pandas-grouping operations (2)-custom grouping and aggregation operations agg

# 自定义分组规则
def get_score_group(score):
    if score <= 4:
        score_group = 'low'
    elif score <= 6:
        score_group = 'middle'
    else:
        score_group = 'high'
    return score_group

# 方法1：传入自定义的函数进行分组按单列分组
data2 = data.set_index('Happiness Score')
data2.groupby(get_score_group).size()

# 方法2：人为构造出一个分组列
data['score group'] = data['Happiness Score'].apply(get_score_group)
data.tail()

data.groupby('score group').size()

Custom aggregation operation

data.groupby('Region').max()
data.groupby('Region').agg(np.max)

# 传入包含多个函数的列表
data.groupby('Region')['Happiness Score'].agg([np.max, np.min, np.mean])

# 通过字典为每个列指定不同的操作方法
data.groupby('Region').agg({
    
    'Happiness Score': np.mean, 'Happiness Rank': np.max})

# 传入自定义函数
def max_min_diff(x):
    return x.max() - x.min()

data.groupby('Region')['Happiness Rank'].agg(max_min_diff)

Exercise

存在DataFrame结构的数据df，对列名为data1的数据依次求最大值、均值、最小值；
利用字典，对列名为data2的数据求均值，对列名data3的数据计算个数；
对列名为data4的数据自定义计算，自定义函数为组内最大值减去均值，且保留两位小数，最后将聚合的数据进行合并。

df = pd.DataFrame({
    
    'key':['one', 'three', 'two', 'two', 'one','three','three','two','one','one'],

     'data1':np.random.randint(25,75,size=10),

    'data2':np.random.randint(1,50,size=10),

    'data3':np.random.randint(50,100,size=10),

    'data4':np.random.randint(100,150,size=10)})

data1=df.groupby('key')['data1'].(1)([np.max,np.mean,np.(2)])

data1.columns=['data1_max','data1_mean','data1_min']

data23=df.(3)('key').agg({
    
    'data2':np.(4),'data3':np.size})

 

def get_data4_diff(data):

    return round(data.(5)()-data.mean(),(6))

data4=df.groupby('key')['data4'].agg((7))

pd.concat([data1,data23,(8)],axis=1)

Fill in the blank (1): The correct answer is agg.
Fill in the blank (2): The correct answer is min.
Fill in the blank (3): The correct answer is groupby.
Fill in the blank (4): The correct answer means.
Fill in the blank (5): The correct answer is max.
Fill in the blank (6): Correct answer 2.
Fill in the blank (7): The correct answer is get_data4_diff.
Fill in the blank (8): correct answer data4.