title: pandas read_csv remove blank rows
body: <p>I am reading in a CSV file as a <code>DataFrame</code> while defining each column's data type. This code gives an error if the CSV file has a blank row in it. How do I read the CSV without blank rows? </p>
  
  <pre><code>dtype = {'material_id': object, 'location_id' : object, 'time_period_id' : int, 'demand' : int, 'sales_branch' : object, 'demand_type' : object }
  
  df = pd.read_csv('./demand.csv', dtype = dtype)
  </code></pre>
  
  <p>I thought of one workaround of doing something like this but not sure if this is the efficient way:</p>
  
  <pre><code>df=pd.read_csv('demand.csv')
  df=df.dropna()
  </code></pre>
  
  <p>and then redefining the column data types in the <code>df</code>. </p>
  
  <p>Edit : Code - </p>
  
  <pre><code>import pandas as pd
  dtype1 = {'material_id': object, 'location_id' : object, 'time_period_id' : int, 'demand' : int, 'sales_branch' : object, 'demand_type' : object }
  df = pd.read_csv('./demand.csv', dtype = dtype1)
  df
  </code></pre>
  
  <p>Error  - <code>ValueError: Integer column has NA values in column 2</code></p>
  
  <p>My CSV file's snapshot - <a href="https://i.stack.imgur.com/lDyeg.jpg" rel="noreferrer"><img src="https://i.stack.imgur.com/lDyeg.jpg" alt="enter image description here"></a></p>