Commit 7b4a7f7b authored by Etienne Kornobis's avatar Etienne Kornobis
Browse files

Adding pandas course and practices

parent b7e1d646
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
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%% Cell type:markdown id:expired-highway tags:
%% Cell type:markdown id:cultural-palestine tags:
# Introduction to JupyterLab
## Aim of this section
......@@ -9,32 +9,42 @@
## Install
After creating a folder for the course, use `venv` to create a virtual environment named for example `sp_env`:
```python3 -m venv sp_env```
```shell
python3 -m venv sp_env
```
This will create a folder `sp_env` in your working directory. The corresponding virtual environment can be activated with:
```source sp_env/bin/activate```
```shell
source sp_env/bin/activate
```
You are now in a virtual environment. You can install librairies in it using pip and these will be installed specifically in this environment (and not globally on your machine). For more on virtual environment, [see the documentation](https://docs.python.org/3/library/venv.html).
Once the virtal environment activated, we can start composing this environment, now with jupyterlab
```pip install jupyterlab```
```shell
pip install jupyterlab
```
You can now start the jupyter server as follows:
```jupyter lab```
```shell
jupyter lab
```
And open the specified URL in your internet browser (Chrome or Firefox are
better supported). By default, the address will be http://localhost:8888 and you will be automatically redirected to this tab.
Once all you work is done, you can exit the virtual environment with:
```deactivate```
```shell
deactivate
```
You will need to reactivate it (with `source sp_env/bin/activate`) in order to use it again.
For more exhaustive guidelines on JupyterLab installation, you can see [the official Jupyter documentation](https://jupyter.org/install)
......@@ -94,56 +104,56 @@
Here are some example of useful magic commands:
- Run cell with bash in subprocess:
%% Cell type:code id:billion-actress tags:
%% Cell type:code id:public-nightlife tags:
``` python
%%bash
echo "This is a bash script"
for i in {1..3}; do echo $i; done
echo "Over and out"
```
%% Cell type:markdown id:tight-spring tags:
%% Cell type:markdown id:marine-arctic tags:
- The exclamation mark character ``!`` can be used as well to execute the following line in a bash subprocess. For example:
%% Cell type:code id:helpful-oasis tags:
%% Cell type:code id:considerable-fleet tags:
``` python
! echo "This is executed in a bash subprocess"
```
%% Cell type:markdown id:marked-construction tags:
%% Cell type:markdown id:satellite-disposal tags:
- `%timeit` can be used to check for execution times:
%% Cell type:code id:photographic-premises tags:
%% Cell type:code id:delayed-thunder tags:
``` python
%timeit for _ in range(1000): True
```
%% Cell type:markdown id:sustained-render tags:
%% Cell type:markdown id:vocational-jacksonville tags:
- Load more extension for the notebook, for example `autoreload` is useful extension to automatically reload a module imported in a Jupyter notebook if the module has changed locally:
%% Cell type:code id:posted-pleasure tags:
%% Cell type:code id:physical-steering tags:
``` python
%load_ext autoreload
%autoreload 2
```
%% Cell type:markdown id:insured-entertainment tags:
%% Cell type:markdown id:regular-tiger tags:
# Exercices
%% Cell type:markdown id:helpful-telephone tags:
%% Cell type:markdown id:rotary-bouquet tags:
The aim here is to get comfortable in Jupyterlab.
## Exercise
......@@ -151,34 +161,34 @@
- Create a new notebook with a python3 kernel.
- Create, delete and move cells around using shortcuts and graphical interface.
NB: A kernel provides a programming language support in Jupyter. Kernels are available for Python, R, Julia, and many more.
%% Cell type:code id:congressional-light tags:
%% Cell type:code id:chinese-values tags:
``` python
```
%% Cell type:markdown id:little-questionnaire tags:
%% Cell type:markdown id:useful-segment tags:
## Exercise
In the notebook, create a code cell with simple python code inside with a
``print`` statement, execute the cell and witness its output.
For example::
print("Hello World !")
%% Cell type:code id:behavioral-ethnic tags:
%% Cell type:code id:classical-extraction tags:
``` python
```
%% Cell type:markdown id:trained-advantage tags:
%% Cell type:markdown id:iraqi-wholesale tags:
## Exercise
In the notebook, create a markdown cell with:
......@@ -187,17 +197,17 @@
- A list
- A link to the jupyter documentation ie https://jupyter.org/documentation
Render (execute) the cell to display the cell with a pretty formatting.
%% Cell type:code id:phantom-register tags:
%% Cell type:code id:refined-relation tags:
``` python
```
%% Cell type:markdown id:precise-average tags:
%% Cell type:markdown id:manufactured-treatment tags:
## Exercise
Grasp the concept of cell execution by creating three cells:
......@@ -205,41 +215,41 @@
- 1 cell defining the same variable with a different value from the previous cell (e.g. `myvar=42`)
- 1 cell printing the value of the variable (`print(myvar)`).
Witness how execution order of your cells can affect the result of the cell printing the output. This is potentially dangerous when using notebooks and has to be kept in mind when coded and used.
%% Cell type:code id:solar-auckland tags:
%% Cell type:code id:featured-converter tags:
``` python
```
%% Cell type:markdown id:inside-approval tags:
%% Cell type:markdown id:constant-thriller tags:
## Exercise
Using a Jupyter magic command, create a cell listing the files in the current directory using a bash subprocess.
%% Cell type:code id:worse-husband tags:
%% Cell type:code id:illegal-preserve tags:
``` python
```
%% Cell type:markdown id:dirty-speaker tags:
%% Cell type:markdown id:written-bidding tags:
## Exercise
Using the graphical interface, export your notebook as html file.
%% Cell type:code id:injured-thirty tags:
%% Cell type:code id:waiting-concord tags:
``` python
```
%% Cell type:markdown id:perceived-michael tags:
%% Cell type:markdown id:varying-providence tags:
# More documentation
JupyterLab: https://jupyterlab.readthedocs.io/en/latest/
......
%% Cell type:markdown id:separated-samba tags:
# <center>**TP**</center>
<img src="./images/pandas_logo.svg">
<div style="text-align:center">
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>
%% Cell type:markdown id:hazardous-berry tags:
# Exploring Blast results
%% Cell type:markdown id:union-charleston tags:
- Import the file data/blast.txt into a pandas dataframe variable (named `blast_res`). Verify that its type is a pandas
dataframe and display the dataframe in jupyterlab.
NB: The column names for this blast format are: "qseqid", "sseqid", "pident", "length", "mismatch", "gapopen", "qstart", "qend", "sstart", "send", "evalue", "bitscore"
You going to need to pass an extra argument (`names`) to specify the names of the columns.
%% Cell type:code id:assured-telescope tags:
``` python
```
%% Cell type:markdown id:parallel-algorithm tags:
Explore ``blast_res`` dataframe:
- Display the 5 first lines of the dataframe.
- Display the 8 last lines of the dataframe.
- Display a overall statistical description of the dataframe.
- Display the dimensions of the dataframe.
%% Cell type:code id:nuclear-carrier tags:
``` python
```
%% Cell type:markdown id:still-scheme tags:
- Extract 3rd line from the ``blast_res`` dataframe. Which type of data structure is returned by this extraction ?
%% Cell type:code id:northern-worse tags:
``` python
```
%% Cell type:markdown id:persistent-beijing tags:
- Extract the *sseqid* column from the ``blast_res`` dataframe.
%% Cell type:code id:directed-brazilian tags:
``` python
```
%% Cell type:markdown id:generic-hearts tags:
- Get the minimum and maximum value of a the *evalue* column.
%% Cell type:code id:prescription-appraisal tags:
``` python
```
%% Cell type:markdown id:naughty-brook tags:
- Get the median and the mean of the *bitscore* column.
%% Cell type:code id:extended-chicken tags:
``` python
```
%% Cell type:markdown id:contrary-allah tags:
- Filter in all hits with a percentage of identity (*pident*) superior to 75%.
%% Cell type:code id:liked-shell tags:
``` python
```
%% Cell type:markdown id:neutral-experience tags:
- Based on the bitscore alone, extract only the best hit(s) (i.e. the highest(s) bitscore(s)).
%% Cell type:code id:three-period tags:
``` python
```
%% Cell type:markdown id:blank-digest tags:
- Filter in all hits which are corresponding to human hits in the database (*sseqid*).
%% Cell type:code id:empirical-manhattan tags:
``` python
```
%% Cell type:markdown id:graphic-corruption tags:
- Filter in all hits with a percentage of identity superior to 75% **AND** and is NOT a HUMAN hit (sseqid does not contain "HUMAN"). (Hint: To negate a boolean in a query you can use "~" in front of it).
%% Cell type:code id:numerical-spread tags:
``` python
```
%% Cell type:markdown id:boolean-verse tags:
- Plot a histogram of the bitscores.
%% Cell type:code id:genetic-navigation tags:
``` python
```
%% Cell type:markdown id:destroyed-velvet tags:
- Plot a barplot of the number of hits per species (species are considered the last code after the "_" in the sseqid column)
%% Cell type:code id:composite-twelve tags:
``` python
```
%% Cell type:markdown id:typical-japan tags:
# Extra exercise
%% Cell type:markdown id:electronic-ferry tags:
- Read the 'data/city_temperature.csv'
- Force the City datatype to string by passing `dtype={'City': str}` as argument to the function to read the file.
Don't worry to the warning, it is due to State wich contains Nan for non US contry, but we do not use these data.
%% Cell type:code id:verified-acceptance tags:
``` python
```
%% Cell type:code id:extreme-radio tags:
``` python
```
%% Cell type:markdown id:phantom-inclusion tags:
We will work only on the Europe Region, so create a datafrane named europe with only these data.
Let's explore it a little bit
* how many data?
* which columns?
* index?
%% Cell type:code id:saving-labor tags:
``` python
```
%% Cell type:code id:pleased-collaboration tags:
``` python
```
%% Cell type:code id:tracked-addition tags:
``` python
```
%% Cell type:markdown id:manufactured-hierarchy tags:
- which countries are in Europe ?
%% Cell type:code id:sharing-lawsuit tags:
``` python
```
%% Cell type:markdown id:adopted-eligibility tags:
- Remove the columns 'Region' and 'State' from the data
%% Cell type:code id:korean-nudist tags:
``` python
```
%% Cell type:markdown id:creative-apparatus tags:
- From the Europe dataframe create a new dataset containing countries: 'France', 'Spain', 'Italy'
%% Cell type:code id:solar-nursery tags:
``` python
```
%% Cell type:markdown id:written-spank tags:
- Group the data on 'City' and 'Year' compute the mean of each group and keep only the 'AvgTemperature' column.
%% Cell type:code id:effective-declaration tags:
``` python
```