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Syndicate_Admin's avatar
Syndicate_Admin
Icon for Administrator rankAdministrator
2 years ago

PROBLEMA SCRIPT PYTHON AND POWERBI

Good afternoon,

I'm trying to launch a forecast script inside PowerBi, specifically this one:

import pandas as pd
import statsmodels.api as sm

data = PerfilCliente[['FECHA', 'PESO']]

data['FECHA'] = pd.to_datetime(data['FECHA'])
data.set_index('FECHA', inplace=True)
data = data.sort_index()

data_diff = data.diff().dropna()

model = sm.tsa.ARIMA(data_diff, order=(1, 1, 1))
model_fit = model.fit()

forecast = model_fit.forecast(steps=90)
forecast_dates = pd.date_range(data.index[-1], periods=90, freq='D')


forecast_df = pd.DataFrame(forecast, index=forecast_dates, columns=['Predicción_Peso'])

forecast_df.reset_index(inplace=True)
forecast_df.rename(columns={'index': 'FECHA'}, inplace=True)
forecast_df['FECHA'] = forecast_df['FECHA'].dt.strftime('%Y-%m-%d')

result = forecast_df

The problem turns out that, when I run it, it gives the following error:

Detalles: "ADO.NET: ÞУŧћøñ ŝ¢ѓĭρť έřґσŕ.
<pi>NameError: name 'PerfilCliente' is not defined
</pi>"


I don't know if I'm not referencing the Client Profile table correctly, but when I reference others with a dataset directly written in Python, I don't get the error. The libraries and everything are correctly installed. Could you help me?

Thank you.

1 Reply

  • Anonymous's avatar
    Anonymous
    Not applicable
    2 years ago

    Hi, Syndicate_Admin 

    Make sure that PerfilCliente has been properly imported into the Python script. In Power BI, you need to use the pandas gallery to read datasets from Power BI's data model. You can do this by using the dataset variable provided by Power BI.

    You can also refer to the code below:

    import pandas as pd
import statsmodels.api as sm

# Import the dataset from Power BI
PerfilCliente = dataset

# Ensure the columns are correctly referenced
data = PerfilCliente[['FECHA', 'PESO']]

data['FECHA'] = pd.to_datetime(data['FECHA'])
data.set_index('FECHA', inplace=True)
data = data.sort_index()

data_diff = data.diff().dropna()

model = sm.tsa.ARIMA(data_diff, order=(1, 1, 1))
model_fit = model.fit()

forecast = model_fit.forecast(steps=90)
forecast_dates = pd.date_range(data.index[-1], periods=90, freq='D')

forecast_df = pd.DataFrame(forecast, index=forecast_dates, columns=['Predicción_Peso'])

forecast_df.reset_index(inplace=True)
forecast_df.rename(columns={'index': 'FECHA'}, inplace=True)
forecast_df['FECHA'] = forecast_df['FECHA'].dt.strftime('%Y-%m-%d')

result = forecast_df

     

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