python进行EDA探索性数据分析

1.查看数据的类型概况

cols = [c for c in train.columns]   #返回数据的列名到列表里

print('Number of features: {}'.format(len(cols)))

print('Feature types:')
train[cols].dtypes.value_counts()

 

结果如下：

           Number of features: 376
           Feature types:

                  Out[5]:

             int64     368
             object      8
             dtype: int64

 

2.查看特征的数值范围

counts = [[], [], []]
for c in cols:
    typ = train[c].dtype
    uniq = len(np.unique(train[c]))          #利用np的unique函数看看该列一共有几个不同的数值
    if uniq == 1:                                       #  uniq==1说明该列只有一个数值
        counts[0].append(c)
    elif uniq == 2 and typ == np.int64:   #  uniq==2说明该列有两个数值，往往就是0与1的二类数值
        counts[1].append(c)
    else:
        counts[2].append(c)

print('Constant features: {}\n Binary features: {} \nCategorical features: {}\n'.format(*[len(c) for c in counts]))

print('Constant features:', counts[0])
print('Categorical features:', counts[2])

 

 结果如下：

　　　　Constant features: 12
               Binary features: 356
　　　　Categorical features: 10

　　　　Constant features: ['X11', 'X93', 'X107', 'X233', 'X235', 'X268', 'X289', 'X290', 'X293', 'X297', 'X330', 'X347']
　　　　Categorical features: ['ID', 'y', 'X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8']

 

3.画出类别特征值的分布情况

pal = sns.color_palette()

for c in counts[2]:
　　value_counts = train[c].value_counts()
　　fig, ax = plt.subplots(figsize=(10, 5))
　　plt.title('Categorical feature {} - Cardinality {}'.format(c, len(np.unique(train[c]))))
　　plt.xlabel('Feature value')
　　plt.ylabel('Occurences')
　　plt.bar(range(len(value_counts)), value_counts.values, color=pal[1])
　　ax.set_xticks(range(len(value_counts)))
　　ax.set_xticklabels(value_counts.index, rotation='vertical')
　　plt.show()