problem assigning values to pandas dataframe at specific location

user_14098258 0
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I have a problem assigning values to a dataframe at a specific location (column and index). First I create an empty dataframe:

self.timeseries = pd.DataFrame(
                columns = ["temperature", "state_of_charge", "m_ice", "m_water"],
                index = pd.date_range(
                        start = self.environment.start,
                        end = self.environment.end,
                        freq = self.environment.time_freq,
                        name = "time"
                        )
                )

Later I try to assign values to each column for each index:

        self.timeseries.temperature.loc[timestamp] = self.current_temp
        self.timeseries.state_of_charge.loc[timestamp] = self.state_of_charge
        self.timeseries.m_ice.loc[timestamp] = self.m_ice /self.mass
        self.timeseries.m_water.loc[timestamp] = self.m_water / self.mass

the first line of the code above seems to work. But once it reaches the second line, I get the following error message:

ValueError: No axis named 1 for object type <class 'pandas.core.series.Series'>

In other parts of the code exactly the same procedure is used, and it works.

Many thanks for each help in advance!

P.S.: I dont know, if it is necessary, but additional info:

I work with spyder 3.3.6 (Python 3.7) in anaconda 1.9.12

pandas python
2 Answers
import pandas as pd
timeseries = pd.DataFrame(
                columns = ["temperature", "state_of_charge", "m_ice", "m_water"],
                index =pd.date_range(start ='01-01-2018',
                         end ='01-02-2018', freq ='5H')
                )
print(timeseries)
timeseries.temperature.loc['2018-01-01 00:00:00'] = 15
timeseries.state_of_charge.loc['2018-01-01 00:00:00'] = 10
timeseries.m_ice.loc['2018-01-01 00:00:00'] = 1
timeseries.m_water.loc['2018-01-01 00:00:00'] = 1
print(timeseries)

Result:

                    temperature state_of_charge m_ice m_water
2018-01-01 00:00:00         NaN             NaN   NaN     NaN
2018-01-01 05:00:00         NaN             NaN   NaN     NaN
2018-01-01 10:00:00         NaN             NaN   NaN     NaN
2018-01-01 15:00:00         NaN             NaN   NaN     NaN
2018-01-01 20:00:00         NaN             NaN   NaN     NaN
                    temperature state_of_charge m_ice m_water
2018-01-01 00:00:00          15              10     1       1
2018-01-01 05:00:00         NaN             NaN   NaN     NaN
2018-01-01 10:00:00         NaN             NaN   NaN     NaN
2018-01-01 15:00:00         NaN             NaN   NaN     NaN
2018-01-01 20:00:00         NaN             NaN   NaN     NaN
user_91023980

you can try to iterate over the length of the data frame and use integer index location to fill in the values into the columns. the values can be a calculated values, it does not really matter. The logic will remain the same.

import pandas as pd
timeseries = pd.DataFrame(
                columns = ["temperature", "state_of_charge", "m_ice", "m_water"],
                index =pd.date_range(start ='01-01-2018 00:00:00',
                         end ='01-02-2018 00:00:00', freq='0.25H')
                )

for i in range(len(timeseries)):
   # this an example, can be any calculated value or function return # 
    timeseries.temperature.iloc[i] = i 
    timeseries.state_of_charge.iloc[i] = i+1
    timeseries.m_ice.iloc[i] = i+2
    timeseries.m_water.iloc[i] = i+3
print(timeseries)

                    temperature state_of_charge m_ice m_water
2018-01-01 00:00:00           0               1     2       3
2018-01-01 00:15:00           1               2     3       4
2018-01-01 00:30:00           2               3     4       5
2018-01-01 00:45:00           3               4     5       6
2018-01-01 01:00:00           4               5     6       7
...                         ...             ...   ...     ...
2018-01-01 23:00:00          92              93    94      95
2018-01-01 23:15:00          93              94    95      96
2018-01-01 23:30:00          94              95    96      97
2018-01-01 23:45:00          95              96    97      98
2018-01-02 00:00:00          96              97    98      99
user_93944320
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