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查看数据   2020/5/27=====================================================================================1.1.查看数据维度，类型属性；1.2.简单的数据统计分析；数据的相关系及分布；=====================================================================================2.实例：#说明：在pycharm中数据不美观；在spyder中打印显示对的相当齐import csv,pandas as pd,numpy as nppd.set_option('display.width', 100)pd.set_option('precision', 4)                       # 设置数据的精确度pd.set_option('display.max_columns', 1000)pd.set_option('display.unicode.ambiguous_as_wide', True)#pd.DataFrame打印显示对齐pd.set_option('display.unicode.east_asian_width', True)#这两个函数为print对齐；在pycharm中仍不美观def fillEmptyStr(str0,width=12,precision=4):    n,n_pos=len(str0),str0.find('.')    if n_pos<0:        if n>=width:            result=str0[0:width]        else:            result=str0+' '*(width-n)    else:        if n_pos+1+precision<=n:            result=str0[0:(n_pos+1+precision)]        else:            result=str0+' '*(n-n_pos-1-precision)    return resultdef print_df(df,width=12,precision=4):    if isinstance(df,pd.DataFrame):        str_i='{:<'+str(width)+'}'        rows,cols=df.index,df.columns        data=df.to_numpy()        n=0        r,c=data.shape        print(str_i.format(' '*width),end='')        for col in cols:            tmp=fillEmptyStr(str(col),width,precision)            print(str_i.format(tmp),end='')        print()        for i in range(r):            tmp=fillEmptyStr(str(rows[n]),width,precision)            print(str_i.format(tmp),end='')            n=n+1            for j in range(c):                tmp=fillEmptyStr(str(data[i,j]),width,precision)                print(str_i.format(tmp),end='')            print()    else:        print(df)=====================================================================================# 显示数据的行和列数据filename = 'pima_data.csv'names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']data = pd.read_csv(filename, names=names)print('data.dtypes=')print(data.dtypes)# 显示数据的行和列数据print('data.shape=',data.shape)# 显示数据的行和列数据print('data.class=')print_df(data.groupby('class').size())# 数据分类分布统计print('data.skew=')print_df(data.skew())# 计算数据的高斯偏离print('data.corr=')print_df(data.corr(method='pearson'))# 显示数据的相关性print('data.describe=')print_df(data.describe())# 描述性统计print('data.head(4)=')print_df(data.head(4))# 显示数据最初4行====================================================================================="""data.dtypes=preg       int64plas       int64pres       int64skin       int64test       int64mass     float64pedi     float64age        int64class      int64dtype: objectdata.shape= (768, 9)data.class=class0    5001    268dtype: int64data.skew=preg     0.9017plas     0.1738pres    -1.8436skin     0.1094test     2.2723mass    -0.4290pedi     1.9199age      1.1296class    0.6350dtype: float64data.corr=            preg        plas        pres        skin        test        mass        pedi        age         class       preg        1.0         0.1294      0.1412      -0.0816     -0.0735     0.0176      -0.0335     0.5443      0.2218      plas        0.1294      1.0         0.1525      0.0573      0.3313      0.2210      0.1373      0.2635      0.4665      pres        0.1412      0.1525      1.0         0.2073      0.0889      0.2818      0.0412      0.2395      0.0650      skin        -0.0816     0.0573      0.2073      1.0         0.4367      0.3925      0.1839      -0.1139     0.0747      test        -0.0735     0.3313      0.0889      0.4367      1.0         0.1978      0.1850      -0.0421     0.1305      mass        0.0176      0.2210      0.2818      0.3925      0.1978      1.0         0.1406      0.0362      0.2926      pedi        -0.0335     0.1373      0.0412      0.1839      0.1850      0.1406      1.0         0.0335      0.1738      age         0.5443      0.2635      0.2395      -0.1139     -0.0421     0.0362      0.0335      1.0         0.2383      class       0.2218      0.4665      0.0650      0.0747      0.1305      0.2926      0.1738      0.2383      1.0         data.describe=            preg        plas        pres        skin        test        mass        pedi        age         class       count       768.0       768.0       768.0       768.0       768.0       768.0       768.0       768.0       768.0       mean        3.8450      120.8945    69.1054     20.5364     79.7994     31.9925     0.4718      33.2408     0.3489      std         3.3695      31.9726     19.3558     15.9522     115.2440    7.8841      0.3313      11.7602     0.4769      min         0.0         0.0         0.0         0.0         0.0         0.0         0.078       21.0        0.0         25%         1.0         99.0        62.0        0.0         0.0         27.3        0.2437      24.0        0.0         50%         3.0         117.0       72.0        23.0        30.5        32.0        0.3725      29.0        0.0         75%         6.0         140.25      80.0        32.0        127.25      36.6        0.6262      41.0        1.0         max         17.0        199.0       122.0       99.0        846.0       67.1        2.42        81.0        1.0         data.head(4)=            preg        plas        pres        skin        test        mass        pedi        age         class       0           6.0         148.0       72.0        35.0        0.0         33.6        0.627       50.0        1.0         1           1.0         85.0        66.0        29.0        0.0         26.6        0.3510      31.0        0.0         2           8.0         183.0       64.0        0.0         0.0         23.3        0.672       32.0        1.0         3           1.0         89.0        66.0        23.0        94.0        28.1        0.1669      21.0        0.0         """======================================================================================

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