pandas分析各国家交易情况

数据集来源： https://archive.ics.uci.edu/ml/datasets/Online%20Retail

#coding:utf-8import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as pltRAW_DATA_FILE = './data/online_retail.xlsx'
CLN_DATA_FILE = './output/cln_online_retail.csv'def inspect_data(data_df):"""查看数据集信息参数：- data_df:  DataFrame数据"""print('数据集基本信息')print(data_df.info())print('\n数据集统计信息')print(data_df.describe())print('\n数据集预览')print(data_df.head())def clean_data(data_df):"""数据清洗，包括去除空记录，去除重复记录参数：- data_df:  DataFrame数据返回：- cln_data_df:  清洗后的数据"""# 去除空记录后的数据non_empty_data_df = data_df.dropna()n_empty = data_df.shape[0] - non_empty_data_df.shape[0]# 去重后的记录cln_data_df = non_empty_data_df.drop_duplicates()n_duplicates = data_df.shape[0] - cln_data_df.shape[0]print('原始数据共有{}条记录，清洗后的数据共有{}条有效记录。（其中空记录有{}条，重复记录有{}条。）'.format(data_df.shape[0], cln_data_df.shape[0], n_empty, n_duplicates))# 保存清洗结果cln_data_df.to_csv(CLN_DATA_FILE, index=False, encoding='utf-8')return cln_data_dfdef show_customer_stats(data_df):"""比较各国家的客户数参数：- data_df:  DataFrame数据"""customer_per_country = data_df.drop_duplicates(['CustomerID'])['Country'].value_counts()# 由于'United Kingdom'数据过多，所以这里只考虑其他国家customer_per_country_df = \customer_per_country[customer_per_country.index != 'United Kingdom'].to_frame().T# 可视化结果sns.barplot(data=customer_per_country_df)#xlabel的名字旋转90度plt.xticks(rotation=90)plt.xlabel('Country')plt.ylabel('#Customers')#布局紧凑plt.tight_layout()plt.savefig('./output/customer_per_country.png')plt.show()def show_total_cost_stats(data_df):"""比较各国家的成交额参数：- data_df:  DataFrame数据"""# 过滤掉"取消"的交易记录，以及'United Kingdom'的数据cond1 = ~data_df['InvoiceNo'].str.startswith('C')cond2 = data_df['Country'] != 'United Kingdom'valid_data_df = data_df[cond1 & cond2].copy()valid_data_df['TotalCost'] = valid_data_df['UnitPrice'] * valid_data_df['Quantity']cost_per_country = valid_data_df.groupby('Country')['TotalCost'].sum()# 可视化结果cost_per_country.sort_values(ascending=False).plot(kind='bar')plt.ylabel('Total Cost')plt.tight_layout()plt.savefig('./output/cost_per_country.png')plt.show()def show_trend_by_country(data_df):"""统计各国家交易记录的趋势参数：- data_df:  DataFrame数据"""countries = ['Germany', 'France', 'Spain', 'Belgium', 'Switzerland']data_df = data_df[data_df['Country'].isin(countries)].copy()data_df['InvoiceDate'] = pd.to_datetime(data_df['InvoiceDate'])data_df['InvoiceYear'] = data_df['InvoiceDate'].dt.year.astype(str)data_df['InvoiceMonth'] = data_df['InvoiceDate'].dt.month.astype(str)#年份拼上月份用横线隔开data_df['InvoiceYearMonth'] = data_df['InvoiceYear'].str.cat(data_df['InvoiceMonth'], sep='-')month_country_count = data_df.groupby(['InvoiceYearMonth', 'Country'])['StockCode'].count()print(month_country_count.head())#打散month_country_count_df = month_country_count.unstack()print(month_country_count_df.head())#横轴截至到月份month_country_count_df.index = pd.to_datetime(month_country_count_df.index).to_period('M')print(month_country_count_df.head())month_country_count_df.sort_index(inplace=True)# 可视化结果# 堆叠柱状图month_country_count_df.plot(kind='bar', stacked=True, rot=45)plt.xlabel('Month')plt.ylabel('#Transaction')plt.tight_layout()plt.savefig('./output/country_trend_stacked_bar.png')plt.show()## 热图sns.heatmap(month_country_count_df.T)plt.xlabel('Month')plt.xticks(rotation=90)plt.yticks(rotation=0)plt.tight_layout()plt.savefig('./output/country_trend_heatmap.png')plt.show()def main():"""主函数"""if not os.path.exists(CLN_DATA_FILE):# 如果不存在清洗后的数据集，进行数据清洗raw_data_df = pd.read_excel(RAW_DATA_FILE, dtype={'InvoiceNo': str,'StockCode': str,'CustomerID': str})# 查看数据集信息inspect_data(raw_data_df)# 数据清洗cln_data_df = clean_data(raw_data_df)else:print('读取已清洗的数据')cln_data_df = pd.read_csv(CLN_DATA_FILE)# 数据分析# 1. 比较各国家的客户数show_customer_stats(cln_data_df)# 2. 比较各国家的成交额show_total_cost_stats(cln_data_df)# 3. 统计各国家交易记录的趋势show_trend_by_country(cln_data_df)if __name__ == '__main__':main()

各国家的客户数