import numpy as np
import pandas as pd
import dabest
print("We're using DABEST v{}".format(dabest.__version__))Pre-compiling numba functions for DABEST...Compiling numba functions: 100%|██████████| 11/11 [00:00<00:00, 34.27it/s]Numba compilation complete!
We're using DABEST v2025.03.27def create_demo_prop_dataset(seed=9999, N=40):
import numpy as np
import pandas as pd
np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
# Create samples
n = 1
c1 = np.random.binomial(n, 0.2, size=N)
c2 = np.random.binomial(n, 0.2, size=N)
c3 = np.random.binomial(n, 0.8, size=N)
t1 = np.random.binomial(n, 0.6, size=N)
t2 = np.random.binomial(n, 0.2, size=N)
t3 = np.random.binomial(n, 0.3, size=N)
t4 = np.random.binomial(n, 0.4, size=N)
t5 = np.random.binomial(n, 0.5, size=N)
t6 = np.random.binomial(n, 0.6, size=N)
t7 = np.ones(N)
t8 = np.zeros(N)
t9 = np.zeros(N)
# Add a `gender` column for coloring the data.
females = np.repeat('Female', N / 2).tolist()
males = np.repeat('Male', N / 2).tolist()
gender = females + males
# Add an `id` column for paired data plotting.
id_col = pd.Series(range(1, N + 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,
'Test 7': t7, 'Test 8': t8, 'Test 9': t9,
'Gender': gender, 'ID': id_col
})
return df
df = create_demo_prop_dataset()
df.head()| Control 1 | Test 1 | Control 2 | Test 2 | Control 3 | Test 3 | Test 4 | Test 5 | Test 6 | Test 7 | Test 8 | Test 9 | Gender | ID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1.0 | 0.0 | 0.0 | Female | 1 |
| 1 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1.0 | 0.0 | 0.0 | Female | 2 |
| 2 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1.0 | 0.0 | 0.0 | Female | 3 |
| 3 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1.0 | 0.0 | 0.0 | Female | 4 |
| 4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1.0 | 0.0 | 0.0 | Female | 5 |
dabest.prop_datasetIn DABEST v2024.3.29, we incorporated feedback from biologists who may not have tables of 0’s and 1’s readily available. As a result, a convenient function - dabest.prop_dataset - to generate a binary dataset based on the specified sample sizes is provided. Users can generate a pandas.DataFrame containing the sample sizes for each element in the groups and the group names (optional if the sample sizes are provided in a dict).
sample_size_1 = {'a':[3, 4], 'b':[2, 5]}
sample_size_2 = [3, 4, 2, 5]
names = ['a', 'b']
sample_df_1 = dabest.prop_dataset(sample_size_1)
sample_df_2 = dabest.prop_dataset(sample_size_2, names)
print(all(sample_df_1 == sample_df_2))
sample_df_1.head()True| a | b | ID | |
|---|---|---|---|
| 0 | 0 | 0 | 1 |
| 1 | 0 | 0 | 2 |
| 2 | 0 | 1 | 3 |
| 3 | 1 | 1 | 4 |
| 4 | 1 | 1 | 5 |
When loading data, you need to set the parameter proportional=True.
two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1"), proportional=True)
two_groups_unpairedDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:24 2025.
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.To generate a proportion plot, the dabest library features two effect sizes:
mean_diff)cohens＿h)These are attributes of the Dabest object.
two_groups_unpaired.mean_diffDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:24 2025.
The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].
The p-value of the two-sided permutation t-test is 0.0, 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 of zero 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`Let’s compute the Cohen’s h for our comparison.
two_groups_unpaired.cohens_hDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:25 2025.
The unpaired Cohen's h between Control 1 and Test 1 is 1.24 [95%CI 0.784, 1.66].
The p-value of the two-sided permutation t-test is 0.0, 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 of zero 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 `.cohens_h.statistical_tests`To generate an estimation plot, simply use the .plot() method.
Each effect size instance has access to the .plot() method, allowing you to quickly create plots for different effect sizes with ease.
Unpaired proportion plots utilise the common bar plot. The bar plot displays the proportion of observations in the dataset that belong to the category of interest:
two_groups_unpaired.mean_diff.plot();
two_groups_unpaired.cohens_h.plot();
Instead of a Gardner-Altman plot, you can generate a Cumming estimation plot by setting float_contrast=False in the .plot() method. This will plot the bootstrap effect sizes below the raw data.
two_groups_unpaired.mean_diff.plot(float_contrast=False);
As with regular (non-binary) unpaired data, multi two-group, shared-control, and multi group plots can be generated for binary data.
multi_two_groups_unpaired = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3")),
proportional=True)
multi_two_groups_unpairedDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:25 2025.
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 3
5000 resamples will be used to generate the effect size bootstraps.multi_two_groups_unpaired.mean_diffDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:26 2025.
The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].
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 2 is 0.025 [95%CI -0.15, 0.15].
The p-value of the two-sided permutation t-test is 0.535, calculated for legacy purposes only.
The unpaired mean difference between Control 3 and Test 3 is -0.6 [95%CI -0.75, -0.425].
The p-value of the two-sided permutation t-test is 0.0, 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 of zero 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`multi_two_groups_unpaired.mean_diff.plot();
shared_control = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4"),
proportional=True)
shared_controlDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:26 2025.
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
5000 resamples will be used to generate the effect size bootstraps.shared_control.mean_diffDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:27 2025.
The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].
The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only.
The unpaired mean difference between Control 1 and Test 2 is 0.025 [95%CI -0.15, 0.15].
The p-value of the two-sided permutation t-test is 0.539, calculated for legacy purposes only.
The unpaired mean difference between Control 1 and Test 3 is 0.125 [95%CI -0.025, 0.325].
The p-value of the two-sided permutation t-test is 0.0936, calculated for legacy purposes only.
The unpaired mean difference between Control 1 and Test 4 is 0.15 [95%CI -0.05, 0.3].
The p-value of the two-sided permutation t-test is 0.0604, 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 of zero 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`shared_control.mean_diff.plot();
multi_groups_unpaired = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2", "Test 3", "Test 4")),
proportional=True)
multi_groups_unpairedDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:27 2025.
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 2
5000 resamples will be used to generate the effect size bootstraps.multi_groups_unpaired.mean_diffDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:28 2025.
The unpaired mean difference between Control 1 and Test 1 is 0.575 [95%CI 0.35, 0.725].
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 2 is 0.025 [95%CI -0.15, 0.15].
The p-value of the two-sided permutation t-test is 0.535, calculated for legacy purposes only.
The unpaired mean difference between Control 2 and Test 3 is 0.125 [95%CI -0.05, 0.325].
The p-value of the two-sided permutation t-test is 0.099, calculated for legacy purposes only.
The unpaired mean difference between Control 2 and Test 4 is 0.15 [95%CI -0.05, 0.3].
The p-value of the two-sided permutation t-test is 0.0604, 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 of zero 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`multi_groups_unpaired.mean_diff.plot();
For the paired version of the proportion plot, we adopt the style of a Sankey Diagram. The width of each bar in each xtick represents the proportion of the corresponding label in the group, and the strip denotes the paired relationship for each observation.
Starting from v2024.3.29, the paired version of the proportion plot receives a major upgrade. We introduce the sankey and flow parameters to control the plot. By default, both sankey and flow are set to True to cater the needs of repeated measures. When sankey is set to False, DABEST will generate a bar plot with a similar aesthetic to the paired proportion plot. When flow is set to False, each group of comparsion forms a Sankey diagram that does not connect to other groups of comparison.
Similar to the unpaired version, the .plot() method is used to produce an estimation plot.
two_groups_paired = dabest.load(df, idx=("Control 1", "Test 1"),
proportional=True, paired="baseline", id_col="ID")
two_groups_pairedDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:28 2025.
Paired effect size(s) for repeated measures against baseline
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.two_groups_paired.mean_diffDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:28 2025.
The paired mean difference for repeated measures against baseline
between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].
The p-value of the two-sided permutation t-test is 0.0, 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 of zero 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`two_groups_paired.mean_diff.plot();
The Sankey plots for paired proportions also supports the float_contrast parameter, which can be set to False to produce a Cumming estimation plot.
two_groups_paired.mean_diff.plot(float_contrast=False);
As with regular (non-binary) unpaired data, multi two-group, repeated-measures, and multi group plots can be generated for binary data.
multi_two_groups_paired = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3")),
proportional=True, paired="baseline", id_col="ID")
multi_two_groups_pairedDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:28 2025.
Paired effect size(s) for repeated measures against baseline
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 3
5000 resamples will be used to generate the effect size bootstraps.multi_two_groups_paired.mean_diffDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:29 2025.
The paired mean difference for repeated measures against baseline
between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].
The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only.
The paired mean difference for repeated measures against baseline
between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.175].
The p-value of the two-sided permutation t-test is 0.571, calculated for legacy purposes only.
The paired mean difference for repeated measures against baseline
between Control 3 and Test 3 is -0.6 [95%CI -0.775, -0.425].
The p-value of the two-sided permutation t-test is 0.0, 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 of zero 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`multi_two_groups_paired.mean_diff.plot();
repeated_measures_baseline = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4"),
proportional=True, paired="baseline", id_col="ID")
repeated_measures_baselineDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:29 2025.
Paired effect size(s) for repeated measures against baseline
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
5000 resamples will be used to generate the effect size bootstraps.repeated_measures_baseline.mean_diffDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:31 2025.
The paired mean difference for repeated measures against baseline
between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].
The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only.
The paired mean difference for repeated measures against baseline
between Control 1 and Test 2 is 0.025 [95%CI -0.15, 0.175].
The p-value of the two-sided permutation t-test is 0.555, calculated for legacy purposes only.
The paired mean difference for repeated measures against baseline
between Control 1 and Test 3 is 0.125 [95%CI -0.075, 0.275].
The p-value of the two-sided permutation t-test is 0.277, calculated for legacy purposes only.
The paired mean difference for repeated measures against baseline
between Control 1 and Test 4 is 0.15 [95%CI -0.05, 0.325].
The p-value of the two-sided permutation t-test is 0.075, 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 of zero 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`repeated_measures_baseline.mean_diff.plot();
repeated_measures_sequential = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4"),
proportional=True, paired="sequential", id_col="ID")
repeated_measures_sequentialDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:31 2025.
Paired effect size(s) for the sequential design of repeated-measures experiment
with 95% confidence intervals will be computed for:
1. Test 1 minus Control 1
2. Test 2 minus Test 1
3. Test 3 minus Test 2
4. Test 4 minus Test 3
5000 resamples will be used to generate the effect size bootstraps.repeated_measures_sequential.mean_diffDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:32 2025.
The paired mean difference for the sequential design of repeated-measures experiment
between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].
The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only.
The paired mean difference for the sequential design of repeated-measures experiment
between Test 1 and Test 2 is -0.55 [95%CI -0.725, -0.4].
The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only.
The paired mean difference for the sequential design of repeated-measures experiment
between Test 2 and Test 3 is 0.1 [95%CI -0.075, 0.225].
The p-value of the two-sided permutation t-test is 0.342, calculated for legacy purposes only.
The paired mean difference for the sequential design of repeated-measures experiment
between Test 3 and Test 4 is 0.025 [95%CI -0.2, 0.2].
The p-value of the two-sided permutation t-test is 0.624, 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 of zero 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`repeated_measures_sequential.mean_diff.plot();
multi_groups_baseline = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2", "Test 3", "Test 4")),
proportional=True, paired="baseline", id_col="ID")
multi_groups_baselineDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:32 2025.
Paired effect size(s) for repeated measures against baseline
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 2
5000 resamples will be used to generate the effect size bootstraps.multi_groups_baseline.mean_diffDABEST v2025.03.27
==================
Good afternoon!
The current time is Tue Mar 25 17:22:33 2025.
The paired mean difference for repeated measures against baseline
between Control 1 and Test 1 is 0.575 [95%CI 0.325, 0.725].
The p-value of the two-sided permutation t-test is 0.0, calculated for legacy purposes only.
The paired mean difference for repeated measures against baseline
between Control 2 and Test 2 is 0.025 [95%CI -0.15, 0.175].
The p-value of the two-sided permutation t-test is 0.571, calculated for legacy purposes only.
The paired mean difference for repeated measures against baseline
between Control 2 and Test 3 is 0.125 [95%CI -0.075, 0.3].
The p-value of the two-sided permutation t-test is 0.309, calculated for legacy purposes only.
The paired mean difference for repeated measures against baseline
between Control 2 and Test 4 is 0.15 [95%CI -0.025, 0.3].
The p-value of the two-sided permutation t-test is 0.0362, 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 of zero 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`multi_groups_baseline.mean_diff.plot();
Here we demonstrate a few proportion plot specific aesthetic adjustments.
You can modify the width of the bar plot bars (unpaired data) by setting the parameter bar_width in the .plot() method.
two_groups_unpaired.mean_diff.plot(bar_width=0.3);
The raw_desat is used to control the amount of desaturation applied to the bar plot bar colors (specific to unpaired data). A value of 0.0 means full desaturation (i.e., grayscale), while a value of 1.0 means no desaturation (i.e., full color saturation). The default one is 0.8.
two_groups_unpaired.mean_diff.plot(raw_desat=1.0);
The parameters raw_label and contrast_label can be used to set labels for the y-axis of the bar plot and the contrast plot.
two_groups_unpaired.mean_diff.plot(raw_label="success",contrast_label="difference");
two_groups_paired.mean_diff.plot(raw_label="success",contrast_label="difference");
The parameters barplot_kwargs can be used to alter the aesthetics of the bar plot. This is a dictionary that can be used to pass additional arguments to the bar plot.
two_groups_unpaired.mean_diff.plot(barplot_kwargs={"alpha":0.5, "edgecolor":"red", "linewidth":2, 'errorbar': ('sd', 0.1)});
By changing the sankey and flow parameters, you can generate different types of Sankey plots for paired proportions.
separate_control = dabest.load(df, idx=((("Control 1", "Test 1"),
("Test 2", "Test 3"),
("Test 4", "Test 7", "Test 6"))),
proportional=True, paired="sequential", id_col="ID")
separate_control.mean_diff.plot();
separate_control.mean_diff.plot(sankey_kwargs={'sankey':False});
separate_control.mean_diff.plot(sankey_kwargs={'flow':False});
Several exclusive parameters can be provided to the .plot() method to customize the Sankey plots for paired proportions. By modifying the sankey_kwargs parameter, you can customize the Sankey plot. The following parameters are supported:
repeated_measures_baseline.mean_diff.plot(sankey_kwargs = {"alpha": 0.2,
"bar_width": 0.4});
The custom_palette parameter functions in a similar way for proportion plots as for other plots - however, there are some differences!
A custom_palette dict can be passed for sankey plots, whereby two keys used are 0 and 1. The color associated with these keys will be used to color the bars in the sankey plot.
For bar plots, the custom_palette dict can be passed like a regular plot, with a color associated to each group. The chosen color will then be used to color the filled portion of the bar plot.
repeated_measures_baseline.mean_diff.plot(custom_palette={0: "red", 1: "blue"});
shared_control.mean_diff.plot(custom_palette={'Control 1': "red", 'Test 1': "blue", 'Test 2': "green", 'Test 3': "purple", 'Test 4': "orange"});
Similarly, premade matplotlib/seaborn color palette can be passed. For sankey plots, the first two colors in the palette will be used to color the bars in the sankey plot. For bar plots, the colors will be used to color the filled portion of the bar plot.
repeated_measures_baseline.mean_diff.plot(custom_palette='Set1');
shared_control.mean_diff.plot(custom_palette='Set1');
Passing a custom palette list functions differently for bar plots and sankey plots:
repeated_measures_baseline.mean_diff.plot(custom_palette=['red', 'blue']);
shared_control.mean_diff.plot(custom_palette=['red', 'blue', 'green', 'purple', 'orange']);
By default, the sample counts for each bar in proportion plots are not shown.
This feature can be turned on by setting prop_sample_counts=True in the .plot() method.
Note: This feature is not compatible with flow=False in sankey_kwargs.
two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1"), proportional=True)
two_groups_unpaired.mean_diff.plot(prop_sample_counts=True);
The sample counts kwargs can be utilised via prop_sample_counts_kwargs in the .plot() method.
two_groups_unpaired.mean_diff.plot(prop_sample_counts=True, prop_sample_counts_kwargs={"color":"red"});
For further aesthetic changes, the Plot Aesthetics Tutorial provides detailed examples of how to customize the plot.