Question

我正在使用此数据库：https://cpj.org/data/killed/?status=Killed&motiveConfirmed%5B%5D=Confirmed&type%5B%5D=Journalist&localOrForeign%5B%5D=Foreign&start_year=1992&end_year=2019&group_by=year

我已将其预处理到此csv中（仅显示159的2行）：

year,combinedStatus,fullName,sortName,primaryNationality,secondaryNationality,tertiaryNationality,gender,photoUrl,photoCredit,type,lastStatus,typeOfDeath,status,employedAs,organizations,jobs,coverage,mediums,country,location,region,state,locality,province,localOrForeign,sourcesOfFire,motiveConfirmed,accountabilityCrossfire,accountabilityAssignment,impunityMurder,tortured,captive,threatened,charges,motive,lengthOfSentence,healthProblems,impCountry,entry,sentenceDate,sentence,locationImprisoned
1994,Confirmed,Abdelkader Hireche,,,,,Male,,,Journalist,,Murder,Killed,Staff,Algerian Television (ENTV),Broadcast Reporter,Politics,Television,Algeria,Algiers,,,Algiers,,Foreign,,Confirmed,,,Partial Impunity,No,No,No,,,,,,,,,
2014,Confirmed,Ahmed Hasan Ahmed,,,,,Male,,,Journalist,,Dangerous Assignment,Killed,Staff,Xinhua News Agency,"Camera Operator,Photographer","Human Rights,Politics,War",Internet,Syria,Damascus,,,Damascus,,Foreign,,Confirmed,,,,,,,,,,,,,,,

我想用它制作这种类型的JSON：

"Afghanistan": {"year": 2001, "fullName": "Volker Handloik", "gender": "Male", "typeOfDeath": "Crossfire", "employedAs": "Freelance", "organizations": "freelance reporter", "jobs": "Print Reporter", "coverage": "War", "mediums": "Print", "photoUrl": NaN}, "Somalia": {"year": 1994, "fullName": "Pierre Anceaux", "gender": "Male", "typeOfDeath": "Murder", "employedAs": "Freelance", "organizations": "freelance", "jobs": "Broadcast Reporter", "coverage": "Human Rights", "mediums": "Television", "photoUrl": NaN}

问题在于阿富汗（如您在链接中所见）已经有许多新闻记者死亡。我想在“阿富汗”索引下列出所有这些谋杀案。但是，按照我目前的做法，仅显示csv文件中的最后一种情况（Volker Handloik）。如何获得所有案例？

这是我的代码atm

import pandas as pd
import pprint as pp
import json

# list with stand-ins for empty cells
missing_values = ["n/a", "na", "unknown", "-", ""]

# set missing values to NaN
df = pd.read_csv("data_journalists.csv", na_values = missing_values, skipinitialspace = True, error_bad_lines=False)

# columns
columns_keep = ['year', 'fullName', 'gender', 'typeOfDeath', 'employedAs', 'organizations', 'jobs', 'coverage', 'mediums', 'country', 'photoUrl']

small_df = df[columns_keep]

with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also
    print(small_df)

# create dict with country-column as index
df_dict = small_df.set_index('country').T.to_dict('dict')

print(df_dict)

# make json file from the dict
with open('result.json', 'w') as fp:
    json.dump(df_dict, fp)

    # use pretty print to see if dict matches the json example in the exercise
 pp.pprint(df_dict)

我想将所有这些名称（以及更多名称）包含在JSON的阿富汗索引下

我想我需要一个附在一个国家的索引上的对象列表，以便每个国家都可以显示所有新闻工作者死亡的案例，而不是仅显示1个（每次都由csv中的下一个替换）希望这足够清楚

Answer 1

我将保留您的代码，直到定义small_df。

之后，我们在“国家/地区”列上执行分组，并在其上使用pd.to_json：

country_series = small_df.groupby('country').apply(lambda r : r.drop(['country'], axis=1).to_json())

country_series是一个以国家为索引的pd.Series。之后，我们创建一个嵌套的字典，以便有一个有效的json对象：

fullDict = {}
for ind, a in country_series.iteritems():
    b = json.loads(a)
    c = b['fullName']
    smallDict = {}
    for index, journalist in c.items():
        smallDict[journalist] = {}
        for i in b.keys():
            smallDict[journalist][i] = b[i][index]
    fullDict[ind] = (smallDict)

我的代码部分的命名很糟糕，但是我试图明确地编写所有步骤，以便使事情变得清晰。

最后，我们将结果写入文件：

with open('result.json', 'w') as f:
    json.dump(fullDict, f)

将CSV转换为JSON。如何保持具有相同索引的值？

1 个答案: