将unix时间转换为熊猫数据存储器中的可读日期

I have a data frame with unix times and prices in it. I want to convert the index column so that it shows in human readable dates. 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). For some context, here is the code I'm working with and what I've tried already:

我有一个包含unix时间和价格的数据框架。我想要转换索引列，以便在人类可读日期中显示。例如，我在索引列中有“date”，如1349633705，但我希望它显示为10/07/2012(或至少10/07/2012 18:15)。在某些情况下，这里是我正在处理的代码和我已经尝试过的:

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   
df

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. Thanks.

可以看到，我用的是df。= df.date日期。应用(λd:datetime。这里的strptime(d，“%Y-%m-%d”)不起作用，因为我使用的是整数，而不是字符串。我想我需要使用datetime.date.fromtimestamp，但是我不太确定如何将它应用到整个df.date中。谢谢。

3 个解决方案

#1

111

These appear to be seconds since epoch.

这似乎是几秒后的时代。

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

#2

If you try using:

如果你试着用:

df[DATE_FIELD]=(pd.to_datetime(df[DATE_FIELD],***unit='s'***))

and receive an error :

并收到一个错误:

"pandas.tslib.OutOfBoundsDatetime: cannot convert input with unit 's'"

“pandas.tslib。OutOfBoundsDatetime:不能将输入转换为单位“s”

This means the DATE_FIELD is not specified in seconds.

这意味着DATE_FIELD不是以秒为单位指定的。

In my case, it was milli seconds - EPOCH time.

在我的例子中，那是毫秒——历元时间。

The conversion worked using below:

转换工作使用如下:

df[DATE_FIELD]=(pd.to_datetime(df[DATE_FIELD],unit='ms'))

#3

Assuming we imported pandas as pd and df is our dataframe

假设我们进口的熊猫是pd, df是我们的dataframe

pd.to_datetime(df['date'],unit='s')

this code worked for me

这段代码对我有用

#1

111