csv-stats 0.1.5

A Python package for the life sciences to conduct hypothesis testing from CSV files.

csv-stats

Python package for rapid hypothesis testing on CSV files with data in long table format, which are common in the life sciences. Test results are saved to PDF as a rendered JSON string, or can be returned as a Python dictionary.

Installation

pip install csv-stats

Examples

All code examples below use the following constants:

DATA_PATH = "path/to/data.csv" # Path to your CSV file
DATA_COLUMN = 'values' # The column to run the hypothesis tests on
GROUP_COLUMN = 'groups' # Grouping variable (i.e. statistical factor)
REPEATED_MEASURES_COLUMN = 'subject_id' # Column indicating repeated measures (e.g. subject IDs)

ANOVA

One way ANOVA is currently supported. They include tests of homogeneity of variance and normality of residuals. Repeated measures ANOVA is also supported, including tests of sphericity.

NOTE: Two- and three-way ANOVA support is planned, but not yet implemented.

from csv_stats.anova import anova1way

# One way ANOVA, independent samples
result_anova1way = anova1way(DATA_PATH, 
                            GROUP_COLUMN, 
                            DATA_COLUMN,                            
                            filename = "anova1way_results.pdf", # Default save name. Enter `None` to not save.
                            render_plot = False # For speed, by default no plots are generated
                        )

# One way ANOVA, repeated measures
result_anova1way_rm = anova1way(DATA_PATH, 
                            GROUP_COLUMN, 
                            DATA_COLUMN,                             
                            repeated_measures_column = REPEATED_MEASURES_COLUMN,
                            filename = "anova1way_results.pdf", # Default save name. Enter `None` to not save.     
                            render_plot = False # For speed, by default no plots are generated                       
                        )

t-test

Both independent samples (one and two samples) and paired samples t-tests are supported. They include tests of homogeneity of variance and normality of residuals.

from csv_stats.ttest import ttest_ind, ttest_dep

# Independent samples t-test 
# Two sample when the GROUP_COLUMN has two groups
# One smaple when the GROUP_COLUMN has one group
result_ttest_ind = ttest_ind(DATA_PATH, 
                            GROUP_COLUMN, 
                            DATA_COLUMN,
                            popmean = 0, # Test against a population mean of 0 (default)
                            filename = "ttest_ind_results.pdf", # Default save name. Enter `None` to not save.
                            render_plot = False # For speed, by default no plots are generated
                        )

# Paired samples t-test
result_ttest_rel = ttest_dep(DATA_PATH, 
                            GROUP_COLUMN, 
                            DATA_COLUMN, 
                            repeated_measures_column = REPEATED_MEASURES_COLUMN,
                            filename = "ttest_dep_results.pdf", # Default save name. Enter `None` to not save.
                            render_plot = False # For speed, by default no plots are generated
                        )

Multiple Data Columns

If you have multiple columns of data that you want to run the same test on, you can specify the data_column argument as "_". This will automatically loop over all columns. Note that to save these results to a file, the filename should be an f-string containing f"{data_column}". The test function will replace data_column with the column name in the file name.