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@alimanfoo
Last active October 19, 2020 21:48
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dsx_pi_20201019.ipynb
chrom pos accessible pi
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{
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{
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},
{
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"import intake\n",
"import allel\n",
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],
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},
{
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},
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"cat = intake.open_catalog('https://malariagen.github.io/intake/gcs.yml')\n",
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{
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},
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"callset_all = cat.ag2.snps.to_zarr()\n",
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{
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"data": {
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},
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{
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"source": [
"exon5_start = 48_712_957\n",
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{
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},
"source": [
"pos = allel.SortedIndex(callset_all['2R']['variants/POS'])\n",
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"loc_exon5 = pos.locate_range(exon5_start, exon5_end)\n",
"loc_exon5"
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{
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{
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{
"cell_type": "code",
"metadata": {
"id": "ZpjV3iALv-LK",
"outputId": "ec323a74-07b5-446b-c83f-4a28e7921787",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 309
}
},
"source": [
"df_samples = cat.ag2.samples.read()\n",
"df_samples.head()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ox_code</th>\n",
" <th>src_code</th>\n",
" <th>population</th>\n",
" <th>country</th>\n",
" <th>location</th>\n",
" <th>site</th>\n",
" <th>contributor</th>\n",
" <th>contact</th>\n",
" <th>year</th>\n",
" <th>m_s</th>\n",
" <th>sex</th>\n",
" <th>n_sequences</th>\n",
" <th>mean_coverage</th>\n",
" <th>ebi_sample_acc</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AA0040-C</td>\n",
" <td>Twifo_Praso__E2</td>\n",
" <td>GHcol</td>\n",
" <td>Ghana</td>\n",
" <td>Twifo Praso</td>\n",
" <td>Twifo Praso</td>\n",
" <td>David Weetman</td>\n",
" <td>David Weetman</td>\n",
" <td>2012</td>\n",
" <td>M</td>\n",
" <td>F</td>\n",
" <td>95033368</td>\n",
" <td>30.99</td>\n",
" <td>ERS311878</td>\n",
" <td>5.60858</td>\n",
" <td>-1.54926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AA0041-C</td>\n",
" <td>Twifo_Praso__H3</td>\n",
" <td>GHcol</td>\n",
" <td>Ghana</td>\n",
" <td>Twifo Praso</td>\n",
" <td>Twifo Praso</td>\n",
" <td>David Weetman</td>\n",
" <td>David Weetman</td>\n",
" <td>2012</td>\n",
" <td>M</td>\n",
" <td>F</td>\n",
" <td>95843804</td>\n",
" <td>31.70</td>\n",
" <td>ERS311886</td>\n",
" <td>5.60858</td>\n",
" <td>-1.54926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AA0042-C</td>\n",
" <td>Takoradi_C7</td>\n",
" <td>GHcol</td>\n",
" <td>Ghana</td>\n",
" <td>Takoradi</td>\n",
" <td>Takoradi</td>\n",
" <td>David Weetman</td>\n",
" <td>David Weetman</td>\n",
" <td>2012</td>\n",
" <td>M</td>\n",
" <td>F</td>\n",
" <td>107420666</td>\n",
" <td>35.65</td>\n",
" <td>ERS311894</td>\n",
" <td>4.91217</td>\n",
" <td>-1.77397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AA0043-C</td>\n",
" <td>Takoradi_H8</td>\n",
" <td>GHcol</td>\n",
" <td>Ghana</td>\n",
" <td>Takoradi</td>\n",
" <td>Takoradi</td>\n",
" <td>David Weetman</td>\n",
" <td>David Weetman</td>\n",
" <td>2012</td>\n",
" <td>M</td>\n",
" <td>F</td>\n",
" <td>95993752</td>\n",
" <td>29.46</td>\n",
" <td>ERS311902</td>\n",
" <td>4.91217</td>\n",
" <td>-1.77397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AA0044-C</td>\n",
" <td>Takoradi_D10</td>\n",
" <td>GHcol</td>\n",
" <td>Ghana</td>\n",
" <td>Takoradi</td>\n",
" <td>Takoradi</td>\n",
" <td>David Weetman</td>\n",
" <td>David Weetman</td>\n",
" <td>2012</td>\n",
" <td>M</td>\n",
" <td>F</td>\n",
" <td>103044262</td>\n",
" <td>33.67</td>\n",
" <td>ERS311910</td>\n",
" <td>4.91217</td>\n",
" <td>-1.77397</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ox_code src_code population ... ebi_sample_acc latitude longitude\n",
"0 AA0040-C Twifo_Praso__E2 GHcol ... ERS311878 5.60858 -1.54926\n",
"1 AA0041-C Twifo_Praso__H3 GHcol ... ERS311886 5.60858 -1.54926\n",
"2 AA0042-C Takoradi_C7 GHcol ... ERS311894 4.91217 -1.77397\n",
"3 AA0043-C Takoradi_H8 GHcol ... ERS311902 4.91217 -1.77397\n",
"4 AA0044-C Takoradi_D10 GHcol ... ERS311910 4.91217 -1.77397\n",
"\n",
"[5 rows x 16 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ps-9r0LHv_3l",
"outputId": "04fe90b1-71a4-4269-e8b7-8e776887c8e4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"source": [
"populations = df_samples.population.unique()\n",
"populations"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['GHcol', 'GHgam', 'BFgam', 'BFcol', 'UGgam', 'GM', 'GW', 'KE',\n",
" 'CMgam', 'FRgam', 'GQgam', 'AOcol', 'GAgam', 'GNgam', 'GNcol',\n",
" 'CIcol'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "aHKQ3kStwDGA"
},
"source": [
"# subset genotypes to populations\n",
"gt_exon5_pops = dict()\n",
"for pop in populations:\n",
" loc_pop = df_samples[df_samples.population == pop].index.values\n",
" gt_exon5_pops[pop] = gt_exon5.take(loc_pop, axis=1)\n"
],
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "d6MLhOkzwHCV",
"outputId": "d10bf789-f70a-4575-fde7-149fb2ef489a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"gt_exon5_pops['GHcol']"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"execution_count": 13
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},
{
"cell_type": "code",
"metadata": {
"id": "6JThP2n2wJGN"
},
"source": [
"# compute allele counts\n",
"ac_exon5_pops = dict()\n",
"for pop in populations:\n",
" gt = gt_exon5_pops[pop]\n",
" ac = gt.count_alleles(max_allele=3)\n",
" ac_exon5_pops[pop] = ac\n"
],
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5FiqQm_Qwcf9",
"outputId": "816231d7-baa2-4d68-ec54-96b5ac1bb7ca",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"ac_exon5_pops['GHcol']"
],
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"metadata": {
"id": "zr6aKNT1xUGk",
"outputId": "b45f275a-9a39-47bc-e403-d563e9153b7d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 238
}
},
"source": [
"\n",
"# compute diversity at variant sites\n",
"pi_pop = np.vstack([allel.mean_pairwise_difference(ac_exon5_pops[pop]) for pop in populations])\n",
"pi_pop"
],
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
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"execution_count": 16
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{
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"outputId": "eb68056a-643c-4bc6-8e9b-fb07da2b91b3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"pi_pop.shape"
],
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(16, 352)"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BGgU60rjxXzV",
"outputId": "d703b7a7-c881-45d2-ff2f-33efbda8f073",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# average over all populations\n",
"pi_allpops_mean = pi_pop.mean(axis=0)\n",
"pi_allpops_mean.shape"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(352,)"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Kn3zS81YwezN",
"outputId": "23849817-5cba-4819-ed09-45d1290e479d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# all positions within exon 5\n",
"pos_allsites_exon5 = np.arange(exon5_start, exon5_end + 1)\n",
"pos_allsites_exon5"
],
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([48712957, 48712958, 48712959, ..., 48714646, 48714647, 48714648])"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nvNtD5rqxAl7",
"outputId": "24b49f17-6c45-4cfd-de99-58129f64dd78",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# load accessibility\n",
"accessibility = cat.ag2.accessibility.to_zarr()\n",
"accessibility"
],
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<zarr.hierarchy.Group '/' read-only>"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8byHAsiKwxWU",
"outputId": "12d0a654-4c52-4a6c-b590-efd97762fcd6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"is_accessible_exon5 = accessibility['2R']['is_accessible'][exon5_start - 1:exon5_end]\n",
"is_accessible_exon5"
],
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([ True, True, True, ..., True, True, True])"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "bS5MHNZ2xNCI",
"outputId": "02eee3da-4687-461e-a332-f0b76066af34",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"pi_allsites_exon5 = np.zeros(pos_allsites_exon5.shape, dtype=float)\n",
"pi_allsites_exon5"
],
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0., 0., 0., ..., 0., 0., 0.])"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Oa-3k5F3yBb2",
"outputId": "b0d868f7-7798-4448-9325-cec2c406f71a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"loc_insert = allel.SortedIndex(pos_allsites_exon5).locate_keys(pos_exon5)\n",
"loc_insert"
],
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([False, False, False, ..., False, False, False])"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dXyHXPWZyLED",
"outputId": "e5cc3025-bcf7-48b1-e0e9-fbc880910943",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"loc_insert.shape, np.count_nonzero(loc_insert)"
],
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((1692,), 352)"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KlMHVDIQyNfU"
},
"source": [
"pi_allsites_exon5[loc_insert] = pi_allpops_mean"
],
"execution_count": 25,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-4enWPq_ybn9",
"outputId": "456c42cb-16ea-423c-e233-c9787f6d0394",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"pi_allsites_exon5.mean()"
],
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.006092060506447091"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "63RJoVVXyd8t",
"outputId": "580d1369-dbfc-45be-839d-b2fc8c08c213",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
}
},
"source": [
"df = pd.DataFrame({'chrom': '2R',\n",
" 'pos': pos_allsites_exon5,\n",
" 'accessible': is_accessible_exon5,\n",
" 'pi': pi_allsites_exon5})\n",
"df"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"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>chrom</th>\n",
" <th>pos</th>\n",
" <th>accessible</th>\n",
" <th>pi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2R</td>\n",
" <td>48712957</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2R</td>\n",
" <td>48712958</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2R</td>\n",
" <td>48712959</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2R</td>\n",
" <td>48712960</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2R</td>\n",
" <td>48712961</td>\n",
" <td>True</td>\n",
" <td>0.0</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>1687</th>\n",
" <td>2R</td>\n",
" <td>48714644</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1688</th>\n",
" <td>2R</td>\n",
" <td>48714645</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1689</th>\n",
" <td>2R</td>\n",
" <td>48714646</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1690</th>\n",
" <td>2R</td>\n",
" <td>48714647</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1691</th>\n",
" <td>2R</td>\n",
" <td>48714648</td>\n",
" <td>True</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1692 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" chrom pos accessible pi\n",
"0 2R 48712957 True 0.0\n",
"1 2R 48712958 True 0.0\n",
"2 2R 48712959 True 0.0\n",
"3 2R 48712960 True 0.0\n",
"4 2R 48712961 True 0.0\n",
"... ... ... ... ...\n",
"1687 2R 48714644 True 0.0\n",
"1688 2R 48714645 True 0.0\n",
"1689 2R 48714646 True 0.0\n",
"1690 2R 48714647 True 0.0\n",
"1691 2R 48714648 True 0.0\n",
"\n",
"[1692 rows x 4 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "8oW0ZuYGyvY5"
},
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
],
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3ocHXljyy__G",
"outputId": "6fbdcada-9d66-450a-f57b-7d87729583d8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
}
},
"source": [
"fig, ax = plt.subplots()\n",
"ax.plot(df['pos'], df['pi'])\n",
"ax.set_ylabel('$\\pi$')\n",
"ax.set_xlabel('Position (bp)');"
],
"execution_count": 29,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "peY8wHEIzFzc",
"outputId": "ef8697bd-8797-408a-959c-dde99e8ed0b7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
}
},
"source": [
"fig, ax = plt.subplots()\n",
"ax.plot(df['pos'], df['pi'])\n",
"ax.set_xlim(48_714_400, 48_714_650)\n",
"ax.set_ylabel('$\\pi$')\n",
"ax.set_xlabel('Position (bp)');"
],
"execution_count": 30,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ztnygIP8zPkg"
},
"source": [
"df.to_csv('dsx_exon5_pi.csv', index=False)"
],
"execution_count": 34,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "AdwXfRnnzvoV",
"outputId": "dea49c45-14bc-4624-fcc8-cfa7db6482d3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"np.count_nonzero(df['accessible'])"
],
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1572"
]
},
"metadata": {
"tags": []
},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QPhXgnu90OPH",
"outputId": "0743dcb1-d38f-4984-f2c4-a87409be1ece",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"np.count_nonzero(~df['accessible'])"
],
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"120"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mjgPqY3p0Q1E"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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