diff --git a/notebooks/Solutions/pandas_TP_solution.ipynb b/notebooks/Solutions/pandas_TP_solution.ipynb
index 75f1767d4279ad5553bfcb3c9be07db01ee44fb3..299ece279770b6ca09e1a91bb9b602e0a9212ef9 100644
--- a/notebooks/Solutions/pandas_TP_solution.ipynb
+++ b/notebooks/Solutions/pandas_TP_solution.ipynb
@@ -7,7 +7,7 @@
"source": [
"# <center><b>Hands-on</b></center>\n",
"\n",
- "<img src=\"./images/pandas_logo.svg\">\n",
+ "<img src=\"../images/pandas_logo.svg\">\n",
"<div style=\"text-align:center\">\n",
" Bertrand Néron, François Laurent, Etienne Kornobis\n",
" <br />\n",
@@ -39,7 +39,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "musical-violence",
"metadata": {},
"outputs": [],
@@ -49,7 +49,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "loaded-transfer",
"metadata": {},
"outputs": [],
@@ -60,272 +60,20 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"id": "streaming-regulation",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "pandas.core.frame.DataFrame"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"type(blast_res)"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "unsigned-coast",
"metadata": {},
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- "execution_count": 4,
- "metadata": {},
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+ "outputs": [],
"source": [
"blast_res"
]
@@ -345,543 +93,40 @@
},
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+ "outputs": [],
"source": [
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]
},
{
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- " <td>20</td>\n",
- " <td>1</td>\n",
- " <td>95</td>\n",
- " <td>155</td>\n",
- " <td>15</td>\n",
- " <td>62</td>\n",
- " <td>1.000000e-06</td>\n",
- " <td>50.1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " qseqid sseqid pident length mismatch gapopen \\\n",
- "168 AK1BA_HUMAN sp|Q94A68|Y1669_ARATH 24.08 299 176 9 \n",
- "169 AK1BA_HUMAN sp|P82810|MORA_RABIT 31.18 170 45 5 \n",
- "170 AK1BA_HUMAN sp|P46336|IOLS_BACSU 25.42 295 159 10 \n",
- "171 AK1BA_HUMAN sp|P80874|GS69_BACSU 29.36 218 107 9 \n",
- "172 AK1BA_HUMAN sp|Q56Y42|PLR1_ARATH 23.00 313 178 10 \n",
- "173 AK1BA_HUMAN sp|P25906|YDBC_ECOLI 23.75 299 181 11 \n",
- "174 AK1BA_HUMAN sp|C6TBN2|AKR1_SOYBN 25.32 316 178 13 \n",
- "175 AK1BA_HUMAN sp|P49261|CROB_LEPLU 45.90 61 20 1 \n",
- "\n",
- " qstart qend sstart send evalue bitscore \n",
- "168 25 292 84 362 7.000000e-15 77.8 \n",
- "169 117 286 27 124 9.000000e-13 68.2 \n",
- "170 29 289 38 305 3.000000e-12 69.7 \n",
- "171 16 213 16 206 3.000000e-11 67.0 \n",
- "172 16 285 50 342 6.000000e-09 60.1 \n",
- "173 11 294 19 285 6.000000e-09 59.7 \n",
- "174 9 290 19 310 6.000000e-08 57.0 \n",
- "175 95 155 15 62 1.000000e-06 50.1 "
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.tail(8)"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"id": "identical-guest",
"metadata": {},
- "outputs": [
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- " <th>count</th>\n",
- " <td>176.000000</td>\n",
- " <td>176.000000</td>\n",
- " <td>176.000000</td>\n",
- " <td>176.000000</td>\n",
- " <td>176.000000</td>\n",
- " <td>176.000000</td>\n",
- " <td>176.000000</td>\n",
- " <td>176.000000</td>\n",
- " <td>1.760000e+02</td>\n",
- " <td>176.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>mean</th>\n",
- " <td>42.772500</td>\n",
- " <td>300.250000</td>\n",
- " <td>149.465909</td>\n",
- " <td>4.767045</td>\n",
- " <td>6.948864</td>\n",
- " <td>299.079545</td>\n",
- " <td>9.198864</td>\n",
- " <td>293.971591</td>\n",
- " <td>6.091102e-09</td>\n",
- " <td>231.952841</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>std</th>\n",
- " <td>12.397842</td>\n",
- " <td>29.162108</td>\n",
- " <td>30.489511</td>\n",
- " <td>2.659161</td>\n",
- " <td>11.909789</td>\n",
- " <td>22.140446</td>\n",
- " <td>9.583331</td>\n",
- " <td>35.601834</td>\n",
- " <td>7.548480e-08</td>\n",
- " <td>104.060644</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>min</th>\n",
- " <td>23.000000</td>\n",
- " <td>61.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>0.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>118.000000</td>\n",
- " <td>1.000000</td>\n",
- " <td>62.000000</td>\n",
- " <td>0.000000e+00</td>\n",
- " <td>50.100000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25%</th>\n",
- " <td>34.845000</td>\n",
- " <td>294.000000</td>\n",
- " <td>150.000000</td>\n",
- " <td>2.000000</td>\n",
- " <td>3.000000</td>\n",
- " <td>294.000000</td>\n",
- " <td>4.000000</td>\n",
- " <td>272.000000</td>\n",
- " <td>8.750000e-100</td>\n",
- " <td>167.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>50%</th>\n",
- " <td>40.230000</td>\n",
- " <td>303.000000</td>\n",
- " <td>159.000000</td>\n",
- " <td>5.000000</td>\n",
- " <td>5.000000</td>\n",
- " <td>297.000000</td>\n",
- " <td>8.000000</td>\n",
- " <td>302.500000</td>\n",
- " <td>1.000000e-61</td>\n",
- " <td>205.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>75%</th>\n",
- " <td>47.980000</td>\n",
- " <td>316.000000</td>\n",
- " <td>164.000000</td>\n",
- " <td>6.000000</td>\n",
- " <td>5.000000</td>\n",
- " <td>316.000000</td>\n",
- " <td>11.000000</td>\n",
- " <td>320.000000</td>\n",
- " <td>8.000000e-48</td>\n",
- " <td>303.000000</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>max</th>\n",
- " <td>100.000000</td>\n",
- " <td>329.000000</td>\n",
- " <td>181.000000</td>\n",
- " <td>13.000000</td>\n",
- " <td>117.000000</td>\n",
- " <td>316.000000</td>\n",
- " <td>84.000000</td>\n",
- " <td>362.000000</td>\n",
- " <td>1.000000e-06</td>\n",
- " <td>654.000000</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
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- "text/plain": [
- " pident length mismatch gapopen qstart qend \\\n",
- "count 176.000000 176.000000 176.000000 176.000000 176.000000 176.000000 \n",
- "mean 42.772500 300.250000 149.465909 4.767045 6.948864 299.079545 \n",
- "std 12.397842 29.162108 30.489511 2.659161 11.909789 22.140446 \n",
- "min 23.000000 61.000000 0.000000 0.000000 1.000000 118.000000 \n",
- "25% 34.845000 294.000000 150.000000 2.000000 3.000000 294.000000 \n",
- "50% 40.230000 303.000000 159.000000 5.000000 5.000000 297.000000 \n",
- "75% 47.980000 316.000000 164.000000 6.000000 5.000000 316.000000 \n",
- "max 100.000000 329.000000 181.000000 13.000000 117.000000 316.000000 \n",
- "\n",
- " sstart send evalue bitscore \n",
- "count 176.000000 176.000000 1.760000e+02 176.000000 \n",
- "mean 9.198864 293.971591 6.091102e-09 231.952841 \n",
- "std 9.583331 35.601834 7.548480e-08 104.060644 \n",
- "min 1.000000 62.000000 0.000000e+00 50.100000 \n",
- "25% 4.000000 272.000000 8.750000e-100 167.000000 \n",
- "50% 8.000000 302.500000 1.000000e-61 205.000000 \n",
- "75% 11.000000 320.000000 8.000000e-48 303.000000 \n",
- "max 84.000000 362.000000 1.000000e-06 654.000000 "
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.describe()"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"id": "alpine-cleveland",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(176, 12)"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.shape"
]
@@ -896,54 +141,20 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": null,
"id": "complicated-football",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "qseqid AK1BA_HUMAN\n",
- "sseqid sp|O08782|ALD2_CRIGR\n",
- "pident 83.23\n",
- "length 316\n",
- "mismatch 53\n",
- "gapopen 0\n",
- "qstart 1\n",
- "qend 316\n",
- "sstart 1\n",
- "send 316\n",
- "evalue 0.0\n",
- "bitscore 537.0\n",
- "Name: 2, dtype: object"
- ]
- },
- "execution_count": 60,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.iloc[2]"
]
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": null,
"id": "administrative-biodiversity",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "pandas.core.series.Series"
- ]
- },
- "execution_count": 61,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"type(blast_res.iloc[2])"
]
@@ -958,167 +169,20 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": null,
"id": "0367828c-9201-45ee-898c-36a85fd8817c",
"metadata": {},
- "outputs": [
- {
- "data": {
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- "<table border=\"1\" class=\"dataframe\">\n",
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- " <th></th>\n",
- " <th>qseqid</th>\n",
- " <th>sseqid</th>\n",
- " <th>pident</th>\n",
- " <th>length</th>\n",
- " <th>mismatch</th>\n",
- " <th>gapopen</th>\n",
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- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|O60218|AK1BA_HUMAN</td>\n",
- " <td>100.00</td>\n",
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- " <td>0</td>\n",
- " <td>0</td>\n",
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- " <td>654.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|C9JRZ8|AK1BF_HUMAN</td>\n",
- " <td>91.16</td>\n",
- " <td>294</td>\n",
- " <td>26</td>\n",
- " <td>0</td>\n",
- " <td>23</td>\n",
- " <td>316</td>\n",
- " <td>51</td>\n",
- " <td>344</td>\n",
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- " <td>559.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|O08782|ALD2_CRIGR</td>\n",
- " <td>83.23</td>\n",
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- " <td>53</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>0.0</td>\n",
- " <td>537.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P45377|ALD2_MOUSE</td>\n",
- " <td>82.28</td>\n",
- " <td>316</td>\n",
- " <td>56</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>0.0</td>\n",
- " <td>527.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P21300|ALD1_MOUSE</td>\n",
- " <td>79.75</td>\n",
- " <td>316</td>\n",
- " <td>64</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>0.0</td>\n",
- " <td>515.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " qseqid sseqid pident length mismatch gapopen \\\n",
- "0 AK1BA_HUMAN sp|O60218|AK1BA_HUMAN 100.00 316 0 0 \n",
- "1 AK1BA_HUMAN sp|C9JRZ8|AK1BF_HUMAN 91.16 294 26 0 \n",
- "2 AK1BA_HUMAN sp|O08782|ALD2_CRIGR 83.23 316 53 0 \n",
- "3 AK1BA_HUMAN sp|P45377|ALD2_MOUSE 82.28 316 56 0 \n",
- "4 AK1BA_HUMAN sp|P21300|ALD1_MOUSE 79.75 316 64 0 \n",
- "\n",
- " qstart qend sstart send evalue bitscore \n",
- "0 1 316 1 316 0.0 654.0 \n",
- "1 23 316 51 344 0.0 559.0 \n",
- "2 1 316 1 316 0.0 537.0 \n",
- "3 1 316 1 316 0.0 527.0 \n",
- "4 1 316 1 316 0.0 515.0 "
- ]
- },
- "execution_count": 62,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.iloc[:5]"
]
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": null,
"id": "b6180bb6-44c3-4ab8-9f74-fca7e7e3f733",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "pandas.core.frame.DataFrame"
- ]
- },
- "execution_count": 63,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"type(blast_res.iloc[:5])"
]
@@ -1133,32 +197,10 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"id": "seasonal-europe",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0 sp|O60218|AK1BA_HUMAN\n",
- "1 sp|C9JRZ8|AK1BF_HUMAN\n",
- "2 sp|O08782|ALD2_CRIGR\n",
- "3 sp|P45377|ALD2_MOUSE\n",
- "4 sp|P21300|ALD1_MOUSE\n",
- " ... \n",
- "171 sp|P80874|GS69_BACSU\n",
- "172 sp|Q56Y42|PLR1_ARATH\n",
- "173 sp|P25906|YDBC_ECOLI\n",
- "174 sp|C6TBN2|AKR1_SOYBN\n",
- "175 sp|P49261|CROB_LEPLU\n",
- "Name: sseqid, Length: 176, dtype: object"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.loc[:,'sseqid']\n",
"# Or\n",
@@ -1175,154 +217,10 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": null,
"id": "7cd85008-5b14-4a62-a195-7df323a0b8a1",
"metadata": {},
- "outputs": [
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- "<table border=\"1\" class=\"dataframe\">\n",
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- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>qseqid</th>\n",
- " <th>sseqid</th>\n",
- " <th>pident</th>\n",
- " <th>evalue</th>\n",
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- " <tr>\n",
- " <th>0</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
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- " <td>100.00</td>\n",
- " <td>0.000000e+00</td>\n",
- " <td>654.0</td>\n",
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- " <tr>\n",
- " <th>1</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
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- " <td>0.000000e+00</td>\n",
- " <td>559.0</td>\n",
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- " <tr>\n",
- " <th>2</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|O08782|ALD2_CRIGR</td>\n",
- " <td>83.23</td>\n",
- " <td>0.000000e+00</td>\n",
- " <td>537.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P45377|ALD2_MOUSE</td>\n",
- " <td>82.28</td>\n",
- " <td>0.000000e+00</td>\n",
- " <td>527.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P21300|ALD1_MOUSE</td>\n",
- " <td>79.75</td>\n",
- " <td>0.000000e+00</td>\n",
- " <td>515.0</td>\n",
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- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
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- " <tr>\n",
- " <th>171</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P80874|GS69_BACSU</td>\n",
- " <td>29.36</td>\n",
- " <td>3.000000e-11</td>\n",
- " <td>67.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>172</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|Q56Y42|PLR1_ARATH</td>\n",
- " <td>23.00</td>\n",
- " <td>6.000000e-09</td>\n",
- " <td>60.1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>173</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P25906|YDBC_ECOLI</td>\n",
- " <td>23.75</td>\n",
- " <td>6.000000e-09</td>\n",
- " <td>59.7</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>174</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|C6TBN2|AKR1_SOYBN</td>\n",
- " <td>25.32</td>\n",
- " <td>6.000000e-08</td>\n",
- " <td>57.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>175</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P49261|CROB_LEPLU</td>\n",
- " <td>45.90</td>\n",
- " <td>1.000000e-06</td>\n",
- " <td>50.1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>176 rows × 5 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " qseqid sseqid pident evalue bitscore\n",
- "0 AK1BA_HUMAN sp|O60218|AK1BA_HUMAN 100.00 0.000000e+00 654.0\n",
- "1 AK1BA_HUMAN sp|C9JRZ8|AK1BF_HUMAN 91.16 0.000000e+00 559.0\n",
- "2 AK1BA_HUMAN sp|O08782|ALD2_CRIGR 83.23 0.000000e+00 537.0\n",
- "3 AK1BA_HUMAN sp|P45377|ALD2_MOUSE 82.28 0.000000e+00 527.0\n",
- "4 AK1BA_HUMAN sp|P21300|ALD1_MOUSE 79.75 0.000000e+00 515.0\n",
- ".. ... ... ... ... ...\n",
- "171 AK1BA_HUMAN sp|P80874|GS69_BACSU 29.36 3.000000e-11 67.0\n",
- "172 AK1BA_HUMAN sp|Q56Y42|PLR1_ARATH 23.00 6.000000e-09 60.1\n",
- "173 AK1BA_HUMAN sp|P25906|YDBC_ECOLI 23.75 6.000000e-09 59.7\n",
- "174 AK1BA_HUMAN sp|C6TBN2|AKR1_SOYBN 25.32 6.000000e-08 57.0\n",
- "175 AK1BA_HUMAN sp|P49261|CROB_LEPLU 45.90 1.000000e-06 50.1\n",
- "\n",
- "[176 rows x 5 columns]"
- ]
- },
- "execution_count": 64,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.loc[:, [\"qseqid\", \"sseqid\", \"pident\", \"evalue\", \"bitscore\"]]"
]
@@ -1345,42 +243,20 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"id": "varied-influence",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0.0"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.evalue.min()"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"id": "little-recipient",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1e-06"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.evalue.max()"
]
@@ -1395,42 +271,20 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"id": "polyphonic-retro",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "205.0"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.bitscore.median()"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"id": "advisory-symphony",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "231.9528409090909"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.bitscore.mean()"
]
@@ -1445,326 +299,20 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"id": "rough-globe",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
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- " <th>0</th>\n",
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- " <td>AK1BA_HUMAN</td>\n",
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- " <td>537.0</td>\n",
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- " <th>3</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P45377|ALD2_MOUSE</td>\n",
- " <td>82.28</td>\n",
- " <td>316</td>\n",
- " <td>56</td>\n",
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- " <td>316</td>\n",
- " <td>1</td>\n",
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- " <td>0.000000e+00</td>\n",
- " <td>527.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P21300|ALD1_MOUSE</td>\n",
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- " <td>515.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|Q5RJP0|ALD1_RAT</td>\n",
- " <td>78.16</td>\n",
- " <td>316</td>\n",
- " <td>69</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>2.000000e-177</td>\n",
- " <td>501.0</td>\n",
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- "text/plain": [
- " qseqid sseqid pident length mismatch gapopen \\\n",
- "0 AK1BA_HUMAN sp|O60218|AK1BA_HUMAN 100.00 316 0 0 \n",
- "1 AK1BA_HUMAN sp|C9JRZ8|AK1BF_HUMAN 91.16 294 26 0 \n",
- "2 AK1BA_HUMAN sp|O08782|ALD2_CRIGR 83.23 316 53 0 \n",
- "3 AK1BA_HUMAN sp|P45377|ALD2_MOUSE 82.28 316 56 0 \n",
- "4 AK1BA_HUMAN sp|P21300|ALD1_MOUSE 79.75 316 64 0 \n",
- "5 AK1BA_HUMAN sp|Q5RJP0|ALD1_RAT 78.16 316 69 0 \n",
- "\n",
- " qstart qend sstart send evalue bitscore \n",
- "0 1 316 1 316 0.000000e+00 654.0 \n",
- "1 23 316 51 344 0.000000e+00 559.0 \n",
- "2 1 316 1 316 0.000000e+00 537.0 \n",
- "3 1 316 1 316 0.000000e+00 527.0 \n",
- "4 1 316 1 316 0.000000e+00 515.0 \n",
- "5 1 316 1 316 2.000000e-177 501.0 "
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.loc[blast_res.pident > 75]"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"id": "novel-turkey",
"metadata": {},
- "outputs": [
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- " <td>sp|Q5RJP0|ALD1_RAT</td>\n",
- " <td>78.16</td>\n",
- " <td>316</td>\n",
- " <td>69</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
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- " <td>316</td>\n",
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- "text/plain": [
- " qseqid sseqid pident length mismatch gapopen \\\n",
- "0 AK1BA_HUMAN sp|O60218|AK1BA_HUMAN 100.00 316 0 0 \n",
- "1 AK1BA_HUMAN sp|C9JRZ8|AK1BF_HUMAN 91.16 294 26 0 \n",
- "2 AK1BA_HUMAN sp|O08782|ALD2_CRIGR 83.23 316 53 0 \n",
- "3 AK1BA_HUMAN sp|P45377|ALD2_MOUSE 82.28 316 56 0 \n",
- "4 AK1BA_HUMAN sp|P21300|ALD1_MOUSE 79.75 316 64 0 \n",
- "5 AK1BA_HUMAN sp|Q5RJP0|ALD1_RAT 78.16 316 69 0 \n",
- "\n",
- " qstart qend sstart send evalue bitscore \n",
- "0 1 316 1 316 0.000000e+00 654.0 \n",
- "1 23 316 51 344 0.000000e+00 559.0 \n",
- "2 1 316 1 316 0.000000e+00 537.0 \n",
- "3 1 316 1 316 0.000000e+00 527.0 \n",
- "4 1 316 1 316 0.000000e+00 515.0 \n",
- "5 1 316 1 316 2.000000e-177 501.0 "
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# OR \n",
"blast_res.query(\"pident > 75\")"
@@ -1780,78 +328,10 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"id": "arbitrary-style",
"metadata": {},
- "outputs": [
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- "</table>\n",
- "</div>"
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- "text/plain": [
- " qseqid sseqid pident length mismatch gapopen \\\n",
- "0 AK1BA_HUMAN sp|O60218|AK1BA_HUMAN 100.0 316 0 0 \n",
- "\n",
- " qstart qend sstart send evalue bitscore \n",
- "0 1 316 1 316 0.0 654.0 "
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Getting the highest bitscore value\n",
"max_bitscore = blast_res.bitscore.max()\n",
@@ -1869,248 +349,10 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"id": "failing-crossing",
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- " <td>AK1BA_HUMAN</td>\n",
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- " <td>348.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P51857|AK1D1_HUMAN</td>\n",
- " <td>50.79</td>\n",
- " <td>317</td>\n",
- " <td>151</td>\n",
- " <td>2</td>\n",
- " <td>5</td>\n",
- " <td>316</td>\n",
- " <td>10</td>\n",
- " <td>326</td>\n",
- " <td>8.000000e-111</td>\n",
- " <td>331.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P14550|AK1A1_HUMAN</td>\n",
- " <td>48.92</td>\n",
- " <td>325</td>\n",
- " <td>154</td>\n",
- " <td>3</td>\n",
- " <td>2</td>\n",
- " <td>316</td>\n",
- " <td>3</td>\n",
- " <td>325</td>\n",
- " <td>4.000000e-106</td>\n",
- " <td>319.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P52895|AK1C2_HUMAN</td>\n",
- " <td>48.73</td>\n",
- " <td>316</td>\n",
- " <td>158</td>\n",
- " <td>2</td>\n",
- " <td>5</td>\n",
- " <td>316</td>\n",
- " <td>8</td>\n",
- " <td>323</td>\n",
- " <td>9.000000e-103</td>\n",
- " <td>311.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P17516|AK1C4_HUMAN</td>\n",
- " <td>48.10</td>\n",
- " <td>316</td>\n",
- " <td>160</td>\n",
- " <td>2</td>\n",
- " <td>5</td>\n",
- " <td>316</td>\n",
- " <td>8</td>\n",
- " <td>323</td>\n",
- " <td>1.000000e-101</td>\n",
- " <td>308.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|Q04828|AK1C1_HUMAN</td>\n",
- " <td>48.10</td>\n",
- " <td>316</td>\n",
- " <td>160</td>\n",
- " <td>2</td>\n",
- " <td>5</td>\n",
- " <td>316</td>\n",
- " <td>8</td>\n",
- " <td>323</td>\n",
- " <td>1.000000e-101</td>\n",
- " <td>308.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P42330|AK1C3_HUMAN</td>\n",
- " <td>47.47</td>\n",
- " <td>316</td>\n",
- " <td>162</td>\n",
- " <td>2</td>\n",
- " <td>5</td>\n",
- " <td>316</td>\n",
- " <td>8</td>\n",
- " <td>323</td>\n",
- " <td>9.000000e-100</td>\n",
- " <td>303.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>161</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|Q5T2L2|AKCL1_HUMAN</td>\n",
- " <td>49.57</td>\n",
- " <td>117</td>\n",
- " <td>56</td>\n",
- " <td>1</td>\n",
- " <td>5</td>\n",
- " <td>118</td>\n",
- " <td>11</td>\n",
- " <td>127</td>\n",
- " <td>3.000000e-30</td>\n",
- " <td>116.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " qseqid sseqid pident length mismatch gapopen \\\n",
- "0 AK1BA_HUMAN sp|O60218|AK1BA_HUMAN 100.00 316 0 0 \n",
- "1 AK1BA_HUMAN sp|C9JRZ8|AK1BF_HUMAN 91.16 294 26 0 \n",
- "8 AK1BA_HUMAN sp|P15121|ALDR_HUMAN 70.57 316 93 0 \n",
- "14 AK1BA_HUMAN sp|Q96JD6|AKCL2_HUMAN 54.46 325 123 3 \n",
- "19 AK1BA_HUMAN sp|P51857|AK1D1_HUMAN 50.79 317 151 2 \n",
- "25 AK1BA_HUMAN sp|P14550|AK1A1_HUMAN 48.92 325 154 3 \n",
- "31 AK1BA_HUMAN sp|P52895|AK1C2_HUMAN 48.73 316 158 2 \n",
- "35 AK1BA_HUMAN sp|P17516|AK1C4_HUMAN 48.10 316 160 2 \n",
- "36 AK1BA_HUMAN sp|Q04828|AK1C1_HUMAN 48.10 316 160 2 \n",
- "45 AK1BA_HUMAN sp|P42330|AK1C3_HUMAN 47.47 316 162 2 \n",
- "161 AK1BA_HUMAN sp|Q5T2L2|AKCL1_HUMAN 49.57 117 56 1 \n",
- "\n",
- " qstart qend sstart send evalue bitscore \n",
- "0 1 316 1 316 0.000000e+00 654.0 \n",
- "1 23 316 51 344 0.000000e+00 559.0 \n",
- "8 1 316 1 316 1.000000e-160 458.0 \n",
- "14 11 316 2 320 2.000000e-117 348.0 \n",
- "19 5 316 10 326 8.000000e-111 331.0 \n",
- "25 2 316 3 325 4.000000e-106 319.0 \n",
- "31 5 316 8 323 9.000000e-103 311.0 \n",
- "35 5 316 8 323 1.000000e-101 308.0 \n",
- "36 5 316 8 323 1.000000e-101 308.0 \n",
- "45 5 316 8 323 9.000000e-100 303.0 \n",
- "161 5 118 11 127 3.000000e-30 116.0 "
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# This could be done with list comprehension creating a list of Booleans \n",
"mask =[\"HUMAN\" in x for x in blast_res.sseqid] \n",
@@ -2119,248 +361,10 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"id": "trained-durham",
"metadata": {},
- "outputs": [
- {
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- " <th>0</th>\n",
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- " <td>AK1BA_HUMAN</td>\n",
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- " <td>458.0</td>\n",
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- " <tr>\n",
- " <th>14</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|Q96JD6|AKCL2_HUMAN</td>\n",
- " <td>54.46</td>\n",
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- " <td>123</td>\n",
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- " <td>348.0</td>\n",
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- " <th>19</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P51857|AK1D1_HUMAN</td>\n",
- " <td>50.79</td>\n",
- " <td>317</td>\n",
- " <td>151</td>\n",
- " <td>2</td>\n",
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- " <td>316</td>\n",
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- " <td>326</td>\n",
- " <td>8.000000e-111</td>\n",
- " <td>331.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P14550|AK1A1_HUMAN</td>\n",
- " <td>48.92</td>\n",
- " <td>325</td>\n",
- " <td>154</td>\n",
- " <td>3</td>\n",
- " <td>2</td>\n",
- " <td>316</td>\n",
- " <td>3</td>\n",
- " <td>325</td>\n",
- " <td>4.000000e-106</td>\n",
- " <td>319.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>31</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P52895|AK1C2_HUMAN</td>\n",
- " <td>48.73</td>\n",
- " <td>316</td>\n",
- " <td>158</td>\n",
- " <td>2</td>\n",
- " <td>5</td>\n",
- " <td>316</td>\n",
- " <td>8</td>\n",
- " <td>323</td>\n",
- " <td>9.000000e-103</td>\n",
- " <td>311.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>35</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P17516|AK1C4_HUMAN</td>\n",
- " <td>48.10</td>\n",
- " <td>316</td>\n",
- " <td>160</td>\n",
- " <td>2</td>\n",
- " <td>5</td>\n",
- " <td>316</td>\n",
- " <td>8</td>\n",
- " <td>323</td>\n",
- " <td>1.000000e-101</td>\n",
- " <td>308.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>36</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|Q04828|AK1C1_HUMAN</td>\n",
- " <td>48.10</td>\n",
- " <td>316</td>\n",
- " <td>160</td>\n",
- " <td>2</td>\n",
- " <td>5</td>\n",
- " <td>316</td>\n",
- " <td>8</td>\n",
- " <td>323</td>\n",
- " <td>1.000000e-101</td>\n",
- " <td>308.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>45</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P42330|AK1C3_HUMAN</td>\n",
- " <td>47.47</td>\n",
- " <td>316</td>\n",
- " <td>162</td>\n",
- " <td>2</td>\n",
- " <td>5</td>\n",
- " <td>316</td>\n",
- " <td>8</td>\n",
- " <td>323</td>\n",
- " <td>9.000000e-100</td>\n",
- " <td>303.0</td>\n",
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- " <tr>\n",
- " <th>161</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|Q5T2L2|AKCL1_HUMAN</td>\n",
- " <td>49.57</td>\n",
- " <td>117</td>\n",
- " <td>56</td>\n",
- " <td>1</td>\n",
- " <td>5</td>\n",
- " <td>118</td>\n",
- " <td>11</td>\n",
- " <td>127</td>\n",
- " <td>3.000000e-30</td>\n",
- " <td>116.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " qseqid sseqid pident length mismatch gapopen \\\n",
- "0 AK1BA_HUMAN sp|O60218|AK1BA_HUMAN 100.00 316 0 0 \n",
- "1 AK1BA_HUMAN sp|C9JRZ8|AK1BF_HUMAN 91.16 294 26 0 \n",
- "8 AK1BA_HUMAN sp|P15121|ALDR_HUMAN 70.57 316 93 0 \n",
- "14 AK1BA_HUMAN sp|Q96JD6|AKCL2_HUMAN 54.46 325 123 3 \n",
- "19 AK1BA_HUMAN sp|P51857|AK1D1_HUMAN 50.79 317 151 2 \n",
- "25 AK1BA_HUMAN sp|P14550|AK1A1_HUMAN 48.92 325 154 3 \n",
- "31 AK1BA_HUMAN sp|P52895|AK1C2_HUMAN 48.73 316 158 2 \n",
- "35 AK1BA_HUMAN sp|P17516|AK1C4_HUMAN 48.10 316 160 2 \n",
- "36 AK1BA_HUMAN sp|Q04828|AK1C1_HUMAN 48.10 316 160 2 \n",
- "45 AK1BA_HUMAN sp|P42330|AK1C3_HUMAN 47.47 316 162 2 \n",
- "161 AK1BA_HUMAN sp|Q5T2L2|AKCL1_HUMAN 49.57 117 56 1 \n",
- "\n",
- " qstart qend sstart send evalue bitscore \n",
- "0 1 316 1 316 0.000000e+00 654.0 \n",
- "1 23 316 51 344 0.000000e+00 559.0 \n",
- "8 1 316 1 316 1.000000e-160 458.0 \n",
- "14 11 316 2 320 2.000000e-117 348.0 \n",
- "19 5 316 10 326 8.000000e-111 331.0 \n",
- "25 2 316 3 325 4.000000e-106 319.0 \n",
- "31 5 316 8 323 9.000000e-103 311.0 \n",
- "35 5 316 8 323 1.000000e-101 308.0 \n",
- "36 5 316 8 323 1.000000e-101 308.0 \n",
- "45 5 316 8 323 9.000000e-100 303.0 \n",
- "161 5 118 11 127 3.000000e-30 116.0 "
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# But pandas as a specific syntax to make operation on strings in a Serie: the method str and its method contains\n",
"blast_res.loc[blast_res.sseqid.str.contains(\"HUMAN\")]"
@@ -2368,129 +372,10 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": null,
"id": "structural-hybrid",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
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- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>qseqid</th>\n",
- " <th>sseqid</th>\n",
- " <th>pident</th>\n",
- " <th>length</th>\n",
- " <th>mismatch</th>\n",
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- " <th>bitscore</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|O08782|ALD2_CRIGR</td>\n",
- " <td>83.23</td>\n",
- " <td>316</td>\n",
- " <td>53</td>\n",
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- " <td>537.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P45377|ALD2_MOUSE</td>\n",
- " <td>82.28</td>\n",
- " <td>316</td>\n",
- " <td>56</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>0.000000e+00</td>\n",
- " <td>527.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|P21300|ALD1_MOUSE</td>\n",
- " <td>79.75</td>\n",
- " <td>316</td>\n",
- " <td>64</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>0.000000e+00</td>\n",
- " <td>515.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>AK1BA_HUMAN</td>\n",
- " <td>sp|Q5RJP0|ALD1_RAT</td>\n",
- " <td>78.16</td>\n",
- " <td>316</td>\n",
- " <td>69</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>1</td>\n",
- " <td>316</td>\n",
- " <td>2.000000e-177</td>\n",
- " <td>501.0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " qseqid sseqid pident length mismatch gapopen \\\n",
- "2 AK1BA_HUMAN sp|O08782|ALD2_CRIGR 83.23 316 53 0 \n",
- "3 AK1BA_HUMAN sp|P45377|ALD2_MOUSE 82.28 316 56 0 \n",
- "4 AK1BA_HUMAN sp|P21300|ALD1_MOUSE 79.75 316 64 0 \n",
- "5 AK1BA_HUMAN sp|Q5RJP0|ALD1_RAT 78.16 316 69 0 \n",
- "\n",
- " qstart qend sstart send evalue bitscore \n",
- "2 1 316 1 316 0.000000e+00 537.0 \n",
- "3 1 316 1 316 0.000000e+00 527.0 \n",
- "4 1 316 1 316 0.000000e+00 515.0 \n",
- "5 1 316 1 316 2.000000e-177 501.0 "
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"blast_res.query(\"~sseqid.str.contains('HUMAN') & pident > 75\")"
]
@@ -2505,33 +390,10 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": null,
"id": "liable-wheat",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"blast_res[\"bitscore\"].hist()"
]
@@ -2546,118 +408,10 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"id": "normal-glenn",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>0</th>\n",
- " <th>1</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>sp|O60218|AK1BA</td>\n",
- " <td>HUMAN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>sp|C9JRZ8|AK1BF</td>\n",
- " <td>HUMAN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>sp|O08782|ALD2</td>\n",
- " <td>CRIGR</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>sp|P45377|ALD2</td>\n",
- " <td>MOUSE</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>sp|P21300|ALD1</td>\n",
- " <td>MOUSE</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>171</th>\n",
- " <td>sp|P80874|GS69</td>\n",
- " <td>BACSU</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>172</th>\n",
- " <td>sp|Q56Y42|PLR1</td>\n",
- " <td>ARATH</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>173</th>\n",
- " <td>sp|P25906|YDBC</td>\n",
- " <td>ECOLI</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>174</th>\n",
- " <td>sp|C6TBN2|AKR1</td>\n",
- " <td>SOYBN</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>175</th>\n",
- " <td>sp|P49261|CROB</td>\n",
- " <td>LEPLU</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>176 rows × 2 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " 0 1\n",
- "0 sp|O60218|AK1BA HUMAN\n",
- "1 sp|C9JRZ8|AK1BF HUMAN\n",
- "2 sp|O08782|ALD2 CRIGR\n",
- "3 sp|P45377|ALD2 MOUSE\n",
- "4 sp|P21300|ALD1 MOUSE\n",
- ".. ... ...\n",
- "171 sp|P80874|GS69 BACSU\n",
- "172 sp|Q56Y42|PLR1 ARATH\n",
- "173 sp|P25906|YDBC ECOLI\n",
- "174 sp|C6TBN2|AKR1 SOYBN\n",
- "175 sp|P49261|CROB LEPLU\n",
- "\n",
- "[176 rows x 2 columns]"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# First extract the species information from the sseqid column\n",
"hits_by_sp = blast_res.sseqid.str.split(\"_\", expand=True)\n",
@@ -2666,33 +420,10 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": null,
"id": "arranged-intervention",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 61,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"# Then count their occurences and do the barplot\n",
"hits_by_sp.loc[:, 1].value_counts().plot(kind=\"bar\")"
@@ -2723,42 +454,20 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": null,
"id": "arctic-pickup",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/tmp/ipykernel_3875/2154739564.py:1: DtypeWarning: Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
- " world = pd.read_csv('data/city_temperature.csv' , sep=',', dtype={'City': str})\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "world = pd.read_csv('data/city_temperature.csv' , sep=',', dtype={'City': str})"
+ "world = pd.read_csv('../data/city_temperature.csv' , sep=',', dtype={'City': str})"
]
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": null,
"id": "conventional-section",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['Region', 'Country', 'State', 'City', 'Month', 'Day', 'Year',\n",
- " 'AvgTemperature'],\n",
- " dtype='object')"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"world.columns"
]
@@ -2773,7 +482,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": null,
"id": "strong-skirt",
"metadata": {},
"outputs": [],
@@ -2791,27 +500,10 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": null,
"id": "unable-establishment",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['Albania', 'Austria', 'Belarus', 'Belgium', 'Bulgaria', 'Croatia',\n",
- " 'Cyprus', 'Czech Republic', 'Denmark', 'Finland', 'France',\n",
- " 'Germany', 'Georgia', 'Greece', 'Hungary', 'Iceland', 'Ireland',\n",
- " 'Italy', 'Latvia', 'Macedonia', 'The Netherlands', 'Norway',\n",
- " 'Poland', 'Portugal', 'Romania', 'Russia', 'Serbia-Montenegro',\n",
- " 'Slovakia', 'Spain', 'Sweden', 'Switzerland', 'Ukraine',\n",
- " 'United Kingdom', 'Yugoslavia'], dtype=object)"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"europe.Country.unique()"
]
@@ -2826,7 +518,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": null,
"id": "anticipated-illinois",
"metadata": {},
"outputs": [],
@@ -2844,7 +536,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": null,
"id": "organized-tender",
"metadata": {},
"outputs": [],
@@ -2862,35 +554,12 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": null,
"id": "sacred-secret",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "City Year\n",
- "Barcelona 1995 62.019178\n",
- " 1996 61.125956\n",
- " 1997 62.612329\n",
- " 1998 60.273973\n",
- " 1999 61.204658\n",
- " ... \n",
- "Rome 2016 61.185246\n",
- " 2017 61.377808\n",
- " 2018 60.821370\n",
- " 2019 59.215068\n",
- " 2020 52.676119\n",
- "Name: AvgTemperature, Length: 182, dtype: float64"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fr_sp_it_mean = fr_sp_it.groupby(['City', 'Year']).mean()['AvgTemperature']\n",
+ "outputs": [],
+ "source": [
+ "fr_sp_it_mean = fr_sp_it.groupby(['City', 'Year']).mean(numeric_only=True).AvgTemperature\n",
"fr_sp_it_mean"
]
},
@@ -2904,35 +573,12 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": null,
"id": "absent-envelope",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "City Year\n",
- "Barcelona 1995 9.569756\n",
- " 1996 9.420765\n",
- " 1997 9.827235\n",
- " 1998 19.750126\n",
- " 1999 13.904526\n",
- " ... \n",
- "Rome 2016 15.914193\n",
- " 2017 11.916595\n",
- " 2018 20.327932\n",
- " 2019 23.514064\n",
- " 2020 6.224294\n",
- "Name: AvgTemperature, Length: 182, dtype: float64"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "fr_sp_it_std = fr_sp_it.groupby(['City', 'Year']).std()['AvgTemperature']\n",
+ "outputs": [],
+ "source": [
+ "fr_sp_it_std = fr_sp_it.groupby(['City', 'Year']).std(numeric_only=True)['AvgTemperature']\n",
"fr_sp_it_std"
]
},
@@ -2948,7 +594,17 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": null,
+ "id": "1e1a6c6a-2c4c-4e81-b24c-781b5810154f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fr_sp_it_mean.reset_index()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
"id": "geological-newman",
"metadata": {},
"outputs": [],
@@ -2969,142 +625,10 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": null,
"id": "liquid-brighton",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>City</th>\n",
- " <th>Year</th>\n",
- " <th>Tmp</th>\n",
- " <th>std</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1995</td>\n",
- " <td>62.019178</td>\n",
- " <td>9.569756</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1996</td>\n",
- " <td>61.125956</td>\n",
- " <td>9.420765</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1997</td>\n",
- " <td>62.612329</td>\n",
- " <td>9.827235</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1998</td>\n",
- " <td>60.273973</td>\n",
- " <td>19.750126</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>Barcelona</td>\n",
- " <td>1999</td>\n",
- " <td>61.204658</td>\n",
- " <td>13.904526</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>177</th>\n",
- " <td>Rome</td>\n",
- " <td>2016</td>\n",
- " <td>61.185246</td>\n",
- " <td>15.914193</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>178</th>\n",
- " <td>Rome</td>\n",
- " <td>2017</td>\n",
- " <td>61.377808</td>\n",
- " <td>11.916595</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>179</th>\n",
- " <td>Rome</td>\n",
- " <td>2018</td>\n",
- " <td>60.821370</td>\n",
- " <td>20.327932</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>180</th>\n",
- " <td>Rome</td>\n",
- " <td>2019</td>\n",
- " <td>59.215068</td>\n",
- " <td>23.514064</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>181</th>\n",
- " <td>Rome</td>\n",
- " <td>2020</td>\n",
- " <td>52.676119</td>\n",
- " <td>6.224294</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>182 rows × 4 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " City Year Tmp std\n",
- "0 Barcelona 1995 62.019178 9.569756\n",
- "1 Barcelona 1996 61.125956 9.420765\n",
- "2 Barcelona 1997 62.612329 9.827235\n",
- "3 Barcelona 1998 60.273973 19.750126\n",
- "4 Barcelona 1999 61.204658 13.904526\n",
- ".. ... ... ... ...\n",
- "177 Rome 2016 61.185246 15.914193\n",
- "178 Rome 2017 61.377808 11.916595\n",
- "179 Rome 2018 60.821370 20.327932\n",
- "180 Rome 2019 59.215068 23.514064\n",
- "181 Rome 2020 52.676119 6.224294\n",
- "\n",
- "[182 rows x 4 columns]"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"clean_data = pd.merge(data_mean, data_std, on=['City', 'Year'])\n",
"clean_data"
@@ -3120,12 +644,12 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": null,
"id": "agreed-diesel",
"metadata": {},
"outputs": [],
"source": [
- "clean_data.to_csv('data/fr_sp_it_temp.tsv', sep='\\t')"
+ "clean_data.to_csv('../data/fr_sp_it_temp.tsv', sep='\\t')"
]
},
{
@@ -3140,95 +664,10 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": null,
"id": "animated-alert",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
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- "output_type": "display_data"
- },
- {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
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- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"for city, df in clean_data.groupby('City'):\n",
" df.plot('Year', 'Tmp', label=city)"
@@ -3237,9 +676,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python [conda env:dev]",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
- "name": "conda-env-dev-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -3251,7 +690,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.4"
+ "version": "3.11.10"
}
},
"nbformat": 4,
diff --git a/notebooks/data/fr_sp_it_temp.tsv b/notebooks/data/fr_sp_it_temp.tsv
index 927af2443614d2d81115d7b10e9d1ffdd4146ea4..2f9cb202439d2c19e2e737190cde2b4ed6a9432f 100644
--- a/notebooks/data/fr_sp_it_temp.tsv
+++ b/notebooks/data/fr_sp_it_temp.tsv
@@ -1,183 +1,183 @@
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
+0 Barcelona 1995 62.01917808219178 9.569756297123327
+1 Barcelona 1996 61.125956284153 9.420764506001397
+2 Barcelona 1997 62.61232876712329 9.827234879971101
+3 Barcelona 1998 60.273972602739725 19.75012607691891
+4 Barcelona 1999 61.20465753424658 13.904525518554435
+5 Barcelona 2000 60.06939890710383 9.099817440252128
+6 Barcelona 2001 59.27945205479452 10.523427017015313
+7 Barcelona 2002 58.04410958904109 18.929773327478483
+8 Barcelona 2003 63.139452054794525 15.153889346840758
+9 Barcelona 2004 62.875136612021855 11.071518902264671
+10 Barcelona 2005 62.041917808219175 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
+13 Barcelona 2008 59.65191256830601 15.614665889880865
+14 Barcelona 2009 61.55205479452055 13.90370839128742
+15 Barcelona 2010 60.66794520547945 11.479319072602506
+16 Barcelona 2011 62.834794520547945 10.102326479631735
+17 Barcelona 2012 63.03224043715847 11.292237552376044
+18 Barcelona 2013 62.24657534246575 10.973485554623132
+19 Barcelona 2014 62.4372602739726 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
+21 Barcelona 2016 60.80081967213114 19.506541913124646
+22 Barcelona 2017 62.51479452054795 11.352460171672947
+23 Barcelona 2018 61.39150684931507 18.513200196464584
+24 Barcelona 2019 59.71917808219178 23.21768377728357
+25 Barcelona 2020 55.43731343283582 5.669011817215402
+26 Bilbao 1995 58.94547945205479 9.152938194122601
27 Bilbao 1996 57.40928961748634 8.299521097803076
-28 Bilbao 1997 59.65315068493152 8.59646759755442
+28 Bilbao 1997 59.65315068493151 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
+31 Bilbao 2000 58.17704918032787 9.61060590126113
+32 Bilbao 2001 59.25808219178082 10.785430834692193
+33 Bilbao 2002 59.06191780821917 18.311794423082254
+34 Bilbao 2003 61.981369863013704 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
+36 Bilbao 2005 60.47123287671233 12.180590300525083
+37 Bilbao 2006 60.93150684931507 10.959762529415924
+38 Bilbao 2007 57.3786301369863 12.556410441414583
+39 Bilbao 2008 57.65546448087432 14.55881450004361
+40 Bilbao 2009 58.33150684931507 12.957812746937886
+41 Bilbao 2010 57.31945205479452 10.92548335370651
+42 Bilbao 2011 60.06493150684932 9.37413895288166
+43 Bilbao 2012 58.18196721311475 10.896172632501663
+44 Bilbao 2013 58.078630136986305 10.692211568623678
+45 Bilbao 2014 60.670410958904114 12.315738661838056
+46 Bilbao 2015 59.060109289617486 14.845431712407324
+47 Bilbao 2016 57.56174863387978 18.627752493559164
+48 Bilbao 2017 58.8317808219178 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
+50 Bilbao 2019 57.03232876712329 20.566454771536044
+51 Bilbao 2020 55.11492537313433 7.012797190171634
+52 Bordeaux 1995 57.37013698630137 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
+54 Bordeaux 1997 58.16931506849315 11.011332365506272
+55 Bordeaux 1998 54.70493150684931 19.598395813702982
+56 Bordeaux 1999 56.779178082191784 13.852986125645279
+57 Bordeaux 2000 57.19071038251366 10.422852712768039
58 Bordeaux 2001 56.48027397260274 11.773260583198585
-59 Bordeaux 2002 55.39232876712332 18.522743557653765
-60 Bordeaux 2003 58.219452054794544 12.897343185222015
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