pandas_dataformat02

7. 如何重命名dataframe的特定列

   df1 = pd.DataFrame(data=np.array([[18,50],[19,51],[20,55]]),index=['man1','man2','man3'],columns=['age','weight'])
   print(df1)
   # 修改列名
   print("\nchange columns :\n")
   #方法1
   df1.rename(columns={'weight':'stress'})
   #方法2
   df1.columns.values[1] = 'stress'
   print(df1)
   
   #>          age  weight
       man1   18      50
       man2   19      51
       man3   20      55
       
       change columns :
       
             age  stress
       man1   18      50
       man2   19      51
       man3   20      55

8. 如何检查dataframe中是否有缺失值

   df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
   
   # 若有缺失值，则为Ture
   df.isnull().values.any()
   
   #>    True
   9. 如何统计dataframe的每列中缺失值的个数
   df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
   
   # 获取每列的缺失值个数
   n_missings_each_col = df.apply(lambda x: x.isnull().sum())
   print(n_missings_each_col.head())
   
   #>    Manufacturer    4
       Model           1
       Type            3
       Min.Price       7
       Price           2
       dtype: int64

9. 如何用平均值替换相应列的缺失值

   df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv',nrows=10)
   print(df[['Min.Price','Max.Price']].head())
   # 平均值替换缺失值
   df_out = df[['Min.Price', 'Max.Price']] = df[['Min.Price', 'Max.Price']].apply(lambda x: x.fillna(x.mean()))
   print(df_out.head())
   
   #>       Min.Price  Max.Price
       0       12.9       18.8
       1       29.2       38.7
       2       25.9       32.3
       3        NaN       44.6
       4        NaN        NaN
   
   #>       Min.Price  Max.Price
       0       12.9       18.8
       1       29.2       38.7
       2       25.9       32.3
       3       23.0       44.6
       4       23.0       29.9

10. 如何用全局变量作为apply函数的附加参数处理指定的列

    df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/Cars93_miss.csv')
    print(df[['Min.Price', 'Max.Price']].head())
    # 全局变量
    d = {'Min.Price': np.nanmean, 'Max.Price': np.nanmedian}
    # 列名Min.Price的缺失值用平均值代替，Max.Price的缺失值用中值代替
    df[['Min.Price', 'Max.Price']] = df[['Min.Price', 'Max.Price']].apply(lambda x, d: x.fillna(d[x.name](x)), args=(d, ))
    print(df[['Min.Price', 'Max.Price']].head())
    
    #>       Min.Price  Max.Price
        0       12.9       18.8
        1       29.2       38.7
        2       25.9       32.3
        3        NaN       44.6
        4        NaN        NaN
    
    #>       Min.Price  Max.Price
        0  12.900000      18.80
        1  29.200000      38.70
        2  25.900000      32.30
        3  17.118605      44.60
        4  17.118605      19.15