时间序列分析案例 —— Rossmann 销售数据_销售量与时间的关系

本文翻译自 kaggle 竞赛 Rossmann Store Sales 的一篇笔记，竞赛度主题是销量数据的预测，这里总结一些数据准备与分析方法

源码在：https://github.com/elena-petrova/rossmann_TSA_forecasts

翻译的目的是为了学习商业数据分析的一些套路，师夷长技以自强！

导入必要的包

numpy，pandas，matplotlib， seaborn，statsmodels

import warnings
warnings.filterwarnings("ignore")

# loading packages
# basic + dates 
import numpy as np
import pandas as pd
from pandas import datetime

# data visualization
import matplotlib.pyplot as plt
import seaborn as sns # advanced vizs
%matplotlib inline

# statistics
from statsmodels.distributions.empirical_distribution import ECDF

# time series analysis
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
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pandas 读取 csv

# importing train data to learn
train = pd.read_csv("train.csv", 
                    parse_dates = True, low_memory = False, index_col = 'Date')

# additional store data
store = pd.read_csv("store.csv", 
                    low_memory = False)
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这里可以了解一下参数：parse_dates，low_memory的作用哦