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‎titan.ipynb

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"metadata": {
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"colab": {
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"provenance": [],
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"authorship_tag": "ABX9TyP6CLE4Aseaf5bHq6gItZ+K",
7+
"authorship_tag": "ABX9TyNspALEWfspOU4EISKEpAa1",
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"include_colab_link": true
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},
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"kernelspec": {
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "2T2yFd5ebn4K",
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"outputId": "e9b9474d-6457-430d-b01d-1022948e2e85"
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"outputId": "ac75d61b-e9f8-4363-e0ff-232592ef37ea"
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},
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"outputs": [
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{
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"df_all.info()"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"df.isna"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "-m8AsyshVFZ5",
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"outputId": "7f27a6b0-8d9a-42ef-ef12-0a5804f6abaa"
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},
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"execution_count": 13,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<bound method DataFrame.isna of Column1 Column2 Column3\n",
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"0 10 0 1st\n",
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"1 25 NULL 3rd\n",
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"2 NULL 1 3rd\n",
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"3 70 0 2nd\n",
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"4 18 1 1st>"
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]
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},
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"metadata": {},
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"execution_count": 13
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"# Find columns with NaN values\n",
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"columns_with_nan = df.columns[df.isna().any()].tolist()\n",
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"\n",
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"# Print the column names\n",
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"print(', '.join(columns_with_nan))"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "MaIfl98vaK5U",
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"outputId": "d10e9982-4e94-446c-cb60-950f45519396"
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},
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"execution_count": 15,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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},
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{
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"cell_type": "code",
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"source": [],
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"source": [
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"df.drop({'Column1','Column2'}, axis=1)"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 206
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"id": "gpX1wsiUBdVN",
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"outputId": "38586426-b0ad-447b-995f-7a75da743bab"
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},
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"outputs": []
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" Column3\n",
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"0 1st\n",
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"1 3rd\n",
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"2 3rd\n",
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"4 1st"
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],
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"text/html": [
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"\n",
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" <div id=\"df-787fd3a7-4838-4e96-a920-e37ad624d125\">\n",
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" <div class=\"colab-df-container\">\n",
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" <div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" .dataframe thead th {\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Column3</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1st</td>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>3rd</td>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>3rd</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>2nd</td>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>1st</td>\n",
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" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-787fd3a7-4838-4e96-a920-e37ad624d125')\"\n",
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"\n",
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" [theme=dark] .colab-df-convert {\n",
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" background-color: #3B4455;\n",
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"\n",
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" [theme=dark] .colab-df-convert:hover {\n",
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" }\n",
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" </style>\n",
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"\n",
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" <script>\n",
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" const buttonEl =\n",
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" document.querySelector('#df-787fd3a7-4838-4e96-a920-e37ad624d125 button.colab-df-convert');\n",
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" buttonEl.style.display =\n",
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" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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"\n",
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" async function convertToInteractive(key) {\n",
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" const element = document.querySelector('#df-787fd3a7-4838-4e96-a920-e37ad624d125');\n",
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" const dataTable =\n",
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" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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" [key], {});\n",
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" if (!dataTable) return;\n",
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"\n",
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" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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" + ' to learn more about interactive tables.';\n",
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" element.innerHTML = '';\n",
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" dataTable['output_type'] = 'display_data';\n",
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" await google.colab.output.renderOutput(dataTable, element);\n",
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" const docLink = document.createElement('div');\n",
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" docLink.innerHTML = docLinkHtml;\n",
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" element.appendChild(docLink);\n",
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" }\n",
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" </div>\n",
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" </div>\n",
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" "
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]
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},
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"metadata": {},
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"execution_count": 3
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import pandas as pd\n",
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"data = pd.read_csv('/content/task4_1_939291.csv')\n",
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"data.info()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "4fAtb7IUif1J",
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"outputId": "ab33d3c2-6530-4cc0-9783-d00520d746a2"
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},
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"execution_count": 23,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 200 entries, 0 to 199\n",
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"Data columns (total 6 columns):\n",
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" # Column Non-Null Count Dtype\n",
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"--- ------ -------------- -----\n",
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" 0 ID 200 non-null int64\n",
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" 1 DISTANCE 200 non-null int64\n",
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" 2 STOP_COUNT 200 non-null int64\n",
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" 3 COST 200 non-null int64\n",
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" 4 FITNESS 200 non-null int64\n",
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" 5 INTERNET 200 non-null int64\n",
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"dtypes: int64(6)\n",
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"memory usage: 9.5 KB\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Присвоение имен столбцам\n",
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"data.columns = ['ID', 'Расстояние до метро (мин)', 'Количество остановок на метро', 'Стоимость аренды', 'Расстояние до фитнес-клуба (мин)', 'Количество интернет-провайдеров']\n",
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"\n",
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"# Экспоненциальная нормировка\n",
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"normalized_distance = data['Расстояние до метро (мин)'].apply(lambda x: 1 / (2 ** x))\n",
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"normalized_stops = data['Количество остановок на метро'].apply(lambda x: 1 / (2 ** x))\n",
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"normalized_rent = data['Стоимость аренды'].apply(lambda x: 1 / x)\n",
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"normalized_fitness = data['Расстояние до фитнес-клуба (мин)'].apply(lambda x: 1 / (2 ** x))\n",
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"normalized_providers = -0.4 * data['Количество интернет-провайдеров']\n",
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"\n",
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"# Вычисление целевой функции\n",
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"target_function = normalized_distance + normalized_stops + normalized_rent + 0.2 * normalized_fitness + normalized_providers\n",
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"\n",
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"# Выбор трех лучших вариантов\n",
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"best_options = target_function.nsmallest(3)\n",
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"\n",
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"# Получение идентификаторов трех лучших вариантов\n",
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"best_ids = best_options.index.tolist()\n",
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"\n",
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"# Вывод результатов\n",
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"print('Идентификаторы трех лучших вариантов:', ', '.join(map(str, best_ids)))\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "uB8Kv6IHUzjJ",
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"outputId": "781304fc-30aa-44df-9540-ba1fbbdfed9d"
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},
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"execution_count": 18,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Идентификаторы трех лучших вариантов: 125, 4, 44\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"data = pd.read_csv('/content/task4_1_939291.csv')\n",
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"\n",
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"data['distance_walk_norm'] = 1 - np.exp(1 - data['DISTANCE'] / data['DISTANCE'].min())\n",
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"data['metro_stations_norm'] = 1 - np.exp(1 - data['STOP_COUNT'] / data['STOP_COUNT'].min())\n",
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"data['rent_norm'] = 1 - np.exp(1 - data['COST'] / data['COST'].min())\n",
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"data['fitness_distance_norm'] = 1 - np.exp(1 - data['FITNESS'] / data['FITNESS'].min())\n",
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"data['internet_providers_norm'] = -1 * np.exp(1 - data['INTERNET'] / data['INTERNET'].max())\n",
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"\n",
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"data['target_value'] = (\n",
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" data['distance_walk_norm'] +\n",
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" data['metro_stations_norm'] +\n",
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" 0.2 * data['fitness_distance_norm'] +\n",
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" 0.4 * data['internet_providers_norm']\n",
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")\n",
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"\n",
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"best_options = data.nsmallest(3, 'target_value')['ID'].tolist()\n",
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"\n",
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"best_options_str = \",\".join(map(str, best_options))\n",
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"print(best_options_str)\n"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "sPOx3Bz2nYwi",
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"outputId": "f4eb3ace-9150-4a03-83f1-d0025474315d"
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},
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"execution_count": 24,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"180,17,9\n"
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]
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}
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]
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}
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]
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}

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