title: Pandas to_sql - Increase table's index when appending DataFrame
body: <p>I've been working to develop a product which centers in the daily execution of a data analysis Python 3.7.0 script. Everyday at midnight it will proccess a huge amount of data, and then export the result to two MySQL tables. The first one will only contain the data relative to the current day, while the other table will contain the concatenated data of all executions.</p>
  
  <p>To exemplify what I current have, see the code below, supposing <code>df</code> would be the final DataFrame generated from the data analysis:</p>
  
  <pre><code>import pandas as pd
  import sqlalchemy
  
  engine = sqlalchemy.create_engine(r"mysql+pymysql://user:psswd@localhost/pathToMyDB")
  
  df = pd.DataFrame({'Something':['a','b','c']})
  
  df.to_sql('DReg', engine, index = True, if_exists='replace') #daily database
  df.to_sql('AReg', engine, index = False, if_exists='append') #anual database
  </code></pre>
  
  <p>As you can see in the parameters of my second <code>to_sql</code> function, I ain't setting an index to the anual database. However, my manager asked me to do so, creating an index that would center around a simple rule: it would be an auto increasing numeric index, that would automatically attribute a number to every row saved on the database corresponding to its position. </p>
  
  <p>So basically, the first time I saved <code>df</code>, the database should look like:</p>
  
  <pre><code>index   Something
  0       a
  1       b
  2       c
  </code></pre>
  
  <p>And in my second execution:</p>
  
  <pre><code>index   Something
  0       a
  1       b
  2       c
  3       a
  4       b
  5       c
  </code></pre>
  
  <p>However, when I set my index to <code>True</code> in the second <code>df.to_sql</code> command (turning it into <code>df.to_sql('AReg', engine, index = True, if_exists='append')</code>), after two executions my database ends up looking like:</p>
  
  <pre><code>index   Something
  0       a
  1       b
  2       c
  0       a
  1       b
  2       c
  </code></pre>
  
  <p>I did some research, but could not find a way to allow this auto increase on the index. I considered reading the anual database at every execution and then adapting my dataframe's index to it, but my database can easily get REALLY huge, which would make it's execution absurdly slow (and also forbid me to simultaneously execute the same data analysis in two computers without compromising my index).</p>
  
  <p>So what is the best solution to make this index work? What am I missing here?</p>