title: How divide or multiply every non-string columns of a PySpark dataframe with a float constant?
body: <p>My input dataframe looks like the below</p>
  
  <pre><code>from pyspark.sql import SparkSession
  spark = SparkSession.builder.appName("Basics").getOrCreate()
  
  df=spark.createDataFrame(data=[('Alice',4.300,None),('Bob',float('nan'),897)],schema=['name','High','Low'])
  
  +-----+----+----+
  | name|High| Low|
  +-----+----+----+
  |Alice| 4.3|null|
  |  Bob| NaN| 897|
  +-----+----+----+
  </code></pre>
  
  <p>Expected Output if divided by 10.0</p>
  
  <pre><code>+-----+----+----+
  | name|High| Low|
  +-----+----+----+
  |Alice| 0.43|null|
  |  Bob| NaN| 89.7|
  +-----+----+----+
  </code></pre>