Forum Discussion

Anonymous's avatar
Anonymous
Not applicable
7 years ago
Solved

Python: DateTimeIndex not showing in PowerBI

Hi, my code runs smoothly in Spyder, so I don't think it's an issue with my code.   However, most of my variables are dataframes, and when I copy the whole code over to the Python Script in Power B...
Need Help
  • v-yuta-msft's avatar
    v-yuta-msft
    7 years ago

    Anonymous,

     

    I can reproduce your issue, just add a statement in the button of your code like pattern below:

    # 'dataset' holds the input data for this script
import datetime
import pandas as pd
import numpy as np

def datetimeix(df):
    df['Date\t'] = pd.DatetimeIndex(df['Date\t'])
    df.set_index('Date\t', inplace = True)
    return df

df = pd.read_csv(r'C:\Users\JimmyTao\Desktop\Test.csv') 

dataset = df

     

     

    After expand, the result is like this:

     

     

    Community Support Team _ Jimmy Tao

    If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

HALI322's avatar
HALI322
New Member
1 year ago

I got similar issue in getting data from a API datasource (Wind Financial System-A Chinese financial terminal like Bloomberg). Python script code is as follow:

 

import pandas as pd
from WindPy import *
w.start()

Wind_Data = w.edb("M0039354", "2000-01-01", "2024-11-11","Fill=Previous")
GDP = pd.DataFrame(Wind_Data.Data, columns=Wind_Data.Times, index=Wind_Data.Fields).T
GDP

 

In Python window, it did feedback transposed dataset include a date index and a column of GDP data. But in Power BI, the return left only GDP data column. The date index column disappeared. Python command line running result below:

>>> import pandas as pd
>>> from WindPy import *
>>> w.start()
.ErrorCode=0
.Data=[Already connected!]
>>>
>>> Wind_Data = w.edb("M0039354", "2000-01-01", "2024-11-11","Fill=Previous")
>>> GDP = pd.DataFrame(Wind_Data.Data, columns=Wind_Data.Times, index=Wind_Data.Fields).T
>>> GDP
                CLOSE
2000-03-31 8.7
2000-06-30 9.1
2000-09-30 8.8
2000-12-31 7.5
2001-03-31 9.5
... ...
2023-09-30 4.9
2023-12-31 5.2
2024-03-31 5.3
2024-06-30 4.7
2024-09-30 4.6

[99 rows x 1 columns]
>>>

Someone help?

(don't worry, all data included are publicly available China GDP growth rate data. No sensitive/private information included.)