text: Title: Misdetected data type when numerical column contains null
  Body: When a column contains null values, it can get misclassified as a nominal type, even if it has high cardinality and is largely quantitative. For instance, in this example, the `# Instances` and `# Attributes` are detected as `nominal` but they are better suited as `quantitative`. 
  
  ```python
  import pandas as pd
  import lux
  df = pd.read_html("https://archive.ics.uci.edu/ml/datasets.php?format=&task=&att=&area=&numAtt=&numIns=&type=&sort=nameUp&view=table")[5]
  df.columns = df.loc[0]
  df = df.loc[1:]
  df['Year'] = pd.to_datetime(df['Year'], format='%Y')
  df.data_type
  ```
  
  As a result, even when dropna occurs on these columns, the data type remains `nominal`.
  ```python
  df[['# Instances','# Attributes']].dropna()
  ```
  ![image](https://user-images.githubusercontent.com/5554675/106254017-366f3f00-6253-11eb-871c-aa85881f60d2.png)
  