Pandas数值分箱操作的几种方法

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分箱是一种常见的数据处理技术有时也被称为分桶或离散化，可用于将连续数据的间隔分组到箱或桶中。

创建以下合成数据用于演示

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import pandas as pd
import numpy as np 

def create_df(): 
    df = pd.DataFrame({'score': np.random.randint(0,101,1000)}) 
    return df 
 
df = create_df() 
print(df.head())

between & loc

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import pandas as pd
import numpy as np 

def create_df(): 
    df = pd.DataFrame({'score': np.random.randint(0,101,1000)}) 
    return df 
 
df = create_df()

df.loc[df['score'].between(0, 50, 'both'), 'grade'] = 'C' 
df.loc[df['score'].between(50, 80, 'right'), 'grade'] = 'B' 
df.loc[df['score'].between(80, 100, 'right'), 'grade'] = 'A'
print(df.grade.value_counts())

cut

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import pandas as pd
import numpy as np 

def create_df(): 
    df = pd.DataFrame({'score': np.random.randint(0,101,1000)}) 
    return df 
 
df = create_df()

bins = [0, 50, 80, 100] 
labels = ['C', 'B', 'A'] 
df['grade'] = pd.cut(x = df['score'], 
                    bins = bins, 
                    labels = labels, 
                    include_lowest = True)
print(df.grade.value_counts())

qcut

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import pandas as pd
import numpy as np 

def create_df(): 
    df = pd.DataFrame({'score': np.random.randint(0,101,1000)}) 
    return df 
 
df = create_df()

df['grade'], cut_bin = pd.qcut(df['score'], 
                               q = 3, 
                               labels = ['C', 'B', 'A'], 
                               retbins = True) 
print(df.grade.value_counts())
# 如果 retbins 设置为 True 则会返回 bin 边界
print(cut_bin)

value_counts

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import pandas as pd
import numpy as np 

def create_df(): 
    df = pd.DataFrame({'score': np.random.randint(0,101,1000)}) 
    return df 
 
df = create_df()

print(df['score'].value_counts(bins = 3, sort = False))
print(df['score'].value_counts(bins = [0,50,80,100], sort = False))