如何将文本列表从 json 数据排序到 pandas？

Question

我已将 json 数据从单个文件夹转换为 pandas 数据框。但这份名单并没有按顺序列出。有人知道如何对数据进行排序吗？

这是 json_files 的输出：

['BuzzFeed_Real_5-Webpage.json',
 'BuzzFeed_Fake_9-Webpage.json',
 'BuzzFeed_Fake_6-Webpage.json',
 'BuzzFeed_Fake_5-Webpage.json',
 'BuzzFeed_Fake_8-Webpage.json',
 'BuzzFeed_Real_6-Webpage.json',
 'BuzzFeed_Real_7-Webpage.json',
 'BuzzFeed_Real_8-Webpage.json',
 'BuzzFeed_Real_9-Webpage.json',
 'BuzzFeed_Real_2-Webpage.json',
 'BuzzFeed_Real_4-Webpage.json',
 'BuzzFeed_Real_1-Webpage.json',
 'BuzzFeed_Real_10-Webpage.json',
 'BuzzFeed_Fake_4-Webpage.json',
 'BuzzFeed_Fake_10-Webpage.json',
 'BuzzFeed_Fake_1-Webpage.json',
 'BuzzFeed_Fake_2-Webpage.json',
 'BuzzFeed_Real_3-Webpage.json',
 'BuzzFeed_Fake_3-Webpage.json',
 'BuzzFeed_Fake_7-Webpage.json']

但是，我的标签顺序如下： 标签

    label
0   BuzzFeed_Real_1
1   BuzzFeed_Real_2
2   BuzzFeed_Real_3
3   BuzzFeed_Real_4
4   BuzzFeed_Real_5
5   BuzzFeed_Real_6
6   BuzzFeed_Real_7
7   BuzzFeed_Real_8
8   BuzzFeed_Real_9
9   BuzzFeed_Real_10
10  BuzzFeed_Fake_1
11  BuzzFeed_Fake_2
12  BuzzFeed_Fake_3
13  BuzzFeed_Fake_4
14  BuzzFeed_Fake_5
15  BuzzFeed_Fake_6
16  BuzzFeed_Fake_7
17  BuzzFeed_Fake_8
18  BuzzFeed_Fake_9
19  BuzzFeed_Fake_10

有人知道如何根据标签对数据进行排序吗？谢谢

这是我的代码：

        import os, json
import pandas as pd
import numpy as np

path_to_json = 'data/'
json_files = [pos_json for pos_json in os.listdir(path_to_json) if pos_json.endswith('json')]
print(json_files)

#Here I define my pandas dataframe with the colums I want to get from json
jsons_data = pd.DataFrame(columns=['text','title'])

#We need both json and an index number so use enumerate()
for index, js in enumerate(json_files):
    with open(os.path.join(path_to_json,js)) as json_file:
        json_text = json.load(json_file)


        #the same structure 
        text = json_text['text']
        title = json_text['title']

        #Here I push a list of data into pandas DataFrame at row given by 'index'
        jsons_data.loc[index] = [text,title]

#Now that we have the pertinen json data in our DataFrame 
print(jsons_data)

这是 jsons_data 的输出：

text    title
0   Story highlights Obams reaffirms US commitment...   Obama in NYC: 'We all have a role to play' in ...
1   Well THAT’S Weird. If the Birther movement is ...   The AP, In 2004, Said Your Boy Obama Was BORN ...
2   The man arrested Monday in connection with the...   Bombing Suspect Filed Anti-Muslim Discriminati...
3   The Haitians in the audience have some newswor...   'Reporters' FLEE When Clintons Get EXPOSED!
4   Chicago Environmentalist Scumbags\n\nLeftists ...   The Black Sphere with Kevin Jackson
5   Obama weighs in on the debate\n\nPresident Bar...   Obama weighs in on the debate
6   Story highlights Ted Cruz refused to endorse T...   Donald Trump's rise puts Ted Cruz in a bind
7   Last week I wrote an article titled “Donald Tr...   More Milestone Moments for Donald Trump! – Eag...
8   Story highlights Trump has 45%, Clinton 42% an...   Georgia poll: Donald Trump, Hillary Clinton in...
9   Story highlights "This, though, is certain: to...   Hillary Clinton on police shootings: 'too many...
10  McCain Criticized Trump for Arpaio’s Pardon… S...   NFL Superstar Unleashes 4 Word Bombshell on Re...
11  On Saturday, September 17 at 8:30 pm EST, an e...   Another Terrorist Attack in NYC…Why Are we STI...
12  Less than a day after protests over the police...   Donald Trump: Drugs a 'Very, Very Big Factor' ...
13  Dolly Kyle has written a scathing “tell all” b...   HILLARY ON DISABLED CHILDREN During Easter Egg...
14  Former President Bill Clinton and his Clinton ...   Charity: Clinton Foundation Distributed “Water...
15  I woke up this morning to find a variation of ...   Proof The Mainstream Media Is Manipulating The...
16  Thanks in part to the declassification of Defe...   Declassified Docs Show That Obama Admin Create...
17  Critical Counties is a CNN series exploring 11...   Critical counties: Wake County, NC, could put ...
18  The Democrats are using an intimidation tactic...   Why is it “RACIST” to Question Someone’s Birth...
19  Back when the news first broke about the pay-t...   Clinton Foundation Spent 5.7% on Charity; Rest...

Answer 1

您可以使用this 的解决方案，将Fake 和Real 字符串的拆分值降序排序，数字升序排序：

L = ['BuzzFeed_Real_5-Webpage.json',
 'BuzzFeed_Fake_9-Webpage.json',
 'BuzzFeed_Fake_6-Webpage.json',
 'BuzzFeed_Fake_5-Webpage.json',
 'BuzzFeed_Fake_8-Webpage.json',
 'BuzzFeed_Real_6-Webpage.json',
 'BuzzFeed_Real_7-Webpage.json',
 'BuzzFeed_Real_8-Webpage.json',
 'BuzzFeed_Real_9-Webpage.json',
 'BuzzFeed_Real_2-Webpage.json',
 'BuzzFeed_Real_4-Webpage.json',
 'BuzzFeed_Real_1-Webpage.json',
 'BuzzFeed_Real_10-Webpage.json',
 'BuzzFeed_Fake_4-Webpage.json',
 'BuzzFeed_Fake_10-Webpage.json',
 'BuzzFeed_Fake_1-Webpage.json',
 'BuzzFeed_Fake_2-Webpage.json',
 'BuzzFeed_Real_3-Webpage.json',
 'BuzzFeed_Fake_3-Webpage.json',
 'BuzzFeed_Fake_7-Webpage.json']

---

class reversor:
    def __init__(self, obj):
        self.obj = obj

    def __eq__(self, other):
        return other.obj == self.obj

    def __lt__(self, other):
        return other.obj < self.obj

a = sorted(L, key=lambda x: (reversor(x.split('_')[1]), int(x.split('_')[2].split('-')[0])))
print (a)
['BuzzFeed_Real_1-Webpage.json', 'BuzzFeed_Real_2-Webpage.json',
 'BuzzFeed_Real_3-Webpage.json', 'BuzzFeed_Real_4-Webpage.json', 
 'BuzzFeed_Real_5-Webpage.json', 'BuzzFeed_Real_6-Webpage.json', 
 'BuzzFeed_Real_7-Webpage.json', 'BuzzFeed_Real_8-Webpage.json', 
 'BuzzFeed_Real_9-Webpage.json', 'BuzzFeed_Real_10-Webpage.json', 
 'BuzzFeed_Fake_1-Webpage.json', 'BuzzFeed_Fake_2-Webpage.json', 
 'BuzzFeed_Fake_3-Webpage.json', 'BuzzFeed_Fake_4-Webpage.json', 
 'BuzzFeed_Fake_5-Webpage.json', 'BuzzFeed_Fake_6-Webpage.json', 
 'BuzzFeed_Fake_7-Webpage.json', 'BuzzFeed_Fake_8-Webpage.json', 
 'BuzzFeed_Fake_9-Webpage.json', 'BuzzFeed_Fake_10-Webpage.json']

pandas 的另一个类似想法 - 将值拆分为新列，最后按 DataFrame.sort_values 排序：

df = pd.DataFrame({'a':L})
df = df.join(df['a'].str.split('_', expand=True))
df['num'] = df[2].str.extract('(\d+)', expand=False).astype(int)
df = df.sort_values([1, 'num'], ascending=[False, True])
print (df)
                                a         0     1                2  num
11   BuzzFeed_Real_1-Webpage.json  BuzzFeed  Real   1-Webpage.json    1
9    BuzzFeed_Real_2-Webpage.json  BuzzFeed  Real   2-Webpage.json    2
17   BuzzFeed_Real_3-Webpage.json  BuzzFeed  Real   3-Webpage.json    3
10   BuzzFeed_Real_4-Webpage.json  BuzzFeed  Real   4-Webpage.json    4
0    BuzzFeed_Real_5-Webpage.json  BuzzFeed  Real   5-Webpage.json    5
5    BuzzFeed_Real_6-Webpage.json  BuzzFeed  Real   6-Webpage.json    6
6    BuzzFeed_Real_7-Webpage.json  BuzzFeed  Real   7-Webpage.json    7
7    BuzzFeed_Real_8-Webpage.json  BuzzFeed  Real   8-Webpage.json    8
8    BuzzFeed_Real_9-Webpage.json  BuzzFeed  Real   9-Webpage.json    9
12  BuzzFeed_Real_10-Webpage.json  BuzzFeed  Real  10-Webpage.json   10
15   BuzzFeed_Fake_1-Webpage.json  BuzzFeed  Fake   1-Webpage.json    1
16   BuzzFeed_Fake_2-Webpage.json  BuzzFeed  Fake   2-Webpage.json    2
18   BuzzFeed_Fake_3-Webpage.json  BuzzFeed  Fake   3-Webpage.json    3
13   BuzzFeed_Fake_4-Webpage.json  BuzzFeed  Fake   4-Webpage.json    4
3    BuzzFeed_Fake_5-Webpage.json  BuzzFeed  Fake   5-Webpage.json    5
2    BuzzFeed_Fake_6-Webpage.json  BuzzFeed  Fake   6-Webpage.json    6
19   BuzzFeed_Fake_7-Webpage.json  BuzzFeed  Fake   7-Webpage.json    7
4    BuzzFeed_Fake_8-Webpage.json  BuzzFeed  Fake   8-Webpage.json    8
1    BuzzFeed_Fake_9-Webpage.json  BuzzFeed  Fake   9-Webpage.json    9
14  BuzzFeed_Fake_10-Webpage.json  BuzzFeed  Fake  10-Webpage.json   10

---

a = df['a'].tolist()
print (a)
['BuzzFeed_Real_1-Webpage.json', 'BuzzFeed_Real_2-Webpage.json',
 'BuzzFeed_Real_3-Webpage.json', 'BuzzFeed_Real_4-Webpage.json', 
 'BuzzFeed_Real_5-Webpage.json', 'BuzzFeed_Real_6-Webpage.json', 
 'BuzzFeed_Real_7-Webpage.json', 'BuzzFeed_Real_8-Webpage.json', 
 'BuzzFeed_Real_9-Webpage.json', 'BuzzFeed_Real_10-Webpage.json', 
 'BuzzFeed_Fake_1-Webpage.json', 'BuzzFeed_Fake_2-Webpage.json', 
 'BuzzFeed_Fake_3-Webpage.json', 'BuzzFeed_Fake_4-Webpage.json', 
 'BuzzFeed_Fake_5-Webpage.json', 'BuzzFeed_Fake_6-Webpage.json', 
 'BuzzFeed_Fake_7-Webpage.json', 'BuzzFeed_Fake_8-Webpage.json', 
 'BuzzFeed_Fake_9-Webpage.json', 'BuzzFeed_Fake_10-Webpage.json']

Answer 2

这应该为您提供从文件名创建索引所需的内容。让我知道您是否需要设置索引的帮助，以及是否需要双索引或合并为单个索引：

import os, json
import pandas as pd
import numpy as np

path_to_json = 'data/'
json_files = [pos_json for pos_json in os.listdir(path_to_json) if pos_json.endswith('json')]
print(json_files)

#Here I define my pandas dataframe with the colums I want to get from json
jsons_data = pd.DataFrame(columns=['text','title'])

#We need both json and an index number so use enumerate()
for index, js in enumerate(json_files):
    with open(os.path.join(path_to_json,js)) as json_file:
        json_text = json.load(json_file)


        #the same structure 
        text = json_text['text']
        title = json_text['title']

        #Here I push a list of data into pandas DataFrame at row given by 'index'
        jsons_data.loc[index] = [text,title]

# Add column to your data frame containing 'json_files' list values
jsons_data['json_files'] = json_files

import re

# Create Regex to identify 'Fake' or 'Real' BuzzFeed
news_type = r"(Fake|Real)"

# Create Regex to extract numeric count
news_type_count = r"(\d+)"

# Extract new type to column
jsons_data['news_type'] = jsons_data['json_files'].str.extract(pat=news_type)

# Extract numeric count to columne
jsons_data['news_type_count'] = jsons_data['json_files'].str.extract(pat=news_type_count)

# Convert numeric count to integer
jsons_data['news_type_count'] = jsons_data['news_type_count'].astype(int)

# Sort dataframe by 'news_type' and 'news_type_count'
jsons_data = jsons_data.sort_values(by=['news_type', 'news_type_count'])

# Print head of dataframe
print(jsons_data.head())