问题描述
我有一个包含 unix 时间和价格的数据框.我想转换索引列，以便它以人类可读的日期显示.
I have a dataframe with unix times and prices in it. I want to convert the index column so that it shows in human readable dates.
例如，我在索引列中有 date 作为 1349633705 但我希望它显示为 10/07/2012(或至少 10/07/2012 18:15).
So for instance I have date as 1349633705 in the index column but I'd want it to show as 10/07/2012 (or at least 10/07/2012 18:15).
对于某些上下文，这是我正在使用的代码以及我已经尝试过的代码:
For some context, here is the code I'm working with and what I've tried already:
import json
import urllib2
from datetime import datetime
response = urllib2.urlopen('http://blockchain.info/charts/market-price?&format=json')
data = json.load(response)
df = DataFrame(data['values'])
df.columns = ["date","price"]
#convert dates
df.date = df.date.apply(lambda d: datetime.strptime(d, "%Y-%m-%d"))
df.index = df.date
如你所见，我正在使用df.date = df.date.apply(lambda d: datetime.strptime(d, "%Y-%m-%d")) 此处不起作用，因为我正在使用整数，而不是字符串.我想我需要使用 datetime.date.fromtimestamp 但我不太确定如何将它应用于整个 df.date.
As you can see I'm usingdf.date = df.date.apply(lambda d: datetime.strptime(d, "%Y-%m-%d")) here which doesn't work since I'm working with integers, not strings. I think I need to use datetime.date.fromtimestamp but I'm not quite sure how to apply this to the whole of df.date.
谢谢.
推荐答案
这些似乎是自纪元以来的秒数.
These appear to be seconds since epoch.
In [20]: df = DataFrame(data['values'])
In [21]: df.columns = ["date","price"]
In [22]: df
Out[22]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 358 entries, 0 to 357
Data columns (total 2 columns):
date 358 non-null values
price 358 non-null values
dtypes: float64(1), int64(1)
In [23]: df.head()
Out[23]:
date price
0 1349720105 12.08
1 1349806505 12.35
2 1349892905 12.15
3 1349979305 12.19
4 1350065705 12.15
In [25]: df['date'] = pd.to_datetime(df['date'],unit='s')
In [26]: df.head()
Out[26]:
date price
0 2012-10-08 18:15:05 12.08
1 2012-10-09 18:15:05 12.35
2 2012-10-10 18:15:05 12.15
3 2012-10-11 18:15:05 12.19
4 2012-10-12 18:15:05 12.15
In [27]: df.dtypes
Out[27]:
date datetime64[ns]
price float64
dtype: object
这篇关于将unix时间转换为pandas数据帧中的可读日期的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！