Stats

class aws_cdk.aws_cloudwatch.Stats

Bases: object

Factory functions for standard statistics strings.

ExampleMetadata:: infused

Example:

# dashboard: cloudwatch.Dashboard
# execution_count_metric: cloudwatch.Metric
# error_count_metric: cloudwatch.Metric


dashboard.add_widgets(cloudwatch.GraphWidget(
    title="Executions vs error rate",

    left=[execution_count_metric],

    right=[error_count_metric.with(
        statistic=cloudwatch.Stats.AVERAGE,
        label="Error rate",
        color=cloudwatch.Color.GREEN
    )]
))

Attributes

AVERAGE = 'Average'

IQM = 'IQM'

MAXIMUM = 'Maximum'

MINIMUM = 'Minimum'

SAMPLE_COUNT = 'SampleCount'

SUM = 'Sum'

Static Methods

classmethod p(percentile)

A shorter alias for percentile().

Parameters:

percentile (Union[int, float]) –

Return type:

str

classmethod percentile(percentile)

Percentile indicates the relative standing of a value in a dataset.

Percentiles help you get a better understanding of the distribution of your metric data.

For example, p(90) is the 90th percentile and means that 90% of the data within the period is lower than this value and 10% of the data is higher than this value.

Parameters:

percentile (Union[int, float]) –

Return type:

str

classmethod percentile_rank(v1, v2=None)

Percentile rank (PR) is the percentage of values that meet a fixed threshold.

If two numbers are given, they define the lower and upper bounds in absolute values, respectively.
If one number is given, it defines the upper bound (the lower bound is assumed to be 0).

For example, percentileRank(300) returns the percentage of data points that have a value of 300 or less. percentileRank(100, 2000) returns the percentage of data points that have a value between 100 and 2000.

Parameters:

v1 (Union[int, float]) –
v2 (Union[int, float, None]) –

Return type:

str

classmethod pr(v1, v2=None)

Shorter alias for percentileRank().

Parameters:

v1 (Union[int, float]) –
v2 (Union[int, float, None]) –

Return type:

str

classmethod tc(p1, p2=None)

Shorter alias for trimmedCount().

Parameters:

p1 (Union[int, float]) –
p2 (Union[int, float, None]) –

Return type:

str

classmethod tm(p1, p2=None)

A shorter alias for trimmedMean().

Parameters:

p1 (Union[int, float]) –
p2 (Union[int, float, None]) –

Return type:

str

classmethod trimmed_count(p1, p2=None)

Trimmed count (TC) is the number of data points in the chosen range for a trimmed mean statistic.

If two numbers are given, they define the lower and upper bounds in percentages, respectively.
If one number is given, it defines the upper bound (the lower bound is assumed to be 0).

For example, tc(90) returns the number of data points not including any data points that fall in the highest 10% of the values. tc(10, 90) returns the number of data points not including any data points that fall in the lowest 10% of the values and the highest 90% of the values.

Parameters:

p1 (Union[int, float]) –
p2 (Union[int, float, None]) –

Return type:

str

classmethod trimmed_mean(p1, p2=None)

Trimmed mean (TM) is the mean of all values that are between two specified boundaries.

Values outside of the boundaries are ignored when the mean is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places. The numbers are percentages.

If two numbers are given, they define the lower and upper bounds in percentages, respectively.
If one number is given, it defines the upper bound (the lower bound is assumed to be 0).

For example, tm(90) calculates the average after removing the 10% of data points with the highest values; tm(10, 90) calculates the average after removing the 10% with the lowest and 10% with the highest values.

Parameters:

p1 (Union[int, float]) –
p2 (Union[int, float, None]) –

Return type:

str

classmethod trimmed_sum(p1, p2=None)

Trimmed sum (TS) is the sum of the values of data points in a chosen range for a trimmed mean statistic.

It is equivalent to (Trimmed Mean) * (Trimmed count).

If two numbers are given, they define the lower and upper bounds in percentages, respectively.
If one number is given, it defines the upper bound (the lower bound is assumed to be 0).

For example, ts(90) returns the sum of the data points not including any data points that fall in the highest 10% of the values. ts(10, 90) returns the sum of the data points not including any data points that fall in the lowest 10% of the values and the highest 90% of the values.

Parameters:

p1 (Union[int, float]) –
p2 (Union[int, float, None]) –

Return type:

str

classmethod ts(p1, p2=None)

Shorter alias for trimmedSum().

Parameters:

p1 (Union[int, float]) –
p2 (Union[int, float, None]) –

Return type:

str

classmethod winsorized_mean(p1, p2=None)

Winsorized mean (WM) is similar to trimmed mean.

However, with winsorized mean, the values that are outside the boundary are not ignored, but instead are considered to be equal to the value at the edge of the appropriate boundary. After this normalization, the average is calculated. You define the boundaries as one or two numbers between 0 and 100, up to 10 decimal places.

If two numbers are given, they define the lower and upper bounds in percentages, respectively.
If one number is given, it defines the upper bound (the lower bound is assumed to be 0).

For example, tm(90) calculates the average after removing the 10% of data points with the highest values; tm(10, 90) calculates the average after removing the 10% with the lowest and 10% with the highest values.

For example, wm(90) calculates the average while treating the 10% of the highest values to be equal to the value at the 90th percentile. wm(10, 90) calculates the average while treaing the bottom 10% and the top 10% of values to be equal to the boundary values.

Parameters:

p1 (Union[int, float]) –
p2 (Union[int, float, None]) –

Return type:

str

classmethod wm(p1, p2=None)

A shorter alias for winsorizedMean().

Parameters:

p1 (Union[int, float]) –
p2 (Union[int, float, None]) –

Return type:

str