Reference documentation and code samples for the Dataplex V1 API class Google::Cloud::Dataplex::V1::DataProfileResult::Profile::Field::ProfileInfo::IntegerFieldInfo.
IntegerFieldInfo defines output for any integer type field.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#average
def average() -> ::Float
Returns
- (::Float) — The average of non-null values of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
#average=
def average=(value) -> ::Float
Parameter
- value (::Float) — The average of non-null values of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
Returns
- (::Float) — The average of non-null values of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
#max
def max() -> ::Integer
Returns
- (::Integer) — The maximum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
#max=
def max=(value) -> ::Integer
Parameter
- value (::Integer) — The maximum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
Returns
- (::Integer) — The maximum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
#min
def min() -> ::Integer
Returns
- (::Integer) — The minimum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
#min=
def min=(value) -> ::Integer
Parameter
- value (::Integer) — The minimum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
Returns
- (::Integer) — The minimum value of an integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
#quartiles
def quartiles() -> ::Array<::Integer>
Returns
- (::Array<::Integer>) — A quartile divide 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. So, here the quartiles is provided as an ordered list of quartile values, occurring in order Q1, median, Q3.
#quartiles=
def quartiles=(value) -> ::Array<::Integer>
Parameter
- value (::Array<::Integer>) — A quartile divide 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. So, here the quartiles is provided as an ordered list of quartile values, occurring in order Q1, median, Q3.
Returns
- (::Array<::Integer>) — A quartile divide 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. So, here the quartiles is provided as an ordered list of quartile values, occurring in order Q1, median, Q3.
#standard_deviation
def standard_deviation() -> ::Float
Returns
- (::Float) — The standard deviation of non-null of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
#standard_deviation=
def standard_deviation=(value) -> ::Float
Parameter
- value (::Float) — The standard deviation of non-null of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.
Returns
- (::Float) — The standard deviation of non-null of integer field in the sampled data. Return NaN, if the field has a NaN. Optional if zero non-null rows.