pandas - 如何使用熊猫中的列表添加一对多列

Question

“fav_colour”和“names”中的第一个列表来自“Tower Hamlets”。

“fav_colour”和“names”中的第二个列表来自“Waltham Forrest”。

请参阅下面的脚本和当前输出：

import pandas as pd
import numpy as np


fav_colour = [['blue', 'yello', 'indigo', 'jasmine', 'green', 'black'], ['yellow','purple', 'red', 'beige']]

names = [['melanie', 'jess', 'sean', 'tom', 'arjun', 'brandon'],['scotty', 'harry', 'chloe', 'emily']]

boroughs = ['Tower Hamlets','Waltham Forrest']

No_of_rows = [len(name) for name in names] #using length to repeat rows in some way??


indexs4 = list(range(0,2))
df1 = [pd.DataFrame(zip(names[i], fav_colour[i], boroughs[i]), columns = ['names','fav', 'boroughs']) for i in indexs4]
df = pd.concat(df1)

“fav_colour”和“names”具有一对一的关系。

“names”和“fav_colour”与自治市镇有一对多的关系

我想与“fav_colour”、“name”和“boroughs”建立一对多的关系，如下所示：

期望的输出：

     names      fav boroughs
0  melanie     blue   Tower Hamlets
1     jess    yello   Tower Hamlets
2     sean   indigo   Tower Hamlets
3      tom  jasmine   Tower Hamlets
4    arjun    green   Tower Hamlets
5  brandon    black   Tower Hamlets  
0   scotty   yellow    Waltham Forrest
1    harry   purple    Waltham Forrest
2    chloe      red    Waltham Forrest
3    emily    beige    Waltham Forrest

电流输出：

 names      fav boroughs
0  melanie     blue        T
1     jess    yello        o
2     sean   indigo        w
3      tom  jasmine        e
4    arjun    green        r
5  brandon    black         
0   scotty   yellow        W
1    harry   purple        a
2    chloe      red        l
3    emily    beige        t

标签： pandasdataframearraylistpandas-groupbyone-to-many

Answer 1

import pandas as pd

fav_colour = [['blue', 'yello', 'indigo', 'jasmine', 'green', 'black'], ['yellow','purple', 'red', 'beige']]

names = [['melanie', 'jess', 'sean', 'tom', 'arjun', 'brandon'],['scotty', 'harry', 'chloe', 'emily']]

boroughs = ['Tower Hamlets','Waltham Forrest']

df = pd.DataFrame(columns=['names','fav', 'boroughs'])

for i  in range(len(fav_colour)):
    tmp = pd.DataFrame(list(zip(names[i],fav_colour[i])),columns=['names','fav'])
    tmp["boroughs"] = boroughs[i]
    df = df.append(tmp,ignore_index= True)