agate-stats 0.4.1#
agate-stats adds statistical methods to agate.
Important links:
agate https://agate.rtfd.org
Documentation: https://agate-stats.rtfd.org
Repository: https://github.com/wireservice/agate-stats
Install#
To install:
pip install agate-stats
For details on development or supported platforms see the agate documentation.
Usage#
agate-stats uses a monkey patching pattern to add additional statistical methods to all agate.Table
instances.
import agate
import agatestats
Importing agate-stats adds methods to agate.Table
. For example, to filter a table to only those rows whose cost
value is an outliers by more than 3 standard deviations you would use TableStats.stdev_outliers()
:
outliers = table.stdev_outliers('price')
In addition to Table methods agatestats also includes a variety of additional aggregations and computations. See the API section of the docs for a complete list of all the added features.
API#
- agatestats.table.stdev_outliers(self, column_name, deviations=3, reject=False)#
A wrapper around
Table.where
that filters the dataset to rows where the value of the column are more than some number of standard deviations from the mean.This method makes no attempt to validate that the distribution of your data is normal.
There are well-known cases in which this algorithm will fail to identify outliers. For a more robust measure see
TableStats.mad_outliers()
.
- agatestats.table.mad_outliers(self, column_name, deviations=3, reject=False)#
A wrapper around
Table.where
that filters the dataset to rows where the value of the column are more than some number of median absolute deviations from the median.This method makes no attempt to validate that the distribution of your data is normal.
- agatestats.tableset.stdev_outliers(self, column_name, deviations=3, reject=False)#
A wrapper around
Table.where
that filters the dataset to rows where the value of the column are more than some number of standard deviations from the mean.This method makes no attempt to validate that the distribution of your data is normal.
There are well-known cases in which this algorithm will fail to identify outliers. For a more robust measure see
TableStats.mad_outliers()
.
- agatestats.tableset.mad_outliers(self, column_name, deviations=3, reject=False)#
A wrapper around
Table.where
that filters the dataset to rows where the value of the column are more than some number of median absolute deviations from the median.This method makes no attempt to validate that the distribution of your data is normal.
- class agatestats.aggregations.PearsonCorrelation(x_column_name, y_column_name)#
Bases:
Aggregation
Calculates the Pearson correlation coefficient for
x_column_name
andy_column_name
.Returns a number between -1 and 1 with 0 implying no correlation. A correlation close to 1 implies a high positive correlation i.e. as x increases so does y. A correlation close to -1 implies a high negative correlation i.e. as x increases, y decreases.
Note: this implementation is borrowed from the MIT licensed latimes-calculate. Thanks, LAT!
- Parameters:
x_column_name – The name of a column.
y_column_name – The name of a column.
- get_aggregate_data_type(table)#
Get the data type that should be used when using this aggregation with a
TableSet
to produce a new column.Should raise
UnsupportedAggregationError
if this column does not support aggregation into aTableSet
. (For example, if it does not return a single value.)
- run(table)#
- Returns:
- class agatestats.computations.ZScores(column_name)#
Bases:
Computation
Computes the z-scores (standard scores) of a given column.
- get_computed_data_type(table)#
Returns an instantiated
DataType
which will be appended to the table.
- validate(table)#
Perform any checks necessary to verify this computation can run on the provided table without errors. This is called by
Table.compute()
beforerun()
.
- run(table)#
When invoked with a table, returns a sequence of new column values.
Changelog#
Unreleased#
Add Python 3.8, 3.9, 3.10, 3.11, 3.12 support.
Drop support for Python 2.7 (EOL 2020-01-01), 3.4 (2019-03-18), 3.5 (2020-09-13), 3.6 (2021-12-23), 3.7 (2023-06-27).
0.4.1 - October 3, 2023#
Remove six dependency.
Fix
ZScores.run()
method.
0.4.0 - December 19, 2016#
Update
ZScores
to use newComputation
interface.Remove monkey patching.
Upgrade agate dependency to
1.5.0
.
0.3.1 - November 5, 2015#
Fix packaging issue.
0.3.0 - November 5, 2015#
Added usage documentation.
Convert
PearsonCorrelation
to an aggregation.Update required version of agate to 1.1.0.
Removed Python 2.6 support.
0.2.0 - October 22, 2015#
Update to support agate 1.0.0.
0.1.0 - October 6, 2015#
Initial version.
License#
The MIT License
Copyright (c) 2015 Christopher Groskopf and contributors
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.