簡(jiǎn)單總結(jié)

關(guān)聯(lián)分析氮双、數(shù)值比較：散點(diǎn)圖慕蔚、曲線圖

分布分析：灰度圖适秩、密度圖

涉及分類的分析：柱狀圖余爆、箱式圖

1.Matplotlib基礎(chǔ)知識(shí)

使用Python進(jìn)行數(shù)據(jù)分析并可視化離不開(kāi)的一個(gè)重要包纷宇，matplotlib.這是一個(gè)開(kāi)源的包，里面有非常豐富的可視化圖形處理蛾方。官方文檔?Matplotlib像捶，里面有詳細(xì)的介紹，以及圖形種類桩砰。

本文主要是學(xué)習(xí)Matplotlib的基本使用方法拓春，需要使用到更多的方法是可以詳細(xì)的閱讀官方文檔。

安裝

a.使用 pip install -U matplotlib b.安裝Anaconda 自帶這個(gè)包

導(dǎo)入包

import matplotlib.pyplot as plt%matplotlib inline #魔法命令行亚隅，使用是能直接再輸出行顯示圖形

幾種基本圖形繪制

x = np.linspace(0, 2, 100)plt.plot(x, x, label='linear')plt.plot(x, x**2, label='quadratic')plt.plot(x, x**3, label='cubic')plt.xlabel('x label')plt.ylabel('y label')plt.title("Simple Plot")plt.legend()plt.show()

def my_plotter(ax, data1, data2, param_dict):? ? """? ? A helper function to make a graph? ? Parameters? ? ----------? ? ax : Axes? ? ? ? The axes to draw to? ? data1 : array? ? ? The x data? ? data2 : array? ? ? The y data? ? param_dict : dict? ? ? Dictionary of kwargs to pass to ax.plot? ? Returns? ? -------? ? out : list? ? ? ? list of artists added? ? """? ? out = ax.plot(data1, data2, **param_dict)? ? return out# which you would then use as:data1, data2, data3, data4 = np.random.randn(4, 100)fig, ax = plt.subplots(1, 1)my_plotter(ax, data1, data2, {'marker': 'x'})

子圖集

fig, (ax1, ax2) = plt.subplots(1, 2)my_plotter(ax1, data1, data2, {'marker': 'x'})my_plotter(ax2, data3, data4, {'marker': 'o'})

import numpy as npimport matplotlib.pyplot as pltN = 5menMeans = (20, 35, 30, 35, 27)womenMeans = (25, 32, 34, 20, 25)menStd = (2, 3, 4, 1, 2)womenStd = (3, 5, 2, 3, 3)ind = np.arange(N)? ? # the x locations for the groupswidth = 0.35? ? ? # the width of the bars: can also be len(x) sequencep1 = plt.bar(ind, menMeans, width, yerr=menStd)p2 = plt.bar(ind, womenMeans, width,? ? ? ? ? ? bottom=menMeans, yerr=womenStd)plt.ylabel('Scores')plt.title('Scores by group and gender')plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))plt.yticks(np.arange(0, 81, 10))plt.legend((p1[0], p2[0]), ('Men', 'Women'))plt.show()

def scatterplot(x_data, y_data, x_label, y_label, title):? ? fig, ax = plt.subplots()? ? ax.scatter(x_data, y_data, s = 10, color = '#539caf', alpha = 0.75)? ? ax.set_title(title)? ? ax.set_xlabel(x_label)? ? ax.set_ylabel(y_label)scatterplot(x_data = daily_data['temp']? ? ? ? ? ? , y_data = daily_data['cnt']? ? ? ? ? ? , x_label = 'Normalized temperature (C)'? ? ? ? ? ? , y_label = 'Check outs'? ? ? ? ? ? , title = 'Number of Check Outs vs Temperature')

import numpy as npimport matplotlib.pyplot as pltmen_means, men_std = (20, 35, 30, 35, 27), (2, 3, 4, 1, 2)women_means, women_std = (25, 32, 34, 20, 25), (3, 5, 2, 3, 3)ind = np.arange(len(men_means))? # the x locations for the groupswidth = 0.35? # the width of the barsfig, ax = plt.subplots()rects1 = ax.bar(ind - width/2, men_means, width, yerr=men_std,? ? ? ? ? ? ? ? color='SkyBlue', label='Men')rects2 = ax.bar(ind + width/2, women_means, width, yerr=women_std,? ? ? ? ? ? ? ? color='IndianRed', label='Women')# Add some text for labels, title and custom x-axis tick labels, etc.ax.set_ylabel('Scores')ax.set_title('Scores by group and gender')ax.set_xticks(ind)ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))ax.legend()def autolabel(rects, xpos='center'):? ? """? ? Attach a text label above each bar in *rects*, displaying its height.? ? *xpos* indicates which side to place the text w.r.t. the center of? ? the bar. It can be one of the following {'center', 'right', 'left'}.? ? """? ? xpos = xpos.lower()? # normalize the case of the parameter? ? ha = {'center': 'center', 'right': 'left', 'left': 'right'}? ? offset = {'center': 0.5, 'right': 0.57, 'left': 0.43}? # x_txt = x + w*off? ? for rect in rects:? ? ? ? height = rect.get_height()? ? ? ? ax.text(rect.get_x() + rect.get_width()*offset[xpos], 1.01*height,? ? ? ? ? ? ? ? '{}'.format(height), ha=ha[xpos], va='bottom')autolabel(rects1, "left")autolabel(rects2, "right")plt.show()

2. GAFAJA股票數(shù)據(jù)可視化分析

(谷歌硼莽，亞馬遜，F(xiàn)acebook煮纵，蘋果懂鸵，京東，阿里巴巴)幾大互聯(lián)網(wǎng)科技巨頭的股票數(shù)據(jù)行疏，可以直接從雅虎財(cái)經(jīng)網(wǎng)站上下載匆光，選擇一年的數(shù)據(jù)進(jìn)行分析。根據(jù)分析我們可以看到科技大佬也隨著2018年經(jīng)濟(jì)不景氣酿联，受到了很大的影響终息。但是這也許對(duì)很多人來(lái)說(shuō)又是一個(gè)最佳的投資時(shí)期！不過(guò)還是有一家公司做到了從年初到年末漲了24.3％得優(yōu)秀業(yè)績(jī)贞让。我們來(lái)看看是哪家周崭？

數(shù)據(jù)下載地址：根據(jù)公司名字搜索一下，然后選擇Historcal Data,設(shè)置查詢期間震桶，apply,然后download.

BABA Historical Prices | Alibaba Group Holding Limited A Stock - Yahoo Finance?finance.yahoo.com

#coding:utf-8%matplotlibinlineimportpandasaspdimportnumpyasnpimportmatplotlib.pyplotaspltappDF=pd.read_csv(r"Downloads\AAPL.csv")fbDF=pd.read_csv(r"Downloads\FB.csv")jdDF=pd.read_csv(r"Downloads\JD.csv")babaDF=pd.read_csv(r"Downloads\BABA.csv")amznDF=pd.read_csv(r"Downloads\AMZN.csv")googleDF=pd.read_csv(r"Downloads\GOOGL.csv")

APPLE

appDF.dtypesDateobjectOpenfloat64Highfloat64Lowfloat64Closefloat64AdjClosefloat64Volumeint64dtype:objectappDF['Date']=pd.to_datetime(appDF['Date'])# change datatypeappDF.set_index(appDF.Date,inplace=True)# reset indexappDF.indexDatetimeIndex(['2018-01-02','2018-01-03','2018-01-04','2018-01-05','2018-01-08','2018-01-09','2018-01-10','2018-01-11','2018-01-12','2018-01-16',...'2018-12-14','2018-12-17','2018-12-18','2018-12-19','2018-12-20','2018-12-21','2018-12-24','2018-12-26','2018-12-27','2018-12-28'],dtype='datetime64[ns]',name='Date',length=250,freq=None)appDF.info()<class'pandas.core.frame.DataFrame'>DatetimeIndex:250entries,2018-01-02to2018-12-28Datacolumns(total7columns):Date250non-nulldatetime64[ns]Open250non-nullfloat64High250non-nullfloat64Low250non-nullfloat64Close250non-nullfloat64AdjClose250non-nullfloat64Volume250non-nullint64dtypes:datetime64[ns](1),float64(5),int64(1)memoryusage:15.6KBappDF.describe()

Open High Low Close Adj Close Volume count 250.000000 250.000000 250.000000 250.000000 250.000000 2.500000e+02 mean 189.233760 191.120640 187.306320 189.178680 187.908454 3.401187e+07 std 20.456809 20.528269 20.387978 20.539151 20.836598 1.465514e+07 min 148.149994 151.550003 146.589996 146.830002 146.830002 1.251390e+07 25% 173.452503 174.962498 172.080002 173.472500 171.660324 2.351965e+07 50% 186.319999 187.534996 184.965003 186.180001 185.077881 3.161740e+07 75% 207.840000 209.437500 205.937496 207.875003 206.795952 4.077780e+07 max 230.779999 233.470001 229.779999 232.070007 231.263092 9.624670e+07

'''定義函數(shù)函數(shù)功能：計(jì)算股票漲跌幅=（現(xiàn)在股價(jià)-買入價(jià)格）/買入價(jià)格輸入?yún)?shù)：column是收盤價(jià)這一列的數(shù)據(jù)返回?cái)?shù)據(jù)：漲跌幅'''defchange(column):#買入價(jià)格buyPrice=column[0]#現(xiàn)在股價(jià)#column.size是總共數(shù)據(jù)條數(shù)休傍，序號(hào)是從0開(kāi)始的，所以最后一條數(shù)據(jù)的序號(hào)是總數(shù)目-1curPrice=column[column.size-1]#累計(jì)漲跌幅priceChange=(curPrice-buyPrice)/buyPrice#判斷股票是上漲蹲姐，還是下跌if(priceChange>0):print('股票累計(jì)上漲=',priceChange*100,'%')elif(priceChange==0):print('股票累沒(méi)有變化=',priceChange*100,'%')else:print('股票累計(jì)下跌',priceChange*100,'%')#返回?cái)?shù)據(jù)returnpriceChangecloseCol=appDF['Close']appChange=change(closeCol)股票累計(jì)下跌-9.305700374599455%

FACEBOOK

fbDF.dtypesDateobjectOpenfloat64Highfloat64Lowfloat64Closefloat64AdjClosefloat64Volumeint64dtype:objectfbDF['Date']=pd.to_datetime(fbDF['Date'])# change datatypefbDF.set_index(fbDF.Date,inplace=True)# reset indexfbDF.indexDatetimeIndex(['2018-01-02','2018-01-03','2018-01-04','2018-01-05','2018-01-08','2018-01-09','2018-01-10','2018-01-11','2018-01-12','2018-01-16',...'2018-12-14','2018-12-17','2018-12-18','2018-12-19','2018-12-20','2018-12-21','2018-12-24','2018-12-26','2018-12-27','2018-12-28'],dtype='datetime64[ns]',name='Date',length=250,freq=None)fbDF.describe()

Open High Low Close Adj Close Volume count 250.000000 250.000000 250.000000 250.000000 250.000000 2.500000e+02 mean 171.621040 173.769240 169.460560 171.672640 171.672640 2.766240e+07 std 19.595486 19.305557 19.958815 19.852291 19.852291 1.920073e+07 min 123.099998 129.740005 123.019997 124.059998 124.059998 9.588600e+06 25% 157.847503 160.939995 156.077495 158.142494 158.142494 1.779380e+07 50% 175.010002 177.040001 172.875000 174.794998 174.794998 2.179760e+07 75% 184.922493 186.510006 183.424996 185.289998 185.289998 3.031708e+07 max 215.720001 218.619995 214.270004 217.500000 217.500000 1.698037e+08

closeCol=fbDF['Close']fbChange=change(closeCol)股票累計(jì)下跌-26.57920931076187%

JingDong

jdDF.dtypesDateobjectOpenfloat64Highfloat64Lowfloat64Closefloat64AdjClosefloat64Volumeint64dtype:objectjdDF['Date']=pd.to_datetime(jdDF['Date'])# change datatypejdDF.set_index(jdDF.Date,inplace=True)# reset indexjdDF.indexDatetimeIndex(['2018-01-02','2018-01-03','2018-01-04','2018-01-05','2018-01-08','2018-01-09','2018-01-10','2018-01-11','2018-01-12','2018-01-16',...'2018-12-14','2018-12-17','2018-12-18','2018-12-19','2018-12-20','2018-12-21','2018-12-24','2018-12-26','2018-12-27','2018-12-28'],dtype='datetime64[ns]',name='Date',length=250,freq=None)closeCol=jdDF['Close']jdChange=change(closeCol)股票累計(jì)下跌-49.838263628425686%

Alibaba

babaDF.dtypesDateobjectOpenfloat64Highfloat64Lowfloat64Closefloat64AdjClosefloat64Volumeint64dtype:objectbabaDF['Date']=pd.to_datetime(babaDF['Date'])# change datatypebabaDF.set_index(babaDF.Date,inplace=True)# reset indexbabaDF.indexDatetimeIndex(['2018-01-02','2018-01-03','2018-01-04','2018-01-05','2018-01-08','2018-01-09','2018-01-10','2018-01-11','2018-01-12','2018-01-16',...'2018-12-14','2018-12-17','2018-12-18','2018-12-19','2018-12-20','2018-12-21','2018-12-24','2018-12-26','2018-12-27','2018-12-28'],dtype='datetime64[ns]',name='Date',length=250,freq=None)closeCol=babaDF['Close']babaChange=change(closeCol)股票累計(jì)下跌-24.26354448996062%

AMAZON

amznDF.dtypesDateobjectOpenfloat64Highfloat64Lowfloat64Closefloat64AdjClosefloat64Volumeint64dtype:objectamznDF['Date']=pd.to_datetime(amznDF['Date'])# change datatypeamznDF.set_index(amznDF.Date,inplace=True)# reset indexamznDF.indexDatetimeIndex(['2018-01-02','2018-01-03','2018-01-04','2018-01-05','2018-01-08','2018-01-09','2018-01-10','2018-01-11','2018-01-12','2018-01-16',...'2018-12-14','2018-12-17','2018-12-18','2018-12-19','2018-12-20','2018-12-21','2018-12-24','2018-12-26','2018-12-27','2018-12-28'],dtype='datetime64[ns]',name='Date',length=250,freq=None)closeCol=amznDF['Close']amznChange=change(closeCol)股票累計(jì)上漲=24.306776862206565%(amznDF.Close[-1]-amznDF.Close[0])/amznDF.Close[0]*100# amazon24.306776862206565amznDF.head(1)

Date Open High Low Close Adj Close Volume Date 2018-01-02 2018-01-02 1172.0 1190.0 1170.51001 1189.01001 1189.01001 2694500

amznDF.tail(1)

Date Open High Low Close Adj Close Volume Date 2018-12-28 2018-12-28 1473.349976 1513.469971 1449.0 1478.02002 1478.02002 8825600

GOOGLE

googleDF.dtypesDateobjectOpenfloat64Highfloat64Lowfloat64Closefloat64AdjClosefloat64Volumeint64dtype:objectgoogleDF['Date']=pd.to_datetime(googleDF['Date'])# change datatypegoogleDF.set_index(googleDF.Date,inplace=True)# reset indexgoogleDF.indexDatetimeIndex(['2018-01-02','2018-01-03','2018-01-04','2018-01-05','2018-01-08','2018-01-09','2018-01-10','2018-01-11','2018-01-12','2018-01-16',...'2018-12-14','2018-12-17','2018-12-18','2018-12-19','2018-12-20','2018-12-21','2018-12-24','2018-12-26','2018-12-27','2018-12-28'],dtype='datetime64[ns]',name='Date',length=250,freq=None)closeCol=googleDF['Close']googleChange=change(closeCol)股票累計(jì)下跌-2.472014607028055%babaDF.plot(x=babaDF.index,y='Close')#x坐標(biāo)軸文本plt.xlabel('時(shí)間')#y坐標(biāo)軸文本plt.ylabel('股價(jià)（美元）')#圖片標(biāo)題plt.title('2018年阿里巴巴股價(jià)走勢(shì)')#顯示網(wǎng)格plt.grid(True)#顯示圖形plt.show()

babaDF.plot(x='Volume',y='Close',kind='scatter')#x坐標(biāo)軸文本plt.xlabel('成交量')#y坐標(biāo)軸文本plt.ylabel('股價(jià)（美元）')#圖片標(biāo)題plt.title('成交量和股價(jià)')#顯示網(wǎng)格plt.grid(True)#顯示圖形plt.show()

babaDF.corr()

Open High Low Close Adj Close Volume Open 1.000000 0.995051 0.993075 0.985336 0.985336 -0.133665 High 0.995051 1.000000 0.994309 0.993553 0.993553 -0.106145 Low 0.993075 0.994309 1.000000 0.994654 0.994654 -0.168921 Close 0.985336 0.993553 0.994654 1.000000 1.000000 -0.145040 Adj Close 0.985336 0.993553 0.994654 1.000000 1.000000 -0.145040 Volume -0.133665 -0.106145 -0.168921 -0.145040 -0.145040 1.000000

#繪制谷歌的畫紙1ax1=googleDF.plot(x=googleDF.index,y='Close',label='谷歌')#通過(guò)指定畫紙ax磨取，在同一張畫紙上繪圖#亞馬遜amznDF.plot(ax=ax1,x=amznDF.index,y='Close',label='亞馬遜')#FacebookfbDF.plot(ax=ax1,x=fbDF.index,y='Close',label='Facebook')#蘋果appDF.plot(ax=ax1,x=appDF.index,y='Close',label='蘋果')#阿里巴巴babaDF.plot(ax=ax1,x=babaDF.index,y='Close',label='阿里巴巴')#騰訊jdDF.plot(ax=ax1,x=jdDF.index,y='Close',label='京東')#x坐標(biāo)軸文本plt.xlabel('時(shí)間')#y坐標(biāo)軸文本plt.ylabel('股價(jià)（美元）')#圖片標(biāo)題plt.title('2018年GAFATA股價(jià)累計(jì)漲幅比較')#顯示網(wǎng)格plt.grid(True)plt.show()

#繪制谷歌的畫紙1ax2=googleDF.plot(x=googleDF.index,y='Close',label='谷歌')#通過(guò)指定畫紙ax人柿，在同一張畫紙上繪圖#亞馬遜amznDF.plot(ax=ax2,x=amznDF.index,y='Close',label='亞馬遜')plt.xlabel('時(shí)間')#y坐標(biāo)軸文本plt.ylabel('股價(jià)（美元）')#圖片標(biāo)題plt.title('2018年GAFATA股價(jià)累計(jì)漲幅比較')#顯示網(wǎng)格plt.grid(True)plt.show()

#Facebookax3=fbDF.plot(x=fbDF.index,y='Close',label='Facebook')#蘋果appDF.plot(ax=ax3,x=appDF.index,y='Close',label='蘋果')#阿里巴巴babaDF.plot(ax=ax3,x=babaDF.index,y='Close',label='阿里巴巴')#騰訊jdDF.plot(ax=ax3,x=jdDF.index,y='Close',label='京東')#x坐標(biāo)軸文本plt.xlabel('時(shí)間')#y坐標(biāo)軸文本plt.ylabel('股價(jià)（美元）')#圖片標(biāo)題plt.title('2018年GAFATA股價(jià)累計(jì)漲幅比較')#顯示網(wǎng)格plt.grid(True)plt.show()

#6家公司股票收盤價(jià)平均值gafataMeanList=[googleDF['Close'].mean(),#谷歌amznDF['Close'].mean(),#亞馬遜fbDF['Close'].mean(),#FacebookappDF['Close'].mean(),#蘋果babaDF['Close'].mean(),#阿里巴巴jdDF['Close'].mean()#騰訊]#創(chuàng)建pandas一維數(shù)組SeriesgafataMeanSer=pd.Series(gafataMeanList,index=['谷歌','亞馬遜','Facebook','蘋果','阿里巴巴','騰訊'])gafataMeanSer.plot(kind='bar',label='GAFAJA')#圖片標(biāo)題plt.title('2018年GAFAJA股價(jià)平均值')#x坐標(biāo)軸文本plt.xlabel('公司名稱')#y坐標(biāo)軸文本plt.ylabel('股價(jià)平均值（美元）')plt.grid(True)plt.show()

#存放6家公司的收盤價(jià)closeDf=pd.DataFrame()#合并6家公司的收盤價(jià)closeDf=pd.concat([closeDf,googleDF['Close'],#谷歌amznDF['Close'],#亞馬遜fbDF['Close'],#FacebookappDF['Close'],#蘋果babaDF['Close'],#阿里巴巴jdDF['Close']#JD],axis=1)#重命名列名為公司名稱closeDf.columns=['谷歌','亞馬遜','Facebook','APPLE','阿里巴巴','JD']closeDf.head()

谷歌 亞馬遜 Facebook APPLE 阿里巴巴 JD Date 2018-01-02 1073.209961 1189.010010 181.419998 172.259995 183.649994 43.279999 2018-01-03 1091.520020 1204.199951 184.669998 172.229996 184.000000 43.509998 2018-01-04 1095.760010 1209.589966 184.330002 173.029999 185.710007 43.669998 2018-01-05 1110.290039 1229.140015 186.850006 175.000000 190.699997 45.639999 2018-01-08 1114.209961 1246.869995 188.279999 174.350006 190.330002 46.099998

closeDf.plot(kind='box')plt.grid(True)plt.show()

股票總結(jié):在2018年，京東的表現(xiàn)最差忙厌，股票總計(jì)快跌了一半凫岖，總市值還剩300億。亞馬遜表現(xiàn)最好逢净，上漲24.3％哥放，總市值7200多億。其他幾個(gè)股票都下行了爹土！蘋果在去年最好的市值達(dá)到了一萬(wàn)億甥雕，現(xiàn)在7400多億。