1. Basic operations¶

In [ ]:

import pipeGEM as pg
from pipeGEM.data.fetching import list_models, load_remote_model
from pipeGEM.utils import get_subsystems
import cobra

import pipeGEM as pg from pipeGEM.data.fetching import list_models, load_remote_model from pipeGEM.utils import get_subsystems import cobra

Get model from BiGG or Metabolic atlas¶

In [3]:

model_list = list_models(organism="E. coli")

model_list = list_models(organism="E. coli")

In [4]:

model_list.head()

model_list.head()

Out[4]:

	id	organism	reaction_count	metabolite_count	gene_count	database
0	e_coli_core	Escherichia coli str. K-12 substr. MG1655	95	72	137	BiGG
2	iAF1260	Escherichia coli str. K-12 substr. MG1655	2382	1668	1261	BiGG
3	iAF1260b	Escherichia coli str. K-12 substr. MG1655	2388	1668	1261	BiGG
11	iAPECO1_1312	Escherichia coli APEC O1	2735	1942	1313	BiGG
13	iB21_1397	Escherichia coli BL21(DE3)	2741	1943	1337	BiGG

In [5]:

ecoli = load_remote_model("e_coli_core")

ecoli = load_remote_model("e_coli_core")

Set parameter Username
Academic license - for non-commercial use only - expires 2024-06-25

In [6]:

ecoli

ecoli

Out[6]:

Name	e_coli_core
Memory address	286c3ad3940
Number of metabolites	72
Number of reactions	95
Number of genes	137
Number of groups	0
Objective expression	1.0*BIOMASS_Ecoli_core_w_GAM - 1.0*BIOMASS_Ecoli_core_w_GAM_reverse_712e5
Compartments	extracellular space, cytosol

Create Model object¶

In [7]:

pg_mod = pg.Model(model=ecoli, name_tag="ecoli")

pg_mod = pg.Model(model=ecoli, name_tag="ecoli")

In [8]:

pg_mod

pg_mod

Out[8]:

Name	e_coli_core
Memory address	286c3ad3940
Number of metabolites	72
Number of reactions	95
Number of genes	137
Number of groups	0
Objective expression	1.0*BIOMASS_Ecoli_core_w_GAM - 1.0*BIOMASS_Ecoli_core_w_GAM_reverse_712e5
Compartments	extracellular space, cytosol

In [9]:

# basic information

pg_mod.n_rxns, pg_mod.n_genes, pg_mod.n_mets

# basic information pg_mod.n_rxns, pg_mod.n_genes, pg_mod.n_mets

Out[9]:

(95, 137, 72)

In [10]:

# flux analysis using get_flux

pFBA_result = pg_mod.do_flux_analysis(method="pFBA")

# flux analysis using get_flux pFBA_result = pg_mod.do_flux_analysis(method="pFBA")

In [11]:

pFBA_result

pFBA_result

Out[11]:

FBA_Analysis at 0x286c4e2ece0

-----------
Parameters:
{'solver': 'gurobi', 'name': 'ecoli'}
-----------
Result keys:
solution, flux_df

In [12]:

pFBA_result.plot(rxn_ids=["PFK", "PGI", "PGK"], aspect=1)

pFBA_result.plot(rxn_ids=["PFK", "PGI", "PGK"], aspect=1)

Use index as the reaction IDs

C:\Users\qwert\miniconda3\envs\py310\lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)

In [13]:

# perform flux sampling

sampling_result = pg_mod.do_flux_analysis(method="sampling", n=30)

# perform flux sampling sampling_result = pg_mod.do_flux_analysis(method="sampling", n=30)

Read LP format model from file C:\Users\qwert\AppData\Local\Temp\tmpulgyhdc7.lp
Reading time = 0.00 seconds
: 73 rows, 190 columns, 722 nonzeros

In [16]:

sampling_result.flux_df

sampling_result.flux_df

Out[16]:

	PFK	PFL	PGI	PGK	PGL	ACALD	AKGt2r	PGM	PIt2r	ALCD2x	...	MALS	MALt2_2	MDH	ME1	ME2	NADH16	NADTRHD	NH4t	O2t	PDH
0	8.136455	2.012919	7.306230	-15.589323	1.481313	-0.030135	-0.200670	-14.596565	2.441216	-0.030048	...	0.169376	0.0	3.109480	0.135734	2.319586	33.497332	3.471805	3.653353	19.446378	7.417451
1	8.954002	0.240421	8.366933	-17.139615	1.158407	-0.709859	-0.104804	-16.124940	2.495111	-0.669748	...	0.089351	0.0	2.147939	0.824229	2.153128	34.876927	1.498010	3.771994	19.956436	10.998865
2	10.096441	1.250305	2.093831	-15.423786	7.625897	-1.420546	-0.194995	-14.443195	2.411297	-0.901694	...	0.595585	0.0	2.347636	0.477438	2.466342	42.375032	15.547232	3.600201	23.535431	9.260710
3	12.278966	0.235281	3.851723	-16.069436	5.908252	-1.404621	-0.067023	-15.083927	2.423389	-1.049668	...	0.209606	0.0	1.465244	0.540197	2.646111	39.958497	8.282598	3.658258	22.200222	9.811926
4	11.080502	0.382634	1.570119	-15.294609	8.178705	-0.749271	-0.088928	-14.311955	2.416368	-0.600418	...	0.048774	0.0	-1.544365	2.018775	3.254201	41.687133	12.908287	3.715260	22.683485	9.044146
5	10.019731	0.672508	7.212477	-16.935753	2.400832	-0.577644	-0.121697	-15.943929	2.438920	-0.400386	...	0.614702	0.0	2.129748	1.471419	3.024100	39.080042	2.129578	3.727398	22.545303	10.239605
6	9.177922	0.102155	4.994711	-16.178629	4.613388	-0.648812	-0.112531	-15.180693	2.453948	-0.420103	...	0.556610	0.0	2.220185	1.837915	1.919416	40.870235	5.624063	3.694209	23.145570	10.082072
7	9.440105	1.299468	5.966434	-16.573700	3.682486	-0.514376	-0.113538	-15.577845	2.448832	-0.432155	...	1.301527	0.0	2.572240	1.498822	3.187130	41.211082	11.525291	3.988394	23.583873	10.676144
8	11.580519	0.596962	5.004591	-16.488093	4.782244	-0.657669	-0.025846	-15.495664	2.440406	-0.182540	...	1.162209	0.0	2.517418	0.959341	3.849965	44.579464	10.242524	3.903854	25.371990	11.093913
9	10.497716	0.094072	3.729291	-15.911775	5.959669	-0.458000	-0.004626	-14.927270	2.420921	-0.379534	...	1.138196	0.0	3.617052	0.235262	2.491668	43.527412	9.159283	3.904477	24.366599	10.909853
10	8.594738	0.334689	2.381024	-15.610308	7.398913	-0.179816	-0.006238	-14.623358	2.426933	-0.156647	...	1.720865	0.0	5.294070	0.066960	1.124705	45.006790	15.205796	3.948078	24.885829	10.786646
11	7.676377	0.750941	1.722096	-15.458097	8.093573	-0.341925	-0.125353	-14.476389	2.414043	-0.160405	...	1.971772	0.0	1.970178	0.911331	1.387143	37.795696	7.086741	4.051644	20.046288	10.385220
12	8.524917	2.255143	5.266372	-16.475340	4.452593	-0.190875	-0.080386	-15.491631	2.418965	-0.171672	...	1.589978	0.0	5.985909	0.674670	0.257122	39.626628	4.865646	3.931122	22.477175	9.258432
13	8.726486	0.508259	3.669878	-15.977336	6.073240	-0.699419	-0.080289	-14.989571	2.428939	-0.383230	...	1.065819	0.0	5.117041	0.902220	0.648532	44.636862	18.382548	3.801737	25.119418	10.549767
14	6.530685	0.327690	-2.338757	-14.052112	12.126658	-0.213595	-0.036666	-13.066353	2.424003	-0.041840	...	1.252535	0.0	2.828086	0.193327	1.640627	46.343355	30.033825	3.653438	24.876430	8.596707
15	6.701907	1.292554	-1.448027	-13.992400	11.025024	-0.248971	-0.052682	-13.003240	2.432370	-0.113375	...	2.364627	0.0	3.334523	1.163366	0.846579	40.828698	13.248844	3.626366	21.904270	8.618416
16	6.683623	0.504603	-0.262243	-14.296111	9.778741	-0.500560	-0.231228	-13.313125	2.417187	-0.279287	...	2.655525	0.0	4.267146	1.233703	0.397984	41.314863	14.382960	3.805572	22.279086	9.908399
17	6.321001	0.139619	0.233939	-14.847214	9.578719	-0.376581	-0.068872	-13.792437	2.593721	-0.046214	...	2.686600	0.0	4.416011	0.699252	1.904393	45.926038	13.263607	3.916039	25.129547	11.359474
18	7.468760	0.135630	2.318945	-15.474716	7.454056	-0.110775	-0.018750	-14.419006	2.596017	-0.050667	...	3.841531	0.0	5.192882	0.659422	3.324699	45.758452	10.246609	3.851713	25.546962	12.866478
19	8.635064	1.334673	4.841663	-16.183376	4.788767	-0.164825	-0.289537	-15.197778	2.423608	-0.014169	...	4.070242	0.0	7.656381	0.084117	2.733826	44.965515	4.182371	3.599911	25.684798	13.040156
20	7.438722	0.834091	3.471059	-15.968861	6.320180	-0.535849	-0.297572	-14.966188	2.465596	-0.082465	...	2.921712	0.0	6.315916	0.858168	0.094941	41.211653	4.690610	3.717724	22.779483	11.600635
21	9.932326	0.056536	2.985607	-15.496409	6.612082	-0.549095	-0.380374	-14.501688	2.446043	-0.262910	...	3.255248	0.0	6.487042	0.544322	0.925062	42.903789	4.951957	3.762972	23.802483	12.432069
22	5.559067	1.137912	-0.892495	-14.122291	10.432131	-0.376143	-0.008757	-13.137774	2.420949	-0.341187	...	3.283372	0.0	6.310739	0.539467	0.726783	45.670989	15.880548	3.758344	24.982303	10.684963
23	6.801159	2.040517	2.467637	-15.440833	7.197741	-0.151915	-0.053099	-14.448949	2.439065	-0.133132	...	3.841465	0.0	7.420246	0.067719	1.898980	44.420136	9.915391	3.759556	24.982808	11.104344
24	11.726714	0.506196	2.881029	-15.507101	6.735412	-0.676988	-0.173926	-14.521893	2.422651	-0.103889	...	2.617814	0.0	6.002064	0.195729	0.973592	42.200157	8.580079	3.690792	23.376864	11.512099
25	8.189568	3.062280	2.755845	-15.465524	6.863114	-0.817450	-0.116705	-14.477624	2.429270	-0.223582	...	3.882292	0.0	6.464094	1.325315	0.271023	38.977847	6.716438	3.782454	21.577993	10.135452
26	7.198730	2.965524	3.107013	-15.707176	6.583788	-0.366566	-0.054003	-14.722509	2.421320	-0.273997	...	3.889910	0.0	8.913350	0.130629	1.065421	44.957756	12.568777	3.672070	25.588623	11.036387
27	8.336019	0.833860	7.311014	-16.956838	2.292328	-0.589042	-0.070404	-15.968241	2.430983	-0.553924	...	2.688126	0.0	6.631404	0.300389	2.495469	42.298245	9.646642	3.632546	24.518691	12.805026
28	9.583324	0.424274	5.310166	-16.373247	4.347281	-0.367505	-0.011608	-15.380114	2.442137	-0.298192	...	2.525792	0.0	2.957092	2.228504	3.623146	44.355802	8.690727	3.651118	25.319376	12.800113
29	12.649507	0.188138	6.671566	-16.752808	2.932795	-0.277455	-0.006899	-15.769149	2.418841	-0.178576	...	2.208087	0.0	2.805009	0.115785	4.507240	39.284972	4.158298	3.606105	22.252459	12.420734

30 rows × 95 columns

Create Group object¶

In [17]:

from pipeGEM.utils import random_perturb

from pipeGEM.utils import random_perturb

In [18]:

# create different models
models = [random_perturb(ecoli, 
                         in_place=False, 
                         structure_ratio=0.8,
                         constr_ratio=0.8, 
                         random_state=i) for i in range(9)]

# create different models models = [random_perturb(ecoli, in_place=False, structure_ratio=0.8, constr_ratio=0.8, random_state=i) for i in range(9)]

Read LP format model from file C:\Users\qwert\AppData\Local\Temp\tmpc1033azf.lp
Reading time = 0.01 seconds
: 72 rows, 190 columns, 720 nonzeros
Read LP format model from file C:\Users\qwert\AppData\Local\Temp\tmp3ahmn46r.lp
Reading time = 0.01 seconds
: 72 rows, 190 columns, 720 nonzeros
Read LP format model from file C:\Users\qwert\AppData\Local\Temp\tmpgzsmgof8.lp
Reading time = 0.01 seconds
: 72 rows, 190 columns, 720 nonzeros
Read LP format model from file C:\Users\qwert\AppData\Local\Temp\tmpgaz934us.lp
Reading time = 0.01 seconds
: 72 rows, 190 columns, 720 nonzeros
Read LP format model from file C:\Users\qwert\AppData\Local\Temp\tmpweuxq9_q.lp
Reading time = 0.01 seconds
: 72 rows, 190 columns, 720 nonzeros
Read LP format model from file C:\Users\qwert\AppData\Local\Temp\tmpcdcu6f4_.lp
Reading time = 0.01 seconds
: 72 rows, 190 columns, 720 nonzeros
Read LP format model from file C:\Users\qwert\AppData\Local\Temp\tmpysqt70xh.lp
Reading time = 0.01 seconds
: 72 rows, 190 columns, 720 nonzeros
Read LP format model from file C:\Users\qwert\AppData\Local\Temp\tmpmq5rm20f.lp
Reading time = 0.01 seconds
: 72 rows, 190 columns, 720 nonzeros
Read LP format model from file C:\Users\qwert\AppData\Local\Temp\tmpzcknlua9.lp
Reading time = 0.01 seconds
: 72 rows, 190 columns, 720 nonzeros

In [19]:

# The first way to initialize Group is to use a dict of cobra.model dicts (str as keys)

grp = pg.Group({"G1": {f"m{i+1}": models[i] for i in range(3)},
               "G2": {f"m{i+1}": models[i] for i in range(3, 6)},
               "G3": {f"m{i+1}": models[i] for i in range(6, 9)}}, name_tag="group")

# The first way to initialize Group is to use a dict of cobra.model dicts (str as keys) grp = pg.Group({"G1": {f"m{i+1}": models[i] for i in range(3)}, "G2": {f"m{i+1}": models[i] for i in range(3, 6)}, "G3": {f"m{i+1}": models[i] for i in range(6, 9)}}, name_tag="group")

In [20]:

# browse the group table

grp.get_info(features=["group_name", "n_rxns", "n_mets", "n_genes"])

# browse the group table grp.get_info(features=["group_name", "n_rxns", "n_mets", "n_genes"])

Out[20]:

	group_name	n_rxns	n_mets	n_genes
m1	G1	71	72	95
m2	G1	69	72	94
m3	G1	65	67	83
m4	G2	63	69	85
m5	G2	76	72	122
m6	G2	74	72	112
m7	G3	69	69	107
m8	G3	71	70	94
m9	G3	66	70	92

In [21]:

# The second way to initialize Group is to use a dict of pipeGEM.Model lists

grp = pg.Group({"G1": [pg.Model(f"m{i+1}", models[i]) for i in range(3)],
               "G2": [pg.Model(f"m{i+1}", models[i]) for i in range(3, 6)],
               "G3": [pg.Model(f"m{i+1}", models[i]) for i in range(6, 9)]}, name_tag="group")

# The second way to initialize Group is to use a dict of pipeGEM.Model lists grp = pg.Group({"G1": [pg.Model(f"m{i+1}", models[i]) for i in range(3)], "G2": [pg.Model(f"m{i+1}", models[i]) for i in range(3, 6)], "G3": [pg.Model(f"m{i+1}", models[i]) for i in range(6, 9)]}, name_tag="group")

In [22]:

# This should return the same result with the first case

grp.get_info(features=["group_name", "n_rxns", "n_mets", "n_genes"])

# This should return the same result with the first case grp.get_info(features=["group_name", "n_rxns", "n_mets", "n_genes"])

Out[22]:

	group_name	n_rxns	n_mets	n_genes
m1	G1	71	72	95
m2	G1	69	72	94
m3	G1	65	67	83
m4	G2	63	69	85
m5	G2	76	72	122
m6	G2	74	72	112
m7	G3	69	69	107
m8	G3	71	70	94
m9	G3	66	70	92

Group operations¶

In [23]:

# indexing

grp["m1"]

# indexing grp["m1"]

Out[23]:

Name	e_coli_core
Memory address	286c2ef5b10
Number of metabolites	72
Number of reactions	71
Number of genes	95
Number of groups	0
Objective expression	1.0*BIOMASS_Ecoli_core_w_GAM - 1.0*BIOMASS_Ecoli_core_w_GAM_reverse_712e5
Compartments	extracellular space, cytosol

In [24]:

# select multiple models

grp[["m1", "m2", "m4"]].get_info()

# select multiple models grp[["m1", "m2", "m4"]].get_info()

Out[24]:

	group_name	n_rxns	n_mets	n_genes
m1	G1	71	72	95
m2	G1	69	72	94
m4	G2	63	69	85

In [25]:

# aggregate models with thier group name into a dict

grp_dic = grp.aggregate_models("group_name")
grp_dic

# aggregate models with thier group name into a dict grp_dic = grp.aggregate_models("group_name") grp_dic

create a new group containing {'m1': pipeGEM.Model [m1] (g,m,r)=(95, 72, 71)
, 'm2': pipeGEM.Model [m2] (g,m,r)=(94, 72, 69)
, 'm3': pipeGEM.Model [m3] (g,m,r)=(83, 67, 65)
}
create a new group containing {'m4': pipeGEM.Model [m4] (g,m,r)=(85, 69, 63)
, 'm5': pipeGEM.Model [m5] (g,m,r)=(122, 72, 76)
, 'm6': pipeGEM.Model [m6] (g,m,r)=(112, 72, 74)
}
create a new group containing {'m7': pipeGEM.Model [m7] (g,m,r)=(107, 69, 69)
, 'm8': pipeGEM.Model [m8] (g,m,r)=(94, 70, 71)
, 'm9': pipeGEM.Model [m9] (g,m,r)=(92, 70, 66)
}

Out[25]:

{'G1': Group [G1] containing 3 models,
 'G2': Group [G2] containing 3 models,
 'G3': Group [G3] containing 3 models}

In [26]:

grp_dic["G2"].get_info()

grp_dic["G2"].get_info()

Out[26]:

	group_name	n_rxns	n_mets	n_genes
m4	G2	63	69	85
m5	G2	76	72	122
m6	G2	74	72	112

In [27]:

# The models inside the pg.Group are pg.Models

isinstance(grp_dic["G2"]["m4"], pg.Model)

# The models inside the pg.Group are pg.Models isinstance(grp_dic["G2"]["m4"], pg.Model)

Out[27]:

True

In [28]:

grp = pg.Group([pg_mod, pg_mod.copy()], )
pfba_result = grp.do_flux_analysis(method="pFBA")

grp = pg.Group([pg_mod, pg_mod.copy()], ) pfba_result = grp.do_flux_analysis(method="pFBA")

Read LP format model from file C:\Users\qwert\AppData\Local\Temp\tmphgv0f41t.lp
Reading time = 0.01 seconds
: 72 rows, 190 columns, 720 nonzeros

In [29]:

pfba_result

pfba_result

Out[29]:

FBA_Analysis at 0x286c77fca90

-----------
Parameters:
{'categorical': {'model'}, 'name': 'Unnamed_group', 'group_by': 'model', 'group': {'model': ['ecoli', 'copied_ecoli']}}
-----------
Result keys:
group_annotation, rxn_annotation, flux_df

In [30]:

pfba_result.log

pfba_result.log

Out[30]:

{'categorical': {'model'},
 'name': 'Unnamed_group',
 'group_by': 'model',
 'group': {'model': ['ecoli', 'copied_ecoli']}}

In [31]:

pfba_result.plot(rxn_ids=["PFK", "PGI", "PGK"], 
                 group_by="model", aspect=1.5, kind="bar")

pfba_result.plot(rxn_ids=["PFK", "PGI", "PGK"], group_by="model", aspect=1.5, kind="bar")

C:\Users\qwert\miniconda3\envs\py310\lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)

Comparisons¶

In [32]:

# compare model components
grp = pg.Group({"G1": [pg.Model(f"m{i+1}", models[i]) for i in range(3)],
               "G2": [pg.Model(f"m{i+1}", models[i]) for i in range(3, 6)],
               "G3": [pg.Model(f"m{i+1}", models[i]) for i in range(6, 9)]}, name_tag="group")
comparison = grp.compare(method="jaccard")

# compare model components grp = pg.Group({"G1": [pg.Model(f"m{i+1}", models[i]) for i in range(3)], "G2": [pg.Model(f"m{i+1}", models[i]) for i in range(3, 6)], "G3": [pg.Model(f"m{i+1}", models[i]) for i in range(6, 9)]}, name_tag="group") comparison = grp.compare(method="jaccard")

create a new group containing {'m1': pipeGEM.Model [m1] (g,m,r)=(95, 72, 71)
, 'm2': pipeGEM.Model [m2] (g,m,r)=(94, 72, 69)
, 'm3': pipeGEM.Model [m3] (g,m,r)=(83, 67, 65)
}
create a new group containing {'m4': pipeGEM.Model [m4] (g,m,r)=(85, 69, 63)
, 'm5': pipeGEM.Model [m5] (g,m,r)=(122, 72, 76)
, 'm6': pipeGEM.Model [m6] (g,m,r)=(112, 72, 74)
}
create a new group containing {'m7': pipeGEM.Model [m7] (g,m,r)=(107, 69, 69)
, 'm8': pipeGEM.Model [m8] (g,m,r)=(94, 70, 71)
, 'm9': pipeGEM.Model [m9] (g,m,r)=(92, 70, 66)
}

In [39]:

comparison.group_annotation

comparison.group_annotation

Out[39]:

	group_name	0
0	G1	[m1, m2, m3]
1	G2	[m4, m5, m6]
2	G3	[m7, m8, m9]

In [41]:

# visualization

comparison.plot(row_color_by="group_name", col_color_by="group_name")

# visualization comparison.plot(row_color_by="group_name", col_color_by="group_name")

                                          group_name
0  (0.2980392156862745, 0.4470588235294118, 0.690...
1  (0.8666666666666667, 0.5176470588235295, 0.321...
2  (0.3333333333333333, 0.6588235294117647, 0.407...

In [ ]:

# data frame showing the jaccard values
comparison

# data frame showing the jaccard values comparison

In [42]:

comparison = grp.compare(method="jaccard", group_by=None)

comparison = grp.compare(method="jaccard", group_by=None)

In [44]:

comparison.group_annotation

comparison.group_annotation

Out[44]:

	n_rxns	n_genes	n_mets	group_name
m1	71	95	72	G1
m2	69	94	72	G1
m3	65	83	67	G1
m4	63	85	69	G2
m5	76	122	72	G2
m6	74	112	72	G2
m7	69	107	69	G3
m8	71	94	70	G3
m9	66	92	70	G3

In [45]:

comparison.plot(annot=True, row_color_by="group_name", col_color_by="group_name")

comparison.plot(annot=True, row_color_by="group_name", col_color_by="group_name")

                                           group_name
m1  (0.2980392156862745, 0.4470588235294118, 0.690...
m2  (0.2980392156862745, 0.4470588235294118, 0.690...
m3  (0.2980392156862745, 0.4470588235294118, 0.690...
m4  (0.8666666666666667, 0.5176470588235295, 0.321...
m5  (0.8666666666666667, 0.5176470588235295, 0.321...
m6  (0.8666666666666667, 0.5176470588235295, 0.321...
m7  (0.3333333333333333, 0.6588235294117647, 0.407...
m8  (0.3333333333333333, 0.6588235294117647, 0.407...
m9  (0.3333333333333333, 0.6588235294117647, 0.407...

In [46]:

comparison = grp.compare(method="PCA", group_by=None, n_components=3)

comparison = grp.compare(method="PCA", group_by=None, n_components=3)

In [47]:

comparison.plot(file_name="PCA.png", prefix="")

comparison.plot(file_name="PCA.png", prefix="")

saving  PCA.png

In [48]:

comparison = grp.compare(group_by="group_name", method="num")

comparison = grp.compare(group_by="group_name", method="num")

In [49]:

comparison.plot(name_order="default")

comparison.plot(name_order="default")