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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Showcase of various CogStat analyses
Below you can see a few examples what analyses are perfomed for a specific task in CogStat. Note that the specific analyses that are applied depend on the task, number of variables, variable measurement levels, various other properties (e.g. normality), therefore, these examples show only some of the possibilities. See a more extensive list of the available analyses details in the online help.
(The table of contents below may not be visible on all systems.)
<h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div>
<script src="https
Step1:  Data
Step2:  Explore variable in interval, ordinal and nominal variables
Step3:  Explore relation pairs in interval, ordinal and nominal variables
Step4:  Compare repeated measures variables with interval, ordinal and nominal variables
Step5:  Compare groups in interval, ordinal and nominal dependent variables, with one or two grouping variables with 2 or 3 group levels
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Python Code:
%matplotlib inline
import os
import warnings
warnings.filterwarnings('ignore')
from cogstat import cogstat as cs
print(cs.__version__)
cs_dir, dummy_filename = os.path.split(cs.__file__)  # We use this for the demo data
Explanation: Showcase of various CogStat analyses
Below you can see a few examples what analyses are perfomed for a specific task in CogStat. Note that the specific analyses that are applied depend on the task, number of variables, variable measurement levels, various other properties (e.g. normality), therefore, these examples show only some of the possibilities. See a more extensive list of the available analyses details in the online help.
(The table of contents below may not be visible on all systems.)
<h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div>
<script src="https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js"></script>
End of explanation
# Load some data
data = cs.CogStatData(data=os.path.join(cs_dir, 'sample_data', 'example_data.csv'))
# Display the data
cs.display(data.print_data())
Explanation: Data
End of explanation
### Explore variable ###
# Get the most important statistics of a single variable
cs.display(data.explore_variable('X'))
cs.display(data.explore_variable('Z'))
cs.display(data.explore_variable('CONDITION'))
Explanation: Explore variable in interval, ordinal and nominal variables
End of explanation
### Explore variable pair ###
# Get the statistics of a variable pair
cs.display(data.explore_variable_pair('X', 'Y'))
cs.display(data.explore_variable_pair('Z', 'ZZ'))
cs.display(data.explore_variable_pair('TIME', 'CONDITION'))
### Behavioral data diffusion analyses ###
# cs.display(data.diffusion(error_name=['error'], RT_name=['RT'], participant_name=['participant_id'], condition_names=['loudness', 'side']))
Explanation: Explore relation pairs in interval, ordinal and nominal variables
End of explanation
### Compare variables ###
cs.display(data.compare_variables(['X', 'Y'], factors=[]))
cs.display(data.compare_variables(['Z', 'ZZ'], factors=[]))
cs.display(data.compare_variables(['CONDITION', 'CONDITION2'], factors=[]))
Explanation: Compare repeated measures variables with interval, ordinal and nominal variables
End of explanation
### Compare groups ###
cs.display(data.compare_groups('X', grouping_variables=['TIME']))
cs.display(data.compare_groups('X', grouping_variables=['TIME3']))
cs.display(data.compare_groups('Y', grouping_variables=['TIME']))
cs.display(data.compare_groups('Y', grouping_variables=['TIME3']))
cs.display(data.compare_groups('CONDITION', grouping_variables=['TIME']))
cs.display(data.compare_groups('X', grouping_variables=['TIME', 'CONDITION']))
Explanation: Compare groups in interval, ordinal and nominal dependent variables, with one or two grouping variables with 2 or 3 group levels
End of explanation
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