oreilly python for data analysis

1, remove the label of the page, such as from Import beautifulrsoup predata=beautifulsoup (data,'html.parser'). Get_text ()2. Remove punctuation, etc., with regular expressions.Import RE#表示将data中的除了大小写字母之外的符号换成空格preData=re.sub (R'[^a-za-z]',' , data)3. Lowercase the words in the text and separate the data with a space

]: B=np.ones ([3,4])#generate all 1 arrays - +in [5]: b -Out[5]: +Array ([[1., 1., 1., 1.], A[1., 1., 1., 1.], at[1., 1., 1., 1.]]) - -In [6]: C=np.random.rand (3,4)#generating a random array - -in [7]: C -Out[7]: inArray ([[[0.36417168, 0.24336724, 0.78826727, 0.42894367], -[0.77198615, 0.95897315, 0.25628233, 0.53995372], to[0.02777746, 0.25093856, 0.14544893, 0.10475779]]) + -In [8]: D=np.eye (3)#Generating a unit array the *in [9]: D $Out[9]:Panax NotoginsengArray ([[1., 0., 0.], -[0.,

Introduction URL: Https://www.kaggle.com/benhamner/d/uciml/iris/python-data-visualizations/notebookImport Matplotlib.pyplot as PltImport Seaborn as SNSImport Pandas as PDImport data:Iris=pd.read_csv (' E:\\data\\iris.csv ')Iris.head ()To make a histogram:Plt.hist (iris[' SEPALLENGTHCM '],bins=15)Plt.xlabel (' SEPALLENGTHCM ')Plt.ylabel (' quantity ')Plt.title ('

element is the index of the item whose index number is smaller than the previous one. So we see that the value of index 2,3 is 1, and the value of index 1 If you want to use the element following the newly inserted index, you need to use the Bfill method The replacement index can be extended from series to dataframe, not only to replace the row index, but also to replace the column index or even replace both Second, delete ① Deleting a series Pandas specificall

Python Data Analysis Prerequisites:1.Anaconda operationFirst, you should set the local data directory as the working directory, so that you can load the local data set into memoryImport Osos.chdir ("d:/bigdata/workspace/testdata/"# Sets the current path to the working path O

python data analysis requirements are not software development requirements , indeed, for a tool, different purposes of the user, the required skills are not the same, such as knife This tool, the butcher used it to kill pigs, the chef used it is cut vegetables, military use it is defend, the guests use it is cut steak, Everyone uses different ways, there are spe

???IndexP.asfreq (' M ', ' Start ') #将年度数据转换为月度的形式, converted to the month of the yearP.asfreq (' M ', ' End ') #将年度数据转换为月度的形式, converted to December of the yearP1=PD. Period (' freq= ', ' A-jun ')P1.asfreq (' m ', ' Start ') #Period (' 2015-07 ', ' m ')P1.asfreq (' m ', ' End ') #Period (' 2016-06 ', ' m ')P2=PD. Period (' 2016-09 ', ' M ')P2.asfreq (' A-jun ') #2016年9月进行频率转换, equivalent to 2017 years in the time frequency ending in JuneRng=pd.period_range (' 2006 ', ' freq= ', ' A-dec ')Ts=ser

‘) #颜色深蓝cup_style = bra.groupby(‘cup‘)[‘cup‘].count() #cup列唯一值得数量cup_styleplt.figure(figsize=(8,6),dpi=80)labels = list(cup_style.index)plt.xlabel(‘cup‘) #x轴为cupplt.ylabel(‘count‘) #y轴为count数量plt.bar(range(len(labels)),cup_style,color=‘royalblue‘,alpha=0.7) #alpha为透明度plt.xticks(range(len(labels)),labels,fontsize=12)plt.grid(color=‘#95a5a6‘,linestyle=‘--‘,linewidth=1,axis=‘y‘,alpha=0.6)plt.legend([‘user-count‘])for x,y in zip(range(len(labels)),cup_style):plt.text(x,y,y,ha=‘center‘,va=‘bottom‘)co

="bfill"))‘‘‘------Back fill------One, threea-0.211055-2.869212 0.022179b-0.870090-0.878423 1.071588c-0.870090-0.878423 1.071588d-0.203259 0.315897 0.495306e-0.203259 0.315897 0.495306f 0.490568-0.968058-0.999899g 1.437819-0.370934-0.482307H 1.437819-0.370934- 0.482307 ‘‘‘Print ('------Average fill------') Print (Df.fillna (Df.mean ()))‘‘‘------Average fill------One, threea-0.211055-2.869212 0.022179b 0.128797-0.954146 0.021373c-0.870090-0.878423 1.071588d 0.128797-0.95

Use Python for data analysis _ Pandas _ basic _ 2, _ pandas_2Reindex method of Series reindex In [15]: obj = Series([3,2,5,7,6,9,0,1,4,8],index=['a','b','c','d','e','f','g', ...: 'h','i','j'])In [16]: obj1 = obj.reindex(['a','b','c','d','e','f','g','h','i','j','k'])In [17]: obj1Out[17]:a 3.0b 2.0c 5.0d 7.0e 6.0f 9.0g 0.0h 1.0i 4.0j