Dataplex V1 API - Class Google::Cloud::Dataplex::V1::DataProfileResult::Profile::Field::ProfileInfo::DoubleFieldInfo (v0.22.0)

Reference documentation and code samples for the Dataplex V1 API class Google::Cloud::Dataplex::V1::DataProfileResult::Profile::Field::ProfileInfo::DoubleFieldInfo.

The profile information for a double type field.

Inherits

  • Object

Extended By

  • Google::Protobuf::MessageExts::ClassMethods

Includes

  • Google::Protobuf::MessageExts

Methods

#average

def average() -> ::Float
Returns
  • (::Float) — Average of non-null values in the scanned data. NaN, if the field has a NaN.

#average=

def average=(value) -> ::Float
Parameter
  • value (::Float) — Average of non-null values in the scanned data. NaN, if the field has a NaN.
Returns
  • (::Float) — Average of non-null values in the scanned data. NaN, if the field has a NaN.

#max

def max() -> ::Float
Returns
  • (::Float) — Maximum of non-null values in the scanned data. NaN, if the field has a NaN.

#max=

def max=(value) -> ::Float
Parameter
  • value (::Float) — Maximum of non-null values in the scanned data. NaN, if the field has a NaN.
Returns
  • (::Float) — Maximum of non-null values in the scanned data. NaN, if the field has a NaN.

#min

def min() -> ::Float
Returns
  • (::Float) — Minimum of non-null values in the scanned data. NaN, if the field has a NaN.

#min=

def min=(value) -> ::Float
Parameter
  • value (::Float) — Minimum of non-null values in the scanned data. NaN, if the field has a NaN.
Returns
  • (::Float) — Minimum of non-null values in the scanned data. NaN, if the field has a NaN.

#quartiles

def quartiles() -> ::Array<::Float>
Returns
  • (::Array<::Float>) — A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of quartile values for the scanned data, occurring in order Q1, median, Q3.

#quartiles=

def quartiles=(value) -> ::Array<::Float>
Parameter
  • value (::Array<::Float>) — A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of quartile values for the scanned data, occurring in order Q1, median, Q3.
Returns
  • (::Array<::Float>) — A quartile divides the number of data points into four parts, or quarters, of more-or-less equal size. Three main quartiles used are: The first quartile (Q1) splits off the lowest 25% of data from the highest 75%. It is also known as the lower or 25th empirical quartile, as 25% of the data is below this point. The second quartile (Q2) is the median of a data set. So, 50% of the data lies below this point. The third quartile (Q3) splits off the highest 25% of data from the lowest 75%. It is known as the upper or 75th empirical quartile, as 75% of the data lies below this point. Here, the quartiles is provided as an ordered list of quartile values for the scanned data, occurring in order Q1, median, Q3.

#standard_deviation

def standard_deviation() -> ::Float
Returns
  • (::Float) — Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.

#standard_deviation=

def standard_deviation=(value) -> ::Float
Parameter
  • value (::Float) — Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.
Returns
  • (::Float) — Standard deviation of non-null values in the scanned data. NaN, if the field has a NaN.