title: Dask Equivalent of pd.to_numeric
body: <p>I am trying to read multiple CSV files, each around 15 GB using dask read_csv. While performing this task, dask interprets a particular column as float, however it has some few values which are of string type and later on it fails when I try to perform some operation stating it cannot convert string to float. Hence I used dtype=str argument to read all the columns as string. Now I want to convert the particular column to numeric with errors='coerce' so that I those records contain string are converted to NaN values and rest get converted to float correctly. Can you please advise how this can be achieved using dask?</p>
  
  <p>Have already tried: astype conversion</p>
  
  <pre class="lang-py prettyprint-override"><code>import dask.dataframe as dd
  df = dd.read_csv("./*.csv", encoding='utf8', 
                   assume_missing = True, 
                   usecols =col_names.values.tolist(),
                      dtype=str)
  df["mycol"] = df["mycol"].astype(float)
  search_df = df.query('mycol &gt;0').compute()
  </code></pre>
  
  <pre><code>ValueError: Mismatched dtypes found in `pd.read_csv`/`pd.read_table`.
  
  +-----------------------------------+--------+----------+
  | Column                            | Found  | Expected |
  +-----------------------------------+--------+----------+
  | mycol                             | object | float64  |
  +-----------------------------------+--------+----------+
  
  The following columns also raised exceptions on conversion:
  
  - mycol
    ValueError("could not convert string to float: 'cliqz.com/tracking'")
  </code></pre>
  
  <pre><code>#Reproducible example
  import dask.dataframe as dd
  df = dd.read_csv("mydata.csv", encoding='utf8', 
                   assume_missing = True)
  df.dtypes #count column will appear as float but it has a couple of dirty values as string
  search_df = df.query('count &gt;0').compute() #This line will give the type conversion error
  </code></pre>
  
  <pre><code>#Edit with one possible solution, but is this optimal while using dask?
  import dask.dataframe as dd
  import pandas as pd
  to_n = lambda x: pd.to_numeric(x, errors="coerce")
  df = dd.read_csv("mydata.csv", encoding='utf8', 
                   assume_missing = True,
                   converters={"count":to_n}
                  )
  df.dtypes 
  search_df = df.query('count &gt;0').compute() 
  </code></pre>