Lianne & Justin @ Just into Data liannewriting

## arima_model_manual.py
from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(df_train, order=(2,1,0))
model_fit = model.fit()
print(model_fit.summary())

## adf_test_difference.py
adf_test = adfuller(df_train_diff)
print(f'p-value: {adf_test[1]}')

## difference_acf_pacf_plots.py
acf_diff = plot_acf(df_train_diff)

pacf_diff = plot_pacf(df_train_diff)

## difference_training.py
df_train_diff = df_train.diff().dropna()
df_train_diff.plot()

## statsmodels_adf_test.py
from statsmodels.tsa.stattools import adfuller
adf_test = adfuller(df_train)
print(f'p-value: {adf_test[1]}')

## statsmodels_acf_pacf_plots.py
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

acf_original = plot_acf(df_train)

pacf_original = plot_pacf(df_train)

## train_test_split.py
msk = (df.index < len(df)-30)
df_train = df[msk].copy()
df_test = df[~msk].copy()

## transform_data_log.py
import numpy as np
df = np.log(df) # don't forget to transform the data back when making real predictions

df.plot()

## import_time_series_data.py
import pandas as pd
df = pd.read_csv('website_data.csv')
df.info()

df.plot()

## streamlit_python_tutorial_example.py
import streamlit as st
import pandas as pd
import plotly.express as px

st.write('# Avocado Prices dashboard')  #st.title('Avocado Prices dashboard')

st.markdown('''
This is a dashboard showing the *average prices* of different types of :avocado:
Data source: [Kaggle](https://www.kaggle.com/datasets/timmate/avocado-prices-2020)
''')
	from statsmodels.tsa.arima.model import ARIMA
	model = ARIMA(df_train, order=(2,1,0))
	model_fit = model.fit()
	print(model_fit.summary())
	adf_test = adfuller(df_train_diff)
	print(f'p-value: {adf_test[1]}')
	acf_diff = plot_acf(df_train_diff)

	pacf_diff = plot_pacf(df_train_diff)
	df_train_diff = df_train.diff().dropna()
	df_train_diff.plot()
	from statsmodels.tsa.stattools import adfuller
	adf_test = adfuller(df_train)
	print(f'p-value: {adf_test[1]}')
	from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

	acf_original = plot_acf(df_train)

	pacf_original = plot_pacf(df_train)
	msk = (df.index < len(df)-30)
	df_train = df[msk].copy()
	df_test = df[~msk].copy()
	import numpy as np
	df = np.log(df) # don't forget to transform the data back when making real predictions

	df.plot()
	import pandas as pd
	df = pd.read_csv('website_data.csv')
	df.info()

	df.plot()
	import streamlit as st
	import pandas as pd
	import plotly.express as px

	st.write('# Avocado Prices dashboard') #st.title('Avocado Prices dashboard')

	st.markdown('''
	This is a dashboard showing the average prices of different types of :avocado:
	Data source: [Kaggle](https://www.kaggle.com/datasets/timmate/avocado-prices-2020)
	''')