title: Create a 10 minute running average of data with uneven time steps
body: <p>I'm attempting to create a 10 minute running average of wind speeds in order to plot on a graph. My data has numerous uneven time steps within it. I am currently using the CSV module to read in and gather my data, I am not very familiar with pandas and have had issues in the past with it. </p>
  
  <pre><code>import matplotlib.pyplot as plt
  import csv
  from datetime import datetime
  
  x=[]
  y=[]
  
  with open('KART_201901010000_201912310000.txt') as csvfile:
      plots = csv.reader(csvfile, delimiter=',')
      for row in plots:
          if 'M' == row[1]:
              continue
          else:           
              x.append(datetime.strptime(row[0],'%Y-%m-%d %H:%M'))            
              y.append(int(row[1]))   
  
  plt.plot(x,y, label='Wind Speed')
  plt.xlabel('Date and Time')
  plt.ylabel('Wind Speed (Kts)')
  plt.title('Wind Speed\nVersus Time')
  plt.legend()
  plt.show()
  </code></pre>
  
  <p>Here is a snippet of my data set showing one of the many uneven time steps.</p>
  
  <pre><code>2019-11-01 11:40,30
  2019-11-01 11:45,35
  2019-11-01 11:50,32
  2019-11-01 11:55,34
  2019-11-01 11:56,33
  2019-11-01 12:00,33
  2019-11-01 12:05,36
  2019-11-01 12:10,31
  </code></pre>
  
  <p>The obvious general idea is to use a for loop to continue the calculations that I would need to average the data. The issue I am running into is how do I account for the uneven steps? Is there a way to use datetime to achieve this that I have no idea about?</p>