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messa/2020_02_20_radky.ipynb

Created February 20, 2020 11:55
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2020_02_20_radky.ipynb
{
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"metadata": {
"colab": {
"name": "2020-02-20 radky.ipynb",
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
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"cell_type": "code",
"metadata": {
"id": "Y0GR-wCKaJN5",
"colab_type": "code",
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},
"source": [
"import pandas as pd\n",
"from pandas import DataFrame"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "QfUWbjjLaeTf",
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"source": [
"tabulka_a = DataFrame(data=[\n",
" dict(a=22, b='dd', c='ee', d='ff'),\n",
" dict(a=11, b='aa', c='bb', d='cc'),\n",
" dict(a=33, b='gg', c='hh', d='ii'),\n",
"])\n",
"tabulka_b = DataFrame(data=[\n",
" dict(a=11, b='aa', c='hh', d='cc'),\n",
" dict(a=11, b='aa', c='bb', d='cc'),\n",
" dict(a=55, b='hh', c='nn', d='mm'),\n",
" dict(a=22, b='dd', c='ee', d='ff'),\n",
"])"
],
"execution_count": 0,
"outputs": []
},
{
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"height": 142
},
"outputId": "105f3554-cfad-4f3c-af9c-158a91046b5a"
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"source": [
"tabulka_a"
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"outputs": [
{
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"data": {
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" <td>gg</td>\n",
" <td>hh</td>\n",
" <td>ii</td>\n",
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],
"text/plain": [
" a b c d\n",
"0 22 dd ee ff\n",
"1 11 aa bb cc\n",
"2 33 gg hh ii"
]
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"execution_count": 8
}
]
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"id": "wf3AwrVkasXG",
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"height": 173
},
"outputId": "53208926-6f80-4d9b-e7e2-2d80dde70642"
},
"source": [
"tabulka_b"
],
"execution_count": 9,
"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",
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"\n",
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"\n",
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" text-align: right;\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>a</th>\n",
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" <th>c</th>\n",
" <th>d</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>aa</td>\n",
" <td>hh</td>\n",
" <td>cc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11</td>\n",
" <td>aa</td>\n",
" <td>bb</td>\n",
" <td>cc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>55</td>\n",
" <td>hh</td>\n",
" <td>nn</td>\n",
" <td>mm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>22</td>\n",
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" <td>ee</td>\n",
" <td>ff</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" a b c d\n",
"0 11 aa hh cc\n",
"1 11 aa bb cc\n",
"2 55 hh nn mm\n",
"3 22 dd ee ff"
]
},
"metadata": {
"tags": []
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"execution_count": 9
}
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{
"cell_type": "code",
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"id": "UBUjUfWsbN4L",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 142
},
"outputId": "868ee409-fb50-4cf5-9557-cf839cd579f0"
},
"source": [
"pd.merge(tabulka_a, tabulka_b, how='left', indicator=True)"
],
"execution_count": 17,
"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",
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"</style>\n",
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" <th></th>\n",
" <th>a</th>\n",
" <th>b</th>\n",
" <th>c</th>\n",
" <th>d</th>\n",
" <th>_merge</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>22</td>\n",
" <td>dd</td>\n",
" <td>ee</td>\n",
" <td>ff</td>\n",
" <td>both</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11</td>\n",
" <td>aa</td>\n",
" <td>bb</td>\n",
" <td>cc</td>\n",
" <td>both</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>33</td>\n",
" <td>gg</td>\n",
" <td>hh</td>\n",
" <td>ii</td>\n",
" <td>left_only</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a b c d _merge\n",
"0 22 dd ee ff both\n",
"1 11 aa bb cc both\n",
"2 33 gg hh ii left_only"
]
},
"metadata": {
"tags": []
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"execution_count": 17
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LVuJyvgZcA3q",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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