.gitlab-ci.yml

+image: python:3.9-slim-buster
+
+stages:
+    - build
+    - deploy
+
+before_script:
+    - pip install jupyter nbconvert
+
+build_notebooks:
+    stage: build
+    script:
+        - mkdir -p public
+        - jupyter nbconvert --to html notebooks/*.ipynb --output-dir=public
+    artifacts:
+        paths:
+            - public
+        
+pages:
+    stage: deploy
+    script:
+        - echo "Deploying Pages"
+    artifacts:
+        paths:
+            - public
+    only:
+        - master

README.md

-# Scientific Python Course (2022)
+# Scientific Python Course (2024)
 
 1. Jupyter
 2. Numpy

data/bar_data.tsv

-# generated with fooo software version 12bis
-# 2021/02/31
-cond1	cond2	cond3	control
-14.644417316782045	2.9453091400880465	24.81171864537413	5.114340165446571
-12.071043262601615	4.406424332565544	21.574601309211538	2.5071180945299716
-8.22746914709182	3.1852515050248806	20.651622951732826	4.449592659096083
-8.980799267050571	9.233559928496131	24.859737015171184	4.127918884772492
-9.080358618317588	5.6291920085265135	18.443503656148863	4.268572385815164
-7.694230104854875	6.503020711301696	18.642541613874208	3.5498483909671847
-7.599781675347266	0.7177137140372931	18.03202862910317	3.1828829793978084
-11.698701634107119	5.233623279246819	23.233583470920532	4.551872236377363
-12.85118587601311	4.164908508298177	16.777059698760876	4.012697826284397
-10.222007465214453	3.8530606053667746	18.405248139687373	4.903494599422023
-9.035341050392997	9.868423421164657	10.61345524543305	3.117720428019477
-11.834986953457499	4.963792795860263	19.390888914271695	5.255737684774923
-16.631862710777938	5.160719789626399	22.031801649843125	3.458114946454981
-7.022382345586635	5.477115208396248	20.53377003464508	3.891374022263035
-11.985764144031462	3.361795864423141	23.416100873409878	3.828626778977407
-10.59188104130614	3.77317240684574	23.904882625215564	3.1120633325977027
-15.35918866611064	4.036201248179169	17.908928899888497	3.939218000053589
-7.7070476362557825	7.2307829291524195	20.88685879579244	3.021976077990768
-13.369820519999111	1.265825280316033	17.216926090913454	3.3039869741919916
-17.400218295628783	7.277575910141663	13.258902467740844	4.05865692313663

data/blast.txt

-AK1BA_HUMAN	sp|O60218|AK1BA_HUMAN	100.00	316	0	0	1	316	1	316	0.0	 654
-AK1BA_HUMAN	sp|C9JRZ8|AK1BF_HUMAN	91.16	294	26	0	23	316	51	344	0.0	 559
-AK1BA_HUMAN	sp|O08782|ALD2_CRIGR	83.23	316	53	0	1	316	1	316	0.0	 537
-AK1BA_HUMAN	sp|P45377|ALD2_MOUSE	82.28	316	56	0	1	316	1	316	0.0	 527
-AK1BA_HUMAN	sp|P21300|ALD1_MOUSE	79.75	316	64	0	1	316	1	316	0.0	 515
-AK1BA_HUMAN	sp|Q5RJP0|ALD1_RAT	78.16	316	69	0	1	316	1	316	2e-177	 501
-AK1BA_HUMAN	sp|P15122|ALDR_RABIT	72.15	316	88	0	1	316	1	316	1e-162	 462
-AK1BA_HUMAN	sp|P07943|ALDR_RAT	71.11	315	91	0	1	315	1	315	3e-161	 459
-AK1BA_HUMAN	sp|P15121|ALDR_HUMAN	70.57	316	93	0	1	316	1	316	1e-160	 458
-AK1BA_HUMAN	sp|P45376|ALDR_MOUSE	70.48	315	93	0	1	315	1	315	2e-160	 457
-AK1BA_HUMAN	sp|P16116|ALDR_BOVIN	72.12	312	87	0	5	316	4	315	4e-159	 454
-AK1BA_HUMAN	sp|P80276|ALDR_PIG	71.52	316	90	0	1	316	1	316	7e-158	 451
-AK1BA_HUMAN	sp|P82125|AKCL2_PIG	60.00	305	116	1	12	316	3	301	7e-131	 382
-AK1BA_HUMAN	sp|Q4R802|AKCL2_MACFA	54.46	325	123	2	11	316	2	320	2e-119	 353
-AK1BA_HUMAN	sp|Q96JD6|AKCL2_HUMAN	54.46	325	123	3	11	316	2	320	2e-117	 348
-AK1BA_HUMAN	sp|Q9DCT1|AKCL2_MOUSE	56.91	304	125	1	13	316	4	301	4e-117	 347
-AK1BA_HUMAN	sp|Q6AZW2|A1A1A_DANRE	56.04	298	128	2	1	297	1	296	1e-116	 346
-AK1BA_HUMAN	sp|Q5U1Y4|AKCL2_RAT	56.39	305	127	1	12	316	3	301	3e-116	 344
-AK1BA_HUMAN	sp|Q8VCX1|AK1D1_MOUSE	51.90	316	148	2	5	316	10	325	5e-111	 332
-AK1BA_HUMAN	sp|P51857|AK1D1_HUMAN	50.79	317	151	2	5	316	10	326	8e-111	 331
-AK1BA_HUMAN	sp|Q9TV64|AK1D1_RABIT	50.79	317	151	2	5	316	10	326	3e-110	 330
-AK1BA_HUMAN	sp|Q9JII6|AK1A1_MOUSE	50.15	325	150	3	2	316	3	325	1e-108	 326
-AK1BA_HUMAN	sp|P31210|AK1D1_RAT	51.74	317	148	3	5	316	10	326	1e-108	 326
-AK1BA_HUMAN	sp|P51635|AK1A1_RAT	50.15	325	150	3	2	316	3	325	1e-108	 325
-AK1BA_HUMAN	sp|Q5R5D5|AK1A1_PONAB	48.92	325	154	3	2	316	3	325	3e-106	 320
-AK1BA_HUMAN	sp|P14550|AK1A1_HUMAN	48.92	325	154	3	2	316	3	325	4e-106	 319
-AK1BA_HUMAN	sp|P80508|PE2R_RABIT	50.63	316	152	2	5	316	8	323	6e-106	 319
-AK1BA_HUMAN	sp|P50578|AK1A1_PIG	49.54	325	152	3	2	316	3	325	3e-105	 317
-AK1BA_HUMAN	sp|Q5ZK84|AK1A1_CHICK	52.01	323	143	3	4	316	7	327	6e-105	 317
-AK1BA_HUMAN	sp|Q6GMC7|AK1A1_XENLA	51.37	329	145	4	1	316	1	327	3e-104	 315
-AK1BA_HUMAN	sp|Q28FD1|AK1A1_XENTR	51.37	329	145	4	1	316	1	327	3e-103	 312
-AK1BA_HUMAN	sp|P52895|AK1C2_HUMAN	48.73	316	158	2	5	316	8	323	9e-103	 311
-AK1BA_HUMAN	sp|Q3ZCJ2|AK1A1_BOVIN	48.31	325	156	3	2	316	3	325	1e-102	 310
-AK1BA_HUMAN	sp|P52898|DDBX_BOVIN	49.05	316	157	2	5	316	8	323	5e-102	 309
-AK1BA_HUMAN	sp|Q5REQ0|AK1C1_PONAB	48.10	316	160	2	5	316	8	323	6e-102	 308
-AK1BA_HUMAN	sp|P17516|AK1C4_HUMAN	48.10	316	160	2	5	316	8	323	1e-101	 308
-AK1BA_HUMAN	sp|Q04828|AK1C1_HUMAN	48.10	316	160	2	5	316	8	323	1e-101	 308
-AK1BA_HUMAN	sp|Q5R7C9|AK1C3_PONAB	48.42	316	159	2	5	316	8	323	1e-101	 308
-AK1BA_HUMAN	sp|Q1XAA8|AK1CN_HORSE	47.94	315	161	1	5	316	8	322	1e-100	 305
-AK1BA_HUMAN	sp|Q6W8P9|AK1CO_HORSE	48.70	308	154	2	13	316	16	323	1e-100	 305
-AK1BA_HUMAN	sp|P70694|DHB5_MOUSE	48.10	316	160	2	5	316	8	323	3e-100	 304
-AK1BA_HUMAN	sp|Q95JH5|AK1C4_MACFA	47.47	316	162	2	5	316	8	323	3e-100	 304
-AK1BA_HUMAN	sp|P52897|PGFS2_BOVIN	48.38	308	155	2	13	316	16	323	4e-100	 304
-AK1BA_HUMAN	sp|Q95JH4|AK1C4_MACFU	47.15	316	163	2	5	316	8	323	8e-100	 303
-AK1BA_HUMAN	sp|P05980|PGFS1_BOVIN	47.47	316	162	2	5	316	8	323	9e-100	 303
-AK1BA_HUMAN	sp|P42330|AK1C3_HUMAN	47.47	316	162	2	5	316	8	323	9e-100	 303
-AK1BA_HUMAN	sp|Q95JH6|AK1C1_MACFU	47.78	316	161	2	5	316	8	323	1e-99	 303
-AK1BA_HUMAN	sp|Q568L5|A1A1B_DANRE	49.08	326	154	3	1	316	1	324	2e-99	 302
-AK1BA_HUMAN	sp|Q95JH7|AK1C1_MACFA	47.47	316	162	2	5	316	8	323	3e-99	 301
-AK1BA_HUMAN	sp|P17264|CRO_LITCT	47.17	318	164	2	3	316	7	324	2e-98	 300
-AK1BA_HUMAN	sp|P02532|CRO_RANTE	46.54	318	166	2	3	316	7	324	2e-98	 299
-AK1BA_HUMAN	sp|Q8VC28|AK1CD_MOUSE	47.34	319	158	4	5	316	8	323	2e-97	 297
-AK1BA_HUMAN	sp|P51652|AKC1H_RAT	44.65	318	168	4	5	316	8	323	3e-96	 294
-AK1BA_HUMAN	sp|P23457|DIDH_RAT	46.03	315	166	2	5	315	8	322	1e-94	 290
-AK1BA_HUMAN	sp|Q8K023|AKC1H_MOUSE	44.62	316	171	2	5	316	8	323	4e-94	 288
-AK1BA_HUMAN	sp|Q91WR5|AK1CL_MOUSE	44.48	308	167	2	13	316	16	323	4e-90	 278
-AK1BA_HUMAN	sp|P82809|AK1CD_MESAU	43.32	307	170	2	13	315	16	322	2e-85	 266
-AK1BA_HUMAN	sp|Q6AYQ2|AK1CL_RAT	43.71	318	166	5	5	316	8	318	2e-84	 263
-AK1BA_HUMAN	sp|Q54NZ7|ALRB_DICDI	47.10	293	139	5	13	299	17	299	1e-82	 259
-AK1BA_HUMAN	sp|Q6IMN8|ALRA_DICDI	44.11	297	148	5	6	300	6	286	5e-79	 249
-AK1BA_HUMAN	sp|O70473|AK1A1_CRIGR	51.74	230	108	2	15	243	1	228	3e-78	 244
-AK1BA_HUMAN	sp|Q0PGJ6|AKRC9_ARATH	44.33	291	140	4	3	287	6	280	5e-75	 239
-AK1BA_HUMAN	sp|P49378|XYL1_KLULA	42.68	314	159	7	1	297	4	313	2e-70	 228
-AK1BA_HUMAN	sp|Q55FL3|ALRC_DICDI	41.67	300	159	4	6	299	18	307	9e-70	 226
-AK1BA_HUMAN	sp|H9JTG9|AK2E4_BOMMO	39.56	316	169	5	2	311	5	304	2e-69	 224
-AK1BA_HUMAN	sp|Q84TF0|AKRCA_ARATH	41.03	290	149	4	4	287	7	280	5e-69	 224
-AK1BA_HUMAN	sp|P27800|ALDX_SPOSA	43.79	306	156	6	1	301	1	295	2e-68	 222
-AK1BA_HUMAN	sp|Q6Y0Z3|XYL1_CANPA	40.81	321	156	7	5	301	10	320	3e-68	 222
-AK1BA_HUMAN	sp|O80944|AKRC8_ARATH	41.91	303	161	6	4	306	7	294	7e-68	 221
-AK1BA_HUMAN	sp|P22045|PGFS_LEIMA	42.91	296	140	6	3	297	7	274	9e-68	 219
-AK1BA_HUMAN	sp|Q5BGA7|XYL1_EMENI	42.38	302	163	5	5	297	6	305	2e-67	 219
-AK1BA_HUMAN	sp|P14065|GCY1_YEAST	41.89	296	150	6	4	291	11	292	6e-67	 218
-AK1BA_HUMAN	sp|Q10494|YDG7_SCHPO	43.06	288	153	5	7	292	18	296	3e-66	 217
-AK1BA_HUMAN	sp|Q9GV41|PGFS_TRYBB	41.84	294	134	5	5	297	7	264	3e-66	 215
-AK1BA_HUMAN	sp|O13283|XYL1_CANTR	40.75	319	159	7	5	301	10	320	4e-66	 216
-AK1BA_HUMAN	sp|P87039|XYL2_CANTR	40.75	319	159	7	5	301	10	320	5e-66	 216
-AK1BA_HUMAN	sp|Q4DJ07|PGFS_TRYCC	40.20	296	139	7	5	297	8	268	1e-65	 214
-AK1BA_HUMAN	sp|O94735|XYL1_PICGU	40.89	313	157	7	5	296	3	308	5e-65	 213
-AK1BA_HUMAN	sp|P38715|GRE3_YEAST	40.38	317	163	7	1	297	1	311	2e-64	 212
-AK1BA_HUMAN	sp|A1D4E3|XYL1_NEOFI	40.62	320	171	6	5	311	6	319	3e-64	 212
-AK1BA_HUMAN	sp|P78736|XYL1_PACTA	41.75	309	156	7	4	297	5	304	3e-64	 211
-AK1BA_HUMAN	sp|Q12458|YPR1_YEAST	41.95	298	149	8	5	294	12	293	5e-64	 211
-AK1BA_HUMAN	sp|A0QV10|Y2408_MYCS2	40.07	297	144	5	1	297	1	263	1e-63	 209
-AK1BA_HUMAN	sp|Q9M338|AKRCB_ARATH	41.46	287	146	4	4	284	7	277	1e-63	 209
-AK1BA_HUMAN	sp|P28475|S6PD_MALDO	37.70	313	164	6	1	298	1	297	3e-63	 209
-AK1BA_HUMAN	sp|Q9P430|XYL1_SCHSH	40.38	312	168	5	6	301	10	319	5e-62	 206
-AK1BA_HUMAN	sp|A1CRI1|XYL1_ASPCL	40.52	306	163	4	5	297	6	305	8e-62	 206
-AK1BA_HUMAN	sp|Q4WJT9|XYL1_ASPFU	40.20	306	164	6	5	297	6	305	1e-61	 205
-AK1BA_HUMAN	sp|B0XNR0|XYL1_ASPFC	40.20	306	164	6	5	297	6	305	1e-61	 205
-AK1BA_HUMAN	sp|Q3ZFI7|GAR1_HYPJE	39.80	299	160	9	1	295	2	284	2e-61	 204
-AK1BA_HUMAN	sp|Q9P8R5|XYL1_ASPNG	39.40	302	172	4	5	297	6	305	2e-61	 204
-AK1BA_HUMAN	sp|A2Q8B5|XYL1_ASPNC	39.40	302	172	4	5	297	6	305	2e-61	 204
-AK1BA_HUMAN	sp|Q2UKD0|XYL1_ASPOR	40.20	306	164	4	5	297	6	305	4e-61	 203
-AK1BA_HUMAN	sp|B8N195|XYL1_ASPFN	40.20	306	164	4	5	297	6	305	4e-61	 203
-AK1BA_HUMAN	sp|C5FFQ7|XYL1_ARTOC	39.94	308	174	3	2	300	10	315	9e-61	 202
-AK1BA_HUMAN	sp|O74237|XYL1_CANTE	39.62	313	171	5	5	301	8	318	1e-60	 202
-AK1BA_HUMAN	sp|P31867|XYL1_PICST	40.26	308	162	6	5	297	4	304	2e-60	 202
-AK1BA_HUMAN	sp|Q01213|DTDH_MUCMU	39.93	298	168	4	9	297	11	306	2e-60	 201
-AK1BA_HUMAN	sp|Q8X195|XYL1_CANBO	39.87	311	165	7	4	296	5	311	2e-59	 199
-AK1BA_HUMAN	sp|Q0GYU4|GLD2_HYPJE	39.31	290	163	7	7	289	8	291	2e-59	 199
-AK1BA_HUMAN	sp|P23901|ALDR_HORVU	40.00	290	151	7	7	292	18	288	2e-59	 199
-AK1BA_HUMAN	sp|Q876L8|XYL1_HYPJE	39.34	305	170	6	5	297	6	307	6e-59	 198
-AK1BA_HUMAN	sp|O42888|YBN4_SCHPO	38.89	288	165	5	4	289	14	292	3e-58	 196
-AK1BA_HUMAN	sp|Q0CUL0|XYL1_ASPTN	39.16	309	173	6	1	297	1	306	5e-58	 195
-AK1BA_HUMAN	sp|Q46857|DKGA_ECOLI	35.93	295	155	6	3	297	5	265	7e-58	 193
-AK1BA_HUMAN	sp|G4N708|XYL1_MAGO7	39.34	305	170	6	5	297	6	307	2e-57	 194
-AK1BA_HUMAN	sp|O34678|YTBE_BACSU	41.10	292	139	5	7	297	11	270	4e-57	 192
-AK1BA_HUMAN	sp|Q8XBT6|DKGA_ECO57	35.59	295	156	6	3	297	5	265	4e-57	 191
-AK1BA_HUMAN	sp|Q8ZI40|DKGA_YERPE	35.84	293	154	6	1	293	3	261	2e-56	 190
-AK1BA_HUMAN	sp|Q8SSK6|ALDR_ENCCU	37.88	293	170	5	6	297	7	288	2e-56	 191
-AK1BA_HUMAN	sp|P38115|ARA1_YEAST	36.81	307	166	8	4	296	24	316	2e-56	 192
-AK1BA_HUMAN	sp|G4MZI3|PRD1_MAGO7	37.05	305	171	6	3	289	4	305	3e-56	 191
-AK1BA_HUMAN	sp|P26690|6DCS_SOYBN	38.28	303	160	5	3	295	11	296	3e-56	 190
-AK1BA_HUMAN	sp|O32210|GR_BACSU	40.82	294	137	5	5	297	9	266	5e-56	 189
-AK1BA_HUMAN	sp|Q9SQ64|COR2_PAPSO	38.05	297	156	6	5	289	9	289	6e-56	 190
-AK1BA_HUMAN	sp|A1UEC6|Y1985_MYCSK	37.50	296	151	4	2	297	3	264	5e-55	 186
-AK1BA_HUMAN	sp|A3PXT0|Y1919_MYCSJ	37.80	291	147	4	2	292	3	259	6e-55	 186
-AK1BA_HUMAN	sp|O14088|YER5_SCHPO	33.66	303	164	5	1	299	2	271	1e-54	 185
-AK1BA_HUMAN	sp|O49133|GALUR_FRAAN	37.77	278	161	6	13	286	19	288	2e-54	 186
-AK1BA_HUMAN	sp|Q9SQ67|COR14_PAPSO	36.91	298	160	7	4	289	8	289	3e-53	 182
-AK1BA_HUMAN	sp|Q9SQ69|COR12_PAPSO	37.46	299	157	7	4	289	8	289	5e-53	 182
-AK1BA_HUMAN	sp|Q8ZM06|DKGA_SALTY	37.63	295	150	6	3	297	5	265	6e-52	 178
-AK1BA_HUMAN	sp|P58744|DKGA_SALTI	37.63	295	150	6	3	297	5	265	8e-52	 177
-AK1BA_HUMAN	sp|Q0GYU5|GLD1_HYPJE	40.14	294	157	7	5	289	7	290	9e-52	 179
-AK1BA_HUMAN	sp|P47137|YJ66_YEAST	34.97	286	155	4	4	286	5	262	3e-51	 176
-AK1BA_HUMAN	sp|Q02198|MORA_PSEPU	36.33	289	157	6	4	289	7	271	1e-50	 175
-AK1BA_HUMAN	sp|A1T726|Y2161_MYCVP	34.35	294	157	5	5	297	10	268	6e-50	 173
-AK1BA_HUMAN	sp|Q7G765|NADO2_ORYSJ	34.35	294	175	7	1	285	3	287	1e-48	 171
-AK1BA_HUMAN	sp|Q7G764|NADO1_ORYSJ	33.89	298	179	7	1	289	1	289	1e-48	 171
-AK1BA_HUMAN	sp|A4TE41|Y4205_MYCGI	33.45	293	161	4	5	297	10	268	4e-48	 168
-AK1BA_HUMAN	sp|A1UEC5|Y1984_MYCSK	32.42	293	164	5	5	297	14	272	8e-48	 167
-AK1BA_HUMAN	sp|Q1BAN7|Y1938_MYCSS	32.42	293	164	5	5	297	14	272	8e-48	 167
-AK1BA_HUMAN	sp|A3PXS9|Y1918_MYCSJ	32.42	293	164	5	5	297	14	272	8e-48	 167
-AK1BA_HUMAN	sp|Q9C1X5|YKW2_SCHPO	34.11	299	162	7	2	298	8	273	8e-47	 165
-AK1BA_HUMAN	sp|A0QV09|Y2407_MYCS2	31.97	294	164	5	5	297	14	272	2e-45	 161
-AK1BA_HUMAN	sp|Q9SQ68|COR13_PAPSO	36.58	298	161	7	4	289	8	289	1e-44	 160
-AK1BA_HUMAN	sp|Q9SQ70|COR11_PAPSO	36.54	301	157	8	4	289	8	289	9e-44	 158
-AK1BA_HUMAN	sp|Q09632|YOF5_CAEEL	35.67	314	166	10	7	314	7	290	1e-43	 158
-AK1BA_HUMAN	sp|E7C196|MER_ERYCB	37.67	300	152	6	5	285	8	291	1e-43	 158
-AK1BA_HUMAN	sp|B9VRJ2|COR15_PAPSO	36.75	302	155	8	4	289	8	289	2e-43	 157
-AK1BA_HUMAN	sp|A5U6Y1|Y2999_MYCTA	33.78	296	156	8	5	297	13	271	4e-43	 155
-AK1BA_HUMAN	sp|P9WQA5|Y2971_MYCTU	33.78	296	156	8	5	297	13	271	4e-43	 155
-AK1BA_HUMAN	sp|P9WQA4|Y2971_MYCTO	33.78	296	156	8	5	297	13	271	4e-43	 155
-AK1BA_HUMAN	sp|Q7TXI6|Y2996_MYCBO	33.78	296	156	8	5	297	13	271	4e-43	 155
-AK1BA_HUMAN	sp|A1KMW6|Y2993_MYCBP	33.78	296	156	8	5	297	13	271	4e-43	 155
-AK1BA_HUMAN	sp|P06632|DKGA_CORSC	32.40	287	162	5	7	293	8	262	3e-42	 153
-AK1BA_HUMAN	sp|A0QL30|Y4483_MYCA1	34.47	293	158	5	5	297	17	275	4e-39	 144
-AK1BA_HUMAN	sp|Q76L36|CPRC2_CANPA	32.89	301	165	10	3	289	10	287	2e-38	 143
-AK1BA_HUMAN	sp|Q73SC5|Y4149_MYCPA	33.79	293	160	5	5	297	17	275	3e-37	 140
-AK1BA_HUMAN	sp|Q8ZH36|DKGB_YERPE	31.14	289	161	7	12	297	2	255	5e-37	 138
-AK1BA_HUMAN	sp|Q73VK6|Y3007_MYCPA	30.27	294	169	6	5	297	12	270	3e-36	 137
-AK1BA_HUMAN	sp|A0QJ99|Y3816_MYCA1	30.27	294	169	6	5	297	15	273	4e-36	 137
-AK1BA_HUMAN	sp|A0PQ11|Y1987_MYCUA	29.25	294	172	6	5	297	13	271	5e-36	 136
-AK1BA_HUMAN	sp|B2HIJ9|Y1744_MYCMM	29.25	294	172	6	5	297	12	270	5e-36	 136
-AK1BA_HUMAN	sp|P15339|DKGB_CORSS	32.26	279	156	5	17	295	19	264	3e-34	 131
-AK1BA_HUMAN	sp|Q8X7Z7|DKGB_ECO57	30.88	285	165	6	13	297	3	255	5e-34	 130
-AK1BA_HUMAN	sp|Q8ZRM7|DKGB_SALTY	30.53	285	166	6	13	297	3	255	6e-34	 130
-AK1BA_HUMAN	sp|Q8Z988|DKGB_SALTI	30.18	285	167	6	13	297	3	255	1e-33	 129
-AK1BA_HUMAN	sp|P30863|DKGB_ECOLI	30.18	285	167	6	13	297	3	255	3e-33	 128
-AK1BA_HUMAN	sp|O69462|Y1669_MYCLE	28.27	283	167	6	5	286	13	260	2e-30	 121
-AK1BA_HUMAN	sp|B8ZS00|Y1669_MYCLB	28.27	283	167	6	5	286	13	260	2e-30	 121
-AK1BA_HUMAN	sp|Q5T2L2|AKCL1_HUMAN	49.57	117	56	1	5	118	11	127	3e-30	 116
-AK1BA_HUMAN	sp|O13848|I3ACR_SCHPO	31.60	288	159	9	7	286	6	263	2e-29	 118
-AK1BA_HUMAN	sp|P76234|YEAE_ECOLI	30.30	297	163	8	4	289	5	268	3e-29	 117
-AK1BA_HUMAN	sp|Q76L37|CPRC1_CANPA	27.18	309	167	9	6	289	9	284	1e-28	 116
-AK1BA_HUMAN	sp|Q07551|KAR_YEAST	29.04	303	173	10	4	289	7	284	1e-25	 108
-AK1BA_HUMAN	sp|Q9USV2|YHH5_SCHPO	30.20	255	142	8	35	286	33	254	2e-23	 101
-AK1BA_HUMAN	sp|P46905|YCCK_BACSU	25.08	299	154	10	29	289	39	305	1e-17	85.1
-AK1BA_HUMAN	sp|Q94A68|Y1669_ARATH	24.08	299	176	9	25	292	84	362	7e-15	77.8
-AK1BA_HUMAN	sp|P82810|MORA_RABIT	31.18	170	45	5	117	286	27	124	9e-13	68.2
-AK1BA_HUMAN	sp|P46336|IOLS_BACSU	25.42	295	159	10	29	289	38	305	3e-12	69.7
-AK1BA_HUMAN	sp|P80874|GS69_BACSU	29.36	218	107	9	16	213	16	206	3e-11	67.0
-AK1BA_HUMAN	sp|Q56Y42|PLR1_ARATH	23.00	313	178	10	16	285	50	342	6e-09	60.1
-AK1BA_HUMAN	sp|P25906|YDBC_ECOLI	23.75	299	181	11	11	294	19	285	6e-09	59.7
-AK1BA_HUMAN	sp|C6TBN2|AKR1_SOYBN	25.32	316	178	13	9	290	19	310	6e-08	57.0
-AK1BA_HUMAN	sp|P49261|CROB_LEPLU	45.90	61	20	1	95	155	15	62	1e-06	50.1

data/fr_sp_it_temp.tsv

-	City	Year	Tmp	std
-0	Barcelona	1995	62.01917808219179	9.569756297123327
-1	Barcelona	1996	61.12595628415301	9.420764506001397
-2	Barcelona	1997	62.61232876712331	9.827234879971101
-3	Barcelona	1998	60.2739726027397	19.75012607691891
-4	Barcelona	1999	61.20465753424656	13.904525518554435
-5	Barcelona	2000	60.069398907103846	9.099817440252128
-6	Barcelona	2001	59.27945205479454	10.523427017015313
-7	Barcelona	2002	58.044109589041135	18.929773327478483
-8	Barcelona	2003	63.13945205479458	15.153889346840758
-9	Barcelona	2004	62.87513661202182	11.071518902264671
-10	Barcelona	2005	62.041917808219225	12.211059284082598
-11	Barcelona	2006	61.9854794520548	10.383211438819417
-12	Barcelona	2007	60.556164383561644	13.128345584082622
-13	Barcelona	2008	59.65191256830599	15.614665889880865
-14	Barcelona	2009	61.552054794520586	13.90370839128742
-15	Barcelona	2010	60.66794520547943	11.479319072602506
-16	Barcelona	2011	62.83479452054788	10.102326479631735
-17	Barcelona	2012	63.03224043715851	11.292237552376044
-18	Barcelona	2013	62.24657534246573	10.973485554623132
-19	Barcelona	2014	62.43726027397264	13.027373258325785
-20	Barcelona	2015	61.795081967213115	16.233531266810775
-21	Barcelona	2016	60.80081967213118	19.506541913124646
-22	Barcelona	2017	62.51479452054798	11.352460171672947
-23	Barcelona	2018	61.391506849315086	18.513200196464584
-24	Barcelona	2019	59.71917808219182	23.21768377728357
-25	Barcelona	2020	55.43731343283583	5.669011817215402
-26	Bilbao	1995	58.94547945205482	9.152938194122601
-27	Bilbao	1996	57.40928961748634	8.299521097803076
-28	Bilbao	1997	59.65315068493152	8.59646759755442
-29	Bilbao	1998	56.50794520547946	18.5087198753874
-30	Bilbao	1999	57.86356164383562	13.254929144303365
-31	Bilbao	2000	58.17704918032782	9.61060590126113
-32	Bilbao	2001	59.258082191780844	10.785430834692193
-33	Bilbao	2002	59.06191780821921	18.311794423082254
-34	Bilbao	2003	61.98136986301364	11.109533738036083
-35	Bilbao	2004	60.1620218579235	10.887947603849794
-36	Bilbao	2005	60.47123287671232	12.180590300525083
-37	Bilbao	2006	60.931506849315106	10.959762529415924
-38	Bilbao	2007	57.378630136986295	12.556410441414583
-39	Bilbao	2008	57.65546448087436	14.55881450004361
-40	Bilbao	2009	58.33150684931509	12.957812746937886
-41	Bilbao	2010	57.31945205479447	10.92548335370651
-42	Bilbao	2011	60.064931506849376	9.37413895288166
-43	Bilbao	2012	58.18196721311478	10.896172632501663
-44	Bilbao	2013	58.078630136986355	10.692211568623678
-45	Bilbao	2014	60.67041095890412	12.315738661838056
-46	Bilbao	2015	59.06010928961746	14.845431712407324
-47	Bilbao	2016	57.561748633879795	18.627752493559164
-48	Bilbao	2017	58.831780821917825	9.579137034485704
-49	Bilbao	2018	58.11095890410959	17.318878742319427
-50	Bilbao	2019	57.03232876712337	20.566454771536044
-51	Bilbao	2020	55.114925373134334	7.012797190171634
-52	Bordeaux	1995	57.370136986301354	11.044040769557341
-53	Bordeaux	1996	55.93497267759563	10.784892064762007
-54	Bordeaux	1997	58.16931506849312	11.011332365506272
-55	Bordeaux	1998	54.70493150684932	19.598395813702982
-56	Bordeaux	1999	56.77917808219176	13.852986125645279
-57	Bordeaux	2000	57.19071038251365	10.422852712768039
-58	Bordeaux	2001	56.48027397260274	11.773260583198585
-59	Bordeaux	2002	55.39232876712332	18.522743557653765
-60	Bordeaux	2003	58.219452054794544	12.897343185222015
-61	Bordeaux	2004	56.27841530054645	11.751133956460096
-62	Bordeaux	2005	56.29835616438355	13.255407253879639
-63	Bordeaux	2006	57.610410958904126	12.919541851158483
-64	Bordeaux	2007	55.84027397260273	13.31117414308708
-65	Bordeaux	2008	55.45765027322398	12.808973051265317
-66	Bordeaux	2009	56.567671232876755	14.223581499075609
-67	Bordeaux	2010	55.150136986301355	12.747677281767228
-68	Bordeaux	2011	58.351506849315086	10.258827834959664
-69	Bordeaux	2012	56.359016393442666	12.014168034051115
-70	Bordeaux	2013	55.9295890410959	11.872014423803591
-71	Bordeaux	2014	56.46931506849318	12.483597626262037
-72	Bordeaux	2015	56.23169398907107	15.68360389372104
-73	Bordeaux	2016	55.93715846994539	15.626511950502655
-74	Bordeaux	2017	56.857260273972656	11.833635389820595
-75	Bordeaux	2018	57.26931506849317	14.359770607228285
-76	Bordeaux	2019	55.98712328767125	19.774109604678415
-77	Bordeaux	2020	52.31268656716417	6.887914900422134
-78	Madrid	1995	60.57424657534247	12.999120629367042
-79	Madrid	1996	58.82896174863385	12.834437087673026
-80	Madrid	1997	60.21095890410956	12.277039698083023
-81	Madrid	1998	56.99123287671236	21.365555361221524
-82	Madrid	1999	57.7158904109589	16.28990685923228
-83	Madrid	2000	57.804371584699425	13.537680848457656
-84	Madrid	2001	57.30356164383564	14.361118272929666
-85	Madrid	2002	56.16986301369865	20.734617527028856
-86	Madrid	2003	58.95178082191778	14.6895054246955
-87	Madrid	2004	57.572677595628384	14.328101211175488
-88	Madrid	2005	58.578630136986256	15.897967362219234
-89	Madrid	2006	59.54575342465752	14.645098748612648
-90	Madrid	2007	56.18767123287673	15.54338968721232
-91	Madrid	2008	56.802459016393456	17.454694998719265
-92	Madrid	2009	59.486849315068476	16.846571444704047
-93	Madrid	2010	58.35260273972605	14.880927577599886
-94	Madrid	2011	60.0972602739726	14.08172547926141
-95	Madrid	2012	59.22103825136618	15.704333928077313
-96	Madrid	2013	58.5898630136986	15.061813341527952
-97	Madrid	2014	60.00684931506852	15.530396717043965
-98	Madrid	2015	59.7360655737705	19.216008162034054
-99	Madrid	2016	58.30437158469944	22.135516183890996
-100	Madrid	2017	60.827123287671206	15.335287673221936
-101	Madrid	2018	57.8893150684931	20.697793342645358
-102	Madrid	2019	58.01808219178085	23.572917074236493
-103	Madrid	2020	50.94477611940298	8.01996162933257
-104	Milan	1995	51.81013698630133	17.66463472693756
-105	Milan	1996	52.536338797814224	13.116517527084948
-106	Milan	1997	54.71835616438357	13.622209061233912
-107	Milan	1998	52.61287671232878	21.59552695083426
-108	Milan	1999	54.47424657534245	16.44477420566477
-109	Milan	2000	55.345355191256836	13.650382814023335
-110	Milan	2001	54.585479452054756	14.728662443197207
-111	Milan	2002	52.71205479452063	20.805079615305182
-112	Milan	2003	55.6578082191781	16.522557860689197
-113	Milan	2004	53.69234972677599	14.1368864235138
-114	Milan	2005	53.44958904109592	15.527562558446604
-115	Milan	2006	54.07835616438361	15.096444437450113
-116	Milan	2007	54.0361643835616	15.97246623271815
-117	Milan	2008	53.66912568306015	18.110549496608105
-118	Milan	2009	54.2654794520548	17.799552797282026
-119	Milan	2010	53.26958904109586	15.442853928856914
-120	Milan	2011	55.66027397260276	14.933169251059093
-121	Milan	2012	55.05191256830602	16.075172183078543
-122	Milan	2013	54.34520547945205	14.6871237720894
-123	Milan	2014	55.45205479452057	14.366357503176557
-124	Milan	2015	55.02267759562845	18.553332365155743
-125	Milan	2016	54.121584699453535	21.339925888669406
-126	Milan	2017	56.38410958904112	15.184990043635842
-127	Milan	2018	55.711780821917834	22.21152305528249
-128	Milan	2019	54.58164383561645	24.353680984815433
-129	Milan	2020	47.80373134328358	9.138676025892822
-130	Paris	1995	53.742191780821955	20.406326470437165
-131	Paris	1996	52.293169398907125	15.207324562142714
-132	Paris	1997	55.57999999999997	12.745184826582006
-133	Paris	1998	50.31753424657538	27.794294802597282
-134	Paris	1999	54.565753424657565	13.99020869517814
-135	Paris	2000	54.33770491803271	10.34568531230199
-136	Paris	2001	53.94493150684927	12.074808387359592
-137	Paris	2002	52.743013698630136	18.72207478617854
-138	Paris	2003	54.56219178082192	13.721165395048772
-139	Paris	2004	53.58524590163939	11.761756034192336
-140	Paris	2005	53.40767123287677	14.98337160011056
-141	Paris	2006	54.19972602739725	13.030265906537082
-142	Paris	2007	53.57205479452048	12.96249396864273
-143	Paris	2008	52.38169398907109	15.655254039470176
-144	Paris	2009	53.061095890410954	14.640168245899794
-145	Paris	2010	51.64821917808218	13.601742307439258
-146	Paris	2011	55.00191780821926	10.339225513741106
-147	Paris	2012	53.256557377049155	11.453752063627668
-148	Paris	2013	52.08849315068489	14.922240911540998
-149	Paris	2014	53.65041095890415	11.968850647395499
-150	Paris	2015	53.43497267759561	15.680118495299984
-151	Paris	2016	51.122950819672184	21.198085156034864
-152	Paris	2017	54.36794520547941	11.949198207377224
-153	Paris	2018	45.7731506849315	36.811172339412884
-154	Paris	2019	52.20821917808223	22.72281465588924
-155	Paris	2020	49.32014925373133	7.458857376369018
-156	Rome	1995	59.67780821917805	10.85141534377517
-157	Rome	1996	59.125956284152984	10.481723613267679
-158	Rome	1997	60.45260273972603	10.581853899170923
-159	Rome	1998	58.78575342465755	20.341707676662004
-160	Rome	1999	60.2827397260274	14.662355669944747
-161	Rome	2000	60.301366120218574	13.68558578952963
-162	Rome	2001	60.5652054794521	10.709699325458592
-163	Rome	2002	58.55150684931504	19.576252129773003
-164	Rome	2003	61.05999999999997	12.978538497784012
-165	Rome	2004	59.942896174863485	11.47532391640649
-166	Rome	2005	59.00958904109588	12.478535024958282
-167	Rome	2006	60.395890410958906	11.92679638779288
-168	Rome	2007	60.57671232876712	13.676997217875977
-169	Rome	2008	60.20819672131149	16.17226743532121
-170	Rome	2009	61.091506849315024	14.458681531324565
-171	Rome	2010	60.28109589041099	11.563938305153439
-172	Rome	2011	60.90219178082182	11.206865812382413
-173	Rome	2012	61.075136612021915	12.547324524416043
-174	Rome	2013	61.049315068493144	11.625454301505501
-175	Rome	2014	61.882465753424654	12.959953934766476
-176	Rome	2015	60.57213114754101	16.531134240619526
-177	Rome	2016	61.185245901639334	15.914192883864587
-178	Rome	2017	61.3778082191781	11.916595484036199
-179	Rome	2018	60.82136986301364	20.327931936122738
-180	Rome	2019	59.215068493150675	23.514064479810376
-181	Rome	2020	52.67611940298508	6.224293650229102

notebooks/scipy_material.py → notebooks/Courses/scipy_material.py

@@ -28,7 +28,7 @@ def illustration_confidence_interval(m=46, s=1, alpha=0.05):
     plt.axhline(0, color='k', linestyle=':', linewidth=1)
     plt.xlabel('population mean')
     plt.ylabel('probability density')
-    plt.axvline(m, color='k', linestyle=':', linewidth=1, label='sample mean')
+    plt.axvline(m, color='g', linestyle=':', linewidth=1, label='sample mean')
 
     u = stats.norm().isf(alpha / 2)
     ci_low = m - u * s
@@ -40,14 +40,14 @@ def illustration_confidence_interval(m=46, s=1, alpha=0.05):
 
     ml = (grid[0]+4*ci_low)/5
     pl = (2*pdf(ci_low)+pdf(m))/3
-    plt.annotate(f'${alpha/2*100}\%$',
+    plt.annotate(f'${alpha/2*100}\\%$',
         [ml, .1*pdf(ml)], [ml, pl],
         arrowprops=dict(arrowstyle="->"),
         horizontalalignment='center')
 
     ml1 = (4*m+ci_high)/5
     ml2 = (m+ci_high)/2
-    plt.annotate(f'${(1-alpha)*100:.0f}\%$ prob. mass',
+    plt.annotate(f'${(1-alpha)*100:.0f}\\%$ prob. mass',
         [ml1, pl], [ml2, (pdf(ml2)+pdf(m))/2],
         arrowprops=dict(arrowstyle="->"))
 
@@ -56,7 +56,7 @@ def illustration_confidence_interval(m=46, s=1, alpha=0.05):
         width=line_width, head_length=head_length, linestyle='none')
     t = plt.arrow(ci_high-head_length, height, ci_low-ci_high+2*head_length, 0,
         width=line_width, head_length=head_length, linestyle='none')
-    plt.text(m, height+line_width, f'${(1-alpha)*100:.0f}\%$ confidence interval',
+    plt.text(m, height+line_width, f'${(1-alpha)*100:.0f}\\%$ confidence interval',
         ha='center')
 
     plt.legend(loc='upper left')
@@ -76,7 +76,7 @@ def illustration_onesided_probabilitymass(z, N = stats.norm(0, 1), onesided_pval
     plt.xlim(grid[[0,-1]])
     plt.xlabel('$X$')
     plt.ylabel('probability density')
-    plt.legend([pdf_curve, z_line], ['$\mathcal{N}(0,1)$', '$z$'])
+    plt.legend([pdf_curve, z_line], [r'$\mathcal{N}(0,1)$', '$z$'])
     plt.annotate(f'$\\approx {onesided_pvalue:.2f}$', (1.8, .03), xytext=(2, .13), arrowprops=dict(arrowstyle="->"))
 
 def illustration_t_pdfs():
@@ -121,7 +121,7 @@ def illustration_skewness_kurtosis():
 
     ax = axes[0]
     for sigma, color in zip((.25, .5, 1), colors):
-        ax.plot(grid, skewed_dist(sigma, grid), '-', color=color, label=f'$\sigma={sigma:.2f}$')
+        ax.plot(grid, skewed_dist(sigma, grid), '-', color=color, label=f'$\\sigma={sigma:.2f}$')
 
     ax.axhline(0, linestyle='--', color='grey', linewidth=1)
     ax.set_xlim(grid[[0,-1]])
@@ -159,7 +159,7 @@ def illustration_chi2():
 
     ax.axhline(0, linestyle='--', color='grey', linewidth=1)
     ax.set_xlim(grid[0],grid[-1])
-    ax.set_xlabel('$\chi^2$')
+    ax.set_xlabel(r'$\chi^2$')
     ax.set_ylabel('probability density')
     ax.legend([ f'$df={df}$' for df in dfs ])
 
@@ -169,7 +169,7 @@ def illustration_chi2():
     ax.plot(grid, chi2, '-', color=color)
     ax.axhline(0, linestyle='--', color='grey', linewidth=1)
     ax.set_xlim(grid[0],grid[-1])
-    ax.set_xlabel('$\chi^2$')
+    ax.set_xlabel(r'$\chi^2$')
     ax.set_ylabel('probability density');
 
     A = [85, 86, 88, 75, 78, 94, 98, 79, 71, 80]

notebooks/Courses/seaborn_cours.ipynb

+%% Cell type:markdown id:vocal-argument tags:
+
+# <center><b>Course</b></center>
+
+<div style="text-align:center">
+    <img src="../images/seaborn.png" width="600px">
+    <div>
+       Bertrand Néron, François Laurent, Etienne Kornobis
+       <br />
+       <a src=" https://research.pasteur.fr/en/team/bioinformatics-and-biostatistics-hub/">Bioinformatics and Biostatistiqucs HUB</a>
+       <br />
+       © Institut Pasteur, 2021
+    </div>
+</div>
+
+%% Cell type:markdown id:minute-stylus tags:
+
+# A glimpse at Seaborn
+
+%% Cell type:markdown id:monthly-royalty tags:
+
+Seaborn is a Python data visualization library based on **Matplotlib**. It provides a high-level interface for drawing attractive and informative statistical graphics while still being able to use matplotlib features.
+
+It is organized depending on the type of data you want to represent:
+
+%% Cell type:markdown id:suffering-attendance tags:
+
+<img src="../images/seaborn_plots.png" width="600px">
+
+%% Cell type:markdown id:legislative-currency tags:
+
+You can use the `relplot`, `displot`, `catplot` group functions or directly call the function corresponding to a specific plot.
+
+%% Cell type:code id:apparent-logan tags:
+
+``` python
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+```
+
+%% Cell type:code id:3c569d0b-7cdc-4d33-b42b-67fd34bdf4ca tags:
+
+``` python
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning, lineno=1498)
+warnings.simplefilter(action='ignore', category=UserWarning, lineno=118)
+```
+
+%% Cell type:markdown id:99f2823d-fbc0-44ff-958e-9ef35297307d tags:
+
+## Simple plot with 2 variables (relplot)
+
+%% Cell type:code id:96dbb85d-f996-4c73-9d51-da2699539ca0 tags:
+
+``` python
+temp = pd.read_csv("../data/fr_sp_it_temp.tsv", sep="\t", header=0, index_col=0)
+temp.head()
+```
+
+%% Cell type:code id:a199f0f2-4f6d-4a1d-bbc7-bd47e826d3d2 tags:
+
+``` python
+paris = temp[temp.City == 'Paris']
+paris.head()
+```
+
+%% Cell type:code id:e3985842-23f3-4ce8-ab5e-5cbd1d9ec2ee tags:
+
+``` python
+sns.relplot(data=paris, x="Year", y="Tmp")
+```
+
+%% Cell type:code id:7ec21722-b094-4052-bb99-0f9306684f8a tags:
+
+``` python
+sns.lineplot(data=paris, x="Year", y="Tmp")
+```
+
+%% Cell type:code id:885b9d4b-5543-40fb-a878-c171f68c60c9 tags:
+
+``` python
+sns.lineplot(data=paris, x="Year", y="Tmp", marker="o")
+```
+
+%% Cell type:code id:49c86b68-18d9-48ef-9812-fa114a54dafb tags:
+
+``` python
+sns.lineplot(data=paris, x="Year", y="Tmp", marker="o", linestyle="--", color="green")
+```
+
+%% Cell type:markdown id:d7039e35-0b13-4af8-b290-4533e4011d45 tags:
+
+Seaborn is using matplotlib under the hood, so all available linestyles, markers and colors are described in https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html
+
+%% Cell type:markdown id:b6c2f491-83b6-47d1-a01a-0c95551a0abd tags:
+
+## Several plots
+
+%% Cell type:code id:9ba40313-00ec-4af5-835f-53e8e8c8a3dd tags:
+
+``` python
+sns.lineplot(data=temp, x="Year", y="Tmp", hue="City")
+```
+
+%% Cell type:markdown id:8b1a4d48-39fe-4808-8d27-6fba456ada0f tags:
+
+## Formatting
+
+%% Cell type:markdown id:510057ce-aa3a-4ab2-9d60-fe1e4e901c4c tags:
+
+### Annotations
+
+%% Cell type:markdown id:5049f6ef-aff4-495e-9660-966ee4a96e13 tags:
+
+In order to pretty format our graph, we can use matplotlib.pyplot (here `plt`) functionalities to control the legend, add title and x and y labels:
+
+%% Cell type:code id:fd32ce6a-0d94-4e62-a09f-defb568cf00c tags:
+
+``` python
+sns.lineplot(data=temp, x="Year", y="Tmp", hue="City")
+
+plt.legend(ncol=2)
+plt.xlabel("Year")
+plt.ylabel("Tp in °F")
+plt.title("Average Temperature")
+```
+
+%% Cell type:markdown id:2691f293-228f-4670-b92b-81e0e1ac92e4 tags:
+
+### xlim, ylim
+
+%% Cell type:code id:b5a7a48d-fed8-4cac-86b3-7299ade9b3ac tags:
+
+``` python
+sns.lineplot(data=temp, x="Year", y="Tmp", hue="City")
+plt.legend(ncol=2)
+plt.xlabel("Year")
+plt.ylabel("Tp in °F")
+plt.title("Average Temperature")
+
+plt.xlim([2000,2010])
+plt.ylim([50,70])
+```
+
+%% Cell type:markdown id:bd7a6c33-6d57-4d92-a03b-a81b66ce6f4a tags:
+
+### axvline, axhline
+
+%% Cell type:code id:c35ab008-cb2b-4faa-b86a-b136fc349d5b tags:
+
+``` python
+sns.lineplot(data=temp, x="Year", y="Tmp", hue="City")
+plt.legend(ncol=2)
+plt.xlabel("Year")
+plt.ylabel("Tp in °F")
+plt.title("Average Temperature")
+
+# Adding horizontal and vertical lines with an alpha parameter
+plt.axvline(2008, color="grey", linestyle="--", alpha=0.5)
+plt.axhline(55, color="grey", linestyle="-.", alpha=0.5)
+```
+
+%% Cell type:markdown id:ecological-memphis tags:
+
+## Distribution plots
+### Histograms and KDE plots
+
+A histogram is displaying a frequency distribution of a **continuous** dataset using bars.
+
+%% Cell type:code id:smooth-persian tags:
+
+``` python
+df = pd.read_csv("../data/titanic.csv")
+```
+
+%% Cell type:code id:brazilian-greene tags:
+
+``` python
+sns.displot(data=df, x="Age")
+```
+
+%% Cell type:markdown id:0e500391-b081-4e3f-a87b-014abd847f68 tags:
+
+To overplot different distributions segregated by a categorical variable, you can use the `hue` parameter:
+
+%% Cell type:code id:5cd0ce9b-288a-45c8-9b9e-769e75e1bd63 tags:
+
+``` python
+sns.displot(data=df, x="Age", hue="Survived")
+```
+
+%% Cell type:markdown id:3289632b-63f9-4e0a-841a-b17a7c42c506 tags:
+
+#### Influence of bins
+
+%% Cell type:code id:vietnamese-proceeding tags:
+
+``` python
+sns.displot(data=df, x="Age", hue="Survived", bins=50)
+```
+
+%% Cell type:code id:dd5a9532-82ee-4d71-bd0a-c3a24c0ec444 tags:
+
+``` python
+sns.displot(data=df, x="Age", hue="Survived", bins=10)
+```
+
+%% Cell type:markdown id:f23420fc-d840-4bd9-9763-74ef506b1c65 tags:
+
+#### KDE curve
+
+%% Cell type:markdown id:unknown-yacht tags:
+
+Here is the corresponding continuous probability density curve (kde):
+
+%% Cell type:code id:active-stephen tags:
+
+``` python
+sns.displot(data=df, x="Age", hue="Survived", kind="kde")
+```
+
+%% Cell type:code id:narrative-illinois tags:
+
+``` python
+sns.displot(data=df, x="Age", hue="Survived", kde=True)
+```
+
+%% Cell type:markdown id:floral-supervisor tags:
+
+## Categorical plots
+### Barplot
+
+A barplot is a way of displaying for example counts, frequencies or averages for different categories.
+
+%% Cell type:code id:about-participant tags:
+
+``` python
+sns.catplot(data=df, x="Sex", y="Age", kind="bar")
+```
+
+%% Cell type:markdown id:designing-deviation tags:
+
+### Swarmplot
+
+%% Cell type:code id:laughing-contributor tags:
+
+``` python
+sns.catplot(data=df, x="Pclass", y="Fare", kind="swarm")
+```
+
+%% Cell type:markdown id:polished-developer tags:
+
+### Boxplot
+
+Box plots visually show the distribution of numerical data and skewness through displaying the data quartiles (or percentiles) and averages.
+
+Box plots show the five-number summary of a set of data: including the minimum score, first (lower) quartile, median, third (upper) quartile, and maximum score.
+
+<div>
+ <img src="../images/boxplot_explanation.png" />
+</div>
+
+for more explanation visit
+https://www.simplypsychology.org/boxplots.html
+
+%% Cell type:code id:demographic-original tags:
+
+``` python
+sns.catplot(data=df, x="Pclass", y="Fare", kind="box")
+```
+
+%% Cell type:markdown id:27290d30-3c5d-4161-8cf8-0b0cb9f22654 tags:
+
+### Violinplot
+
+Sometimes the median and mean aren't enough to understand a dataset. Are most of the values clustered around the median? Or are they clustered around the minimum and the maximum with nothing in the middle? When you have questions like these, distribution plots are your friends.
+
+The box plot is an old standby for visualizing basic distributions. It's convenient for comparing summary statistics (such as range and quartiles), but it doesn't let you see variations in the data. For multimodal distributions (those with multiple peaks) this can be particularly limiting.
+
+But fret not—this is where the violin plot comes in. A violin plot is a hybrid of a box plot and a kernel density plot, which shows peaks in the data.
+
+formore explanation visit: https://mode.com/blog/violin-plot-examples/
+
+%% Cell type:code id:359478ff-ec41-43b4-87fe-227e93e2d41f tags:
+
+``` python
+sns.catplot(data=df, x="Pclass", y="Fare", kind="violin")
+```
+
+%% Cell type:markdown id:facial-spell tags:
+
+## Scatterplot
+
+A scatter plot (aka scatter chart, scatter graph) uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. Scatter plots are used to observe relationships between variables.
+
+https://chartio.com/learn/charts/what-is-a-scatter-plot/
+
+%% Cell type:code id:academic-kazakhstan tags:
+
+``` python
+sns.scatterplot(data=df, x="Age", y="Fare", hue="Sex", size="Pclass", alpha=0.5)
+```
+
+%% Cell type:markdown id:differential-sauce tags:
+
+## Heatmap
+
+%% Cell type:code id:mighty-barrier tags:
+
+``` python
+uniform_data = np.random.rand(5, 5)
+sns.heatmap(uniform_data, vmin=0, vmax=1)
+```
+
+%% Cell type:code id:0f988aac-b37a-4163-917c-03597e74b8b4 tags:
+
+``` python
+sns.heatmap(uniform_data, vmin=0, vmax=1, annot=True)
+```
+
+%% Cell type:markdown id:8fc43d40-d07b-4052-9579-fc0e93d621d1 tags:
+
+## How to make subplots (Matplotlib)
+
+We can pack several plots in a figure.
+There is several way to do that, here we describe the *pyplot.subplots* function
+> https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html
+
+When using seaborn plotting function the high level catplot, displot and relplot methods will not work with subplots. You should be specific and use the lower level methods such as histplot, boxplot etc...
+
+%% Cell type:code id:f57d267f-bcc4-4ed2-8aa1-557fe666ccce tags:
+
+``` python
+fig, axs = plt.subplots(2,2, figsize=(9,7)) # 2 rows, 2 columns
+
+sns.histplot(data=df, x="Age", hue="Survived", kde=True, ax=axs[0,0], palette=["grey", "black"])
+sns.boxplot(data=df, x="Pclass", y="Fare", ax=axs[0,1], palette=["lightgreen", "green", "darkgreen"])
+sns.barplot(data=df, x="Sex", y="Age", ax=axs[1,0])
+sns.scatterplot(data=df, x="Age", y="Fare", hue="Sex", size="Pclass", alpha=0.5, ax=axs[1,1])
+```
+
+%% Cell type:code id:bf1edc04-10a3-4c3c-8c6c-072594953bf6 tags:
+
+``` python
+# More complex visualisations can be incorporated in a function
+def titanic_graph():
+
+    fig, axs = plt.subplots(2,2, figsize=(9,7)) # 2 rows, 2 columns
+
+    sns.histplot(data=df, x="Age", hue="Survived", kde=True, ax=axs[0,0], palette=["grey", "black"])
+    sns.boxplot(data=df, x="Pclass", y="Fare", ax=axs[0,1], palette=["lightgreen", "green", "darkgreen"])
+    sns.barplot(data=df, x="Sex", y="Age", ax=axs[1,0])
+    sns.scatterplot(data=df, x="Age", y="Fare", hue="Sex", size="Pclass", alpha=0.5, ax=axs[1,1])
+```
+
+%% Cell type:code id:05bdefdd-8474-4241-a9a3-be576adbc941 tags:
+
+``` python
+titanic_graph()
+```
+
+%% Cell type:markdown id:07f3defa-f36d-4bf2-a7ae-5c05e4190412 tags:
+
+## Saving figures (Matplotlib)
+
+%% Cell type:code id:1c0c03bc-f3f4-4a62-ae30-fedd26e5ec46 tags:
+
+``` python
+titanic_graph()
+plt.savefig("titanic_visualization.pdf")
+```