在 pandas 中按自定义列表排序 [英] sorting by a custom list in pandas

问题描述

通读后:http://pandas.pydata.org/pandas-docs/version/0.13.1/generated/pandas.DataFrame.sort.html

我似乎仍然无法弄清楚如何按自定义列表对列进行排序.显然，默认排序是按字母顺序排列的.我举个例子.这是我的(非常精简的)数据框:

I still can't seem to figure out how to sort a column by a custom list. Obviously, the default sort is alphabetical. I'll give an example. Here is my (very abridged) dataframe:

             Player      Year   Age   Tm     G
2967     Cedric Hunter   1991    27  CHH     6
5335     Maurice Baker   2004    25  VAN     7
13950    Ratko Varda     2001    22  TOT     60
6141     Ryan Bowen      2009    34  OKC     52
6169     Adrian Caldwell 1997    31  DAL     81

我希望能够按玩家、年份和 Tm 排序.按玩家和年份的默认排序对我来说很好，按正常顺序排列.但是，我不希望团队按字母顺序 b/c 排序，我希望 TOT 始终位于顶部.

I want to be able to sort by Player, Year and then Tm. The default sort by Player and Year is fine for me, in normal order. However, I do not want Team sorted alphabetically b/c I want TOT always at the top.

这是我创建的列表:

sorter = ['TOT', 'ATL', 'BOS', 'BRK', 'CHA', 'CHH', 'CHI', 'CLE', 'DAL', 'DEN',
   'DET', 'GSW', 'HOU', 'IND', 'LAC', 'LAL', 'MEM', 'MIA', 'MIL',
   'MIN', 'NJN', 'NOH', 'NOK', 'NOP', 'NYK', 'OKC', 'ORL', 'PHI',
   'PHO', 'POR', 'SAC', 'SAS', 'SEA', 'TOR', 'UTA', 'VAN',
   'WAS', 'WSB']

阅读上面的链接后，我认为这会奏效，但没有:

After reading through the link above, I thought this would work but it didn't:

df.sort(['Player', 'Year', 'Tm'], ascending = [True, True, sorter])

它仍然在顶部有 ATL，这意味着它按字母顺序排序，而不是根据我的自定义列表.任何帮助将不胜感激，我只是想不通.

It still has ATL at the top, meaning that it sorted alphabetically and not according to my custom list. Any help would really be greatly appreciated, I just can't figure this out.

推荐答案

下面是对数据框执行字典排序的示例.这个想法是基于特定的排序创建一个数字索引.然后根据索引执行数字排序.为此将一列添加到数据框中，然后将其删除.

Below is an example that performs lexicographic sort on a dataframe. The idea is to create an numerical index based on the specific sort. Then to perform a numerical sort based on the index. A column is added to the dataframe to do so, and is then removed.

import pandas as pd

# Create DataFrame
df = pd.DataFrame(
{'id':[2967, 5335, 13950, 6141, 6169],
    'Player': ['Cedric Hunter', 'Maurice Baker',
               'Ratko Varda' ,'Ryan Bowen' ,'Adrian Caldwell'],
    'Year': [1991, 2004, 2001, 2009, 1997],
    'Age': [27, 25, 22, 34, 31],
    'Tm': ['CHH' ,'VAN' ,'TOT' ,'OKC', 'DAL'],
    'G': [6, 7, 60, 52, 81]})

# Define the sorter
sorter = ['TOT', 'ATL', 'BOS', 'BRK', 'CHA', 'CHH', 'CHI', 'CLE', 'DAL','DEN',
          'DET', 'GSW', 'HOU', 'IND', 'LAC', 'LAL', 'MEM', 'MIA', 'MIL',
          'MIN', 'NJN', 'NOH', 'NOK', 'NOP', 'NYK', 'OKC', 'ORL', 'PHI',
          'PHO', 'POR', 'SAC', 'SAS', 'SEA', 'TOR', 'UTA', 'VAN',
          'WAS', 'WSB']

# Create the dictionary that defines the order for sorting
sorterIndex = dict(zip(sorter, range(len(sorter))))

# Generate a rank column that will be used to sort
# the dataframe numerically
df['Tm_Rank'] = df['Tm'].map(sorterIndex)

# Here is the result asked with the lexicographic sort
# Result may be hard to analyze, so a second sorting is
# proposed next
## NOTE: 
## Newer versions of pandas use 'sort_values' instead of 'sort'
df.sort_values(['Player', 'Year', 'Tm_Rank'],
        ascending = [True, True, True], inplace = True)
df.drop('Tm_Rank', 1, inplace = True)
print(df)

# Here is an example where 'Tm' is sorted first, that will 
# give the first row of the DataFrame df to contain TOT as 'Tm'
df['Tm_Rank'] = df['Tm'].map(sorterIndex)
## NOTE: 
## Newer versions of pandas use 'sort_values' instead of 'sort'
df.sort_values(['Tm_Rank', 'Player', 'Year'],
        ascending = [True , True, True], inplace = True)
df.drop('Tm_Rank', 1, inplace = True)
print(df)

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