title: Fill the missing date values in a Pandas Dataframe column
body: <p>I'm using Pandas to store stock prices data using Data Frames. There are 2940 rows in the dataset. The Dataset snapshot is displayed below:</p>
  
  <p><a href="https://i.stack.imgur.com/ptPzP.png" rel="noreferrer"><img src="https://i.stack.imgur.com/ptPzP.png" alt="enter image description here"></a></p>
  
  <p>The time series data does not contain the values for Saturday and Sunday. Hence missing values have to be filled. 
  <br>Here is the code I've written but it is not solving the problem:</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  import os
  os.chdir('C:/Users/Admin/Analytics/stock-prices')
  
  data  = pd.read_csv('stock-data.csv')
  
  # PriceDate Column - Does not contain Saturday and Sunday stock entries
  data['PriceDate'] =  pd.to_datetime(data['PriceDate'], format='%m/%d/%Y')
  data = data.sort_index(by=['PriceDate'], ascending=[True])
  
  
  # Starting date is Aug 25 2004
  idx = pd.date_range('08-25-2004',periods=2940,freq='D')
  
  
  data = data.set_index(idx)
  data['newdate']=data.index
  newdate=data['newdate'].values   # Create a time series column   
  
  
  data = pd.merge(newdate, data, on='PriceDate', how='outer')
  </code></pre>
  
  <p><strong>How to fill the missing values for Saturday and Sunday?</strong></p>