title: Specifying different percisions in Pandas saving functions
body: <pre><code>  1 import pandas as pd
    2 
    3 values = {
    4     'Name': ['a', 'b', 'c'],
    5     'Val': [1.2341467, 12, 10000234.01]}
    6 
    7 df = pd.DataFrame(values)
    8 
    9 print(df)
   10 df.to_csv("pandastest.csv")
  </code></pre>
  
  <p><strong>Output</strong></p>
  
  <pre><code>,Name,Val
  0,a,1.2341467
  1,b,12.0
  2,c,10000234.01
  </code></pre>
  
  <p>While saving to CSV sometimes I may have various types of data formats. Considering the above example perhaps I would like <code>a</code> to be formated as <code>1.23</code>, <code>b</code> to be formated as <code>12</code> and <code>c</code> to be formated as <code>1.0e7</code>. However, there is only as single format option while saving which forces all values to be the same</p>
  
  <pre><code>  1 import pandas as pd
    2 
    3 values = {
    4     'Name': ['a', 'b', 'c'],
    5     'Val': [1.2341467, 12, 10000234.01]}
    6 
    7 df = pd.DataFrame(values)
    8 
    9 print(df)
   10 df.to_csv("pandastest.csv", float_format='%.2f')
   11 
  </code></pre>
  
  <p><strong>Output</strong></p>
  
  <pre><code>,Name,Val
  0,a,1.23
  1,b,12.00
  2,c,10000234.01
  </code></pre>
  
  <p>Is there a way to pass a dictionary or a list of formats to <code>float_format</code> to change the number of significant digits independently?</p>