import pandas as pd import numpy as np import matplotlib.pyplot as plt #1.加載并查看數據基本信息 def read_dataset(file_root):dataframe=pd.read_csv(file_root)print("數據的基本信息：")print(dataframe.info())print("數據集有%i行，%i列"%(dataframe.shape[0],dataframe.shape[1]))print("數據預覽：")print(dataframe.head())return dataframe #2.處理缺失數據 def processing_missing_data(dataframe):if (dataframe.isnull().values.any()):dataframe.fillna(0.)#dataframe.dropna()return dataframe #3.可視化數據 def visualize_data(dataframe,save_fig=True):fig=plt.figure()ax1=fig.add_subplot(2,2,1)ax2=fig.add_subplot(2,2,2)ax3=fig.add_subplot(2,2,3)ax4=fig.add_subplot(2,2,4)#解決matplotlib顯示中文的問題plt.rcParams["font.sans-serif"]=["SimHei"]#指定默認字體plt.rcParams["axes.unicode_minus"]=False#解決保存圖像是負號顯示為方塊問題fig.suptitle("戰隊屬性")ax1.scatter(dataframe["LeagueIndex"],dataframe["Age"])ax1.set_xlabel("戰隊")ax1.set_ylabel("年齡")ax2.scatter(dataframe["LeagueIndex"],dataframe["HoursPerWeek"])ax2.set_xlabel("戰隊")ax2.set_ylabel("每周游戲時間")ax3.scatter(dataframe["LeagueIndex"], dataframe["APM"])ax3.set_xlabel("戰隊")ax3.set_ylabel("APM")ax4.scatter(dataframe["LeagueIndex"], dataframe["WorkersMade"])ax4.set_xlabel("戰隊")ax4.set_ylabel("單位時間建造數")if save_fig:plt.savefig("H:/pythonfigure/league.png")plt.show() #4.可視化數據統計 def visualize_league_data(dataframe,attribution,save_fig=True):min_data=[]max_data=[]mean_data=[]for i in range(1,9):filter_data=dataframe.ix[dataframe["LeagueIndex"]==i,attribution]min_data.append(filter_data.min())max_data.append(filter_data.max())mean_data.append(filter_data.mean())league_series=pd.Series(range(1,9),name="LeagueIndex")min_series=pd.Series(min_data,name="min")max_series=pd.Series(max_data,name="max")mean_series=pd.Series(mean_data,name="mean")dataframe_rst=pd.concat([league_series,min_series,max_series,mean_series],axis=1)if save_fig:dataframe_rst.to_csv("H:/pythonfigure/min_max_mean.csv")#統計值可視化fig=plt.figure()ax1=fig.add_subplot(1,1,1)#解決matplotlib顯示中文問題plt.rcParams["font.sans-serif"]=["SimHei"]#指定默認字體plt.rcParams['axes.unicode_minus'] = False # 解決保存圖像是負號'-'顯示為方塊的問題ax1.plot(dataframe_rst["LeagueIndex"],dataframe_rst["min"],color='b')ax1.plot(dataframe_rst["LeagueIndex"],dataframe_rst["max"],color='g')ax1.plot(dataframe_rst["LeagueIndex"],dataframe_rst["mean"],color="r")ax1.set_xlabel("戰隊")ax1.set_ylabel("attribution")ax1.legend()if save_fig:plt.savefig("H:/pythonfigure/min_max_mean.png")plt.show()#加載數據 dataframe=read_dataset("H:/pythonfigure/starcraft.csv") #處理缺失數據 df_data=processing_missing_data(dataframe) #可視化數據 visualize_data(df_data,save_fig=True) #可視化統計指標 visualize_league_data(df_data,"Age",save_fig=True) visualize_league_data(df_data,"HoursPerWeek",save_fig=True) visualize_league_data(df_data,"APM",save_fig=True) visualize_league_data(df_data,"WorkersMade",save_fig=True)

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