這篇文章主要是最近整理《數(shù)據(jù)挖掘與分析》課程中的作品及課件過程中吧雹，收集了幾段比較好的代碼供大家學習骨杂。同時，做數(shù)據(jù)分析到后面吮炕，除非是研究算法創(chuàng)新的腊脱，否則越來越覺得數(shù)據(jù)非常重要访得，才是有價值的東西龙亲。后面的課程會慢慢講解Python應(yīng)用在Hadoop和Spark中，以及networkx數(shù)據(jù)科學等知識悍抑。

如果文章中存在錯誤或不足之處鳄炉，還請海涵~希望文章對你有所幫助。

一. Pandas獲取數(shù)據(jù)集并顯示

采用Pandas對2002年~2014年的商品房價數(shù)據(jù)集作時間序列分析搜骡，從中抽取幾個城市與貴陽做對比拂盯，并對貴陽商品房作出分析。

數(shù)據(jù)集位32.csv记靡，具體值如下：（讀者可直接復(fù)制）

year????Beijing?Chongqing???Shenzhen????Guiyang?Kunming?Shanghai????Wuhai???Changsha??

2002????4764.00?????1556.00?????5802.00?????1643.00?????2276.00?????4134.00?????1928.00?????1802.00???

2003????4737.00?????1596.00?????6256.00?????1949.00?????2233.00?????5118.00?????2072.00?????2040.00???

2004????5020.93?????1766.24?????6756.24?????1801.68?????2473.78?????5855.00?????2516.32?????2039.09???

2005????6788.09?????2134.99?????7582.27?????2168.90?????2639.72?????6842.00?????3061.77?????2313.73???

2006????8279.51?????2269.21?????9385.34?????2372.66?????2903.32?????7196.00?????3689.64?????2644.15???

2007????11553.26????2722.58?????14049.69????2901.63?????3108.12?????8361.00?????4664.03?????3304.74???

2008????12418.00????2785.00?????12665.00????3149.00?????3750.00?????8195.00?????4781.00?????3288.00???

2009????13799.00????3442.00?????14615.00????3762.00?????3807.00?????12840.00????5329.00?????3648.00???

2010????17782.00????4281.00?????19170.00????4410.00?????3660.00?????14464.00????5746.00?????4418.00???

2011????16851.95????4733.84?????21350.13????5069.52?????4715.23?????14603.24????7192.90?????5862.39???

2012????17021.63????5079.93?????19589.82????4846.14?????5744.68?????14061.37????7344.05?????6100.87???

2013????18553.00????5569.00?????24402.00????5025.00?????5795.00?????16420.00????7717.00?????6292.00???

2014????18833.00????5519.00?????24723.00????5608.00?????6384.00?????16787.00????7951.00?????6116.00??

繪制對比各個城市的商品房價數(shù)據(jù)代碼如下所示：

#?-*-?coding:?utf-8?-*-??

"""

Created?on?Mon?Mar?06?10:55:17?2017

@author:?eastmount

"""??

import?pandas?as?pd??

data?=?pd.read_csv("32.csv",index_col='year')?#index_col用作行索引的列名???

#顯示前6行數(shù)據(jù)???

print(data.shape)????

print(data.head(6))??

import?matplotlib.pyplot?as?plt??

plt.rcParams['font.sans-serif']?=?['simHei']?#用來正常顯示中文標簽??

plt.rcParams['axes.unicode_minus']?=?False???#用來正常顯示負號??

data.plot()??

plt.savefig(u'時序圖.png',?dpi=500)??

plt.show()??

輸出如下所示：

重點知識：

1谈竿、plt.rcParams顯示中文及負號团驱；

2、plt.savefig保存圖片至本地空凸；

3嚎花、pandas直接讀取數(shù)據(jù)顯示繪制圖形，index_col獲取索引呀洲。

二. Pandas獲取某列數(shù)據(jù)繪制柱狀圖

接著上面的實驗紊选，我們需要獲取貴陽那列數(shù)據(jù)，再繪制相關(guān)圖形道逗。

#?-*-?coding:?utf-8?-*-??

"""

Created?on?Mon?Mar?06?10:55:17?2017

@author:?eastmount

"""??

import?pandas?as?pd??

data?=?pd.read_csv("32.csv",index_col='year')?#index_col用作行索引的列名???

#顯示前6行數(shù)據(jù)???

print(data.shape)????

print(data.head(6))??

import?matplotlib.pyplot?as?plt??

plt.rcParams['font.sans-serif']?=?['simHei']?#用來正常顯示中文標簽??

plt.rcParams['axes.unicode_minus']?=?False???#用來正常顯示負號??

data.plot()??

plt.savefig(u'時序圖.png',?dpi=500)??

plt.show()??

#獲取貴陽數(shù)據(jù)集并繪圖??

gy?=?data['Guiyang']??

print?u'輸出貴陽數(shù)據(jù)'??

print?gy??

gy.plot()??

plt.show()??

通過data['Guiyang']獲取某列數(shù)據(jù)兵罢，然后再進行繪制如下所示：

通過這個數(shù)據(jù)集調(diào)用bar函數(shù)可以繪制對應(yīng)的柱狀圖，如下所示滓窍，需要注意x軸位年份卖词，獲取兩列數(shù)據(jù)進行繪圖。

#?-*-?coding:?utf-8?-*-??

"""

Created?on?Mon?Mar?06?10:55:17?2017

@author:?eastmount

"""??

import?pandas?as?pd??

data?=?pd.read_csv("32.csv",index_col='year')?#index_col用作行索引的列名???

#顯示前6行數(shù)據(jù)???

print(data.shape)????

print(data.head(6))??

#獲取貴陽數(shù)據(jù)集并繪圖??

gy?=?data['Guiyang']??

print?u'輸出貴陽數(shù)據(jù)'??

print?gy??

import?numpy?as?np??

x?=?['2002','2003','2004','2005','2006','2007','2008',??

'2009','2010','2011','2012','2013','2014']??

N?=13??

ind?=?np.arange(N)#賦值0-13??

width=0.35??

plt.bar(ind,?gy,?width,?color='r',?label='sum?num')???

#設(shè)置底部名稱????

plt.xticks(ind+width/2,?x,?rotation=40)?#旋轉(zhuǎn)40度????

plt.title('The?price?of?Guiyang')????

plt.xlabel('year')????

plt.ylabel('price')????

plt.savefig('guiyang.png',dpi=400)????

plt.show()??

輸出如下圖所示：

補充一段hist繪制柱狀圖的代碼：

import?numpy?as?np??

import?pylab?as?pl??

#?make?an?array?of?random?numbers?with?a?gaussian?distribution?with??

#?mean?=?5.0??

#?rms?=?3.0??

#?number?of?points?=?1000??

data?=?np.random.normal(5.0,?3.0,?1000)??

#?make?a?histogram?of?the?data?array??

pl.hist(data,?histtype='stepfilled')?#去掉黑色輪廓??

#?make?plot?labels??

pl.xlabel('data')???

pl.show()??

輸出如下圖所示：

推薦文章：http://www.cnblogs.com/jasonfreak/p/5441512.html

三. Python繪制時間序列-自相關(guān)圖

核心代碼如下所示：

#?-*-?coding:?utf-8?-*-??

"""

Created?on?Mon?Mar?06?10:55:17?2017

@author:?yxz15

"""??

import?pandas?as?pd??

data?=?pd.read_csv("32.csv",index_col='year')??

#顯示前6行數(shù)據(jù)????

print(data.shape)????

print(data.head(6))??

import?matplotlib.pyplot?as?plt??

plt.rcParams['font.sans-serif']?=?['simHei']??

plt.rcParams['axes.unicode_minus']?=?False??

data.plot()??

plt.savefig(u'時序圖.png',?dpi=500)??

plt.show()??

from?statsmodels.graphics.tsaplots?import?plot_acf??

gy?=?data['Guiyang']??

print?gy??

plot_acf(gy).show()??

plt.savefig(u'貴陽自相關(guān)圖',dpi=300)??

from?statsmodels.tsa.stattools?import?adfuller?as?ADF??

print?'ADF:',ADF(gy)??

輸出結(jié)果如下所示：

時間序列相關(guān)文章推薦：

python時間序列分析

個股與指數(shù)的回歸分析（python）

Python_Statsmodels包_時間序列分析_ARIMA模型

四. 聚類分析大連交易所數(shù)據(jù)集

這部分主要提供一個網(wǎng)址給大家下載數(shù)據(jù)集吏夯，前面文章說過sklearn自帶一些數(shù)據(jù)集以及UCI官網(wǎng)提供大量的數(shù)據(jù)集坏平。這里講述一個大連商品交易所的數(shù)據(jù)集。

地址：http://www.dce.com.cn/dalianshangpin/xqsj/lssj/index.html#

比如下載"焦炭"數(shù)據(jù)集锦亦，命名為"35.csv"舶替，在對其進行聚類分析。

代碼如下：

#?-*-?coding:?utf-8?-*-??

"""

Created?on?Mon?Mar?06?10:19:15?2017

@author:?yxz15

"""??

#第一部分：導(dǎo)入數(shù)據(jù)集??

import?pandas?as?pd??

Coke1?=pd.read_csv("35.csv")??

print?Coke1?[:4]??

#第二部分：聚類??

from?sklearn.cluster?import?KMeans??

clf=KMeans(n_clusters=3)??

pre=clf.fit_predict(Coke1)??

print?pre[:4]??

#第三部分：降維??

from?sklearn.decomposition?import?PCA??

pca=PCA(n_components=2)??

newData=pca.fit_transform(Coke1)??

print?newData[:4]??

x1=[n[0]?for?n?in?newData]??

x2=[n[1]?for?n?in?newData]??

#第四部分：用matplotlib包畫圖??

import?matplotlib.pyplot?as?plt??

plt.title??

plt.xlabel("x?feature")??

plt.ylabel("y?feature")??

plt.scatter(x1,x2,c=pre,?marker='x')??

plt.savefig("bankloan.png",dpi=400)??

plt.show()??

? ??出如下圖所示：

五. PCA降維及繪圖代碼

PCA降維繪圖參考這篇博客杠园。

http://blog.csdn.net/xiaolewennofollow/article/details/46127485

代碼如下：

#?-*-?coding:?utf-8?-*-??

"""

Created?on?Mon?Mar?06?21:47:46?2017

@author:?yxz

"""??

from?numpy?import?*??

def?loadDataSet(fileName,delim='\t'):??

????fr=open(fileName)??

stringArr=[line.strip().split(delim)for?line?in?fr.readlines()]??

datArr=[map(float,line)for?line?in?stringArr]??

return?mat(datArr)??

def?pca(dataMat,topNfeat=9999999):??

meanVals=mean(dataMat,axis=0)??

????meanRemoved=dataMat-meanVals??

covMat=cov(meanRemoved,rowvar=0)??

????eigVals,eigVets=linalg.eig(mat(covMat))??

????eigValInd=argsort(eigVals)??

eigValInd=eigValInd[:-(topNfeat+1):-1]??

????redEigVects=eigVets[:,eigValInd]??

print?meanRemoved??

print?redEigVects??

????lowDDatMat=meanRemoved*redEigVects??

????reconMat=(lowDDatMat*redEigVects.T)+meanVals??

return?lowDDatMat,reconMat??

dataMat=loadDataSet('41.txt')??

lowDMat,reconMat=pca(dataMat,1)??

def?plotPCA(dataMat,reconMat):??

import?matplotlib??

import?matplotlib.pyplot?as?plt??

????datArr=array(dataMat)??

????reconArr=array(reconMat)??

n1=shape(datArr)[0]??

n2=shape(reconArr)[0]??

????xcord1=[];ycord1=[]??

????xcord2=[];ycord2=[]??

for?i?in?range(n1):??

xcord1.append(datArr[i,0]);ycord1.append(datArr[i,1])??

for?i?in?range(n2):??

xcord2.append(reconArr[i,0]);ycord2.append(reconArr[i,1])??

????fig=plt.figure()??

ax=fig.add_subplot(111)??

ax.scatter(xcord1,ycord1,s=90,c='red',marker='^')??

ax.scatter(xcord2,ycord2,s=50,c='yellow',marker='o')??

plt.title('PCA')??

plt.savefig('ccc.png',dpi=400)??

????plt.show()??

plotPCA(dataMat,reconMat)??

輸出結(jié)果如下圖所示：

采用PCA方法對數(shù)據(jù)集進行降維操作漠趁，即將紅色三角形數(shù)據(jù)降維至黃色直線上，一個平面降低成一條直線废恋。PCA的本質(zhì)就是對角化協(xié)方差矩陣辩恼，對一個n*n的對稱矩陣進行分解，然后把矩陣投影到這N個基上瞧甩。

數(shù)據(jù)集為41.txt钉跷，值如下：

61.5????55??

59.8????61??

56.9????65??

62.4????58??

63.3????58??

62.8????57??

62.3????57??

61.9????55??

65.1????61??

59.4????61??

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60.4????61??

62.2????54??

60.2????62??

60.9????58??

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63.4????54??

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62.7????59??

63.3????56??

63.8????55??

61??57??

59.4????62??

58.1????62??

60.4????58??

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62.2????57??

60.5????61??

60.9????57??

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60.7????59??

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58.2????59??

64.1????59??

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60.8????59??

61.8????55??

61.2????56??

61.1????56??

65.2????56??

58.4????63??

63.1????56??

62.4????58??

61.8????55??

63.8????56??

63.3????60??

60.7????60??

60.9????61??

61.9????54??

60.9????55??

61.6????58??

59.3????62??

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59.3????61??

62.6????57??

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60.9????57??

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62.5????57??

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63.3????57??

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57.9????60??

62.3????59??

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60.7????58??

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62.5????57??

55.1????69??

61.6????56??

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66.3????62??

61.6????59??

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原文參考：http://blog.csdn.net/eastmount/article/details/60675865