如果這非常簡(jiǎn)單或已經(jīng)被問(wèn)到，我是 Python 新手并且使用 json 文件，所以我很困惑。我有一個(gè)從網(wǎng)站上抓取的 9 GB json 文件。這些數(shù)據(jù)包含大約 300 萬(wàn)人的信息。每個(gè)人都有屬性，但并不是所有的人都具有相同的屬性。一個(gè)屬性對(duì)應(yīng)于 json 文件中的一個(gè)鍵，如下所示：{ "_id": "in-00000001", "name": { "family_name": "Trump", "given_name": "Donald" }, "locality": "United States", "skills": [ "Twitter", "Real Estate", "Golf" ], "industry": "Government", "experience": [ { "org": "Republican", "end": "Present", "start": "January 2017", "title": "President of the United States" }, { "org": "The Apprentice", "end": "2015", "start": "2003", "title": "The guy that fires people" }]}所以在這里，、 、 、_id和name是locality屬性（鍵）。另一個(gè)配置文件可能具有其他屬性，例如、、，或者缺少在另一個(gè)配置文件中找到的某些屬性，例如屬性等。skillsindustryexperienceeducationawardsinterestsskills我想做的是掃描 json 文件中的每個(gè)配置文件，如果配置文件包含屬性和skills，我想提取該信息并將其插入數(shù)據(jù)框中（我想我需要 Pandas ？）。從中，我想具體提取他們當(dāng)前雇主的姓名，即最近在. 數(shù)據(jù)框如下所示：industryexperienceexperienceorg Industry | Current employer | Skills ___________________________________________________________________ Government | Republican | Twitter, Real Estate, Golf Marketing | Marketers R Us | Branding, Social Media, Advertising...對(duì)于具有這三個(gè)屬性的所有配置文件，依此類推。我正在努力尋找一個(gè)很好的資源來(lái)解釋如何做這種事情，因此我的問(wèn)題是。我想粗略的偽代碼是：for each profile in open(path to .json file): if profile has keys "experience", "industry" AND "skills": on the same row of the data frame: insert current employer into "current employer" column of data frame insert industry into "industry" column of data frame insert list of skills into "skills" column of data frame我只需要知道如何用 Python 編寫(xiě)它。
1 回答
炎炎設(shè)計(jì)
TA貢獻(xiàn)1808條經(jīng)驗(yàn) 獲得超4個(gè)贊
我假設(shè)該文件包含所有配置文件，例如
{
"profile 1" : {
# Full object as in the example above
},
"profile 2" : {
#Full object as in the example above
}
}
在繼續(xù)之前，讓我展示一個(gè)使用 Pandas DataFrames 的正確方法。
更好地使用 Pandas DataFrames 的示例：
Pandas DataFrame 中的值不能是列表。因此，我們將不得不復(fù)制行，如下例所示。查看此問(wèn)題和 JD Long 的答案以獲取更多詳細(xì)信息：如何在 pandas 數(shù)據(jù)框中使用列表作為值？
ID | Industry | Current employer | Skill
___________________________________________________________________
in-01 | Government | Republican | Twitter
in-01 | Government | Republican | Real Estate
in-01 | Government | Republican | Golf
in-02 | Marketing | Marketers R Us | Branding
in-02 | Marketing | Marketers R Us | Social Media
in-02 | Marketing | Marketers R Us | Advertising
在以下代碼的注釋中查找解釋：
import json
import pandas as pd
# Create a DataFrame df with the columns as in the example
df = pd.DataFrame(data, columns = ['ID', 'Industry','Employer','Skill'])
#Load the file as json.
with open(path to .json file) as file:
#readlines() reads the file as string and loads() loads it into a dict
obj = json.loads(''.join(file.readlines()))
#Then iterate its items() as key value pairs
#But the line of code below depends on my first assumption.
#Depending on the file format, the line below might have to differ.
for prof_key, profile in obj.items():
# Verify if a profile contains all the required keys
if all(key in profile.keys() for key in ("_id","experience", "industry","skills")):
for skill in profile["skills"]:
df.loc[-1] = [profile["_id"],
profile["industry"],
[x for x in profile["experience"] if x["end"] == "Present"][0]["org"],
skill]
上面的行在df.loc[-1] = ...數(shù)據(jù)框中插入一行作為最后一行（索引-1）。
當(dāng)您稍后希望使用此信息時(shí)，您將不得不使用df.groupby('ID')
讓我知道您的文件中是否有不同的格式，以及此說(shuō)明是否足以讓您入門(mén)或您需要更多。
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