我需要将两列中的值合并到另一列中。
假设以下是我的 pandas df:
data = {'material':['Matl_A', 'Matl_B', 'Matl_B', 'Matl_A'],
'strength':[10, 20, 30, 100]
df = pd.DataFrame(data)
所以我的 df 是:
material strength
---------- ----------
Matl_A 10
Matl_B 20
Matl_B 30
Matl_A 100
我想做这样的事情:
material strength grade
---------- ---------- ---------
Matl_A 10 1
Matl_B 20 4
Matl_B 80 5
Matl_A 100 2
执行此操作的最佳方法是什么？
编辑:
我在下面使用了 Michael Gardner 的回答并对其进行了扩展，因为我们有很多 Material 。希望这次修订能提供更清晰的画面。如果我需要对 20 种具有不同条件范围的 Material 进行分类，那么更优雅的方法是什么:
import numpy as np
import pandas as pd
strength = np.random.randint(low=1, high=30, size=20)
material = ['matl_a', 'matl_b', 'matl_b', 'matl_a', 'matl_d',
'matl_b', 'matl_d', 'matl_a', 'matl_a', 'matl_b',
'matl_a', 'matl_b', 'matl_e', 'matl_a', 'matl_c',
'matl_b', 'matl_c', 'matl_a', 'matl_a', 'matl_b']
data = {'material':material,
'strength':strength }
df = pd.DataFrame(data)
def grading(df):
if df['material'] == 'matl_a':
if 0 <= df['strength'] <=10:
return 1
elif 11 <= df['strength'] <= 20:
return 2
elif 21 <= df['strength'] <= 30:
return 3
elif 31 <= df['strength'] <= 40:
return 4
else:
return 5
elif df['material'] == 'matl_b':
if 0 <= df['strength'] <=10:
return 6
elif 11 <= df['strength'] <= 20:
return 7
elif 21 <= df['strength'] <= 30:
return 8
elif 31 <= df['strength'] <= 40:
return 9
else:
return 10
elif df['material'] == 'matl_c':
if 0 <= df['strength'] <=10:
return 11
elif 11 <= df['strength'] <= 20:
return 12
elif 21 <= df['strength'] <= 30:
return 13
elif 31 <= df['strength'] <= 40:
return 14
else:
return 15
else:
if 0 <= df['strength'] <=10:
return 16
elif 11 <= df['strength'] <= 20:
return 17
elif 21 <= df['strength'] <= 30:
return 18
elif 31 <= df['strength'] <= 40:
return 19
else:
return 20
df['grade'] = df.apply(grading, axis=1)
最佳答案
使用np.select
a = df.material.eq('Matl_A')
b = df.material.eq('Matl_B')
df['grade'] = np.select([a & df.strength.between(5,10),
a & df.strength.between(11,20),
b & df.strength.between(10,50),
b & df.strength.between(50,100)],
['A', 'B', 'A', 'B'],
default='C')
关于python - 基于多列分箱(分类值)的最佳方式，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/57916603/