Python數(shù)據(jù)分析與應(yīng)用 課件 第10章 pandas

第10章Pandas

《Python數(shù)據(jù)分析與應(yīng)用》pandaspandas是基于NumPy的數(shù)據(jù)分析工具，官方網(wǎng)址是。pandas提供了快速，靈活和富有表現(xiàn)力的數(shù)據(jù)結(jié)構(gòu)，目的是使“關(guān)系”或“標(biāo)記”數(shù)據(jù)的工作既簡(jiǎn)單又直觀。Pandas用于數(shù)據(jù)清洗，對(duì)噪音等數(shù)據(jù)進(jìn)行處理，從而便于機(jī)器學(xué)習(xí)和數(shù)據(jù)分析。pandaspandas常用6個(gè)類Series：基本數(shù)據(jù)結(jié)構(gòu)，一維標(biāo)簽數(shù)組，能夠保存任何數(shù)據(jù)類型DataFrame：基本數(shù)據(jù)結(jié)構(gòu)，一般為二維數(shù)組，是一組有序的列Index：索引對(duì)象，負(fù)責(zé)管理軸標(biāo)簽和其他元數(shù)據(jù)（比如軸名稱）groupby：分組對(duì)象，通過(guò)傳入需要分組的參數(shù)實(shí)現(xiàn)對(duì)數(shù)據(jù)分組Timestamp：時(shí)間戳對(duì)象，表示時(shí)間軸上的一個(gè)時(shí)刻Timedelta：時(shí)間差對(duì)象，用來(lái)計(jì)算兩個(gè)時(shí)間點(diǎn)的差值Pandas2個(gè)重要類創(chuàng)建Series

創(chuàng)建Series對(duì)象的函數(shù)是Series，它的主要參數(shù)是data和index，其基本語(yǔ)法格式如下。pandas.Series(data=None,

index=None,

name=None)

參數(shù)說(shuō)明如下：data：接收array或dict。表示接收的數(shù)據(jù)。默認(rèn)為None。index：接收array或list。表示索引，必須與數(shù)據(jù)長(zhǎng)度相同name：接收string或list。表示Series對(duì)象的名稱。默認(rèn)為None。通過(guò)ndarray創(chuàng)建Seriesimportpandasaspdimportnumpyasnpprint('通過(guò)ndarray創(chuàng)建的Series為：\n',pd.Series(np.arange(5),index=['a','b','c','d','e'],name='ndarray'))通過(guò)dict創(chuàng)建Seriesdict的鍵作為Series的索引，dict的值作為Series的值，無(wú)須傳入index參數(shù)。通過(guò)dict創(chuàng)建Series對(duì)象，代碼如下所示：importpandasaspddict={'a':0,'b':1,'c':5,'d':3,'e':4}print('通過(guò)dict創(chuàng)建的Series為：\n',pd.Series(dict))通過(guò)list創(chuàng)建Seriesimportpandasaspdlist1=[0,1,5,3,4]print('通過(guò)list創(chuàng)建的Series為：\n',pd.Series(list1,index=['a','b','c','d','e'],name='list'))Series屬性Series擁有8個(gè)常用屬性，如下所示。values：以ndarray的格式返回Series對(duì)象的所有元素index：返回Series對(duì)象的索引dtype：返回Series對(duì)象的數(shù)據(jù)類型shape：返回Series對(duì)象的形狀nbytes：返回Series對(duì)象的字節(jié)數(shù)ndim：返回Series對(duì)象的維度size：返回Series對(duì)象的個(gè)數(shù)T：返回Series對(duì)象的轉(zhuǎn)置訪問(wèn)Series的屬性importpandasaspdseries1=pd.Series([1,5,3,4])print("series1:\n{}\n".format(series1))print("series1.values:{}\n".format(series1.values))#數(shù)據(jù)print("series1.index:{}\n".format(series1.index))#索引print("series1.shape:{}\n".format(series1.shape))#形狀print("series1.ndim:{}\n".format(series1.ndim))#維度訪問(wèn)Series數(shù)據(jù)通過(guò)索引位置訪問(wèn)Series的數(shù)據(jù)與ndarray相同，importpandasaspdseries5=pd.Series([1,5,3,4,5,6,7],index=["C","D","E","F","G","A","B"])#通過(guò)索引位置訪問(wèn)Series數(shù)據(jù)子集print("series5位于第1位置的數(shù)據(jù)為:",series5[0])#通過(guò)索引名稱（標(biāo)簽）也可以訪問(wèn)Series數(shù)據(jù)print("Eis{}\n".format(series5["E"]))更新Seriesimportpandasaspdseries1=pd.Series(list1,index=['a','b','c','d','e'],name='list')print("series1:\n{}\n".format(series1))#更新元素series1['a']=3print('更新后的Series1為：\n',series1)追加Series和插入單個(gè)值importpandasaspdseries1=pd.Series(list1,index=['a','b','c','d','e'],name='list')print("series1:\n{}\n".format(series1))series1=pd.Series([4,5],index=['f','g'])#追加Seriesprint('在series插入series1后為：\n',series.append(series1))刪除Series元素importpandasaspdseries=pd.Series(list1,index=['a','b','c','d','e'],name='list')print("series:\n{}\n".format(series))#刪除數(shù)據(jù)series.drop('e',inplace=True)print('刪除索引e對(duì)應(yīng)數(shù)據(jù)后的series為：\n',series)。DataFrameDataFrame是pandas基本數(shù)據(jù)結(jié)構(gòu)，類似數(shù)據(jù)庫(kù)中的表。DataFrame既有行索引，也有列索引，可以看作是Series組成的dict，每個(gè)Series是DataFrame的一列

創(chuàng)建DataFrameDataFrame函數(shù)用于創(chuàng)建DataFrame對(duì)象，其基本語(yǔ)法格式如下pandas.DataFrame(data=None,

index=None,

columns=None,

dtype=None,

copy=False)參數(shù)說(shuō)明如下所示：data：接收ndarray、dict、list或DataFrame。表示輸入數(shù)據(jù)。默認(rèn)為None。index：接收Index，ndarray。表示索引。默認(rèn)為None。columns：接收Index，ndarray。表示列標(biāo)簽（列名）。默認(rèn)為None。通過(guò)dict創(chuàng)建DataFrameimportpandasaspddict1={'col1':[0,1,5,3,4],'col5':[5,6,7,8,9]}print('通過(guò)dict創(chuàng)建的DataFrame為：\n',pd.DataFrame(dict1,index=['a','b','c','d','e']))通過(guò)list創(chuàng)建DataFrameimportpandasaspdlist5=[[0,5],[1,6],[5,7],[3,8],[4,9]]print('通過(guò)list創(chuàng)建的DataFrame為：\n',pd.DataFrame(list5,index=['a','b','c','d','e'],columns=['col1','col5']))通過(guò)Series創(chuàng)建DataFrameimportpandasaspdnoteSeries

=

pd.Series(["C",

"D",

"E",

"F",

"G",

"A",

"B"],

index=[1,

5,

3,

4,

5,

6,

7])weekdaySeries

=

pd.Series(["Mon",

"Tue",

"Wed",

"Thu","Fri",

"Sat",

"Sun"],

index=[1,

5,

3,

4,

5,

6,

7])df4

=

pd.DataFrame([noteSeries,

weekdaySeries])print("df4:\n{}\n".format(df4))DataFrame屬性values：以ndarray的格式返回DataFrame對(duì)象的所有元素index：返回DataFrame對(duì)象的Indexcolumns：返回DataFrame對(duì)象的列標(biāo)簽dtypes：返回DataFrame對(duì)象的數(shù)據(jù)類型axes：返回DataFrame對(duì)象的軸標(biāo)簽ndim：返回DataFrame對(duì)象的軸尺寸數(shù)size：返回DataFrame對(duì)象的個(gè)數(shù)shape：返回DataFrame對(duì)象的形狀更新DataFrameimportpandasaspddf=pd.DataFrame({'col1':[0,1,5,3,4],'col5':[5,6,7,8,9]},index=['a','b','c','d','e'])print('DataFrame為：\n',df)#更新列df['col1']=[10,11,15,13,14]print('更新列后的DataFrame為：\n',df)插入和刪除DataFrameimportpandasaspddf3=pd.DataFrame({"note":["C","D","E","F","G","A","B"],"weekday":["Mon","Tue","Wed","Thu","Fri","Sat","Sun"]})print("df3:\n{}\n".format(df3))df3["No."]=pd.Series([1,5,3,4,5,6,7])#采用賦值的方法插入列print("df3:\n{}\n".format(df3))deldf3["weekday"]#刪除列的方法有多種，如del、pop、dropprint("df3:\n{}\n".format(df3))Index

（1）

隱式創(chuàng)建

創(chuàng)建Series或DataFrame等對(duì)象時(shí)，索引會(huì)轉(zhuǎn)換為Index對(duì)象

（2）顯式創(chuàng)建

Index對(duì)象可以通過(guò)pandas.Index()函數(shù)創(chuàng)建

。

plotMatplotlib繪制一張圖表需要各個(gè)基礎(chǔ)組件對(duì)象，工作量較大。而pandas中使用行標(biāo)簽和列標(biāo)簽以及分組信息，較為簡(jiǎn)便的完成圖表的制作。散點(diǎn)圖

importnumpyasnpimportpandasaspdwdf=pd.DataFrame(np.arange(20),columns=['W'])wdf['Y']=wdf['W']*1.5+2wdf.iloc[3,1]=128wdf.iloc[18,1]=150wdf.plot(kind='scatter',x='W',y='Y')

條形圖importpandasaspdimportnumpyasnpimportmatplotlib.pyplotaspltdf2=pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d'])df2.plot.bar()plt.show()

直方圖與密度圖importpandasaspdimportnumpyasnp

n1=np.random.normal(loc=10,scale=5,size=1000)n2=np.random.normal(loc=50,scale=7,size=1000)n=np.hstack((n1,n2))s=pd.DataFrame(data=n)s.plot(kind='hist',bins=100,density=True)s.plot(kind='kde')

箱線圖importnumpyasnpimportpandasaspd

wdf=pd.DataFrame(np.arange(20),columns=['W'])wdf['Y']=wdf['W']*1.5+2wdf.iloc[3,1]=128wdf.iloc[18,1]=150importmatplotlib.pyplotaspltplt.boxplot(wdf)plt.show()面積圖importpandasaspdimportnumpyasnpimportmatplotlib.pyplotaspltdf=pd.Dat