title: Importing financial data into Python Pandas using read_csv
body: <p>I have a .csv with the following structure:</p>
  
  <pre><code>date_begin,date_end,name,name_code,active_accounts,transaction_amount,transaction_count
  1/1/2008,1/31/2008,Name_1,1001,"123,456","$7,890,123.45","67,890"
  2/1/2008,2/29/2008,Name_1,1001,"43,210","$987,654.32","109,876"
  3/1/2008,3/31/2008,Name_1,1001,"485,079","$1,265,789,433.98","777,888"
  ...
  12/1/2008,12/31/2008,Name_1,1001,"87,543","$432,098,987","87,987"
  1/1/2008,1/31/2008,Name_2,1002,"268,456","$890,123.45","97,890"
  2/1/2008,2/29/2008,Name_2,1002,"53,210","$987,654.32","109,876"
  ...
  etc
  </code></pre>
  
  <p>I am trying to read them into into a pandas dataframe by using the following code:</p>
  
  <pre><code>import pandas as pd
  
  data = pd.read_csv('my_awesome_csv.csv'),parse_dates=[[0,1]],
                     infer_datetime_format=True)
  </code></pre>
  
  <p>This works just fine except that I would like to control the data types in each column.  When I run the following code in the interpreter I discover that the numbers in quotes do not get recognized as numbers, either dollars or otherwise.</p>
  
  <pre><code>In [10]: data.dtypes
  Out[10]: 
  date_begin_date_end       object
  name                      object
  name_code                  int64
  active_accounts           object  # Problem, I want this to be a number
  transaction_amount        object  # Ditto, I want this to be a number (it's a dollar amount)
  transaction_count         object  # Still a number!
  dtype: object
  </code></pre>
  
  <p>I have done some snooping around in the <a href="http://pandas.pydata.org/pandas-docs/stable/io.html#io-read-csv-table" rel="noreferrer">Pandas csv documentation</a> but haven't found what I'm looking for about declaring types that are amounts when they are saved as strings with commas and dollar signs in the csv.  My ultimate goal here is to be able to do some arithmetic operations on the values in these columns.</p>
  
  <p>Any thoughts?</p>