首页 > 解决方案 > 使用 python pandas 将 JSON 文件转换为正确的格式

问题描述

我想将 JSON 文件转换为正确的格式。我有一个 JSON 文件,如下所示:

{
    "fruit": "Apple",
    "size": "Large",
    "color": "Red",
    "details":"|seedless:true|,|condition:New|"

},
{
    "fruit": "Almond",
    "size": "small",
    "color": "brown",
    "details":"|Type:dry|,|seedless:true|,|condition:New|"

}

您可以看到详细信息中的数据可能会有所不同。

我想把它改成:

{
    "fruit": "Apple",
    "size": "Large",
    "color": "Red",
    "seedless":"true",
    "condition":"New",

},
{
    "fruit": "Almond",
    "size": "small",
    "color": "brown",
    "Type":"dry",
    "seedless":"true",
    "condition":"New",

}

我曾尝试在 python 中使用 pandas 作为:

import json
import pandas as pd
import re
df = pd.read_json("data.json",lines=True)

#I tried to change the pattern of data in details column as

re1 = re.compile('r/|(.?):(.?)|/')
re2 = re.compile('r\"(.*?)\":\"(.*?)\"')

df.replace({'details' :re1}, {'details' : re2},inplace = True, regex = True);

但是,在详细信息列的所有行中将输出作为“对象”。

标签: pythonjsonregexpandasdesign-patterns

解决方案


You can convert the (list of) dictionaries to a pandas data frame.

import pandas as pd

# data is a list of dictionaries
data = [{
    "fruit": "Apple",
    "size": "Large",
    "color": "Red",
    "details":"|seedless:true|,|condition:New|"

},
{
    "fruit": "Almond",
    "size": "small",
    "color": "brown",
    "details":"|Type:dry,|seedless:true|,|condition:New|"

}]

# convert to data frame
df = pd.DataFrame(data)

# remove '|' from details and convert to list
df['details'] = df['details'].str.replace(r'\|', '').str.split(',')

# explode list => one row for each element
df = df.explode('details')

# split details into name/value pair
df[['name', 'value']] = df['details'].str.split(':').apply(lambda x: pd.Series(x))

# drop details column
df = df.drop(columns='details')

print(df)

    fruit   size  color       name value
0   Apple  Large    Red   seedless  true
0   Apple  Large    Red  condition   New
1  Almond  small  brown       Type   dry
1  Almond  small  brown   seedless  true
1  Almond  small  brown  condition   New

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