title: Why did my DataFrame grow in size after using df.to_csv() and df = pd.read_csv() operations?
body: <p>Doing a quick check to see the size of my DataFrame shows that it's 2.4 GB and all columns are in float32 format.</p>
  <pre><code>df.info(memory_usage=&quot;deep&quot;)
  
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  Int64Index: 1468379 entries, 1516035660 to 1604138340
  Columns: 441 entries, Open to 2099520 EMA
  dtypes: float32(441)
  memory usage: 2.4 GB
  </code></pre>
  <p>I save the DataFrame to my pc and the reload it back using the following:</p>
  <pre><code>df.to_csv(&quot;CSV of BTC Price Plus Indicators.csv&quot;)
  df = pd.read_csv('/Users/Moonboi/Coding/Crypto/CSV of BTC Price Plus Indicators.csv').set_index(&quot;Timestamp&quot;)
  </code></pre>
  <p>Then check the memory usage:</p>
  <pre><code>df.info(memory_usage=&quot;deep&quot;)
  
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  Int64Index: 1468379 entries, 1516035660 to 1604138340
  Columns: 441 entries, Open to 2099520 EMA
  dtypes: float64(441)
  memory usage: 4.8 GB
  </code></pre>
  <p>Now it takes up double the space and uses float64 instead of float32? Whats this all about? Can't I keep it in float32 format?</p>