Question

我在数据框中有多列包含数字数据。我想四分位数的每一列，将每个值更改为q1，q2，q3或q4。

我目前遍历每一列，并使用pandas qcut函数对其进行更改：

for column_name in df.columns:
    df[column_name] = pd.qcut(df[column_name].astype('float'), 4, ['q1','q2','q3','q4'])

这非常慢！有更快的方法吗？

Answer 1

略微处理以下示例。看起来从字符串转换为float会增加时间。尽管没有提供工作示例，所以无法知道原始类型。无论是否复制，df[column].astype(copy=)似乎都很有效。没有什么要追寻的。

import pandas as pd
import numpy as np
import random
import time

random.seed(2)

indexes = [i for i in range(1,10000) for _ in range(10)]
df = pd.DataFrame({'A': indexes, 'B': [str(random.randint(1,99)) for e in indexes], 'C':[str(random.randint(1,99)) for e in indexes], 'D':[str(random.randint(1,99)) for e in indexes]})
#df = pd.DataFrame({'A': indexes, 'B': [random.randint(1,99) for e in indexes], 'C':[random.randint(1,99) for e in indexes], 'D':[random.randint(1,99) for e in indexes]})

df_result = pd.DataFrame({'A': indexes, 'B': [random.randint(1,99) for e in indexes], 'C':[random.randint(1,99) for e in indexes], 'D':[random.randint(1,99) for e in indexes]})

def qcut(copy, x):
    for i, column_name in enumerate(df.columns):
        s = pd.qcut(df[column_name].astype('float', copy=copy), 4, ['q1','q2','q3','q4'])
        df_result["col %d %d"%(x, i)] = s.values

times = []
for x in range(0,10):
    a = time.clock()
    qcut(True, x)
    b = time.clock()
    times.append(b-a)

print np.mean(times)

for x in range(10, 20):
    a = time.clock()
    qcut(False, x)
    b = time.clock()
    times.append(b-a)
print np.mean(times)

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