antonia-had

## gist:22a4c35b28371aeda2d5c7b9027d5184
dddd

## load_data.py
import numpy as np #Package we'll use for numerical calculations
import matplotlib.pyplot as plt #From matplotlib package we import pyplot for plots
import pandas #Package to data manipulation
import scipy.optimize #Package we'll use to optimize
plt.style.use('seaborn-colorblind') #This is a pyplot style (optional)

'''Load the data into a pandas series with the name wine_sales'''
time_series = pandas.Series.from_csv("wine_sales.csv", header=0)

P=12 #number of seasonal periods in a cycle

## wine_sales.csv

          
            Date
            Sales

            
              1/1/80
              464

            
              2/1/80
              675

            
              3/1/80
              703

            
              4/1/80
              887

            
              5/1/80
              1139

            
              6/1/80
              1077

            
              7/1/80
              1318

            
              8/1/80
              1260

            
              9/1/80
              1120
	import numpy as np #Package we'll use for numerical calculations
	import matplotlib.pyplot as plt #From matplotlib package we import pyplot for plots
	import pandas #Package to data manipulation
	import scipy.optimize #Package we'll use to optimize
	plt.style.use('seaborn-colorblind') #This is a pyplot style (optional)

	'''Load the data into a pandas series with the name wine_sales'''
	time_series = pandas.Series.from_csv("wine_sales.csv", header=0)

	P=12 #number of seasonal periods in a cycle
	Date	Sales
	1/1/80	464
	2/1/80	675
	3/1/80	703
	4/1/80	887
	5/1/80	1139
	6/1/80	1077
	7/1/80	1318
	8/1/80	1260
	9/1/80	1120