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olivx/escola.xlsx

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Created December 5, 2018 21:29
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Annotations of Pandas DataFrame
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gen_names_xlsx.ipynb
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gen_random_values.ipynb
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import_xlsx_to_django_database.ipynb
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pandas.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"filename = '/home/dev/fix/348670_ion_annotations.tsv'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(filename, sep='\\t')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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" .dataframe thead tr:only-child th {\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>chrom</th>\n",
" <th>pos</th>\n",
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" <th>alt</th>\n",
" <th>qual</th>\n",
" <th>filter</th>\n",
" <th>alt_pos_strand</th>\n",
" <th>dbsnp_bin</th>\n",
" <th>dbsnp_chrom</th>\n",
" <th>...</th>\n",
" <th>gnomad_an_popmax</th>\n",
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" <th>gnomad_dp_median</th>\n",
" <th>gnomad_dref_median</th>\n",
" <th>gnomad_gq_median</th>\n",
" <th>gnomad_ab_median</th>\n",
" <th>gnomad_as_rf</th>\n",
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" <th>fdvr</th>\n",
" <th>hs_only</th>\n",
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" </tbody>\n",
"</table>\n",
"<p>13 rows × 265 columns</p>\n",
"</div>"
],
"text/plain": [
" chrom pos _id ref alt qual filter alt_pos_strand dbsnp_bin \\\n",
"0 4 0 19 1 1 0 4 1 0 \n",
"1 5 0 19 1 1 0 4 1 0 \n",
"2 5 0 18 1 1 0 4 1 0 \n",
"3 5 0 19 1 1 0 4 1 0 \n",
"4 5 0 19 1 1 0 4 1 0 \n",
"5 5 0 19 1 1 0 4 1 0 \n",
"6 5 0 19 1 1 0 4 1 0 \n",
"7 5 0 19 1 1 0 4 1 0 \n",
"8 5 0 19 1 1 0 4 1 0 \n",
"9 5 0 19 1 1 0 4 1 0 \n",
"10 5 0 19 1 1 0 4 1 0 \n",
"11 5 0 19 1 1 0 4 1 0 \n",
"12 5 0 19 1 1 0 4 1 0 \n",
"\n",
" dbsnp_chrom ... gnomad_an_popmax gnomad_af_popmax \\\n",
"0 4 ... 0 0 \n",
"1 5 ... 0 0 \n",
"2 5 ... 0 0 \n",
"3 5 ... 0 0 \n",
"4 5 ... 5 11 \n",
"5 5 ... 11 23 \n",
"6 5 ... 10 23 \n",
"7 5 ... 10 23 \n",
"8 5 ... 4 11 \n",
"9 5 ... 5 11 \n",
"10 5 ... 0 0 \n",
"11 5 ... 6 11 \n",
"12 5 ... 4 11 \n",
"\n",
" gnomad_dp_median gnomad_dref_median gnomad_gq_median gnomad_ab_median \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 2 11 2 11 \n",
"5 6 25 5 23 \n",
"6 5 24 5 23 \n",
"7 5 24 5 23 \n",
"8 2 12 2 11 \n",
"9 2 11 2 11 \n",
"10 0 0 0 0 \n",
"11 3 12 2 11 \n",
"12 2 12 2 11 \n",
"\n",
" gnomad_as_rf gnomad_as_filterstatus fdvr hs_only \n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 11 4 0 0 \n",
"5 23 9 0 0 \n",
"6 23 9 0 0 \n",
"7 23 9 0 0 \n",
"8 11 4 0 0 \n",
"9 11 4 0 0 \n",
"10 0 0 0 0 \n",
"11 11 4 0 0 \n",
"12 11 4 0 0 \n",
"\n",
"[13 rows x 265 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Como ver o tamanho de todos os campos\n",
"df.applymap(lambda el: len(el) if isinstance(el, str) else 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Django Shell-Plus",
"language": "python",
"name": "django_extensions"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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remove_duplicated.ipynb
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separe_email.ipynb
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