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Created 4 years ago
A tutorial on time series data preparation and EDA.You require following datasets.
- airline-passengers
- champagne
- daily-minimum-temperatures
- shampoo-sales
#Disable warnings
import warnings
warnings.filterwarnings('ignore')
warnings.simplefilter('ignore')# Import required libraries
import pandas as pd
import numpy as np
from pandas.plotting import lag_plot
from pandas.plotting import autocorrelation_plot
from matplotlib import pyplot
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.stattools import adfuller
import math as math
from scipy.stats import boxcox
from random import randrange
from random import seed
from random import random
from random import gauss# load dataset
series = pd.read_csv('daily-minimum-temperatures.csv', header=0, index_col=0, parse_dates=True,
squeeze=True)
print(type(series))<class 'pandas.core.series.Series'>
#Peek into the data
print(series.head())
Date
1981-01-01 20.7
1981-01-02 17.9
1981-01-03 18.8
1981-01-04 14.6
1981-01-05 15.8
Name: Temp, dtype: float64