Hello Pravin,

In the Q research platform, there is local_csv():

local_csv()
If you want to work with your own data in our environment, you can upload a csv to your data directory and load the file with local_csv. By default, local_csv returns a DataFrame with the data from the loaded file.
You can customize the resulting DataFrame by passing the following parameters:
Automatically convert strings to security objects by indicating a symbol_column
Automatically parse dates as Datetime object by indicating a date_column
Specify what timezone to use with timezone
Use the date column as the index with use_date_column_as_index

local_csv?

Signature: local_csv(path, symbol_column=None, date_column=None, use_date_column_as_index=True, timezone='UTC', symbol_reference_date=None, **read_csv_kwargs)  
Docstring:  
Load a CSV from the /data directory.

:param path:
   Path of file to load, relative to /data.  
:param symbol_column:
   Column containing strings to convert to Security objects.  
:param date_column:
   Column to parse as Datetime.  Ignored if `parse_dates` is passed as  
   an additional keyword argument.  
:param use_date_column_as_index:
   If True and date_column is supplied, set it as the frame index.  
:param timezone:
   Interpret date_column as this timezone.  
:param read_csv_kwargs:
   Extra parameters to forward to pandas.read_csv.  
:returns:
   DataFrame with data from the loaded file.  
:rtype:
   pandas.DataFrame  
File:      /home/qexec/src/qexec_repo/qexec/research/api.py  
Type:      function  

For the browser-based backtester, you can use fetcher (see the help docs). And of course you can always just copy-and-paste data into the IDE (I've never seen a limit on the number of lines of code, for example).

Thanks Grant. I will submit an algo so that I have access to research platform.

You can also use Fetcher to import the CSV into your algorithm: https://www.quantopian.com/help#overview-fetcher

Here's a sample algo: https://www.quantopian.com/help#sample-csv-1