本文介绍了在对数据框的一列进行装箱后,如何制作一个新的数据框以计算每个箱中的元素数量?的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
说我有一个数据框,df
:
>>> df
Age Score
19 1
20 2
24 3
19 2
24 3
24 1
24 3
20 1
19 1
20 3
22 2
22 1
我想构造一个新的数据框,将Age
装箱,并将每个箱中的元素总数存储在不同的Score
列中:
I want to construct a new dataframe that bins Age
and stores the total number of elements in each of the bins in different Score
columns:
Age Score 1 Score 2 Score 3
19-21 2 4 3
22-24 2 2 9
这是我的处理方式,我感到非常费解(意思是,这不应该那么困难):
This is my way of doing it, which I feel is highly convoluted (meaning, it shouldn't be this difficult):
import numpy as np
import pandas as pd
data = pd.DataFrame(columns=['Age', 'Score'])
data['Age'] = [19,20,24,19,24,24,24,20,19,20,22,22]
data['Score'] = [1,2,3,2,3,1,3,1,1,3,2,1]
_, bins = np.histogram(data['Age'], 2)
labels = ['{}-{}'.format(i + 1, j) for i, j in zip(bins[:-1], bins[1:])] #dynamically create labels
labels[0] = '{}-{}'.format(bins[0], bins[1])
df = pd.DataFrame(columns=['Score', labels[0], labels[1]])
df['Score'] = data.Score.unique()
for i in labels:
df[i] = np.zeros(3)
for i in range(len(data)):
for j in range(len(labels)):
m1, m2 = labels[j].split('-') # lower & upper bounds of the age interval
if ((float(data['Age'][i])>float(m1)) & (float(data['Age'][i])<float(m2))): # find the age group in which each age lies
if data['Score'][i]==1:
index = 0
elif data['Score'][i]==2:
index = 1
elif data['Score'][i]==3:
index = 2
df[labels[j]][index] += 1
df.sort_values('Score', inplace=True)
df.set_index('Score', inplace=True)
print(df)
这产生
19.0-21.5 22.5-24.0
Score
1 2.0 2.0
2 4.0 2.0
3 3.0 9.0
是否有更好,更清洁,更有效的方法来实现这一目标?
Is there a better, cleaner, more efficient of achieving this?
推荐答案
IIUC,我认为您可以尝试以下方法之一:
IIUC, I think you can try one of these:
1.如果您已经知道垃圾箱:
1.If you already know the bins:
df['Age'] = np.where(df['Age']<=21,'19-21','22-24')
df.groupby(['Age'])['Score'].value_counts().unstack()
2.如果您知道垃圾箱的数量,则需要:
2.If you know number of bins you need:
df.Age = pd.cut(df.Age, bins=2,include_lowest=True)
df.groupby(['Age'])['Score'].value_counts().unstack()
3.乔恩·克莱门茨评论的想法:
pd.crosstab(pd.cut(df.Age, [19, 21, 24],include_lowest=True), df.Score)
这三者均产生以下输出:
All of the three produces following output:
Score 1 2 3
Age
(18.999, 21.0] 3 2 1
(21.0, 24.0] 2 1 3
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