agatestats 0.4.1 (alpha)¶
agatestats adds statistical methods to agate.
Important links:
 agate http://agate.rtfd.org
 Documentation: http://agatestats.rtfd.org
 Repository: https://github.com/wireservice/agatestats
 Issues: https://github.com/wireservice/agatestats/issues
Install¶
To install:
pip install agatestats
For details on development or supported platforms see the agate documentation.
Usage¶
agatestats uses a monkey patching pattern to add additional statistical methods to all agate.Table
instances.
import agate
import agatestats
Importing agatestats 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 wellknown cases in which this algorithm will fail to identify outliers. For a more robust measure see
TableStats.mad_outliers()
.Parameters:  column_name – The name of the column to compute outliers on.
 deviations – The number of deviations from the mean a data point must be to qualify as an outlier.
 reject – If
True
then the newTable
will contain everything except the outliers.
Returns: A new
Table
.

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.
Parameters:  column_name – The name of the column to compute outliers on.
 deviations – The number of deviations from the median a data point must be to qualify as an outlier.
 reject – If
True
then the newTable
will contain everything except the outliers.
Returns: A new
Table
.

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 wellknown cases in which this algorithm will fail to identify outliers. For a more robust measure see
TableStats.mad_outliers()
.Parameters:  column_name – The name of the column to compute outliers on.
 deviations – The number of deviations from the mean a data point must be to qualify as an outlier.
 reject – If
True
then the newTable
will contain everything except the outliers.
Returns: A new
Table
.

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.
Parameters:  column_name – The name of the column to compute outliers on.
 deviations – The number of deviations from the median a data point must be to qualify as an outlier.
 reject – If
True
then the newTable
will contain everything except the outliers.
Returns: A new
Table
.

class
agatestats.aggregations.
PearsonCorrelation
(x_column_name, y_column_name)¶ 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 latimescalculate. Thanks, LAT!
Parameters:  x_column_name – The name of a column.
 y_column_name – The name of a column.

run
(table)¶ Returns: decimal.Decimal
.

class
agatestats.computations.
ZScores
(column_name)¶ Computes the zscores (standard scores) of a given column.
Changelog¶
0.4.1¶
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.