDF file into variable
            
            lat = data.variables['lat'][:]
            lon = data.variables['lon'][:]
        
            #Trying to access the nearest location in the netcdf file
            #squared difference between the specified lat, lon and lat lon of the netCDF 

            sq_diff_lat = (lat - location_latitude)**2
            sq_diff_lon = (lon - location_longitude)**2
        
            #identify the index of the min value for lat and lon
            
            min_index_lat = sq_diff_lat.argmin()
            min_index_lon = sq_diff_lon.argmin()
            
            #accessing precipitation values
        
            precip = data.variables['precip']
        
            #creating date range for every year during iteration
        
            start = str(yr) + '-01-01'
            end = str(yr) + '-12-31'
            d_range = pd.date_range(start = start,
                          end = end,
                          freq = 'D')
            # now a nested loop that goes for all years dates
        
            for t_index in np.arange(0,len(d_range)):
               
                print(d_range[t_index])
          
                #This is the culprit line that throws me error (below)

                df.loc[d_range[t_index]]['Day'] = datestdtojd(str(d_range[t_index]))
            
                #I tried the line below also 
                #df.loc[d_range[t_index]]['Day'] = 
                datestdtojd(datetime.strptime(str(d_range[t_index]), '%Y-%m-%d 
                %H:%M:%S'))

                df.loc[d_range[t_index]]['Year'] = year
                df.loc[d_range[t_index]]['Latitude'] = location_latitude
                df.loc[d_range[t_index]]['Longitude'] = location_longitude
                df.loc[d_range[t_index]]['Precipitation'] = precip[t_index, 
                    min_index_lat, min_index_lon]

        #save as csv

        df = df.pivot_table(values='Precipitation', index=. 
        ['Year','Latitude','Longitude'],columns='Day')      
        df.to_csv('Final_Output_With_368Column_10rows.csv')


ValueError: unconverted data remains:  00:00:00