[python] Convert unix time to readable date in pandas dataframe

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.

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:

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   

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.

Assuming we imported pandas as pd and df is our dataframe

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

works for me.

Alternatively, by changing a line of the above code:

# df.date = df.date.apply(lambda d: datetime.strptime(d, "%Y-%m-%d"))
df.date = df.date.apply(lambda d: datetime.datetime.fromtimestamp(int(d)).strftime('%Y-%m-%d'))

It should also work.

If you try using:

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

and receive an error :

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

This means the DATE_FIELD is not specified in seconds.

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

The conversion worked using below:

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