将unix时间转换为pandas数据帧中的可读日期 [英] Convert unix time to readable date in pandas dataframe

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 using df.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IT屋！