In DABEST v2025.03.27, we introduce a new plotting orientation: horizontal plots.
To access this, provide horizontal=True to the .plot() method.
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, 30.36it/s]Numba compilation complete!
We're using DABEST v2025.03.27from scipy.stats import norm # Used in generation of populations.
np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
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
})two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1"))
two_groups_unpaired.mean_diff.plot(horizontal=True);
two_groups_paired = dabest.load(df, idx=("Control 1", "Test 1"), paired='baseline', id_col='ID')
two_groups_paired.mean_diff.plot(horizontal=True);
shared_control = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5"))
shared_control.mean_diff.plot(horizontal=True);
repeated_measures_baseline = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5"), paired='baseline', id_col='ID')
repeated_measures_baseline.mean_diff.plot(horizontal=True);
repeated_measures_sequential = dabest.load(df, idx=("Control 1", "Test 1", "Test 2", "Test 3", "Test 4", "Test 5"), paired='sequential', id_col='ID')
repeated_measures_sequential.mean_diff.plot(horizontal=True);
multi_2group = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3", "Test 4", "Test 5")))
multi_2group.mean_diff.plot(horizontal=True);
def 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_prop = create_demo_prop_dataset()multi_two_groups_unpaired = dabest.load(df_prop, idx=(("Control 1", "Test 1"), ("Control 2", "Test 2"), ("Test 3", "Test 4"), ("Test 5", "Test 6")), proportional=True)
multi_two_groups_unpaired.mean_diff.plot(horizontal=True);
multi_group_baseline = dabest.load(df_prop, idx=((("Control 1", "Test 1","Test 2", "Test 3"),("Test 4", "Test 5", "Test 6"))),proportional=True, paired="baseline", id_col="ID")
multi_group_baseline.mean_diff.plot(horizontal=True);
from scipy.stats import norm # Used in generation of populations.
np.random.seed(9999) # Fix the seed to ensure reproducibility of results.
# Create samples
N = 20
y = norm.rvs(loc=3, scale=0.4, size=N*4)
y[N:2*N] = y[N:2*N]+1
y[2*N:3*N] = y[2*N:3*N]-0.5
# Add a `Treatment` column
t1 = np.repeat('Placebo', N*2).tolist()
t2 = np.repeat('Drug', N*2).tolist()
treatment = t1 + t2
# Add a `Rep` column as the first variable for the 2 replicates of experiments done
rep = []
for i in range(N*2):
rep.append('Rep1')
rep.append('Rep2')
# Add a `Genotype` column as the second variable
wt = np.repeat('W', N).tolist()
mt = np.repeat('M', N).tolist()
wt2 = np.repeat('W', N).tolist()
mt2 = np.repeat('M', N).tolist()
genotype = wt + mt + wt2 + mt2
# Add an `id` column for paired data plotting.
id = list(range(0, N*2))
id_col = id + id
# Combine all columns into a DataFrame.
df_delta2 = pd.DataFrame({'ID' : id_col,
'Rep' : rep,
'Genotype' : genotype,
'Treatment': treatment,
'Y' : y
})unpaired_delta2 = dabest.load(data = df_delta2, x = ["Genotype", "Genotype"], y = "Y", delta2 = True, experiment = "Treatment")
unpaired_delta2.mean_diff.plot(horizontal=True);
unpaired = dabest.load(df, idx=(("Control 1", "Test 2"), ("Control 2", "Test 3"), ("Test 4", "Test 5")), mini_meta=True)
unpaired.mean_diff.plot(horizontal=True);
As with the vertical plots, horizontal plots can be customized using the same options. Shown below are a few cases where the aesthetics are modified, added functionality, or just less intuitive.
As with the vertical plots, you can specify the side of the swarms via swarm_side in the .plot() method.
In this case, swarm_side='left' would plot the swarms upwards, and swarm_side='right' would plot the swarms downwards.
Default is swarm_side='left'
two_groups_unpaired = dabest.load(df, idx=("Control 1", "Test 1", 'Test 2'), resamples=5000)
two_groups_unpaired.mean_diff.plot(swarm_side='left', horizontal=True);
swarm_side='center'
two_groups_unpaired.mean_diff.plot(swarm_side='center', horizontal=True);
swarm_side='right'
two_groups_unpaired.mean_diff.plot(swarm_side='right', horizontal=True);
The table axis can be customized using the horizontal_table_kwargs argument. A dict of keywords can be passed to customize the table.
If None, the following keywords are passed:
'show' - Whether to show the table. Default is True.'color' - The color of the table. Default is ‘yellow’.'alpha' - The transparency of the table. Default is 0.2.'fontsize' - The fontsize of the table. Default is 12.'text_color' - The color of the text in the table. Default is ‘black’.'text_units' - The units of the text in the table. Default is None.'control_marker' - The marker for the control group. Default is ‘-’.'fontsize_label' - The fontsize of the table x-label. Default is 12.'label' - The table x-label.multi_2group = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3"),("Test 4", "Test 5")))
multi_2group.mean_diff.plot(horizontal=True,
horizontal_table_kwargs={'color': 'red',
'alpha': 0.5,
'text_color':
'white',
'text_units':'mm',
'label': 'delta mm',
'control_marker': 'o',
});
The table axis can be hidden using the 'show':False in the horizontal_table_kwargs dict.
multi_2group = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3"),("Test 4", "Test 5")))
multi_2group.mean_diff.plot(horizontal=True, horizontal_table_kwargs={'show': False});
As with the vertical plots, you can utilise a gridkey table for representing the groupings. This can be reached via gridkey in the .plot() method.
You can either use gridkey='auto' to automatically generate the gridkey, or pass a list of indexes to represent the groupings (e.g., gridkey=['Control', 'Test']).
See the examples in the Plot Aesthetics Tutorial for more information with regards to kwargs.
multi_2group = dabest.load(df, idx=(("Control 1", "Test 1"),("Control 2", "Test 2"),("Control 3", "Test 3"),("Test 4", "Test 5")))
multi_2group.mean_diff.plot(horizontal=True, gridkey=['Control', 'Test']);