Update app.py

app.py

@@ -2,7 +2,7 @@ import matplotlib.pyplot as plt
 from pylab import rcParams
 from statsmodels.tsa.seasonal import seasonal_decompose
 import streamlit as st
-import dask.dataframe as dd 
+import pandas as pd
 
 st.title('Project Canada Goose')
 st.write('Mempertahankan brand "canada goose" agar tetap menjadi penjualan tertinggi (untuk 1 tahun kedepan) dengan metode time series forecasting')
@@ -13,14 +13,14 @@ def load_csv_data():
     data = pd.concat(tp, ignore_index=True)
 
     # Convert data yang bukan datetime yang seperti 0000-0000 ke Datetime agar hasilnya NaT
-    data['sold_at'] = dd.to_datetime(data['sold_at'], errors='coerce')
-    data['created_at'] = dd.to_datetime(data['created_at'], errors='coerce')
-    data['shipped_at'] = dd.to_datetime(data['shipped_at'], errors='coerce')
-    data['delivered_at'] = dd.to_datetime(data['delivered_at'], errors='coerce')
-    data['returned_at'] = dd.to_datetime(data['returned_at'], errors='coerce')
+    data['sold_at'] = pd.to_datetime(data['sold_at'], errors='coerce')
+    data['created_at'] = pd.to_datetime(data['created_at'], errors='coerce')
+    data['shipped_at'] = pd.to_datetime(data['shipped_at'], errors='coerce')
+    data['delivered_at'] = pd.to_datetime(data['delivered_at'], errors='coerce')
+    data['returned_at'] = pd.to_datetime(data['returned_at'], errors='coerce')
 
     # Ambil data date dari data setelahnya.
-    data.fillna(method='bfill')
+    data.fillna(method='bfill', inplace=True)
     return data
 
 data_load_state = st.text('Loading data...')
@@ -158,8 +158,8 @@ arima = auto_arima(y_train,start_p=1, start_q=1, max_p=3, max_q=3, m=12,
 
 n_forecast = len(y_test) + 8
 pred= arima.predict(n_forecast,D=1,seasonal=(1,0,0))
-dates = dd.date_range(y_test.index[-1],periods=n_forecast, freq='M')
-pred= dd.Series(pred, index=dates)
+dates = pd.date_range(y_test.index[-1],periods=n_forecast, freq='M')
+pred= pd.Series(pred, index=dates)
 
 fig = plt.figure(figsize=(15,5))
 plt.title("Prediksi menurut arima untuk 1 tahun kedepan")