text: Title: Feature Request
  Body: ### Missing functionality
  
  As a frequent user of ydata_profiling am encountering the below issue 
  
  In the given dataset for the numeric datatype column we have to exclude empty cell while calculating 'sum'. When we are not including empty cells then the value of sum is coming as 'NaN'. From our side if we are replacing empty with '0' then it is impacting 'min' value, if we are replacing with some other value then it is impacting the data type of the corresponding column.
  
  
  
  ### Proposed feature
  
  we have to exclude empty or null cell of the numeric data type columns while calculating 'sum'. When we are not including empty cells then the value of sum is coming as 'NaN'
  
  ### Alternatives considered
  
  Below logic in describe_numeric_spark.py is the place where 'sum' is been is calculated. Please correct me if am wrong
  
  @describe_numeric_1d.register
  def describe_numeric_1d_spark(
      config: Settings, df: DataFrame, summary: dict
  ) -> Tuple[Settings, DataFrame, dict]:
      """Describe a boolean series.
  
      Args:
          series: The Series to describe.
          summary: The dict containing the series description so far.
  
      Returns:
          A dict containing calculated series description values.
      """
  
      stats = numeric_stats_spark(df, summary)
      summary["min"] = stats["min"]
      summary["max"] = stats["max"]
      summary["mean"] = stats["mean"]
      summary["std"] = stats["std"]
      summary["variance"] = stats["variance"]
      summary["skewness"] = stats["skewness"]
      summary["kurtosis"] = stats["kurtosis"]
      summary["sum"] = stats["sum"]
  
  
  ### Additional context
  
  We are building wheel file from our code and installing the same in databricks cluster and trying to do exploratory data analysis of the given source dataset in CSV file format 