防止 pandas to_json（）将时间分量添加到日期对象 [英] Prevent Pandas to_json() from adding time component to date object

问题描述

我有一个数据框，其中包含一些日期对象。我需要转换为用于JavaScript的json，这需要YYYY-MM-DD，但 to_json（）会不断添加时间分量。我已经看到了许多首先转换为字符串的答案，但这是循环的大约15个查询的查询的一部分，每个查询具有许多列（针对SO问题对其进行了简化），我不想将每个列转换硬编码为

I have a dataframe of that contains some date objects. I need to convert to a json for use in JavaScript, which requires YYYY-MM-DD, but to_json() keeps adding a time component. I've seen a number of answers that convert to a string first, but this is part of a loop of about 15 queries each with many columns (simplified it for the SO question) and I don't want to hardcode each column conversion as there are a lot.

import pandas as pd
from datetime import date
df = pd.DataFrame(data=[[date(year=2018, month=1, day=1)]])    
print df.to_json(orient='records', date_format='iso', date_unit='s')

输出：

[{"0":"2018-01-01T00:00:00Z"}]

所需的输出：

[{"0":"2018-01-01"}]

推荐答案

熊猫当前不具有该功能。对此有一个未解决的问题，如果将来的版本中添加了 date_format 参数的更多选项，您应该订阅此问题（这似乎是一个合理的功能要求） ：

Pandas does not currently have the feature. There is an open issue about this, you should subscribe to the issue in case more options for the date_format argument are added in a future release (which seems like a reasonable feature request):

没办法使用to_json只写过期的日期时间＃16492

No way with to_json to write only date out of datetime #16492

在转储json之前将相关的列手动转换为字符串可能是最好的选择。

Manually converting the relevant columns to string before dumping out json is likely the best option.

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