如何将熊猫数据框的单列拆分为多列与组？

Question

我是 python pandas 的新手。我有一个如下数据框：

df = pd.DataFrame({'Name': ['football', 'ramesh','suresh','pankaj','cricket','rakesh','mohit','mahesh'],
               'age': ['25', '22','21','32','37','26','24','30']})
print df

       Name age
0  football  25
1    ramesh  22
2    suresh  21
3    pankaj  32
4   cricket  37
5    rakesh  26
6     mohit  24
7    mahesh  30

“姓名”栏还包含“运动名称”和“运动人姓名”。我想把它分成两个不同的列，如下所示：

预期输出：

sports_name sport_person_name age
football    ramesh            25
            suresh            22
            pankaj            32
cricket     rakesh            26
            mohit             24
            mahesh            30

如果我在“名称”列上进行分组，我不会得到预期的输出，这显然是直接的输出，因为“名称”列中没有重复。我需要使用什么才能获得预期的输出？

编辑：如果不想硬编码运动名称

df = pd.DataFrame({'Name': ['football', 'ramesh','suresh','pankaj','cricket','rakesh','mohit','mahesh'],
           'age': ['', '22','21','32','','26','24','30']})

df = df.replace('', np.nan, regex=True)

nan_rows = df[df.isnull().T.any().T]
sports = nan_rows['Name'].tolist()

df['sports_name'] = df['Name'].where(df['Name'].isin(sports)).ffill()
d = {'Name':'sport_person_name'}
df = df[df['sports_name'] != df['Name']].reset_index(drop=True).rename(columns=d)
df = df[['sports_name','sport_person_name','age']]
print (df)

我刚刚检查了除了“名称”列之外的行在所有其余列中都包含 NAN 值，并且肯定是体育名称。我创建了该运动名称的列表，并利用以下解决方案创建了 sports_name 和 sports_person_name 列。

Answer 1

你可以使用：

#define list of sports
sports = ['football','cricket']
#create NaNs if no sport in Name, forward filling NaNs
df['sports_name'] = df['Name'].where(df['Name'].isin(sports)).ffill()
#remove same values in columns sports_name and Name, rename column
d = {'Name':'sport_person_name'}
df = df[df['sports_name'] != df['Name']].reset_index(drop=True).rename(columns=d)
#change order of columns
df = df[['sports_name','sport_person_name','age']]
print (df)
  sports_name sport_person_name age
0    football            ramesh  22
1    football            suresh  21
2    football            pankaj  32
3     cricket            rakesh  26
4     cricket             mohit  24
5     cricket            mahesh  30

与DataFrame.insert 类似的解决方案 - 然后不需要重新排序：

#define list of sports
sports = ['football','cricket']
#rename column by dict
d = {'Name':'sport_person_name'}
df = df.rename(columns=d)
#create NaNs if no sport in Name, forward filling NaNs
df.insert(0, 'sports_name', df['sport_person_name'].where(df['sport_person_name'].isin(sports)).ffill())
#remove same values in columns sports_name and Name
df = df[df['sports_name'] != df['sport_person_name']].reset_index(drop=True)
print (df)
  sports_name sport_person_name age
0    football            ramesh  22
1    football            suresh  21
2    football            pankaj  32
3     cricket            rakesh  26
4     cricket             mohit  24
5     cricket            mahesh  30

如果只需要一个运动值，请将limit=1 添加到ffill 并将NaNs 替换为空字符串：

sports = ['football','cricket']
df['sports_name'] = df['Name'].where(df['Name'].isin(sports)).ffill(limit=1).fillna('')
d = {'Name':'sport_person_name'}
df = df[df['sports_name'] != df['Name']].reset_index(drop=True).rename(columns=d)
df = df[['sports_name','sport_person_name','age']]
print (df)
  sports_name sport_person_name age
0    football            ramesh  22
1                        suresh  21
2                        pankaj  32
3     cricket            rakesh  26
4                         mohit  24
5                        mahesh  30

Answer 2

您想要的输出是字典而不是数据框。 字典会看起来：

{'Sport' : {'Player' : age,'Player2' : age}}

如果你真的想要一个数据框： 如果名字总是出现在玩家之前：

import pandas as pd
import numpy as np

df = pd.DataFrame({'Name': ['football','ramesh','suresh','pankaj','cricket' 
                  ,'rakesh','mohit','mahesh'],
                  'age': ['25', '22','21','32','37','26','24','30']})

sports=['football', 'cricket']
wanted_dict={}
current_sport=''

for val in df['sport_person_name']:
    if val in sports:
        current_sport=val
    else:
        wanted_dict[val]=current_sport

#Now you got - {name:sport_name,...}

df['sports_name']=999
for val in df['sport_person_name']
    df['sports_name']=np.where((val not in sports)&
                              (df['sport_person_name']==val),
                               wanted_dict[val],'sport)

df = df[df['sports_name']!='sport']

应该是什么样子：

sports_name sport_person_name age
football    ramesh            25
football    suresh            22
football    pankaj            32
cricket     rakesh            26
cricket     mohit             24
cricket     mahesh            30