errors saving dataframes since 1.8.0

[mihailescu2m]

I am saving a pandas dataframe like this:

db = InfluxDBClient(url=config.DB_URL, token=config.DB_TOKEN, org=config.DB_ORG
db_write_api = db.write_api()
db_write_api.write(config.DB_BUCKET, record=df, data_frame_measurement_name=price_history)
db_write_api.__del__()
db.__del__()

With 1.7.0 it worked well.
With 1.8.0 I am getting errors about the date:

The batch item wasn't processed successfully because: (400)
Reason: Bad Request
HTTP response headers: HTTPHeaderDict({'Content-Type': 'application/json; charset=utf-8', 'X-Platform-Error-Code': 'invalid', 'Date': 'Sat, 27 Jun 2020 21:50:02 GMT', 'Transfer-Encoding': 'chunked'})
HTTP response body: {"code":"invalid","message":"unable to parse 'price_history daily_charge=106.660794520548,actual_kw_price=4.4495000000000005,forecast_kw_price=nan,actual_general_use=21.906245000000002,forecast_general_use=nan,actual_controlled_load=15.229245000000002,forecast_controlled_load=nan,actual_fit=-6.999245000000001,forecast_fit=nan,actual_renewables=25.036933442426097,forecast_renewables=nan 1593207000000000000': invalid number\nunable to parse 'price_history ..... [error continues for each row]

[bednar]

@mihailescu2m thanks, we will take a look

[bednar]

@mihailescu2m could you please share how your Pandas DataFrame looks like?

[mihailescu2m]

@bednar is this enough?

print(df)
                     daily_charge  actual_kw_price  forecast_kw_price  actual_general_use  forecast_general_use  actual_controlled_load  forecast_controlled_load  actual_fit  forecast_fit  actual_renewables  forecast_renewables
ts                                                                                                                                                                                                                                 
2020-06-28 18:30:00    106.660795           1.7347                NaN           18.892817                   NaN               12.215817                       NaN   -3.985817           NaN          95.838662                  NaN
2020-06-28 19:00:00    106.660795           2.6631                NaN           19.923341                   NaN               13.246341                       NaN   -5.016341           NaN          90.276395                  NaN
2020-06-28 19:30:00    106.660795           3.1955                NaN           20.514305                   NaN               13.837305                       NaN   -5.607305           NaN          83.388326                  NaN
2020-06-28 20:00:00    106.660795           4.5749                NaN           22.045439                   NaN               15.368439                       NaN   -7.138439           NaN          68.391742                  NaN
2020-06-28 20:30:00    106.660795           5.7310                NaN           23.328710                   NaN               16.651710                       NaN   -8.421710           NaN          59.522250                  NaN
...                           ...              ...                ...                 ...                   ...                     ...                       ...         ...           ...                ...                  ...
2020-06-30 15:30:00    106.660795              NaN           3.582510                 NaN             20.943886                     NaN                 14.266886         NaN     -6.036886                NaN            57.748487
2020-06-30 16:00:00    106.660795              NaN           3.412366                 NaN             20.755026                     NaN                 14.078026         NaN     -5.848026                NaN            63.197402
2020-06-30 16:30:00    106.660795              NaN           3.138664                 NaN             20.451217                     NaN                 13.774217         NaN     -5.544217                NaN            70.247935
2020-06-30 17:00:00    106.660795              NaN           2.544090                 NaN             19.791240                     NaN                 13.114240         NaN     -4.884240                NaN            75.524903
2020-06-30 17:30:00    106.660795              NaN           2.303061                 NaN             19.523698                     NaN                 12.846698         NaN     -4.616698                NaN            81.681978

[95 rows x 11 columns]

print(df.info())
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 95 entries, 2020-06-28 18:30:00 to 2020-06-30 17:30:00
Data columns (total 11 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   daily_charge              95 non-null     float64
 1   actual_kw_price           49 non-null     float64
 2   forecast_kw_price         46 non-null     float64
 3   actual_general_use        49 non-null     float64
 4   forecast_general_use      46 non-null     float64
 5   actual_controlled_load    49 non-null     float64
 6   forecast_controlled_load  46 non-null     float64
 7   actual_fit                49 non-null     float64
 8   forecast_fit              46 non-null     float64
 9   actual_renewables         49 non-null     float64
 10  forecast_renewables       46 non-null     float64
dtypes: float64(11)
memory usage: 8.9 KB
None

[bednar]

It is perfect. Thanks