Dmitry
98c27fec85
Perform a bulk rename/move of many files and folders: paths previously prefixed with '01', '02', '03', '04', etc. were relocated to equivalent paths prefixed with '8'. Changes are path-only (R100 renames) and do not modify file contents; this reorganizes the repository structure.
681 lines
22 KiB
Plaintext
681 lines
22 KiB
Plaintext
{
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"name": "python3",
|
|
"language": "python"
|
|
},
|
|
"language_info": {
|
|
"name": "python",
|
|
"version": "3.12.13",
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"pygments_lexer": "ipython3",
|
|
"mimetype": "text/x-python",
|
|
"nbconvert_exporter": "python",
|
|
"file_extension": ".py"
|
|
},
|
|
"colab": {
|
|
"provenance": []
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5,
|
|
"cells": [
|
|
{
|
|
"cell_type": "code",
|
|
"id": "2e2f8a4c-7063-4650-ace5-8f230c8d994a",
|
|
"metadata": {
|
|
"id": "5tecyK1Y5y_0"
|
|
},
|
|
"execution_count": 1,
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"import seaborn as sns\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"from scipy.stats import pearsonr, spearmanr, kendalltau\n",
|
|
"import sqlite3\n",
|
|
"import kagglehub\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"/home/ada/Documents/Files/files_mephi/Работы/ИРФМ/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not\n",
|
|
" found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
|
" from .autonotebook import tqdm as notebook_tqdm\n"
|
|
]
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "8f16c784-75c9-44a1-8f9c-6555bdf38a08",
|
|
"metadata": {
|
|
"id": "-kxc18fy5gMU",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "3db947c3-f4c3-4449-91eb-1a5722ba2deb"
|
|
},
|
|
"execution_count": 2,
|
|
"source": [
|
|
"# https://www.kaggle.com/datasets/ziya07/financial-transaction-dataset-for-risk-prediction?select=financial_transactions.csv\n",
|
|
"dataset_dir = kagglehub.dataset_download(\n",
|
|
" \"mchirico/montcoalert\",\n",
|
|
" output_dir=\"../data/montcoalert\",\n",
|
|
" # force_download=True, # раскомментируйте, если нужно перекачать датасет\n",
|
|
")\n",
|
|
"\n",
|
|
"print(\"Path to dataset files:\", dataset_dir)\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"Path to dataset files: ../data/montcoalert\n"
|
|
]
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "8228deb7-1ef3-4b6a-b16d-795c5b80a10b",
|
|
"metadata": {
|
|
"id": "jjxxafuj5twF",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 597
|
|
},
|
|
"outputId": "07ffb386-df17-4830-daa5-20e36a68e0c5"
|
|
},
|
|
"execution_count": 3,
|
|
"source": [
|
|
"import os\n",
|
|
"\n",
|
|
"df = pd.read_csv(os.path.join(dataset_dir, \"911.csv\"))\n",
|
|
"df\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "bcd5db7c-b149-4e05-a1c5-d5df628572d4",
|
|
"metadata": {
|
|
"id": "JFENAQ6x6M-2",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 424
|
|
},
|
|
"outputId": "69f2c9f2-256e-4184-f6cc-7895b3065557"
|
|
},
|
|
"execution_count": 4,
|
|
"source": [
|
|
"df = df.drop(columns=['desc', 'zip', 'addr', 'e'])\n",
|
|
"df['town'] = df['twp']\n",
|
|
"df = df.drop(columns=['twp'])\n",
|
|
"df\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "8322a532-24e5-406f-b019-cbf264676259",
|
|
"metadata": {
|
|
"id": "kjx88uH06yXh",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 241
|
|
},
|
|
"outputId": "b4525822-dc37-4baf-ef06-2429efb9d9b9"
|
|
},
|
|
"execution_count": 5,
|
|
"source": [
|
|
"df.isnull().sum()\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"lat 0\n",
|
|
"lng 0\n",
|
|
"title 0\n",
|
|
"timeStamp 0\n",
|
|
"town 293\n",
|
|
"dtype: int64\n"
|
|
]
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "9ea98f73-7ab5-4b31-a3c7-d4031b65636c",
|
|
"metadata": {
|
|
"id": "CVwu4mZY7j38",
|
|
"outputId": "10c839ff-e230-4429-8ec1-dd1160e0e52a",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 241
|
|
}
|
|
},
|
|
"execution_count": 6,
|
|
"source": [
|
|
"df = df.dropna()\n",
|
|
"df.isnull().sum()\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"lat 0\n",
|
|
"lng 0\n",
|
|
"title 0\n",
|
|
"timeStamp 0\n",
|
|
"town 0\n",
|
|
"dtype: int64\n"
|
|
]
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "1e0859b5-80a7-45d3-b681-4e3f5e97944f",
|
|
"metadata": {
|
|
"id": "BVziGRoT74je",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 300
|
|
},
|
|
"outputId": "cb96454a-a04b-4ea0-d044-981fdddcc008"
|
|
},
|
|
"execution_count": 7,
|
|
"source": [
|
|
"df.describe()\n",
|
|
"\n",
|
|
"# штат Пенсильвания (PA) находится примерно в следующих координатах:\n",
|
|
"# Широта (lat): от 39.7° N до 42.5° N\n",
|
|
"# Долгота (lng): от -80.5° W до -74.7° W\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "6931cd95-26a2-43da-98f0-90d69e3a7afe",
|
|
"metadata": {
|
|
"id": "YZyG4DIl8uQE",
|
|
"outputId": "0f7ec0ed-aeed-426b-f367-640113d8057d",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 300
|
|
}
|
|
},
|
|
"execution_count": 8,
|
|
"source": [
|
|
"df_clean = df[\n",
|
|
" (df['lat'].between(39.7, 42.5)) &\n",
|
|
" (df['lng'].between(-80.5, -74.7))\n",
|
|
"]\n",
|
|
"df_clean.describe()\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "76fd2b05-51a2-420d-a7c4-6a41cf922cfb",
|
|
"metadata": {
|
|
"id": "9UHU8d6s8597",
|
|
"outputId": "f822f2bb-1bcb-44b2-9371-e1bd0480a509",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 424
|
|
}
|
|
},
|
|
"execution_count": 9,
|
|
"source": [
|
|
"df_grouped = df_clean.groupby('town').size().reset_index(name='count')\n",
|
|
"df_grouped = df_grouped[df_grouped['count'] >= 5]\n",
|
|
"df_grouped.sort_values(by='count', ascending=False)\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "7204f62a-c6a3-4b60-8a6c-ae055c771276",
|
|
"metadata": {
|
|
"id": "8XxEu-5I_6oz",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 526
|
|
},
|
|
"outputId": "831e0639-0ffe-4a9d-e309-e6d19745375a"
|
|
},
|
|
"execution_count": 10,
|
|
"source": [
|
|
"plt.figure(figsize=(8, 6))\n",
|
|
"sns.boxplot(y=df_grouped['count'])\n",
|
|
"plt.title('Распределение количества вызовов по городам')\n",
|
|
"plt.ylabel('Количество вызовов')\n",
|
|
"plt.show()\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "8e69711f-ea64-4139-bd42-bab3ffe02750",
|
|
"metadata": {
|
|
"id": "hL-4vAqy_gsN",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 300
|
|
},
|
|
"outputId": "50f07e22-0a9c-4b43-87a4-94c18e7601e6"
|
|
},
|
|
"execution_count": 11,
|
|
"source": [
|
|
"df_grouped.describe()\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "2f9d95cf-4449-4b59-918e-7662c1eba9da",
|
|
"metadata": {
|
|
"id": "lFjeZjICF-C-",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "9c2c08fa-ae97-4b74-f3a4-92148999c037"
|
|
},
|
|
"execution_count": 12,
|
|
"source": [
|
|
"df_grouped = df_grouped.sort_values(by='count')\n",
|
|
"counts = df_grouped['count'].values\n",
|
|
"towns = df_grouped['town'].values\n",
|
|
"n = len(counts)\n",
|
|
"\n",
|
|
"total_range = counts.max() - counts.min() # Размах\n",
|
|
"gap_threshold = 0.1 * total_range # Порог\n",
|
|
"\n",
|
|
"# Отсекаем нижние и верхние 10% по порядку наблюдений\n",
|
|
"bottom_10_idx = int(n * 0.10)\n",
|
|
"top_10_idx = int(n * 0.90)\n",
|
|
"\n",
|
|
"towns_to_drop = set()\n",
|
|
"\n",
|
|
"# Проверка нижних 10% (идем от центра к краям)\n",
|
|
"# Ищем разрыв между y (индекс i) и соседом ближе к центру (индекс i+1)\n",
|
|
"for i in range(bottom_10_idx, -1, -1): # (6, 5, 4, 3, 2, 1, 0)\n",
|
|
" if i + 1 < n:\n",
|
|
" gap = counts[i+1] - counts[i]\n",
|
|
" if gap > gap_threshold:\n",
|
|
" # Нашли разрыв. Удаляем всё от y до края\n",
|
|
" for j in range(i, -1, -1):\n",
|
|
" towns_to_drop.add(towns[j])\n",
|
|
" break\n",
|
|
"\n",
|
|
"# Проверка верхних 10% (идем от центра к краям)\n",
|
|
"# Ищем разрыв между y (индекс i) и соседом ближе к центру (индекс i-1)\n",
|
|
"for i in range(top_10_idx, n):\n",
|
|
" if i - 1 >= 0:\n",
|
|
" gap = counts[i] - counts[i-1]\n",
|
|
" if gap > gap_threshold:\n",
|
|
" # Нашли разрыв. Удаляем всё от y до края\n",
|
|
" for j in range(i, n):\n",
|
|
" towns_to_drop.add(towns[j])\n",
|
|
" break\n",
|
|
"\n",
|
|
"towns_to_drop\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"{'LOWER MERION'}\n"
|
|
]
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "401fa973-2d94-440b-ae9a-65b7fa31c5e2",
|
|
"metadata": {
|
|
"id": "2Nch3p_tHwOf",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "cd95841f-df8d-496f-a39e-a6c4877e067a"
|
|
},
|
|
"execution_count": 13,
|
|
"source": [
|
|
"df_clean = df_clean[~df_clean['town'].isin(towns_to_drop)]\n",
|
|
"\n",
|
|
"df_clean.info()\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"<class 'pandas.DataFrame'>\n",
|
|
"Index: 607557 entries, 0 to 663520\n",
|
|
"Data columns (total 5 columns):\n",
|
|
" # Column Non-Null Count Dtype\n",
|
|
"--- ------ -------------- -----\n",
|
|
" 0 lat 607557 non-null float64\n",
|
|
" 1 lng 607557 non-null float64\n",
|
|
" 2 title 607557 non-null str\n",
|
|
" 3 timeStamp 607557 non-null str\n",
|
|
" 4 town 607557 non-null str\n",
|
|
"dtypes: float64(2), str(3)\n",
|
|
"memory usage: 27.8 MB\n"
|
|
]
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "a71317e7-7d15-4878-88f0-f201c15bf64d",
|
|
"metadata": {
|
|
"id": "pORHOk6xAe6b",
|
|
"outputId": "4151cbca-371f-4cab-d8e1-b91e9712703a",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 526
|
|
}
|
|
},
|
|
"execution_count": 14,
|
|
"source": [
|
|
"df_clean_grouped = df_clean.groupby('town').size().reset_index(name='count')\n",
|
|
"plt.figure(figsize=(8, 6))\n",
|
|
"sns.boxplot(y=df_clean_grouped['count'])\n",
|
|
"plt.title('Распределение количества вызовов по городам')\n",
|
|
"plt.ylabel('Количество вызовов')\n",
|
|
"plt.show()\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "29af2c22-e4b2-401d-80bc-43c812867c4e",
|
|
"metadata": {
|
|
"id": "xFeEAU27DVWV",
|
|
"outputId": "d26f3a5f-6857-45e7-fc33-dce0818419f1",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 300
|
|
}
|
|
},
|
|
"execution_count": 15,
|
|
"source": [
|
|
"df_clean_grouped.describe()\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "6c425257-1b4d-41e8-8c17-1b26ade1bbeb",
|
|
"metadata": {
|
|
"id": "bace27bc",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "6f3cd65b-abb6-4e9d-c880-c0d3162bb064"
|
|
},
|
|
"execution_count": 16,
|
|
"source": [
|
|
"df_clean['timeStamp'] = pd.to_datetime(df_clean['timeStamp'])\n",
|
|
"df_clean.info()\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"<class 'pandas.DataFrame'>\n",
|
|
"Index: 607557 entries, 0 to 663520\n",
|
|
"Data columns (total 5 columns):\n",
|
|
" # Column Non-Null Count Dtype\n",
|
|
"--- ------ -------------- -----\n",
|
|
" 0 lat 607557 non-null float64\n",
|
|
" 1 lng 607557 non-null float64\n",
|
|
" 2 title 607557 non-null str\n",
|
|
" 3 timeStamp 607557 non-null datetime64[us]\n",
|
|
" 4 town 607557 non-null str\n",
|
|
"dtypes: datetime64[us](1), float64(2), str(2)\n",
|
|
"memory usage: 27.8 MB\n"
|
|
]
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "cc0a488b-ce3f-4860-ac73-cc086623ad68",
|
|
"metadata": {
|
|
"id": "9d0eeaf8",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 206
|
|
},
|
|
"outputId": "d158ac82-85e5-464c-854b-f1264a32406b"
|
|
},
|
|
"execution_count": 17,
|
|
"source": [
|
|
"df_clean['hour'] = df_clean['timeStamp'].dt.hour\n",
|
|
"df_clean.head()\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "36b114ae-9647-4555-84ad-96cc3ca7ace4",
|
|
"metadata": {
|
|
"id": "91a1edc3",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 206
|
|
},
|
|
"outputId": "04806088-c1be-4fae-e382-0240b286ffe5"
|
|
},
|
|
"execution_count": 18,
|
|
"source": [
|
|
"calls_by_hour = df_clean.groupby('hour').size().reset_index(name='count')\n",
|
|
"calls_by_hour.head()\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "153435e0-d311-47ce-9934-527aa4918935",
|
|
"metadata": {
|
|
"id": "0ef31758",
|
|
"outputId": "4a0e5512-4174-4037-f749-697c2ca60963",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/",
|
|
"height": 607
|
|
}
|
|
},
|
|
"execution_count": 19,
|
|
"source": [
|
|
"plt.figure(figsize=(10, 6))\n",
|
|
"sns.lineplot(x='hour', y='count', data=calls_by_hour, marker='o', label='Calls Count')\n",
|
|
"plt.title('Общее количество вызовов 911 по часам суток')\n",
|
|
"plt.xlabel('Час суток')\n",
|
|
"plt.ylabel('Количество вызовов')\n",
|
|
"plt.xticks(calls_by_hour['hour'])\n",
|
|
"plt.grid(True, linestyle='--', alpha=0.7)\n",
|
|
"plt.legend()\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "fe76bee3-d0a0-4dad-9867-ce6c3c711d08",
|
|
"metadata": {
|
|
"id": "jg4PAmmUQ_9o",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"outputId": "512127d4-5308-419f-a7dd-cab7399473af"
|
|
},
|
|
"execution_count": 20,
|
|
"source": [
|
|
"def analyze_correlations(df, col1, col2):\n",
|
|
" x = df[col1].values\n",
|
|
" y = df[col2].values\n",
|
|
"\n",
|
|
" results = {\n",
|
|
" 'Пирсон': pearsonr(x, y),\n",
|
|
" 'Спирмен': spearmanr(x, y),\n",
|
|
" 'Кендалл': kendalltau(x, y)\n",
|
|
" }\n",
|
|
"\n",
|
|
" for method, (corr, p_value) in results.items():\n",
|
|
" abs_corr = abs(corr)\n",
|
|
"\n",
|
|
" if method == 'Кендалл':\n",
|
|
" thresholds = [0.2, 0.4, 0.6, 0.8]\n",
|
|
" else:\n",
|
|
" thresholds = [0.3, 0.5, 0.7, 0.9]\n",
|
|
"\n",
|
|
" if abs_corr < thresholds[0]:\n",
|
|
" strength = \"слабая (или отсутствует)\"\n",
|
|
" elif abs_corr < thresholds[1]:\n",
|
|
" strength = \"умеренная\"\n",
|
|
" elif abs_corr < thresholds[2]:\n",
|
|
" strength = \"заметная\"\n",
|
|
" elif abs_corr < thresholds[3]:\n",
|
|
" strength = \"высокая\"\n",
|
|
" else:\n",
|
|
" strength = \"весьма высокая\"\n",
|
|
"\n",
|
|
" status = \"Значима\" if p_value < 0.05 else \"НЕ значима\"\n",
|
|
" direction = \"положительная\" if corr > 0 else \"отрицательная\"\n",
|
|
"\n",
|
|
" print(f\"[{method}]\")\n",
|
|
" print(f\" Коэффициент: {corr:.4f} ({direction})\")\n",
|
|
" print(f\" P-value: {p_value:.4e} ({status})\")\n",
|
|
"\n",
|
|
" if p_value < 0.05:\n",
|
|
" print(f\" Вывод: Наблюдается {strength} связь.\")\n",
|
|
" else:\n",
|
|
" print(f\" Вывод: Недостаточно данных для подтверждения связи.\")\n",
|
|
" print(\"-\" * 40)\n",
|
|
"\n",
|
|
"analyze_correlations(calls_by_hour, 'hour', 'count')\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"name": "stdout",
|
|
"text": [
|
|
"[Пирсон]\n",
|
|
" Коэффициент: 0.5114 (положительная)\n",
|
|
" P-value: 1.0638e-02 (Значима)\n",
|
|
" Вывод: Наблюдается заметная связь.\n",
|
|
"----------------------------------------\n",
|
|
"[Спирмен]\n",
|
|
" Коэффициент: 0.4991 (положительная)\n",
|
|
" P-value: 1.3028e-02 (Значима)\n",
|
|
" Вывод: Наблюдается умеренная связь.\n",
|
|
"----------------------------------------\n",
|
|
"[Кендалл]\n",
|
|
" Коэффициент: 0.3623 (положительная)\n",
|
|
" P-value: 1.2905e-02 (Значима)\n",
|
|
" Вывод: Наблюдается умеренная связь.\n",
|
|
"----------------------------------------\n"
|
|
]
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "8ee4c692-afde-4bad-b7a5-bb0dda83a5aa",
|
|
"metadata": {},
|
|
"execution_count": 24,
|
|
"source": [
|
|
"conn = sqlite3.connect('../data/calls.db')\n",
|
|
"df_clean.to_sql('calls', conn, if_exists='replace', index=False)\n",
|
|
"conn.close()\n"
|
|
],
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"id": "4e3cb3c7-62d9-4d2b-b7d0-5d209464fac2",
|
|
"metadata": {
|
|
"id": "1e5d291c",
|
|
"outputId": "783c15e5-5dce-46e2-c666-1f917a832051",
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
}
|
|
},
|
|
"execution_count": 25,
|
|
"source": [
|
|
"conn = sqlite3.connect('../data/calls.db')\n",
|
|
"query = \"SELECT * FROM calls LIMIT 5\"\n",
|
|
"curr = conn.cursor()\n",
|
|
"\n",
|
|
"data = curr.execute(query).fetchall()\n",
|
|
"\n",
|
|
"conn.close()\n",
|
|
"data\n"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "display_data",
|
|
"data": {
|
|
"text/plain": [
|
|
"[(40.2978759,\n",
|
|
" -75.5812935,\n",
|
|
" 'EMS: BACK PAINS/INJURY',\n",
|
|
" '2015-12-10 17:10:52',\n",
|
|
" 'NEW HANOVER',\n",
|
|
" 17),\n",
|
|
" (40.2580614,\n",
|
|
" -75.2646799,\n",
|
|
" 'EMS: DIABETIC EMERGENCY',\n",
|
|
" '2015-12-10 17:29:21',\n",
|
|
" 'HATFIELD TOWNSHIP',\n",
|
|
" 17),\n",
|
|
" (40.1211818,\n",
|
|
" -75.3519752,\n",
|
|
" 'Fire: GAS-ODOR/LEAK',\n",
|
|
" '2015-12-10 14:39:21',\n",
|
|
" 'NORRISTOWN',\n",
|
|
" 14),\n",
|
|
" (40.116153,\n",
|
|
" -75.343513,\n",
|
|
" 'EMS: CARDIAC EMERGENCY',\n",
|
|
" '2015-12-10 16:47:36',\n",
|
|
" 'NORRISTOWN',\n",
|
|
" 16),\n",
|
|
" (40.251492,\n",
|
|
" -75.6033497,\n",
|
|
" 'EMS: DIZZINESS',\n",
|
|
" '2015-12-10 16:56:52',\n",
|
|
" 'LOWER POTTSGROVE',\n",
|
|
" 16)]\n"
|
|
]
|
|
},
|
|
"metadata": {}
|
|
}
|
|
]
|
|
}
|
|
]
|
|
} |