title: read_csv with dates containing a dot as decimal separator and floats with a comma as decimal separator?
body: <p>I have a csv file with a layout that looks like this:</p>
  
  <pre><code>date    a    b
  2019-08-28 10:43:41.098  -1,9  -2,5
  </code></pre>
  
  <p>When I read this into pandas using</p>
  
  <pre><code>df = pd.read_csv(file,sep=None,parse_dates[0],engine='python',decimal=',')
  </code></pre>
  
  <p>and I look at the resulting dataframe, Pandas recognizes the <code>date</code> as <code>dtype: datetime64[ns]</code> but the other columns as <code>dtype: object</code>. </p>
  
  <p>I have tried to skip the <code>decimal=','</code> part altogether but it doesn't help. I could try to individually replace the commas as dots but I wanted to see if there was a way to do it while importing the csv file directly.</p>
  
  <p>Thanks in advance</p>
  
  <p>EDIT: MWE</p>
  
  <p><code>data.txt</code>:</p>
  
  <pre><code>date    a   b   
  08/28/2019 10:43:41.0980    -1,9    -2,5
  
  </code></pre>
  
  <pre><code>import pandas as pd
  
  df = pd.read_csv('C:/Users/USERNAME/Documents/Python/data.txt',sep=None,parse_dates=[0],engine='python').dropna(how='any',axis='columns')
  
  print(df)
  </code></pre>
  
  <p>This code leads to <code>df</code> being equal to the aforementioned <code>data.txt</code>, but the data types are:</p>
  
  <pre><code>date    datetime64[ns]
  a               object
  b               object
  dtype: object
  </code></pre>
  
  <p>Using <code>sep='\s{2,}'</code> as @furas suggested leads to the <code>df</code> being:</p>
  
  <pre><code>                             date\ta\tb
  0  08/28/2019 10:43:41.0980\t-1,9\t-2,5
  </code></pre>