anchor.diff:  Columns with only `0`, `1`, `Yes`, `No`, `True`, or `False` will be set to `bool`. Columns with numerical values
   will be set to either `int`, `int64`, `decimal`, or `float`, in that order of preference.
   
  -If in any row a value is missing, by default the CSV type provider will infer a nullable (for `int` and `int64`) or an optional
  +If a value is missing in any row, by default the CSV type provider will infer a nullable (for `int` and `int64`) or an optional
   (for `bool`, `DateTime` and `Guid`). When a `decimal` would be inferred but there are missing values, we will infer a
   `float` instead, and use `Double.NaN` to represent those missing values. The `string` type is already inherently nullable,
   so by default we won't generate a `string option`. If you prefer to use optionals in all cases, you can set the static parameter
  
positive.code: 
  It is quite common in statistical datasets for some values to be missing. If
  you open the [`data/AirQuality.csv`](../data/AirQuality.csv) file you will see
  that some values for the Ozone observations are marked `#N/A`. Such values are
  parsed as float and will in F# be marked with `Double.NaN`. The values `#N/A`, `NA`,
  and `:` are recognized as missing values by default, but you can customize it by specifying
  the `MissingValues` static parameter of `CsvProvider`.
  
  
negative.code: (*** include-it:chart1 ***)
  
  (**
  As one more example, we use the `Candlestick` chart to get a more detailed look at the
  data over the last month:
  *)
  
  