title: How to make a multiplication of a dask.Dataframe from dask.Series over rows?
body: <p><code>normalised = data.mul(alpha, axis=1)</code> </p>
  
  <p>In the snippet above <code>data</code> is a dask.Dataframe and <code>alpha</code> is a dask.Series. </p>
  
  <p>Dask raises a <code>ValueError: Unable to mul dd.Series with axis=1</code> error while performing this multiplication. This operation works if I compute alpha (e.g alpha.compute() ), but since alpha is a big task I want to avoid calling compute at this stage of the code. </p>
  
  <p>Can anybody come up with a better to solution to perform this operation?</p>
  
  <p>A reproducible example is below:</p>
  
  <pre><code>import pandas as pd
  import dask.dataframe as dd
  
  df = pd.DataFrame(
      [[1, 2, 3], 
       [1, 2, 3.5], 
       [1, 2, 3.7]], 
      columns = ["A", "B", "C"]
  )
  
  ddf = dd.from_pandas(df, npartitions=1)
  
  dv = df.iloc[0]
  series = dd.from_pandas(dv, npartitions=1)
  
  ddf.mul(series, axis=1)
  </code></pre>