[英]Python - Create nested JSON from dataframe
I have a dataframe which looks like this:我有一个如下所示的数据框:
key text
0 title Lorem ipsum
1 header Lorem ipsum
2 description Lorem ipsum
.
.
.
.
10 pyramid.male Lorem ipsum
11 pyramid.male_surplus Lorem ipsum
12 pyramid.female Lorem ipsum
13 pyramid.female_surplus Lorem ipsum
.
.
.
.
29 jitterplot.title1 Lorem ipsum
30 jitterplot.metric_1.label Lorem ipsum
31 jitterplot.metric_1.tooltip Lorem ipsum
32 jitterplot.metric_2.label Lorem ipsum
33 jitterplot.metric_2.tooltip Lorem ipsum
The keys represent keys in a JSON file.键表示 JSON 文件中的键。 The JSON structure should look like the following: JSON 结构应如下所示:
{
"title": "Lorem ipsum",
"header": "Lorem ipsum",
"description": "Lorem ipsum",
"pyramid": {
"male": "Lorem ipsum",
"male_surplus": "Lorem ipsum",
"female": "Lorem ipsum",
"female_surplus": "Lorem ipsum"
},
"jitterplot": {
"title1": "Lorem ipsum",
"metric_1": {
"label": "Lorem ipsum",
"tooltip": "Lorem ipsum"
},
"metric_2": {
"label": "Lorem ipsum",
"tooltip": "Lorem ipsum"
}
}
}
Meaning, a .
意思是,一个.
in the key
column represents a nested level. key
列中的表示嵌套级别。
Is there a 'Pythonic' way to achieve this?有没有一种“Pythonic”的方式来实现这一点? Currently, I'm just hacking it by manually writing each row to a text file with a custom parser I wrote.目前,我只是通过使用我编写的自定义解析器将每一行手动写入文本文件来破解它。 But obviously this is not very scalable.但显然这不是非常可扩展的。
I've prepared a sample CSV which you can read, and added some additional columns if they help.我准备了一个您可以阅读的示例 CSV,并添加了一些额外的列,如果它们有帮助的话。 Use the following code:使用以下代码:
import pandas as pd
url = 'https://raw.githubusercontent.com/Thevesh/Display/master/i18n_sample.csv'
df = pd.read_csv(url)
df['n_levels'] = df['key'].str.count('\.') # column with number of levels
max_levels = df.n_levels.max() #
df = df.join(df['key'].str.split('.',expand=True))
df.columns = list(df.columns)[:-max_levels-1] + ['key_' + str(x) for x in range(max_levels+1)]
def autonesting_dict():
return collections.defaultdict(autonesting_dict)
json_dict = autonesting_dict()
key, value = 'jitterplot.metric_2.tooltip', "Lorem ipsum"
subkeys = key.split('.')
nested_dict = functools.reduce(lambda d, key: d[key], subkeys[:-1], json_dict)
nested_dict[subkeys[-1]] = value
The above will make it so that:以上将使它:
json_dict['jitterplot']['metric_2']['tooltip'] # 'Lorem ipsum'
Just repeat for all rows.只需对所有行重复。
Sidenote regarding:旁注关于:
I've prepared a sample CSV which you can read, and added some additional columns if they help.我准备了一个您可以阅读的示例 CSV,并添加了一些额外的列,如果它们有帮助的话。 Use the following code:使用以下代码:
Maybe it's just me, but that sounds like something that might be given on an assignment or quiz, not like someone asking for assistance.也许只有我一个人,但这听起来像是在作业或测验中给出的东西,而不是寻求帮助的人。
This seems like a good fit for a recursive function:这似乎很适合递归函数:
# Dataframe with columns key and value:
df = ...
json_data = {}
def set_value(nested_dict, keys, value):
if len(keys) == 1:
nested_dict[keys[0]] = value
return
if keys[0] not in nested_dict:
nested_dict[keys[0]] = {}
set_value(nested_dict[keys[0]], keys[1:], value)
for full_key, value in zip(df.key, df.text):
keys = full_key.split('.')
set_value(json_data, keys, value)
print(json_data)
Similarly but a bit simpler than the other answers:同样,但比其他答案简单一点:
def set_nested_value(d, keys, value):
for key in keys[:-1]:
d = d.setdefault(key, {})
d[keys[-1]] = value
result = {}
for _, row in df.iterrows():
set_nested_value(result, row["key"].split("."), row["text"])
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