.. _Tutorial:

================
Tutorial: Basics
================


Load Libraries
--------------

.. code-block:: python3
  :linenos:


    import numpy as np
    import pandas as pd
    import dabest

    print("We're using DABEST v{}".format(dabest.__version__))


.. parsed-literal::

    We're using DABEST v2023.02.14


Create dataset for demo
-----------------------

Here, we create a dataset to illustrate how ``dabest`` functions. In
this dataset, each column corresponds to a group of observations.

.. code-block:: python3
  :linenos:


    from scipy.stats import norm # Used in generation of populations.

    np.random.seed(9999) # Fix the seed so the results are replicable.
    # pop_size = 10000 # Size of each population.
    Ns = 20 # The number of samples taken from each population

    # Create samples
    c1 = norm.rvs(loc=3, scale=0.4, size=Ns)
    c2 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
    c3 = norm.rvs(loc=3.25, scale=0.4, size=Ns)

    t1 = norm.rvs(loc=3.5, scale=0.5, size=Ns)
    t2 = norm.rvs(loc=2.5, scale=0.6, size=Ns)
    t3 = norm.rvs(loc=3, scale=0.75, size=Ns)
    t4 = norm.rvs(loc=3.5, scale=0.75, size=Ns)
    t5 = norm.rvs(loc=3.25, scale=0.4, size=Ns)
    t6 = norm.rvs(loc=3.25, scale=0.4, size=Ns)


    # Add a `gender` column for coloring the data.
    females = np.repeat('Female', Ns/2).tolist()
    males = np.repeat('Male', Ns/2).tolist()
    gender = females + males

    # Add an `id` column for paired data plotting.
    id_col = pd.Series(range(1, Ns+1))

    # Combine samples and gender into a DataFrame.
    df = pd.DataFrame({'Control 1' : c1,     'Test 1' : t1,
                       'Control 2' : c2,     'Test 2' : t2,
                       'Control 3' : c3,     'Test 3' : t3,
                       'Test 4'    : t4,     'Test 5' : t5, 'Test 6' : t6,
                       'Gender'    : gender, 'ID'  : id_col
                      })

Note that we have 9 groups (3 Control samples and 6 Test samples). Our
dataset also has a non-numerical column indicating gender, and another
column indicating the identity of each observation.

This is known as a ‘wide’ dataset. See this
`writeup <https://sejdemyr.github.io/r-tutorials/basics/wide-and-long/>`__
for more details.

.. code-block:: python3
  :linenos:


    df.head()




.. raw:: html

    <div>
    <style scoped>
      /*  .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        } */
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Control 1</th>
          <th>Test 1</th>
          <th>Control 2</th>
          <th>Test 2</th>
          <th>Control 3</th>
          <th>Test 3</th>
          <th>Test 4</th>
          <th>Test 5</th>
          <th>Test 6</th>
          <th>Gender</th>
          <th>ID</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>2.793984</td>
          <td>3.420875</td>
          <td>3.324661</td>
          <td>1.707467</td>
          <td>3.816940</td>
          <td>1.796581</td>
          <td>4.440050</td>
          <td>2.937284</td>
          <td>3.486127</td>
          <td>Female</td>
          <td>1</td>
        </tr>
        <tr>
          <th>1</th>
          <td>3.236759</td>
          <td>3.467972</td>
          <td>3.685186</td>
          <td>1.121846</td>
          <td>3.750358</td>
          <td>3.944566</td>
          <td>3.723494</td>
          <td>2.837062</td>
          <td>2.338094</td>
          <td>Female</td>
          <td>2</td>
        </tr>
        <tr>
          <th>2</th>
          <td>3.019149</td>
          <td>4.377179</td>
          <td>5.616891</td>
          <td>3.301381</td>
          <td>2.945397</td>
          <td>2.832188</td>
          <td>3.214014</td>
          <td>3.111950</td>
          <td>3.270897</td>
          <td>Female</td>
          <td>3</td>
        </tr>
        <tr>
          <th>3</th>
          <td>2.804638</td>
          <td>4.564780</td>
          <td>2.773152</td>
          <td>2.534018</td>
          <td>3.575179</td>
          <td>3.048267</td>
          <td>4.968278</td>
          <td>3.743378</td>
          <td>3.151188</td>
          <td>Female</td>
          <td>4</td>
        </tr>
        <tr>
          <th>4</th>
          <td>2.858019</td>
          <td>3.220058</td>
          <td>2.550361</td>
          <td>2.796365</td>
          <td>3.692138</td>
          <td>3.276575</td>
          <td>2.662104</td>
          <td>2.977341</td>
          <td>2.328601</td>
          <td>Female</td>
          <td>5</td>
        </tr>
      </tbody>
    </table>
    </div>



Loading Data
------------

Before we create estimation plots and obtain confidence intervals for
our effect sizes, we need to load the data and the relevant groups.

We simply supply the DataFrame to ``dabest.load()``. We also must supply
the two groups you want to compare in the ``idx`` argument as a tuple or
list.

.. code-block:: python3
  :linenos:


    two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1"), resamples=5000)

Calling this ``Dabest`` object gives you a gentle greeting, as well as
the comparisons that can be computed.

.. code-block:: python3
  :linenos:


    two_groups_unpaired




.. parsed-literal::

    DABEST v2023.02.14
    ==================
                
    Good evening!
    The current time is Sun Aug 29 18:00:54 2021.

    Effect size(s) with 95% confidence intervals will be computed for:
    1. Test 1 minus Control 1

    5000 resamples will be used to generate the effect size bootstraps.



Changing statistical parameters
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You can change the width of the confidence interval that will be
produced by manipulating the ``ci`` argument.

.. code-block:: python3
  :linenos:


    two_groups_unpaired_ci90 = dabest.load(df, idx=("Control 1", "Test 1"), ci=90)

.. code-block:: python3
  :linenos:


    two_groups_unpaired_ci90




.. parsed-literal::

    DABEST v2023.02.14
    ==================

    Good afternoon!
    The current time is Mon Oct 19 17:12:44 2020.

    Effect size(s) with 90% confidence intervals will be computed for:
    1. Test 1 minus Control 1

    5000 resamples will be used to generate the effect size bootstraps.



Effect sizes
------------

``dabest`` now features a range of effect sizes:
  - the mean difference (``mean_diff``)
  - the median difference (``median_diff``)
  - `Cohen’s d <https://en.wikipedia.org/wiki/Effect_size#Cohen's_d>`__ (``cohens_d``)
  - `Hedges’ g <https://en.wikipedia.org/wiki/Effect_size#Hedges'_g>`__ (``hedges_g``)
  - `Cliff’s delta <https://en.wikipedia.org/wiki/Effect_size#Effect_size_for_ordinal_data>`__ (``cliffs_delta``)

Each of these are attributes of the ``Dabest`` object.

.. code-block:: python3
  :linenos:


    two_groups_unpaired.mean_diff




.. parsed-literal::

    DABEST v2023.02.14
    ==================
                
    Good evening!
    The current time is Sun Aug 29 18:10:44 2021.

    The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768].
    The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 

    5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
    Any p-value reported is the probability of observing theeffect size (or greater),
    assuming the null hypothesis ofzero difference is true.
    For each p-value, 5000 reshuffles of the control and test labels were performed.

    To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`



For each comparison, the type of effect size is reported (here, it’s the
“unpaired mean difference”). The confidence interval is reported as:
[*confidenceIntervalWidth* *LowerBound*, *UpperBound*]

This confidence interval is generated through bootstrap resampling. See
:doc:`bootstraps` for more details.

Since v0.3.0, DABEST will report the p-value of the `non-parametric two-sided approximate permutation t-test <https://en.wikipedia.org/wiki/Resampling_(statistics)#Permutation_tests>`__. This is also known as the Monte Carlo permutation test.

For unpaired comparisons, the p-values and test statistics of `Welch's t test <https://en.wikipedia.org/wiki/Welch%27s_t-test>`__, `Student's t test <https://en.wikipedia.org/wiki/Student%27s_t-test>`__, and `Mann-Whitney U test <https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test>`__ can be found in addition. For paired comparisons, the p-values and test statistics of the `paired Student's t <https://en.wikipedia.org/wiki/Student%27s_t-test#Paired_samples>`__ and `Wilcoxon <https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test>`__ tests are presented.

.. code-block:: python3
  :linenos:


    pd.options.display.max_columns = 50
    two_groups_unpaired.mean_diff.results




.. raw:: html

    <div>
    <style scoped>
        /* .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        } */
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>control</th>
          <th>test</th>
          <th>control_N</th>
          <th>test_N</th>
          <th>effect_size</th>
          <th>is_paired</th>
          <th>difference</th>
          <th>ci</th>
          <th>bca_low</th>
          <th>bca_high</th>
          <th>bca_interval_idx</th>
          <th>pct_low</th>
          <th>pct_high</th>
          <th>pct_interval_idx</th>
          <th>bootstraps</th>
          <th>resamples</th>
          <th>random_seed</th>
          <th>permutations</th>
          <th>pvalue_permutation</th>
          <th>permutation_count</th>
          <th>permutations_var</th>
          <th>pvalue_welch</th>
          <th>statistic_welch</th>
          <th>pvalue_students_t</th>
          <th>statistic_students_t</th>
          <th>pvalue_mann_whitney</th>
          <th>statistic_mann_whitney</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>Control 1</td>
          <td>Test 1</td>
          <td>20</td>
          <td>20</td>
          <td>mean difference</td>
          <td>None</td>
          <td>0.48029</td>
          <td>95</td>
          <td>0.220869</td>
          <td>0.767721</td>
          <td>(140, 4889)</td>
          <td>0.215697</td>
          <td>0.761716</td>
          <td>(125, 4875)</td>
          <td>[0.6686169333655454, 0.4382051534234943, 0.665...</td>
          <td>5000</td>
          <td>12345</td>
          <td>[-0.17259843762502491, 0.03802293852634886, -0...</td>
          <td>0.001</td>
          <td>5000</td>
          <td>[0.026356588154404337, 0.027102495439046997, 0...</td>
          <td>0.002094</td>
          <td>-3.308806</td>
          <td>0.002057</td>
          <td>-3.308806</td>
          <td>0.001625</td>
          <td>83.0</td>
        </tr>
      </tbody>
    </table>
    </div>



.. code-block:: python3
  :linenos:


    two_groups_unpaired.mean_diff.statistical_tests




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>control</th>
          <th>test</th>
          <th>control_N</th>
          <th>test_N</th>
          <th>effect_size</th>
          <th>is_paired</th>
          <th>difference</th>
          <th>ci</th>
          <th>bca_low</th>
          <th>bca_high</th>
          <th>pvalue_permutation</th>
          <th>pvalue_welch</th>
          <th>statistic_welch</th>
          <th>pvalue_students_t</th>
          <th>statistic_students_t</th>
          <th>pvalue_mann_whitney</th>
          <th>statistic_mann_whitney</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>Control 1</td>
          <td>Test 1</td>
          <td>20</td>
          <td>20</td>
          <td>mean difference</td>
          <td>None</td>
          <td>0.48029</td>
          <td>95</td>
          <td>0.220869</td>
          <td>0.767721</td>
          <td>0.001</td>
          <td>0.002094</td>
          <td>-3.308806</td>
          <td>0.002057</td>
          <td>-3.308806</td>
          <td>0.001625</td>
          <td>83.0</td>
        </tr>
      </tbody>
    </table>
    </div>



Let’s compute the Hedges’ *g* for our comparison.

.. code-block:: python3
  :linenos:


    two_groups_unpaired.hedges_g




.. parsed-literal::

    DABEST v2023.02.14
    ==================
                
    Good evening!
    The current time is Sun Aug 29 18:12:17 2021.

    The unpaired Hedges' g between Control 1 and Test 1 is 1.03 [95%CI 0.349, 1.62].
    The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 

    5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
    Any p-value reported is the probability of observing theeffect size (or greater),
    assuming the null hypothesis ofzero difference is true.
    For each p-value, 5000 reshuffles of the control and test labels were performed.

    To get the results of all valid statistical tests, use `.hedges_g.statistical_tests`



.. code-block:: python3
  :linenos:


    two_groups_unpaired.hedges_g.results




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>control</th>
          <th>test</th>
          <th>control_N</th>
          <th>test_N</th>
          <th>effect_size</th>
          <th>is_paired</th>
          <th>difference</th>
          <th>ci</th>
          <th>bca_low</th>
          <th>bca_high</th>
          <th>bca_interval_idx</th>
          <th>pct_low</th>
          <th>pct_high</th>
          <th>pct_interval_idx</th>
          <th>bootstraps</th>
          <th>resamples</th>
          <th>random_seed</th>
          <th>permutations</th>
          <th>pvalue_permutation</th>
          <th>permutation_count</th>
          <th>permutations_var</th>
          <th>pvalue_welch</th>
          <th>statistic_welch</th>
          <th>pvalue_students_t</th>
          <th>statistic_students_t</th>
          <th>pvalue_mann_whitney</th>
          <th>statistic_mann_whitney</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>Control 1</td>
          <td>Test 1</td>
          <td>20</td>
          <td>20</td>
          <td>Hedges' g</td>
          <td>None</td>
          <td>1.025525</td>
          <td>95</td>
          <td>0.349394</td>
          <td>1.618579</td>
          <td>(42, 4724)</td>
          <td>0.472844</td>
          <td>1.74166</td>
          <td>(125, 4875)</td>
          <td>[1.1337301267831184, 0.8311210968422604, 1.539...</td>
          <td>5000</td>
          <td>12345</td>
          <td>[-0.3295089865590538, 0.07158401210924781, -0....</td>
          <td>0.001</td>
          <td>5000</td>
          <td>[0.026356588154404337, 0.027102495439046997, 0... </td>
          <td>0.002094</td>
          <td>-3.308806</td>
          <td>0.002057</td>
          <td>-3.308806</td>
          <td>0.001625</td>
          <td>83.0</td>
        </tr>
      </tbody>
    </table>
    </div>



Producing estimation plots
--------------------------

To produce a **Gardner-Altman estimation plot**, simply use the
``.plot()`` method. You can read more about its genesis and design
inspiration at :doc:`robust-beautiful`.

Every effect size instance has access to the ``.plot()`` method. This
means you can quickly create plots for different effect sizes easily.

.. code-block:: python3
  :linenos:


  two_groups_unpaired.mean_diff.plot();



.. image:: _images/tutorial_27_0.png


.. code-block:: python3
  :linenos:


    two_groups_unpaired.hedges_g.plot();



.. image:: _images/tutorial_28_0.png


Instead of a Gardner-Altman plot, you can produce a **Cumming estimation
plot** by setting ``float_contrast=False`` in the ``plot()`` method.
This will plot the bootstrap effect sizes below the raw data, and also
displays the the mean (gap) and ± standard deviation of each group
(vertical ends) as gapped lines. This design was inspired by Edward
Tufte’s dictum to maximise the data-ink ratio.

.. code-block:: python3
  :linenos:


    two_groups_unpaired.hedges_g.plot(float_contrast=False);



.. image:: _images/tutorial_30_0.png


The ``dabest`` package also implements a range of estimation plot
designs aimed at depicting common experimental designs.

The **multi-two-group estimation plot** tiles two or more Cumming plots
horizontally, and is created by passing a *nested tuple* to ``idx`` when
``dabest.load()`` is first invoked.

Thus, the lower axes in the Cumming plot is effectively a `forest
plot <https://en.wikipedia.org/wiki/Forest_plot>`__, used in
meta-analyses to aggregate and compare data from different experiments.

.. code-block:: python3
  :linenos:


    multi_2group = dabest.load(df, idx=(("Control 1", "Test 1",),
                                         ("Control 2", "Test 2")
                                       ))

    multi_2group.mean_diff.plot();



.. image:: _images/tutorial_35_0.png


The **shared control plot** displays another common experimental
paradigm, where several test samples are compared against a common
reference sample.

This type of Cumming plot is automatically generated if the tuple passed
to ``idx`` has more than two data columns.

.. code-block:: python3
  :linenos:


    shared_control = dabest.load(df, idx=("Control 1", "Test 1",
                                          "Test 2", "Test 3",
                                          "Test 4", "Test 5", "Test 6")
                                 )

.. code-block:: python3
  :linenos:


    shared_control




.. parsed-literal::

    DABEST v2023.02.14
    ==================
                
    Good evening!
    The current time is Tue Aug 31 23:39:22 2021.

    Effect size(s) with 95% confidence intervals will be computed for:
    1. Test 1 minus Control 1
    2. Test 2 minus Control 1
    3. Test 3 minus Control 1
    4. Test 4 minus Control 1
    5. Test 5 minus Control 1
    6. Test 6 minus Control 1

    5000 resamples will be used to generate the effect size bootstraps.



.. code-block:: python3
  :linenos:


    shared_control.mean_diff




.. parsed-literal::

    DABEST v2023.02.14
    ==================
                
    Good evening!
    The current time is Tue Aug 31 23:42:39 2021.

    The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768].
    The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 

    The unpaired mean difference between Control 1 and Test 2 is -0.542 [95%CI -0.914, -0.211].
    The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. 

    The unpaired mean difference between Control 1 and Test 3 is 0.174 [95%CI -0.295, 0.628].
    The p-value of the two-sided permutation t-test is 0.479, calculated for legacy purposes only. 

    The unpaired mean difference between Control 1 and Test 4 is 0.79 [95%CI 0.306, 1.31].
    The p-value of the two-sided permutation t-test is 0.0042, calculated for legacy purposes only. 

    The unpaired mean difference between Control 1 and Test 5 is 0.265 [95%CI 0.0137, 0.497].
    The p-value of the two-sided permutation t-test is 0.0404, calculated for legacy purposes only. 

    The unpaired mean difference between Control 1 and Test 6 is 0.288 [95%CI -0.00441, 0.515].
    The p-value of the two-sided permutation t-test is 0.0324, calculated for legacy purposes only. 

    5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
    Any p-value reported is the probability of observing theeffect size (or greater),
    assuming the null hypothesis ofzero difference is true.
    For each p-value, 5000 reshuffles of the control and test labels were performed.

    To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`



.. code-block:: python3
  :linenos:


    shared_control.mean_diff.plot();



.. image:: _images/tutorial_42_0.png


``dabest`` thus empowers you to robustly perform and elegantly present
complex visualizations and statistics.

.. code-block:: python3
  :linenos:


    multi_groups = dabest.load(df, idx=(("Control 1", "Test 1",),
                                         ("Control 2", "Test 2","Test 3"),
                                         ("Control 3", "Test 4","Test 5", "Test 6")
                                       ))


.. code-block:: python3
  :linenos:


    multi_groups




.. parsed-literal::

    DABEST v2023.02.14
    ==================
                
    Good evening!
    The current time is Tue Aug 31 23:47:40 2021.

    Effect size(s) with 95% confidence intervals will be computed for:
    1. Test 1 minus Control 1
    2. Test 2 minus Control 2
    3. Test 3 minus Control 2
    4. Test 4 minus Control 3
    5. Test 5 minus Control 3
    6. Test 6 minus Control 3

    5000 resamples will be used to generate the effect size bootstraps.



.. code-block:: python3
  :linenos:


    multi_groups.mean_diff




.. parsed-literal::

    DABEST v2023.02.14
    ==================
                
    Good evening!
    The current time is Tue Aug 31 23:48:17 2021.

    The unpaired mean difference between Control 1 and Test 1 is 0.48 [95%CI 0.221, 0.768].
    The p-value of the two-sided permutation t-test is 0.001, calculated for legacy purposes only. 

    The unpaired mean difference between Control 2 and Test 2 is -1.38 [95%CI -1.93, -0.895].
    The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only. 

    The unpaired mean difference between Control 2 and Test 3 is -0.666 [95%CI -1.3, -0.103].
    The p-value of the two-sided permutation t-test is 0.0352, calculated for legacy purposes only. 

    The unpaired mean difference between Control 3 and Test 4 is 0.362 [95%CI -0.114, 0.887].
    The p-value of the two-sided permutation t-test is 0.161, calculated for legacy purposes only. 

    The unpaired mean difference between Control 3 and Test 5 is -0.164 [95%CI -0.404, 0.0742].
    The p-value of the two-sided permutation t-test is 0.208, calculated for legacy purposes only. 

    The unpaired mean difference between Control 3 and Test 6 is -0.14 [95%CI -0.398, 0.102].
    The p-value of the two-sided permutation t-test is 0.282, calculated for legacy purposes only. 

    5000 bootstrap samples were taken; the confidence interval is bias-corrected and accelerated.
    Any p-value reported is the probability of observing theeffect size (or greater),
    assuming the null hypothesis ofzero difference is true.
    For each p-value, 5000 reshuffles of the control and test labels were performed.

    To get the results of all valid statistical tests, use `.mean_diff.statistical_tests`



.. code-block:: python3
  :linenos:


    multi_groups.mean_diff.plot();



.. image:: _images/tutorial_47_0.png


Using long (aka ‘melted’) data frames
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

``dabest`` can also work with ‘melted’ or ‘long’ data. This term is so
used because each row will now correspond to a single datapoint, with
one column carrying the value and other columns carrying ‘metadata’
describing that datapoint.

More details on wide vs long or ‘melted’ data can be found in this
`Wikipedia
article <https://en.wikipedia.org/wiki/Wide_and_narrow_data>`__. The
`pandas
documentation <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html>`__
gives recipes for melting dataframes.

.. code-block:: python3
  :linenos:


    x='group'
    y='metric'

    value_cols = df.columns[:-2] # select all but the "Gender" and "ID" columns.

    df_melted = pd.melt(df.reset_index(),
                        id_vars=["Gender", "ID"],
                        value_vars=value_cols,
                        value_name=y,
                        var_name=x)

    df_melted.head() # Gives the first five rows of `df_melted`.




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Gender</th>
          <th>ID</th>
          <th>group</th>
          <th>metric</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>Female</td>
          <td>1</td>
          <td>Control 1</td>
          <td>2.793984</td>
        </tr>
        <tr>
          <th>1</th>
          <td>Female</td>
          <td>2</td>
          <td>Control 1</td>
          <td>3.236759</td>
        </tr>
        <tr>
          <th>2</th>
          <td>Female</td>
          <td>3</td>
          <td>Control 1</td>
          <td>3.019149</td>
        </tr>
        <tr>
          <th>3</th>
          <td>Female</td>
          <td>4</td>
          <td>Control 1</td>
          <td>2.804638</td>
        </tr>
        <tr>
          <th>4</th>
          <td>Female</td>
          <td>5</td>
          <td>Control 1</td>
          <td>2.858019</td>
        </tr>
      </tbody>
    </table>
    </div>



When your data is in this format, you will need to specify the ``x`` and
``y`` columns in ``dabest.load()``.

.. code-block:: python3
  :linenos:


    analysis_of_long_df = dabest.load(df_melted, idx=("Control 1", "Test 1"),
                                     x="group", y="metric")

    analysis_of_long_df




.. parsed-literal::

    DABEST v2023.02.14
    ==================
                
    Good evening!
    The current time is Tue Aug 31 23:51:12 2021.

    Effect size(s) with 95% confidence intervals will be computed for:
    1. Test 1 minus Control 1

    5000 resamples will be used to generate the effect size bootstraps.



.. code-block:: python3
  :linenos:


    analysis_of_long_df.mean_diff.plot();



.. image:: _images/tutorial_52_0.png