Convert Column to Date Format (Pandas Dataframe)

I have a pandas dataframe as follows:

Symbol  Date
A       02/20/2015
A       01/15/2016
A       08/21/2015

I want to sort it by Date, but the column is just an object.

I tried to make the column a date object, but I ran into an issue where that format is not the format needed. The format needed is 2015-02-20, etc.

So now I'm trying to figure out how to have numpy convert the 'American' dates into the ISO standard, so that I can make them date objects, so that I can sort by them.

How would I convert these american dates into ISO standard, or is there a more straight forward method I'm missing within pandas?

You can use pd.to_datetime() to convert to a datetime object. It takes a format parameter, but in your case I don't think you need it.

>>> import pandas as pd
>>> df = pd.DataFrame( {'Symbol':['A','A','A'] ,
    'Date':['02/20/2015','01/15/2016','08/21/2015']})
>>> df
         Date Symbol
0  02/20/2015      A
1  01/15/2016      A
2  08/21/2015      A
>>> df['Date'] =pd.to_datetime(df.Date)
>>> df.sort('Date') # This now sorts in date order
        Date Symbol
0 2015-02-20      A
2 2015-08-21      A
1 2016-01-15      A

For future search, you can change the sort statement:

>>> df.sort_values(by='Date') # This now sorts in date order
        Date Symbol
0 2015-02-20      A
2 2015-08-21      A
1 2016-01-15      A

sort method has been deprecated and replaced with sort_values. After converting to datetime object using df['Date']=pd.to_datetime(df['Date'])

df.sort_values(by=['Date'])

Note: to sort in-place and/or in a descending order (the most recent first):

df.sort_values(by=['Date'], inplace=True, ascending=False)

@JAB's answer is fast and concise. But it changes the DataFrame you are trying to sort, which you may or may not want.

_{(Note: You almost certainly will want it, because your date columns should be dates, not strings!)}

In the unlikely event that you don't want to change the dates into dates, you can also do it a different way.

First, get the index from your sorted Date column:

In [25]: pd.to_datetime(df.Date).order().index
Out[25]: Int64Index([0, 2, 1], dtype='int64')

Then use it to index your original DataFrame, leaving it untouched:

In [26]: df.ix[pd.to_datetime(df.Date).order().index]
Out[26]: 
        Date Symbol
0 2015-02-20      A
2 2015-08-21      A
1 2016-01-15      A

Magic!

Note: for Pandas versions 0.20.0 and later, use loc instead of ix, which is now deprecated.

Since pandas >= 1.0.0 we have the key argument in DataFrame.sort_values. This way we can sort the dataframe by specifying a key and without adjusting the original dataframe:

df.sort_values(by="Date", key=pd.to_datetime)

  Symbol        Date
0      A  02/20/2015
2      A  08/21/2015
1      A  01/15/2016

The data containing the date column can be read by using the below code:

data = pd.csv(file_path,parse_dates=[date_column])

Once the data is read by using the above line of code, the column containing the information about the date can be accessed using pd.date_time() like:

pd.date_time(data[date_column], format = '%d/%m/%y')

to change the format of date as per the requirement.

data['Date'] = data['Date'].apply(pd.to_datetime) # non-null datetime64[ns]