4467 lines
410 KiB
Text
4467 lines
410 KiB
Text
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a1e7cf0d-9900-4055-a584-1c3bd0a6b4dd",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Dzień 2"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "e0b20803-4377-4808-9ba4-c1233afaa3ad",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import numpy as np\n",
|
||
"import pandas as pd"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8952bc61-2299-4194-b887-af265e2ae715",
|
||
"metadata": {},
|
||
"source": [
|
||
"# "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "cea8feaa-53d9-4eb3-9aa2-0b118bd88d9d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Response [200]>"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import requests\n",
|
||
"import datetime as dt\n",
|
||
"yesterday = dt.date.today() - dt.timedelta(days=1)\n",
|
||
"api = \"https://earthquake.usgs.gov/fdsnws/event/1/query\"\n",
|
||
"payload = {\n",
|
||
" \"format\": \"geojson\",\n",
|
||
" \"starttime\": yesterday - dt.timedelta(days=21),\n",
|
||
" \"endtime\": yesterday\n",
|
||
"}\n",
|
||
"response = requests.get(api, params=payload)\n",
|
||
"response"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "281db825-3be0-4eec-ad4c-718d643f3887",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 6630 entries, 0 to 6629\n",
|
||
"Data columns (total 26 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 mag 6629 non-null float64\n",
|
||
" 1 place 6630 non-null object \n",
|
||
" 2 time 6630 non-null int64 \n",
|
||
" 3 updated 6630 non-null int64 \n",
|
||
" 4 tz 0 non-null object \n",
|
||
" 5 url 6630 non-null object \n",
|
||
" 6 detail 6630 non-null object \n",
|
||
" 7 felt 454 non-null float64\n",
|
||
" 8 cdi 454 non-null float64\n",
|
||
" 9 mmi 102 non-null float64\n",
|
||
" 10 alert 37 non-null object \n",
|
||
" 11 status 6630 non-null object \n",
|
||
" 12 tsunami 6630 non-null int64 \n",
|
||
" 13 sig 6630 non-null int64 \n",
|
||
" 14 net 6630 non-null object \n",
|
||
" 15 code 6630 non-null object \n",
|
||
" 16 ids 6630 non-null object \n",
|
||
" 17 sources 6630 non-null object \n",
|
||
" 18 types 6630 non-null object \n",
|
||
" 19 nst 5569 non-null float64\n",
|
||
" 20 dmin 5568 non-null float64\n",
|
||
" 21 rms 6629 non-null float64\n",
|
||
" 22 gap 5569 non-null float64\n",
|
||
" 23 magType 6629 non-null object \n",
|
||
" 24 type 6630 non-null object \n",
|
||
" 25 title 6630 non-null object \n",
|
||
"dtypes: float64(8), int64(4), object(14)\n",
|
||
"memory usage: 1.3+ MB\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"json = response.json()\n",
|
||
"\n",
|
||
"data = [quake['properties'] for quake in json['features']]\n",
|
||
"data = pd.DataFrame(data)\n",
|
||
"data.info()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2acda05f-fdac-405c-a92f-a17aee1383e5",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Czyszczenie i przygotowywanie danych\n",
|
||
" - usuwanie duplikatów\n",
|
||
" - skalowanie\n",
|
||
" - normalizacja i standaryzacja wartości numerycznych (dla algo wrażliwych na skalę wartości)\n",
|
||
" - uzupełnianie braków\n",
|
||
" - uzupełnianie NaN w zależności od kontekstu\n",
|
||
" - konwersja typów\n",
|
||
" - najczęściej nietypowe dane są wczytywane jako string, więc jeśli są inne to trzeba je przekonwertować np. na datę"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "343d7ceb-d05e-45aa-adb3-766e82c9d183",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Jednak czytamy z pliku który został podany przez prowadzącego"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 141,
|
||
"id": "56e86530-c7a9-481c-9586-9bfc20bc9cfd",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(9332, 26)"
|
||
]
|
||
},
|
||
"execution_count": 141,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data = pd.read_csv(\"data/earthquakes.csv\")\n",
|
||
"data.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 73,
|
||
"id": "06b77e4f-2e34-4294-9f0d-54178f0560a4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(1866, 26)"
|
||
]
|
||
},
|
||
"execution_count": 73,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# maska\n",
|
||
"data[data.mag >= 0]\n",
|
||
"\n",
|
||
"data.loc[(data.tsunami == 1) & (data.alert == 'red')] \n",
|
||
"data[data.title.str.contains('Alaska')]['title']\n",
|
||
"\n",
|
||
"data.loc[data.mag.between(6.5, 8)]\n",
|
||
"\n",
|
||
"data.loc[data.magType.isin(['mw', 'mwb'])]\n",
|
||
"\n",
|
||
"data.loc[[data.mag.idxmin(), data.mag.idxmax()]]\n",
|
||
"\n",
|
||
"data.filter(items=['mag'])\n",
|
||
"data.filter(like='mag')\n",
|
||
"data.filter(regex=r'^t')\n",
|
||
"\n",
|
||
"test_data = data.sample(frac=0.2, random_state=123)\n",
|
||
"train_data = data.drop(test_data.index)\n",
|
||
"\n",
|
||
"test_data.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 136,
|
||
"id": "2f959ea7-098d-4c85-a3cb-9cb2bc10f660",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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>mag</th>\n",
|
||
" <th>mag_negative</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>39</th>\n",
|
||
" <td>-0.10</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>49</th>\n",
|
||
" <td>-0.10</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>63</th>\n",
|
||
" <td>0.10</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>79</th>\n",
|
||
" <td>0.10</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>87</th>\n",
|
||
" <td>0.00</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9251</th>\n",
|
||
" <td>0.11</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9261</th>\n",
|
||
" <td>-0.02</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9267</th>\n",
|
||
" <td>-0.32</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9280</th>\n",
|
||
" <td>0.11</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9305</th>\n",
|
||
" <td>-0.27</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>771 rows × 2 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" mag mag_negative\n",
|
||
"39 -0.10 True\n",
|
||
"49 -0.10 True\n",
|
||
"63 0.10 False\n",
|
||
"79 0.10 False\n",
|
||
"87 0.00 False\n",
|
||
"... ... ...\n",
|
||
"9251 0.11 False\n",
|
||
"9261 -0.02 True\n",
|
||
"9267 -0.32 True\n",
|
||
"9280 0.11 False\n",
|
||
"9305 -0.27 True\n",
|
||
"\n",
|
||
"[771 rows x 2 columns]"
|
||
]
|
||
},
|
||
"execution_count": 136,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Tworzenie etykiet na podstawie danych w tabeli\n",
|
||
"data['mag_negative'] = data.mag < 0\n",
|
||
"data[data.mag < 0.2][['mag','mag_negative']]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 124,
|
||
"id": "63581df2-ea46-484e-97df-c376a42b2e9f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(9332, 26)"
|
||
]
|
||
},
|
||
"execution_count": 124,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data['place'] = data.place.str.replace(\n",
|
||
" r'.* of ', '', regex=True # remove anything saying <something> of <something>\n",
|
||
").str.replace(\n",
|
||
" 'the ', '' # remove \"the \"\n",
|
||
").str.replace(\n",
|
||
" r'CA$', 'California', regex=True # fix California\n",
|
||
").str.replace(\n",
|
||
" r'NV$', 'Nevada', regex=True # fix Nevada\n",
|
||
").str.replace(\n",
|
||
" r'MX$', 'Mexico', regex=True # fix Mexico\n",
|
||
").str.replace(\n",
|
||
" r' region$', '', regex=True # chop off endings with \" region\"\n",
|
||
").str.replace(\n",
|
||
" 'northern ', '' # remove \"northern \"\n",
|
||
").str.replace(\n",
|
||
" 'Fiji Islands', 'Fiji' # line up the Fiji places\n",
|
||
").str.replace(\n",
|
||
" r'^.*, ', '', regex=True # remove anything brefore comma\n",
|
||
").str.strip() # remove any extra spaces\n",
|
||
"data.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 108,
|
||
"id": "91732b76-8a0c-47f7-8185-bd252f3635bd",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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>alert</th>\n",
|
||
" <th>cdi</th>\n",
|
||
" <th>code</th>\n",
|
||
" <th>detail</th>\n",
|
||
" <th>dmin</th>\n",
|
||
" <th>felt</th>\n",
|
||
" <th>gap</th>\n",
|
||
" <th>ids</th>\n",
|
||
" <th>mag</th>\n",
|
||
" <th>magType</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>time</th>\n",
|
||
" <th>title</th>\n",
|
||
" <th>tsunami</th>\n",
|
||
" <th>type</th>\n",
|
||
" <th>types</th>\n",
|
||
" <th>tz</th>\n",
|
||
" <th>updated</th>\n",
|
||
" <th>url</th>\n",
|
||
" <th>in_ca</th>\n",
|
||
" <th>in_alaska</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1639</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>00660018</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>0.2080</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>269.43</td>\n",
|
||
" <td>,nn00660018,</td>\n",
|
||
" <td>0.8</td>\n",
|
||
" <td>ml</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1538957373093</td>\n",
|
||
" <td>M 0.8 - 60km NNE of Pahrump, Nevada</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>-480.0</td>\n",
|
||
" <td>1539050747271</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8224</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>60297087</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>0.3779</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>138.00</td>\n",
|
||
" <td>,uu60297087,</td>\n",
|
||
" <td>1.4</td>\n",
|
||
" <td>ml</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>1537509006670</td>\n",
|
||
" <td>M 1.4 - 22km NW of Montpelier, Idaho</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>-420.0</td>\n",
|
||
" <td>1537558282160</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>2 rows × 28 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" alert cdi code detail \\\n",
|
||
"1639 NaN NaN 00660018 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"8224 NaN NaN 60297087 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"\n",
|
||
" dmin felt gap ids mag magType ... time \\\n",
|
||
"1639 0.2080 NaN 269.43 ,nn00660018, 0.8 ml ... 1538957373093 \n",
|
||
"8224 0.3779 NaN 138.00 ,uu60297087, 1.4 ml ... 1537509006670 \n",
|
||
"\n",
|
||
" title tsunami type \\\n",
|
||
"1639 M 0.8 - 60km NNE of Pahrump, Nevada 0 earthquake \n",
|
||
"8224 M 1.4 - 22km NW of Montpelier, Idaho 0 earthquake \n",
|
||
"\n",
|
||
" types tz updated \\\n",
|
||
"1639 ,geoserve,origin,phase-data, -480.0 1539050747271 \n",
|
||
"8224 ,geoserve,origin,phase-data, -420.0 1537558282160 \n",
|
||
"\n",
|
||
" url in_ca in_alaska \n",
|
||
"1639 https://earthquake.usgs.gov/earthquakes/eventp... False False \n",
|
||
"8224 https://earthquake.usgs.gov/earthquakes/eventp... False False \n",
|
||
"\n",
|
||
"[2 rows x 28 columns]"
|
||
]
|
||
},
|
||
"execution_count": 108,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data.assign(\n",
|
||
" in_ca = data.place.str.endswith(\"California\"),\n",
|
||
" in_alaska = data.place.str.endswith(\"Alaska\")\n",
|
||
").sample(2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 109,
|
||
"id": "a1f1a474-0138-4594-af8a-db23c32752f0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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>alert</th>\n",
|
||
" <th>cdi</th>\n",
|
||
" <th>code</th>\n",
|
||
" <th>detail</th>\n",
|
||
" <th>dmin</th>\n",
|
||
" <th>felt</th>\n",
|
||
" <th>gap</th>\n",
|
||
" <th>ids</th>\n",
|
||
" <th>mag</th>\n",
|
||
" <th>magType</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>title</th>\n",
|
||
" <th>tsunami</th>\n",
|
||
" <th>type</th>\n",
|
||
" <th>types</th>\n",
|
||
" <th>tz</th>\n",
|
||
" <th>updated</th>\n",
|
||
" <th>url</th>\n",
|
||
" <th>in_ca</th>\n",
|
||
" <th>in_alaska</th>\n",
|
||
" <th>other_places</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1914</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>00659912</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>0.005</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>92.48</td>\n",
|
||
" <td>,nn00659912,</td>\n",
|
||
" <td>0.8</td>\n",
|
||
" <td>ml</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>M 0.8 - 13km NW of Virginia City, Nevada</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>-480.0</td>\n",
|
||
" <td>1538938015357</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>573</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>20277497</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>,ak20277497,</td>\n",
|
||
" <td>1.7</td>\n",
|
||
" <td>ml</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>M 1.7 - 24km NNE of Sterling, Alaska</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,</td>\n",
|
||
" <td>-540.0</td>\n",
|
||
" <td>1539290268489</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>True</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>2 rows × 29 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" alert cdi code detail \\\n",
|
||
"1914 NaN NaN 00659912 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"573 NaN NaN 20277497 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"\n",
|
||
" dmin felt gap ids mag magType ... \\\n",
|
||
"1914 0.005 NaN 92.48 ,nn00659912, 0.8 ml ... \n",
|
||
"573 NaN NaN NaN ,ak20277497, 1.7 ml ... \n",
|
||
"\n",
|
||
" title tsunami type \\\n",
|
||
"1914 M 0.8 - 13km NW of Virginia City, Nevada 0 earthquake \n",
|
||
"573 M 1.7 - 24km NNE of Sterling, Alaska 0 earthquake \n",
|
||
"\n",
|
||
" types tz updated \\\n",
|
||
"1914 ,geoserve,origin,phase-data, -480.0 1538938015357 \n",
|
||
"573 ,geoserve,origin, -540.0 1539290268489 \n",
|
||
"\n",
|
||
" url in_ca in_alaska \\\n",
|
||
"1914 https://earthquake.usgs.gov/earthquakes/eventp... False False \n",
|
||
"573 https://earthquake.usgs.gov/earthquakes/eventp... False True \n",
|
||
"\n",
|
||
" other_places \n",
|
||
"1914 True \n",
|
||
"573 False \n",
|
||
"\n",
|
||
"[2 rows x 29 columns]"
|
||
]
|
||
},
|
||
"execution_count": 109,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# lambda\n",
|
||
"data.assign(\n",
|
||
" in_ca = data.place == \"California\",\n",
|
||
" in_alaska = data.place == \"Alaska\",\n",
|
||
" other_places = lambda x: ~x.in_ca & ~x.in_alaska \n",
|
||
").sample(2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 113,
|
||
"id": "141b0b4e-d92a-4494-bc2f-804e40a4c0f3",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"place\n",
|
||
"Alaska 3665\n",
|
||
"California 2861\n",
|
||
"Nevada 681\n",
|
||
"Hawaii 367\n",
|
||
"Puerto Rico 216\n",
|
||
" ... \n",
|
||
"Colorado 1\n",
|
||
"Socotra 1\n",
|
||
"Iraq 1\n",
|
||
"Somalia 1\n",
|
||
"New Mexico 1\n",
|
||
"Name: count, Length: 110, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 113,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data.place.value_counts()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 114,
|
||
"id": "a2984e2d-3394-4b43-ab68-16d8fc13d531",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"((61, 26), (9271, 26), True)"
|
||
]
|
||
},
|
||
"execution_count": 114,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# dzielenie setów\n",
|
||
"tsunami = data[data.tsunami == 1]\n",
|
||
"no_tsunami = data[data.tsunami == 0]\n",
|
||
"\n",
|
||
"tsunami.shape, no_tsunami.shape, (tsunami+no_tsunami).size == data.size"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 116,
|
||
"id": "8fb8866a-eae0-4ce4-a249-3b6dce9bc0ee",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(9332, 26)"
|
||
]
|
||
},
|
||
"execution_count": 116,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pd.concat([tsunami, no_tsunami]).shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "fd163486-227d-4c9c-b341-db792e636bef",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"try:\n",
|
||
" del data['source']\n",
|
||
"except KeyError:\n",
|
||
" print(\"Kolumna nie istnieje\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 139,
|
||
"id": "33d32c58-a81d-4e09-90cc-a1d1421d6e8e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Kolumna nie istnieje\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Index(['alert', 'cdi', 'code', 'detail', 'dmin', 'felt', 'gap', 'ids', 'mag',\n",
|
||
" 'magType', 'mmi', 'net', 'nst', 'place', 'rms', 'sig', 'sources',\n",
|
||
" 'status', 'time', 'title', 'tsunami', 'type', 'types', 'tz', 'updated',\n",
|
||
" 'url'],\n",
|
||
" dtype='object')"
|
||
]
|
||
},
|
||
"execution_count": 139,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"try:\n",
|
||
" mag_negative = data.pop('mag_negative')\n",
|
||
"except KeyError:\n",
|
||
" print(\"Kolumna nie istnieje\")\n",
|
||
"data.columns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 140,
|
||
"id": "c85c8110-3cb1-45bf-85e9-5fae2d2b7a12",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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>alert</th>\n",
|
||
" <th>mag</th>\n",
|
||
" <th>time</th>\n",
|
||
" <th>title</th>\n",
|
||
" <th>tsunami</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.35</td>\n",
|
||
" <td>1539475168010</td>\n",
|
||
" <td>M 1.4 - 9km NE of Aguanga, CA</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.29</td>\n",
|
||
" <td>1539475129610</td>\n",
|
||
" <td>M 1.3 - 9km NE of Aguanga, CA</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.42</td>\n",
|
||
" <td>1539475062610</td>\n",
|
||
" <td>M 3.4 - 8km NE of Aguanga, CA</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.44</td>\n",
|
||
" <td>1539474978070</td>\n",
|
||
" <td>M 0.4 - 9km NE of Aguanga, CA</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2.16</td>\n",
|
||
" <td>1539474716050</td>\n",
|
||
" <td>M 2.2 - 10km NW of Avenal, CA</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" alert mag time title tsunami\n",
|
||
"0 NaN 1.35 1539475168010 M 1.4 - 9km NE of Aguanga, CA 0\n",
|
||
"1 NaN 1.29 1539475129610 M 1.3 - 9km NE of Aguanga, CA 0\n",
|
||
"2 NaN 3.42 1539475062610 M 3.4 - 8km NE of Aguanga, CA 0\n",
|
||
"3 NaN 0.44 1539474978070 M 0.4 - 9km NE of Aguanga, CA 0\n",
|
||
"4 NaN 2.16 1539474716050 M 2.2 - 10km NW of Avenal, CA 0"
|
||
]
|
||
},
|
||
"execution_count": 140,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"cols_to_drop = [\n",
|
||
" col for col in data.columns\n",
|
||
" if col not in ['alert', 'mag', 'title', 'time', 'tsunami']\n",
|
||
"]\n",
|
||
"data.drop(columns=cols_to_drop).head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bf077e35-2ee2-414f-90b4-063e5042a1fd",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Ćwiczenia"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 161,
|
||
"id": "a5f98a28",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(9332, 26)"
|
||
]
|
||
},
|
||
"execution_count": 161,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data = pd.read_csv(\"data/earthquakes.csv\")\n",
|
||
"data.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 162,
|
||
"id": "1d4c2c91",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(9332, 26)"
|
||
]
|
||
},
|
||
"execution_count": 162,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data['place'] = data.place.str.replace(\n",
|
||
" r'.* of ', '', regex=True # remove anything saying <something> of <something>\n",
|
||
").str.replace(\n",
|
||
" 'the ', '' # remove \"the \"\n",
|
||
").str.replace(\n",
|
||
" r'CA$', 'California', regex=True # fix California\n",
|
||
").str.replace(\n",
|
||
" r'NV$', 'Nevada', regex=True # fix Nevada\n",
|
||
").str.replace(\n",
|
||
" r'MX$', 'Mexico', regex=True # fix Mexico\n",
|
||
").str.replace(\n",
|
||
" r' region$', '', regex=True # chop off endings with \" region\"\n",
|
||
").str.replace(\n",
|
||
" 'northern ', '' # remove \"northern \"\n",
|
||
").str.replace(\n",
|
||
" 'Fiji Islands', 'Fiji' # line up the Fiji places\n",
|
||
").str.replace(\n",
|
||
" r'^.*, ', '', regex=True # remove anything brefore comma\n",
|
||
").str.strip() # remove any extra spaces\n",
|
||
"data.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 155,
|
||
"id": "692b5a25-1f4c-4e71-bb30-1922df47016c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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>alert</th>\n",
|
||
" <th>cdi</th>\n",
|
||
" <th>code</th>\n",
|
||
" <th>detail</th>\n",
|
||
" <th>dmin</th>\n",
|
||
" <th>felt</th>\n",
|
||
" <th>gap</th>\n",
|
||
" <th>ids</th>\n",
|
||
" <th>mag</th>\n",
|
||
" <th>magType</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>sources</th>\n",
|
||
" <th>status</th>\n",
|
||
" <th>time</th>\n",
|
||
" <th>title</th>\n",
|
||
" <th>tsunami</th>\n",
|
||
" <th>type</th>\n",
|
||
" <th>types</th>\n",
|
||
" <th>tz</th>\n",
|
||
" <th>updated</th>\n",
|
||
" <th>url</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>67</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000hbqa</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.415</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>72.0</td>\n",
|
||
" <td>,us1000hbqa,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1539448501800</td>\n",
|
||
" <td>M 4.6 - 160km NNW of Nago, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>480.0</td>\n",
|
||
" <td>1539449501040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>713</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000hah8</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.141</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>82.0</td>\n",
|
||
" <td>,us1000hah8,</td>\n",
|
||
" <td>4.7</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1539238726290</td>\n",
|
||
" <td>M 4.7 - 139km WSW of Naze, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539240344040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1124</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h9la</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.737</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>135.0</td>\n",
|
||
" <td>,us1000h9la,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1539115120470</td>\n",
|
||
" <td>M 4.6 - 53km ESE of Kamaishi, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539119067040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1309</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h952</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>0.897</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>136.0</td>\n",
|
||
" <td>,us1000h952,</td>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1539058606580</td>\n",
|
||
" <td>M 4.5 - 80km ENE of Misawa, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539060436040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1398</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h8zx</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>0.555</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>124.0</td>\n",
|
||
" <td>,us1000h8zx,</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1539034912990</td>\n",
|
||
" <td>M 4.4 - 65km NNE of Hachijo-jima, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539062940040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1422</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>1000h8xg</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>0.686</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>129.0</td>\n",
|
||
" <td>,us1000h8xg,</td>\n",
|
||
" <td>4.7</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1539028322670</td>\n",
|
||
" <td>M 4.7 - 41km E of Namie, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539061047807</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1435</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h8vs</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.152</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>72.0</td>\n",
|
||
" <td>,us1000h8vs,</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1539020712670</td>\n",
|
||
" <td>M 4.4 - 21km E of Tomakomai, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539034924040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1492</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.4</td>\n",
|
||
" <td>1000h8q7</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.059</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>74.0</td>\n",
|
||
" <td>,us1000h8q7,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1539003238480</td>\n",
|
||
" <td>M 4.6 - 29km E of Tomakomai, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539012243124</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1563</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h8mj</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>3.773</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>101.0</td>\n",
|
||
" <td>,us1000h8mj,</td>\n",
|
||
" <td>4.9</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538977532250</td>\n",
|
||
" <td>M 4.9 - 293km ESE of Iwo Jima, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>600.0</td>\n",
|
||
" <td>1538978864040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1732</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000hb41</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.308</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>154.0</td>\n",
|
||
" <td>,us1000hb41,</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538923896600</td>\n",
|
||
" <td>M 4.1 - 89km NE of Miyako, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>600.0</td>\n",
|
||
" <td>1539532511040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1875</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h8c2</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.570</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>82.0</td>\n",
|
||
" <td>,us1000h8c2,</td>\n",
|
||
" <td>4.7</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538881270700</td>\n",
|
||
" <td>M 4.7 - 177km WSW of Chichi-shima, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538882370040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1898</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.8</td>\n",
|
||
" <td>1000h8b9</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>0.516</td>\n",
|
||
" <td>19.0</td>\n",
|
||
" <td>66.0</td>\n",
|
||
" <td>,us1000h8b9,</td>\n",
|
||
" <td>4.8</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538874859870</td>\n",
|
||
" <td>M 4.8 - 12km N of Shinshiro, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539319234277</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2053</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h84n</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.108</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>85.0</td>\n",
|
||
" <td>,us1000h84n,</td>\n",
|
||
" <td>4.8</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538839123780</td>\n",
|
||
" <td>M 4.8 - 147km E of Nago, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538840634040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2191</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2.7</td>\n",
|
||
" <td>1000h800</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.134</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>75.0</td>\n",
|
||
" <td>,us1000h800,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538799265130</td>\n",
|
||
" <td>M 4.6 - 27km ESE of Chitose, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538811844812</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2564</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000hams</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.187</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>118.0</td>\n",
|
||
" <td>,us1000hams,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538701532340</td>\n",
|
||
" <td>M 4.6 - 162km E of Nago, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539306889040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2576</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.1</td>\n",
|
||
" <td>1000h7cn</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.015</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>,us1000h7cn,</td>\n",
|
||
" <td>5.4</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538697528010</td>\n",
|
||
" <td>M 5.4 - 37km E of Tomakomai, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,moment-tensor,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538757701040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2688</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000hajw</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.539</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>41.0</td>\n",
|
||
" <td>,us1000hajw,</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538673414250</td>\n",
|
||
" <td>M 4.0 - 51km WNW of Mikuni, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539363073040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2824</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h6vj</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>2.125</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>131.0</td>\n",
|
||
" <td>,us1000h6vj,</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538641253520</td>\n",
|
||
" <td>M 4.4 - 51km SSE of Kushima, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538642265040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3072</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.1</td>\n",
|
||
" <td>1000h6ac</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>2.082</td>\n",
|
||
" <td>15.0</td>\n",
|
||
" <td>118.0</td>\n",
|
||
" <td>,us1000h6ac,</td>\n",
|
||
" <td>4.9</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538579732490</td>\n",
|
||
" <td>M 4.9 - 15km ENE of Hasaki, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539230846942</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3632</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.1</td>\n",
|
||
" <td>1000h5gh</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.449</td>\n",
|
||
" <td>19.0</td>\n",
|
||
" <td>117.0</td>\n",
|
||
" <td>,us1000h5gh,</td>\n",
|
||
" <td>4.9</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538450871260</td>\n",
|
||
" <td>M 4.9 - 53km ESE of Hitachi, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538581746455</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3771</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h7n9</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.925</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>167.0</td>\n",
|
||
" <td>,us1000h7n9,</td>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538422163330</td>\n",
|
||
" <td>M 4.5 - 87km ESE of Kamaishi, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>600.0</td>\n",
|
||
" <td>1539026579040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3851</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h7mm</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.288</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>98.0</td>\n",
|
||
" <td>,us1000h7mm,</td>\n",
|
||
" <td>4.7</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538406431250</td>\n",
|
||
" <td>M 4.7 - 173km E of Nago, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539118704040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4082</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>1000h4uw</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.178</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>79.0</td>\n",
|
||
" <td>,us1000h4uw,</td>\n",
|
||
" <td>4.8</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538360525340</td>\n",
|
||
" <td>M 4.8 - 31km E of Chitose, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539136962751</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4192</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2.2</td>\n",
|
||
" <td>1000h4r4</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>0.883</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>109.0</td>\n",
|
||
" <td>,us1000h4r4,</td>\n",
|
||
" <td>4.3</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538336412660</td>\n",
|
||
" <td>M 4.3 - 50km NNE of Shizunai, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538367048207</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4257</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h7kb</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>4.830</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>89.0</td>\n",
|
||
" <td>,us1000h7kb,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538323620710</td>\n",
|
||
" <td>M 4.6 - 213km SE of Hachijo-jima, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539103992040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4846</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2.7</td>\n",
|
||
" <td>1000h479</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.163</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>75.0</td>\n",
|
||
" <td>,us1000h479,</td>\n",
|
||
" <td>4.5</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538213154900</td>\n",
|
||
" <td>M 4.5 - 25km ESE of Chitose, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538294859215</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5655</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h382</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.105</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>131.0</td>\n",
|
||
" <td>,us1000h382,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538048754220</td>\n",
|
||
" <td>M 4.6 - 156km ENE of Nago, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538050682040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5915</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h5rx</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>2.044</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>157.0</td>\n",
|
||
" <td>,us1000h5rx,</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1538003928830</td>\n",
|
||
" <td>M 4.4 - 78km ESE of Yamada, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>600.0</td>\n",
|
||
" <td>1539400764040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6769</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2.7</td>\n",
|
||
" <td>2000hjkw</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.426</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>135.0</td>\n",
|
||
" <td>,us2000hjkw,</td>\n",
|
||
" <td>4.8</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537846261100</td>\n",
|
||
" <td>M 4.8 - 58km SE of Ofunato, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1537908537284</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6961</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2000hjc4</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>2.482</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>116.0</td>\n",
|
||
" <td>,us2000hjc4,</td>\n",
|
||
" <td>4.7</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537799121060</td>\n",
|
||
" <td>M 4.7 - 46km ENE of Hirara, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>480.0</td>\n",
|
||
" <td>1538419499040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7001</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h3vt</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.418</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>87.0</td>\n",
|
||
" <td>,us1000h3vt,</td>\n",
|
||
" <td>4.3</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537792438760</td>\n",
|
||
" <td>M 4.3 - 25km ESE of Muroran, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538375544040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7066</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.8</td>\n",
|
||
" <td>2000hja7</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.034</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>75.0</td>\n",
|
||
" <td>,us2000hja7,</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537780626560</td>\n",
|
||
" <td>M 4.4 - 35km E of Tomakomai, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538238102308</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7145</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2000hj80</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.242</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>140.0</td>\n",
|
||
" <td>,us2000hj80,</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537764836900</td>\n",
|
||
" <td>M 4.1 - 97km N of Mombetsu, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>600.0</td>\n",
|
||
" <td>1538716171040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7261</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2.7</td>\n",
|
||
" <td>2000hj4d</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>2.421</td>\n",
|
||
" <td>2.0</td>\n",
|
||
" <td>106.0</td>\n",
|
||
" <td>,us2000hj4d,</td>\n",
|
||
" <td>4.1</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537738421220</td>\n",
|
||
" <td>M 4.1 - 64km ESE of Owase, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1537740803336</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7381</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.3</td>\n",
|
||
" <td>2000hj04</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.373</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" <td>137.0</td>\n",
|
||
" <td>,us2000hj04,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537715134540</td>\n",
|
||
" <td>M 4.6 - 9km ENE of Funaishikawa, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538332135138</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7408</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>2000hizb</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>2.396</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" <td>137.0</td>\n",
|
||
" <td>,us2000hizb,</td>\n",
|
||
" <td>4.8</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537709390500</td>\n",
|
||
" <td>M 4.8 - 65km SSE of Hasaki, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538329898836</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7652</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h3qw</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.228</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>93.0</td>\n",
|
||
" <td>,us1000h3qw,</td>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537654508260</td>\n",
|
||
" <td>M 4.2 - 164km E of Nago, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539135424040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7696</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h3qp</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>0.223</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>146.0</td>\n",
|
||
" <td>,us1000h3qp,</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537640754840</td>\n",
|
||
" <td>M 4.0 - 65km E of Shizunai, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539294775040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7807</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h3qb</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>0.571</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>124.0</td>\n",
|
||
" <td>,us1000h3qb,</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537619327220</td>\n",
|
||
" <td>M 4.4 - 67km NNE of Hachijo-jima, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538226419040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8071</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2000hic7</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>2.666</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>150.0</td>\n",
|
||
" <td>,us2000hic7,</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537550307820</td>\n",
|
||
" <td>M 4.4 - 67km E of Nemuro, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>600.0</td>\n",
|
||
" <td>1539122080040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8311</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h3av</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.230</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>95.0</td>\n",
|
||
" <td>,us1000h3av,</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537487811890</td>\n",
|
||
" <td>M 4.4 - 166km E of Nago, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538880454040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8324</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2000hhxa</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.234</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>82.0</td>\n",
|
||
" <td>,us2000hhxa,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537484762840</td>\n",
|
||
" <td>M 4.6 - 167km E of Nago, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538969995040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8327</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2000hhx3</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.068</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>89.0</td>\n",
|
||
" <td>,us2000hhx3,</td>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537484169100</td>\n",
|
||
" <td>M 4.2 - 34km ESE of Chitose, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538870822040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8376</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h3am</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.272</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>91.0</td>\n",
|
||
" <td>,us1000h3am,</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537470382450</td>\n",
|
||
" <td>M 4.4 - 171km E of Nago, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1539464832040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8431</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2000hhlf</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.270</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>95.0</td>\n",
|
||
" <td>,us2000hhlf,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537455447970</td>\n",
|
||
" <td>M 4.6 - 171km E of Nago, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1537457187040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8550</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2000hhgj</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.844</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>164.0</td>\n",
|
||
" <td>,us2000hhgj,</td>\n",
|
||
" <td>4.4</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537430889800</td>\n",
|
||
" <td>M 4.4 - 105km ENE of Miyako, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>600.0</td>\n",
|
||
" <td>1537510968040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8555</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2000hhga</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.825</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>129.0</td>\n",
|
||
" <td>,us2000hhga,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537429466000</td>\n",
|
||
" <td>M 4.6 - 105km ENE of Miyako, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>600.0</td>\n",
|
||
" <td>1537430366040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8687</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1000h2t5</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>0.485</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>147.0</td>\n",
|
||
" <td>,us1000h2t5,</td>\n",
|
||
" <td>4.2</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537392267190</td>\n",
|
||
" <td>M 4.2 - 39km NE of Misawa, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538890503040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9070</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2000hgfj</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>2.315</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>141.0</td>\n",
|
||
" <td>,us2000hgfj,</td>\n",
|
||
" <td>4.7</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537289668660</td>\n",
|
||
" <td>M 4.7 - 96km E of Hasaki, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1537976170040</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9198</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>3.4</td>\n",
|
||
" <td>2000hgaa</td>\n",
|
||
" <td>https://earthquake.usgs.gov/fdsnws/event/1/que...</td>\n",
|
||
" <td>1.367</td>\n",
|
||
" <td>85.0</td>\n",
|
||
" <td>115.0</td>\n",
|
||
" <td>,us2000hgaa,</td>\n",
|
||
" <td>4.6</td>\n",
|
||
" <td>mb</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>,us,</td>\n",
|
||
" <td>reviewed</td>\n",
|
||
" <td>1537258271290</td>\n",
|
||
" <td>M 4.6 - 3km ESE of Sugito, Japan</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>earthquake</td>\n",
|
||
" <td>,dyfi,geoserve,origin,phase-data,</td>\n",
|
||
" <td>540.0</td>\n",
|
||
" <td>1538220844777</td>\n",
|
||
" <td>https://earthquake.usgs.gov/earthquakes/eventp...</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>50 rows × 26 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" alert cdi code detail \\\n",
|
||
"67 NaN NaN 1000hbqa https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"713 NaN NaN 1000hah8 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"1124 NaN NaN 1000h9la https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"1309 NaN NaN 1000h952 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"1398 NaN NaN 1000h8zx https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"1422 NaN 1.0 1000h8xg https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"1435 NaN NaN 1000h8vs https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"1492 NaN 3.4 1000h8q7 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"1563 NaN NaN 1000h8mj https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"1732 NaN NaN 1000hb41 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"1875 NaN NaN 1000h8c2 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"1898 NaN 3.8 1000h8b9 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"2053 NaN NaN 1000h84n https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"2191 NaN 2.7 1000h800 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"2564 NaN NaN 1000hams https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"2576 NaN 3.1 1000h7cn https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"2688 NaN NaN 1000hajw https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"2824 NaN NaN 1000h6vj https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"3072 NaN 3.1 1000h6ac https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"3632 NaN 3.1 1000h5gh https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"3771 NaN NaN 1000h7n9 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"3851 NaN NaN 1000h7mm https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"4082 NaN 4.1 1000h4uw https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"4192 NaN 2.2 1000h4r4 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"4257 NaN NaN 1000h7kb https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"4846 NaN 2.7 1000h479 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"5655 NaN NaN 1000h382 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"5915 NaN NaN 1000h5rx https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"6769 NaN 2.7 2000hjkw https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"6961 NaN NaN 2000hjc4 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"7001 NaN NaN 1000h3vt https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"7066 NaN 3.8 2000hja7 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"7145 NaN NaN 2000hj80 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"7261 NaN 2.7 2000hj4d https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"7381 NaN 3.3 2000hj04 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"7408 NaN 1.0 2000hizb https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"7652 NaN NaN 1000h3qw https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"7696 NaN NaN 1000h3qp https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"7807 NaN NaN 1000h3qb https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"8071 NaN NaN 2000hic7 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"8311 NaN NaN 1000h3av https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"8324 NaN NaN 2000hhxa https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"8327 NaN NaN 2000hhx3 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"8376 NaN NaN 1000h3am https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"8431 NaN NaN 2000hhlf https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"8550 NaN NaN 2000hhgj https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"8555 NaN NaN 2000hhga https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"8687 NaN NaN 1000h2t5 https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"9070 NaN NaN 2000hgfj https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"9198 NaN 3.4 2000hgaa https://earthquake.usgs.gov/fdsnws/event/1/que... \n",
|
||
"\n",
|
||
" dmin felt gap ids mag magType ... sources status \\\n",
|
||
"67 1.415 NaN 72.0 ,us1000hbqa, 4.6 mb ... ,us, reviewed \n",
|
||
"713 1.141 NaN 82.0 ,us1000hah8, 4.7 mb ... ,us, reviewed \n",
|
||
"1124 1.737 NaN 135.0 ,us1000h9la, 4.6 mb ... ,us, reviewed \n",
|
||
"1309 0.897 NaN 136.0 ,us1000h952, 4.5 mb ... ,us, reviewed \n",
|
||
"1398 0.555 NaN 124.0 ,us1000h8zx, 4.4 mb ... ,us, reviewed \n",
|
||
"1422 0.686 1.0 129.0 ,us1000h8xg, 4.7 mb ... ,us, reviewed \n",
|
||
"1435 1.152 NaN 72.0 ,us1000h8vs, 4.4 mb ... ,us, reviewed \n",
|
||
"1492 1.059 4.0 74.0 ,us1000h8q7, 4.6 mb ... ,us, reviewed \n",
|
||
"1563 3.773 NaN 101.0 ,us1000h8mj, 4.9 mb ... ,us, reviewed \n",
|
||
"1732 1.308 NaN 154.0 ,us1000hb41, 4.1 mb ... ,us, reviewed \n",
|
||
"1875 1.570 NaN 82.0 ,us1000h8c2, 4.7 mb ... ,us, reviewed \n",
|
||
"1898 0.516 19.0 66.0 ,us1000h8b9, 4.8 mb ... ,us, reviewed \n",
|
||
"2053 1.108 NaN 85.0 ,us1000h84n, 4.8 mb ... ,us, reviewed \n",
|
||
"2191 1.134 1.0 75.0 ,us1000h800, 4.6 mb ... ,us, reviewed \n",
|
||
"2564 1.187 NaN 118.0 ,us1000hams, 4.6 mb ... ,us, reviewed \n",
|
||
"2576 1.015 7.0 33.0 ,us1000h7cn, 5.4 mb ... ,us, reviewed \n",
|
||
"2688 1.539 NaN 41.0 ,us1000hajw, 4.0 mb ... ,us, reviewed \n",
|
||
"2824 2.125 NaN 131.0 ,us1000h6vj, 4.4 mb ... ,us, reviewed \n",
|
||
"3072 2.082 15.0 118.0 ,us1000h6ac, 4.9 mb ... ,us, reviewed \n",
|
||
"3632 1.449 19.0 117.0 ,us1000h5gh, 4.9 mb ... ,us, reviewed \n",
|
||
"3771 1.925 NaN 167.0 ,us1000h7n9, 4.5 mb ... ,us, reviewed \n",
|
||
"3851 1.288 NaN 98.0 ,us1000h7mm, 4.7 mb ... ,us, reviewed \n",
|
||
"4082 1.178 7.0 79.0 ,us1000h4uw, 4.8 mb ... ,us, reviewed \n",
|
||
"4192 0.883 1.0 109.0 ,us1000h4r4, 4.3 mb ... ,us, reviewed \n",
|
||
"4257 4.830 NaN 89.0 ,us1000h7kb, 4.6 mb ... ,us, reviewed \n",
|
||
"4846 1.163 3.0 75.0 ,us1000h479, 4.5 mb ... ,us, reviewed \n",
|
||
"5655 1.105 NaN 131.0 ,us1000h382, 4.6 mb ... ,us, reviewed \n",
|
||
"5915 2.044 NaN 157.0 ,us1000h5rx, 4.4 mb ... ,us, reviewed \n",
|
||
"6769 1.426 1.0 135.0 ,us2000hjkw, 4.8 mb ... ,us, reviewed \n",
|
||
"6961 2.482 NaN 116.0 ,us2000hjc4, 4.7 mb ... ,us, reviewed \n",
|
||
"7001 1.418 NaN 87.0 ,us1000h3vt, 4.3 mb ... ,us, reviewed \n",
|
||
"7066 1.034 3.0 75.0 ,us2000hja7, 4.4 mb ... ,us, reviewed \n",
|
||
"7145 1.242 NaN 140.0 ,us2000hj80, 4.1 mb ... ,us, reviewed \n",
|
||
"7261 2.421 2.0 106.0 ,us2000hj4d, 4.1 mb ... ,us, reviewed \n",
|
||
"7381 1.373 10.0 137.0 ,us2000hj04, 4.6 mb ... ,us, reviewed \n",
|
||
"7408 2.396 1.0 137.0 ,us2000hizb, 4.8 mb ... ,us, reviewed \n",
|
||
"7652 1.228 NaN 93.0 ,us1000h3qw, 4.2 mb ... ,us, reviewed \n",
|
||
"7696 0.223 NaN 146.0 ,us1000h3qp, 4.0 mb ... ,us, reviewed \n",
|
||
"7807 0.571 NaN 124.0 ,us1000h3qb, 4.4 mb ... ,us, reviewed \n",
|
||
"8071 2.666 NaN 150.0 ,us2000hic7, 4.4 mb ... ,us, reviewed \n",
|
||
"8311 1.230 NaN 95.0 ,us1000h3av, 4.4 mb ... ,us, reviewed \n",
|
||
"8324 1.234 NaN 82.0 ,us2000hhxa, 4.6 mb ... ,us, reviewed \n",
|
||
"8327 1.068 NaN 89.0 ,us2000hhx3, 4.2 mb ... ,us, reviewed \n",
|
||
"8376 1.272 NaN 91.0 ,us1000h3am, 4.4 mb ... ,us, reviewed \n",
|
||
"8431 1.270 NaN 95.0 ,us2000hhlf, 4.6 mb ... ,us, reviewed \n",
|
||
"8550 1.844 NaN 164.0 ,us2000hhgj, 4.4 mb ... ,us, reviewed \n",
|
||
"8555 1.825 NaN 129.0 ,us2000hhga, 4.6 mb ... ,us, reviewed \n",
|
||
"8687 0.485 NaN 147.0 ,us1000h2t5, 4.2 mb ... ,us, reviewed \n",
|
||
"9070 2.315 NaN 141.0 ,us2000hgfj, 4.7 mb ... ,us, reviewed \n",
|
||
"9198 1.367 85.0 115.0 ,us2000hgaa, 4.6 mb ... ,us, reviewed \n",
|
||
"\n",
|
||
" time title tsunami \\\n",
|
||
"67 1539448501800 M 4.6 - 160km NNW of Nago, Japan 0 \n",
|
||
"713 1539238726290 M 4.7 - 139km WSW of Naze, Japan 0 \n",
|
||
"1124 1539115120470 M 4.6 - 53km ESE of Kamaishi, Japan 0 \n",
|
||
"1309 1539058606580 M 4.5 - 80km ENE of Misawa, Japan 0 \n",
|
||
"1398 1539034912990 M 4.4 - 65km NNE of Hachijo-jima, Japan 0 \n",
|
||
"1422 1539028322670 M 4.7 - 41km E of Namie, Japan 0 \n",
|
||
"1435 1539020712670 M 4.4 - 21km E of Tomakomai, Japan 0 \n",
|
||
"1492 1539003238480 M 4.6 - 29km E of Tomakomai, Japan 0 \n",
|
||
"1563 1538977532250 M 4.9 - 293km ESE of Iwo Jima, Japan 0 \n",
|
||
"1732 1538923896600 M 4.1 - 89km NE of Miyako, Japan 0 \n",
|
||
"1875 1538881270700 M 4.7 - 177km WSW of Chichi-shima, Japan 0 \n",
|
||
"1898 1538874859870 M 4.8 - 12km N of Shinshiro, Japan 0 \n",
|
||
"2053 1538839123780 M 4.8 - 147km E of Nago, Japan 0 \n",
|
||
"2191 1538799265130 M 4.6 - 27km ESE of Chitose, Japan 0 \n",
|
||
"2564 1538701532340 M 4.6 - 162km E of Nago, Japan 0 \n",
|
||
"2576 1538697528010 M 5.4 - 37km E of Tomakomai, Japan 0 \n",
|
||
"2688 1538673414250 M 4.0 - 51km WNW of Mikuni, Japan 0 \n",
|
||
"2824 1538641253520 M 4.4 - 51km SSE of Kushima, Japan 0 \n",
|
||
"3072 1538579732490 M 4.9 - 15km ENE of Hasaki, Japan 0 \n",
|
||
"3632 1538450871260 M 4.9 - 53km ESE of Hitachi, Japan 0 \n",
|
||
"3771 1538422163330 M 4.5 - 87km ESE of Kamaishi, Japan 0 \n",
|
||
"3851 1538406431250 M 4.7 - 173km E of Nago, Japan 0 \n",
|
||
"4082 1538360525340 M 4.8 - 31km E of Chitose, Japan 0 \n",
|
||
"4192 1538336412660 M 4.3 - 50km NNE of Shizunai, Japan 0 \n",
|
||
"4257 1538323620710 M 4.6 - 213km SE of Hachijo-jima, Japan 0 \n",
|
||
"4846 1538213154900 M 4.5 - 25km ESE of Chitose, Japan 0 \n",
|
||
"5655 1538048754220 M 4.6 - 156km ENE of Nago, Japan 0 \n",
|
||
"5915 1538003928830 M 4.4 - 78km ESE of Yamada, Japan 0 \n",
|
||
"6769 1537846261100 M 4.8 - 58km SE of Ofunato, Japan 0 \n",
|
||
"6961 1537799121060 M 4.7 - 46km ENE of Hirara, Japan 0 \n",
|
||
"7001 1537792438760 M 4.3 - 25km ESE of Muroran, Japan 0 \n",
|
||
"7066 1537780626560 M 4.4 - 35km E of Tomakomai, Japan 0 \n",
|
||
"7145 1537764836900 M 4.1 - 97km N of Mombetsu, Japan 0 \n",
|
||
"7261 1537738421220 M 4.1 - 64km ESE of Owase, Japan 0 \n",
|
||
"7381 1537715134540 M 4.6 - 9km ENE of Funaishikawa, Japan 0 \n",
|
||
"7408 1537709390500 M 4.8 - 65km SSE of Hasaki, Japan 0 \n",
|
||
"7652 1537654508260 M 4.2 - 164km E of Nago, Japan 0 \n",
|
||
"7696 1537640754840 M 4.0 - 65km E of Shizunai, Japan 0 \n",
|
||
"7807 1537619327220 M 4.4 - 67km NNE of Hachijo-jima, Japan 0 \n",
|
||
"8071 1537550307820 M 4.4 - 67km E of Nemuro, Japan 0 \n",
|
||
"8311 1537487811890 M 4.4 - 166km E of Nago, Japan 0 \n",
|
||
"8324 1537484762840 M 4.6 - 167km E of Nago, Japan 0 \n",
|
||
"8327 1537484169100 M 4.2 - 34km ESE of Chitose, Japan 0 \n",
|
||
"8376 1537470382450 M 4.4 - 171km E of Nago, Japan 0 \n",
|
||
"8431 1537455447970 M 4.6 - 171km E of Nago, Japan 0 \n",
|
||
"8550 1537430889800 M 4.4 - 105km ENE of Miyako, Japan 0 \n",
|
||
"8555 1537429466000 M 4.6 - 105km ENE of Miyako, Japan 0 \n",
|
||
"8687 1537392267190 M 4.2 - 39km NE of Misawa, Japan 0 \n",
|
||
"9070 1537289668660 M 4.7 - 96km E of Hasaki, Japan 0 \n",
|
||
"9198 1537258271290 M 4.6 - 3km ESE of Sugito, Japan 0 \n",
|
||
"\n",
|
||
" type types tz \\\n",
|
||
"67 earthquake ,geoserve,origin,phase-data, 480.0 \n",
|
||
"713 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"1124 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"1309 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"1398 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"1422 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"1435 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"1492 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"1563 earthquake ,geoserve,origin,phase-data, 600.0 \n",
|
||
"1732 earthquake ,geoserve,origin,phase-data, 600.0 \n",
|
||
"1875 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"1898 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"2053 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"2191 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"2564 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"2576 earthquake ,dyfi,geoserve,moment-tensor,origin,phase-data, 540.0 \n",
|
||
"2688 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"2824 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"3072 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"3632 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"3771 earthquake ,geoserve,origin,phase-data, 600.0 \n",
|
||
"3851 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"4082 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"4192 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"4257 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"4846 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"5655 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"5915 earthquake ,geoserve,origin,phase-data, 600.0 \n",
|
||
"6769 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"6961 earthquake ,geoserve,origin,phase-data, 480.0 \n",
|
||
"7001 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"7066 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"7145 earthquake ,geoserve,origin,phase-data, 600.0 \n",
|
||
"7261 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"7381 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"7408 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"7652 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"7696 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"7807 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"8071 earthquake ,geoserve,origin,phase-data, 600.0 \n",
|
||
"8311 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"8324 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"8327 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"8376 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"8431 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"8550 earthquake ,geoserve,origin,phase-data, 600.0 \n",
|
||
"8555 earthquake ,geoserve,origin,phase-data, 600.0 \n",
|
||
"8687 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"9070 earthquake ,geoserve,origin,phase-data, 540.0 \n",
|
||
"9198 earthquake ,dyfi,geoserve,origin,phase-data, 540.0 \n",
|
||
"\n",
|
||
" updated url \n",
|
||
"67 1539449501040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"713 1539240344040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"1124 1539119067040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"1309 1539060436040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"1398 1539062940040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"1422 1539061047807 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"1435 1539034924040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"1492 1539012243124 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"1563 1538978864040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"1732 1539532511040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"1875 1538882370040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"1898 1539319234277 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"2053 1538840634040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"2191 1538811844812 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"2564 1539306889040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"2576 1538757701040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"2688 1539363073040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"2824 1538642265040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"3072 1539230846942 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"3632 1538581746455 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"3771 1539026579040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"3851 1539118704040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"4082 1539136962751 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"4192 1538367048207 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"4257 1539103992040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"4846 1538294859215 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"5655 1538050682040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"5915 1539400764040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"6769 1537908537284 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"6961 1538419499040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"7001 1538375544040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"7066 1538238102308 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"7145 1538716171040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"7261 1537740803336 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"7381 1538332135138 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"7408 1538329898836 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"7652 1539135424040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"7696 1539294775040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"7807 1538226419040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"8071 1539122080040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"8311 1538880454040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"8324 1538969995040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"8327 1538870822040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"8376 1539464832040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"8431 1537457187040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"8550 1537510968040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"8555 1537430366040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"8687 1538890503040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"9070 1537976170040 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"9198 1538220844777 https://earthquake.usgs.gov/earthquakes/eventp... \n",
|
||
"\n",
|
||
"[50 rows x 26 columns]"
|
||
]
|
||
},
|
||
"execution_count": 155,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# znajdz trzęsienia w japonii z typem mag 'mb'\n",
|
||
"data[(data.place.str.contains('Japan')) & (data.magType == 'mb')]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 147,
|
||
"id": "e7e30b39-871d-4147-bbca-2ded10396eb8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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>cdi</th>\n",
|
||
" <th>dmin</th>\n",
|
||
" <th>felt</th>\n",
|
||
" <th>gap</th>\n",
|
||
" <th>mag</th>\n",
|
||
" <th>mmi</th>\n",
|
||
" <th>nst</th>\n",
|
||
" <th>rms</th>\n",
|
||
" <th>sig</th>\n",
|
||
" <th>time</th>\n",
|
||
" <th>tsunami</th>\n",
|
||
" <th>tz</th>\n",
|
||
" <th>updated</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>15.000000</td>\n",
|
||
" <td>681.000000</td>\n",
|
||
" <td>15.000000</td>\n",
|
||
" <td>681.000000</td>\n",
|
||
" <td>681.000000</td>\n",
|
||
" <td>1.00</td>\n",
|
||
" <td>681.000000</td>\n",
|
||
" <td>681.000000</td>\n",
|
||
" <td>681.000000</td>\n",
|
||
" <td>6.810000e+02</td>\n",
|
||
" <td>681.0</td>\n",
|
||
" <td>681.0</td>\n",
|
||
" <td>6.810000e+02</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>2.440000</td>\n",
|
||
" <td>0.166199</td>\n",
|
||
" <td>2.400000</td>\n",
|
||
" <td>153.668120</td>\n",
|
||
" <td>0.500073</td>\n",
|
||
" <td>2.84</td>\n",
|
||
" <td>12.618209</td>\n",
|
||
" <td>0.151986</td>\n",
|
||
" <td>10.970631</td>\n",
|
||
" <td>1.538314e+12</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>-480.0</td>\n",
|
||
" <td>1.538402e+12</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>0.501142</td>\n",
|
||
" <td>0.166228</td>\n",
|
||
" <td>4.626013</td>\n",
|
||
" <td>68.735302</td>\n",
|
||
" <td>0.696710</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>9.866963</td>\n",
|
||
" <td>0.084662</td>\n",
|
||
" <td>19.607150</td>\n",
|
||
" <td>5.965637e+08</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>6.010951e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>0.001000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>29.140000</td>\n",
|
||
" <td>-0.500000</td>\n",
|
||
" <td>2.84</td>\n",
|
||
" <td>3.000000</td>\n",
|
||
" <td>0.000500</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.537247e+12</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>-480.0</td>\n",
|
||
" <td>1.537307e+12</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>0.053000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>97.380000</td>\n",
|
||
" <td>-0.100000</td>\n",
|
||
" <td>2.84</td>\n",
|
||
" <td>6.000000</td>\n",
|
||
" <td>0.106900</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.537854e+12</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>-480.0</td>\n",
|
||
" <td>1.537928e+12</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>2.200000</td>\n",
|
||
" <td>0.112000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>149.140000</td>\n",
|
||
" <td>0.400000</td>\n",
|
||
" <td>2.84</td>\n",
|
||
" <td>10.000000</td>\n",
|
||
" <td>0.146300</td>\n",
|
||
" <td>2.000000</td>\n",
|
||
" <td>1.538280e+12</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>-480.0</td>\n",
|
||
" <td>1.538428e+12</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>2.900000</td>\n",
|
||
" <td>0.233000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>199.720000</td>\n",
|
||
" <td>0.900000</td>\n",
|
||
" <td>2.84</td>\n",
|
||
" <td>16.000000</td>\n",
|
||
" <td>0.187100</td>\n",
|
||
" <td>12.000000</td>\n",
|
||
" <td>1.538821e+12</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>-480.0</td>\n",
|
||
" <td>1.538878e+12</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>3.300000</td>\n",
|
||
" <td>1.414000</td>\n",
|
||
" <td>19.000000</td>\n",
|
||
" <td>355.910000</td>\n",
|
||
" <td>2.900000</td>\n",
|
||
" <td>2.84</td>\n",
|
||
" <td>61.000000</td>\n",
|
||
" <td>0.863400</td>\n",
|
||
" <td>129.000000</td>\n",
|
||
" <td>1.539461e+12</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>-480.0</td>\n",
|
||
" <td>1.539483e+12</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" cdi dmin felt gap mag mmi \\\n",
|
||
"count 15.000000 681.000000 15.000000 681.000000 681.000000 1.00 \n",
|
||
"mean 2.440000 0.166199 2.400000 153.668120 0.500073 2.84 \n",
|
||
"std 0.501142 0.166228 4.626013 68.735302 0.696710 NaN \n",
|
||
"min 2.000000 0.001000 1.000000 29.140000 -0.500000 2.84 \n",
|
||
"25% 2.000000 0.053000 1.000000 97.380000 -0.100000 2.84 \n",
|
||
"50% 2.200000 0.112000 1.000000 149.140000 0.400000 2.84 \n",
|
||
"75% 2.900000 0.233000 1.000000 199.720000 0.900000 2.84 \n",
|
||
"max 3.300000 1.414000 19.000000 355.910000 2.900000 2.84 \n",
|
||
"\n",
|
||
" nst rms sig time tsunami tz \\\n",
|
||
"count 681.000000 681.000000 681.000000 6.810000e+02 681.0 681.0 \n",
|
||
"mean 12.618209 0.151986 10.970631 1.538314e+12 0.0 -480.0 \n",
|
||
"std 9.866963 0.084662 19.607150 5.965637e+08 0.0 0.0 \n",
|
||
"min 3.000000 0.000500 0.000000 1.537247e+12 0.0 -480.0 \n",
|
||
"25% 6.000000 0.106900 0.000000 1.537854e+12 0.0 -480.0 \n",
|
||
"50% 10.000000 0.146300 2.000000 1.538280e+12 0.0 -480.0 \n",
|
||
"75% 16.000000 0.187100 12.000000 1.538821e+12 0.0 -480.0 \n",
|
||
"max 61.000000 0.863400 129.000000 1.539461e+12 0.0 -480.0 \n",
|
||
"\n",
|
||
" updated \n",
|
||
"count 6.810000e+02 \n",
|
||
"mean 1.538402e+12 \n",
|
||
"std 6.010951e+08 \n",
|
||
"min 1.537307e+12 \n",
|
||
"25% 1.537928e+12 \n",
|
||
"50% 1.538428e+12 \n",
|
||
"75% 1.538878e+12 \n",
|
||
"max 1.539483e+12 "
|
||
]
|
||
},
|
||
"execution_count": 147,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# wywołaj opis (describe) który będzie brał pod uwagę tylko Nevadę\n",
|
||
"data[data.place == 'Nevada'].describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 171,
|
||
"id": "527f0fb8-7217-41f2-a930-7de1439fc139",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"130"
|
||
]
|
||
},
|
||
"execution_count": 171,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# sprawdź ilość tsunami w japonii\n",
|
||
"data[(data.place.str.contains('Japan')) & (data.tsunami == 1)].size"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 170,
|
||
"id": "8629a223-fcac-4cc9-bb82-cfb2f0086e41",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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>cdi</th>\n",
|
||
" <th>dmin</th>\n",
|
||
" <th>felt</th>\n",
|
||
" <th>gap</th>\n",
|
||
" <th>mag</th>\n",
|
||
" <th>mmi</th>\n",
|
||
" <th>nst</th>\n",
|
||
" <th>rms</th>\n",
|
||
" <th>sig</th>\n",
|
||
" <th>time</th>\n",
|
||
" <th>tsunami</th>\n",
|
||
" <th>tz</th>\n",
|
||
" <th>updated</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>16.000000</td>\n",
|
||
" <td>50.000000</td>\n",
|
||
" <td>16.000000</td>\n",
|
||
" <td>50.000000</td>\n",
|
||
" <td>50.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>50.000000</td>\n",
|
||
" <td>50.000000</td>\n",
|
||
" <td>5.000000e+01</td>\n",
|
||
" <td>50.0</td>\n",
|
||
" <td>50.000000</td>\n",
|
||
" <td>5.000000e+01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>2.881250</td>\n",
|
||
" <td>1.485080</td>\n",
|
||
" <td>11.187500</td>\n",
|
||
" <td>108.880000</td>\n",
|
||
" <td>4.530000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.801800</td>\n",
|
||
" <td>318.080000</td>\n",
|
||
" <td>1.538218e+12</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>547.200000</td>\n",
|
||
" <td>1.538746e+12</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>0.883341</td>\n",
|
||
" <td>0.802257</td>\n",
|
||
" <td>20.672748</td>\n",
|
||
" <td>31.676709</td>\n",
|
||
" <td>0.266688</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.258589</td>\n",
|
||
" <td>38.570286</td>\n",
|
||
" <td>6.404535e+08</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>26.111222</td>\n",
|
||
" <td>5.518279e+08</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>0.223000</td>\n",
|
||
" <td>1.000000</td>\n",
|
||
" <td>33.000000</td>\n",
|
||
" <td>4.000000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.270000</td>\n",
|
||
" <td>246.000000</td>\n",
|
||
" <td>1.537258e+12</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>480.000000</td>\n",
|
||
" <td>1.537430e+12</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>3.100000</td>\n",
|
||
" <td>1.271000</td>\n",
|
||
" <td>3.500000</td>\n",
|
||
" <td>112.000000</td>\n",
|
||
" <td>4.600000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>0.810000</td>\n",
|
||
" <td>326.000000</td>\n",
|
||
" <td>1.538268e+12</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>540.000000</td>\n",
|
||
" <td>1.538886e+12</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>95%</th>\n",
|
||
" <td>3.875000</td>\n",
|
||
" <td>2.583200</td>\n",
|
||
" <td>35.500000</td>\n",
|
||
" <td>155.650000</td>\n",
|
||
" <td>4.900000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.335500</td>\n",
|
||
" <td>371.750000</td>\n",
|
||
" <td>1.539090e+12</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>600.000000</td>\n",
|
||
" <td>1.539428e+12</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>4.100000</td>\n",
|
||
" <td>4.830000</td>\n",
|
||
" <td>85.000000</td>\n",
|
||
" <td>167.000000</td>\n",
|
||
" <td>5.400000</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>1.400000</td>\n",
|
||
" <td>451.000000</td>\n",
|
||
" <td>1.539449e+12</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>600.000000</td>\n",
|
||
" <td>1.539533e+12</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" cdi dmin felt gap mag mmi nst \\\n",
|
||
"count 16.000000 50.000000 16.000000 50.000000 50.000000 0.0 0.0 \n",
|
||
"mean 2.881250 1.485080 11.187500 108.880000 4.530000 NaN NaN \n",
|
||
"std 0.883341 0.802257 20.672748 31.676709 0.266688 NaN NaN \n",
|
||
"min 1.000000 0.223000 1.000000 33.000000 4.000000 NaN NaN \n",
|
||
"50% 3.100000 1.271000 3.500000 112.000000 4.600000 NaN NaN \n",
|
||
"95% 3.875000 2.583200 35.500000 155.650000 4.900000 NaN NaN \n",
|
||
"max 4.100000 4.830000 85.000000 167.000000 5.400000 NaN NaN \n",
|
||
"\n",
|
||
" rms sig time tsunami tz updated \n",
|
||
"count 50.000000 50.000000 5.000000e+01 50.0 50.000000 5.000000e+01 \n",
|
||
"mean 0.801800 318.080000 1.538218e+12 0.0 547.200000 1.538746e+12 \n",
|
||
"std 0.258589 38.570286 6.404535e+08 0.0 26.111222 5.518279e+08 \n",
|
||
"min 0.270000 246.000000 1.537258e+12 0.0 480.000000 1.537430e+12 \n",
|
||
"50% 0.810000 326.000000 1.538268e+12 0.0 540.000000 1.538886e+12 \n",
|
||
"95% 1.335500 371.750000 1.539090e+12 0.0 600.000000 1.539428e+12 \n",
|
||
"max 1.400000 451.000000 1.539449e+12 0.0 600.000000 1.539533e+12 "
|
||
]
|
||
},
|
||
"execution_count": 170,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# oblicz kwantyl 95% trzęsień w Japonii z magnitudą 'mb'\n",
|
||
"data[(data.place.str.contains('Japan')) & (data.magType == 'mb')].describe(percentiles=[0.95])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 178,
|
||
"id": "6bd47e02-8b68-4bfa-b2cd-bcc838b127ae",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.23129251700680273"
|
||
]
|
||
},
|
||
"execution_count": 178,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# oblicz trzęsienia a indonezji z tsunami - podaj wartość w procentach\n",
|
||
"x = data[data.place.str.contains('Indonesia') & data.tsunami == 1].size\n",
|
||
"y = data[data.place.str.contains('Indonesia')].size\n",
|
||
"x/y"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "82ff9d7c-695f-4dad-b2c2-2584ab429d02",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Long i Wide"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "abd12ae2-ce7d-4d14-b996-78f1c6429c6d",
|
||
"metadata": {},
|
||
"source": [
|
||
"## matplotlib"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 315,
|
||
"id": "3de3ed9f-6905-4eba-bc21-165e5aea097d",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 215,
|
||
"id": "de87d64c-13d0-4458-be5f-4bd1b9b90f67",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(93, 3)"
|
||
]
|
||
},
|
||
"execution_count": 215,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_long = pd.read_csv(\"data/long_data.csv\", parse_dates=['date'], usecols=['date', 'datatype', 'value'])\n",
|
||
"df_wide = pd.read_csv(\"data/wide_data.csv\", parse_dates=['date'])\n",
|
||
"\n",
|
||
"df_long.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 225,
|
||
"id": "387352e3-c1f4-4845-b624-f1ab8305e6b5",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, '˚C')"
|
||
]
|
||
},
|
||
"execution_count": 225,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x200 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_wide.plot(kind='bar', x='date', figsize=(12,2), title=\"Tempertura\").set_ylabel('˚C')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "28b5c0a4-5a46-4c8c-a22b-5d3e52102111",
|
||
"metadata": {},
|
||
"source": [
|
||
"## seaborn"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 230,
|
||
"id": "058c4598-8802-4f19-8f88-11d0dfec6b44",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import seaborn as sns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 248,
|
||
"id": "d56db132-d27c-4d8e-9615-1b7ca85322d0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_2480/557238536.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
|
||
" ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"''"
|
||
]
|
||
},
|
||
"execution_count": 248,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x300 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.set(rc={'figure.figsize':(12,3)})\n",
|
||
"ax = sns.lineplot(\n",
|
||
" data = df_long,\n",
|
||
" x = 'date',\n",
|
||
" y = 'value',\n",
|
||
" hue = 'datatype' \n",
|
||
")\n",
|
||
"ax.set_ylabel('°C')\n",
|
||
"ax.set_xticklabels(ax.get_xticklabels(), rotation=45)\n",
|
||
";"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "00412f08-ec65-479f-9fe8-1f5eb8c395d6",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "12090f1a-bbe8-46cf-b40a-67f9ac4fd848",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Czyszczenie danych"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 305,
|
||
"id": "25ec7d8f-f487-468f-8d4b-384960d203be",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"(502, 5)"
|
||
]
|
||
},
|
||
"execution_count": 305,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# df_sp = pd.read_csv(\"data/nyc_temperatures.csv\", usecols=['date', 'datatype', 'value'], parse_dates=['date'])\n",
|
||
"df_sp = pd.read_csv(\"data/sp500.csv\", index_col='date', parse_dates=['date']).drop(columns=['adj_close'])\n",
|
||
"df_sp.shape"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 306,
|
||
"id": "7f7e4ca8-77a1-4409-85f9-616872a45e12",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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>high</th>\n",
|
||
" <th>low</th>\n",
|
||
" <th>open</th>\n",
|
||
" <th>close</th>\n",
|
||
" <th>volume</th>\n",
|
||
" <th>day_of_week</th>\n",
|
||
" <th>month</th>\n",
|
||
" <th>quarter</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>date</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2017-01-03</th>\n",
|
||
" <td>2263.879883</td>\n",
|
||
" <td>2245.129883</td>\n",
|
||
" <td>2251.570068</td>\n",
|
||
" <td>2257.830078</td>\n",
|
||
" <td>3770530000</td>\n",
|
||
" <td>Tuesday</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-01-04</th>\n",
|
||
" <td>2272.820068</td>\n",
|
||
" <td>2261.600098</td>\n",
|
||
" <td>2261.600098</td>\n",
|
||
" <td>2270.750000</td>\n",
|
||
" <td>3764890000</td>\n",
|
||
" <td>Wednesday</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-01-05</th>\n",
|
||
" <td>2271.500000</td>\n",
|
||
" <td>2260.449951</td>\n",
|
||
" <td>2268.179932</td>\n",
|
||
" <td>2269.000000</td>\n",
|
||
" <td>3761820000</td>\n",
|
||
" <td>Thursday</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-01-06</th>\n",
|
||
" <td>2282.100098</td>\n",
|
||
" <td>2264.060059</td>\n",
|
||
" <td>2271.139893</td>\n",
|
||
" <td>2276.979980</td>\n",
|
||
" <td>3339890000</td>\n",
|
||
" <td>Friday</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-01-09</th>\n",
|
||
" <td>2275.489990</td>\n",
|
||
" <td>2268.899902</td>\n",
|
||
" <td>2273.590088</td>\n",
|
||
" <td>2268.899902</td>\n",
|
||
" <td>3217610000</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2018-12-24</th>\n",
|
||
" <td>2410.340088</td>\n",
|
||
" <td>2351.100098</td>\n",
|
||
" <td>2400.560059</td>\n",
|
||
" <td>2351.100098</td>\n",
|
||
" <td>2613930000</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2018-12-26</th>\n",
|
||
" <td>2467.760010</td>\n",
|
||
" <td>2346.580078</td>\n",
|
||
" <td>2363.120117</td>\n",
|
||
" <td>2467.699951</td>\n",
|
||
" <td>4233990000</td>\n",
|
||
" <td>Wednesday</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2018-12-27</th>\n",
|
||
" <td>2489.100098</td>\n",
|
||
" <td>2397.939941</td>\n",
|
||
" <td>2442.500000</td>\n",
|
||
" <td>2488.830078</td>\n",
|
||
" <td>4096610000</td>\n",
|
||
" <td>Thursday</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2018-12-28</th>\n",
|
||
" <td>2520.270020</td>\n",
|
||
" <td>2472.889893</td>\n",
|
||
" <td>2498.770020</td>\n",
|
||
" <td>2485.739990</td>\n",
|
||
" <td>3702620000</td>\n",
|
||
" <td>Friday</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2018-12-31</th>\n",
|
||
" <td>2509.239990</td>\n",
|
||
" <td>2482.820068</td>\n",
|
||
" <td>2498.939941</td>\n",
|
||
" <td>2506.850098</td>\n",
|
||
" <td>3442870000</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" <td>12</td>\n",
|
||
" <td>4</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>502 rows × 8 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" high low open close volume \\\n",
|
||
"date \n",
|
||
"2017-01-03 2263.879883 2245.129883 2251.570068 2257.830078 3770530000 \n",
|
||
"2017-01-04 2272.820068 2261.600098 2261.600098 2270.750000 3764890000 \n",
|
||
"2017-01-05 2271.500000 2260.449951 2268.179932 2269.000000 3761820000 \n",
|
||
"2017-01-06 2282.100098 2264.060059 2271.139893 2276.979980 3339890000 \n",
|
||
"2017-01-09 2275.489990 2268.899902 2273.590088 2268.899902 3217610000 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"2018-12-24 2410.340088 2351.100098 2400.560059 2351.100098 2613930000 \n",
|
||
"2018-12-26 2467.760010 2346.580078 2363.120117 2467.699951 4233990000 \n",
|
||
"2018-12-27 2489.100098 2397.939941 2442.500000 2488.830078 4096610000 \n",
|
||
"2018-12-28 2520.270020 2472.889893 2498.770020 2485.739990 3702620000 \n",
|
||
"2018-12-31 2509.239990 2482.820068 2498.939941 2506.850098 3442870000 \n",
|
||
"\n",
|
||
" day_of_week month quarter \n",
|
||
"date \n",
|
||
"2017-01-03 Tuesday 1 1 \n",
|
||
"2017-01-04 Wednesday 1 1 \n",
|
||
"2017-01-05 Thursday 1 1 \n",
|
||
"2017-01-06 Friday 1 1 \n",
|
||
"2017-01-09 Monday 1 1 \n",
|
||
"... ... ... ... \n",
|
||
"2018-12-24 Monday 12 4 \n",
|
||
"2018-12-26 Wednesday 12 4 \n",
|
||
"2018-12-27 Thursday 12 4 \n",
|
||
"2018-12-28 Friday 12 4 \n",
|
||
"2018-12-31 Monday 12 4 \n",
|
||
"\n",
|
||
"[502 rows x 8 columns]"
|
||
]
|
||
},
|
||
"execution_count": 306,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_sp.assign(\n",
|
||
" day_of_week = lambda x: x.index.day_name(),\n",
|
||
" month = lambda x: x.index.month,\n",
|
||
" quarter = lambda x: x.index.quarter\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "1b44e64f-b738-4751-9685-b441ff332f93",
|
||
"metadata": {},
|
||
"source": [
|
||
"# Ćwiczenie"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 313,
|
||
"id": "bb0e5b0e-0262-4df8-bb15-74d19fd36f99",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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>open</th>\n",
|
||
" <th>high</th>\n",
|
||
" <th>low</th>\n",
|
||
" <th>close</th>\n",
|
||
" <th>volume</th>\n",
|
||
" <th>day_of_week</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>date</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>2017-01-01</th>\n",
|
||
" <td>963.66</td>\n",
|
||
" <td>1003.08</td>\n",
|
||
" <td>958.70</td>\n",
|
||
" <td>998.33</td>\n",
|
||
" <td>147775008</td>\n",
|
||
" <td>Sunday</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-01-02</th>\n",
|
||
" <td>998.62</td>\n",
|
||
" <td>1031.39</td>\n",
|
||
" <td>996.70</td>\n",
|
||
" <td>1021.75</td>\n",
|
||
" <td>222184992</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-01-03</th>\n",
|
||
" <td>1021.60</td>\n",
|
||
" <td>1044.08</td>\n",
|
||
" <td>1021.60</td>\n",
|
||
" <td>1043.84</td>\n",
|
||
" <td>185168000</td>\n",
|
||
" <td>Tuesday</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-01-04</th>\n",
|
||
" <td>1044.40</td>\n",
|
||
" <td>1159.42</td>\n",
|
||
" <td>1044.40</td>\n",
|
||
" <td>1154.73</td>\n",
|
||
" <td>344945984</td>\n",
|
||
" <td>Wednesday</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2017-01-05</th>\n",
|
||
" <td>1156.73</td>\n",
|
||
" <td>1191.10</td>\n",
|
||
" <td>910.42</td>\n",
|
||
" <td>1013.38</td>\n",
|
||
" <td>510199008</td>\n",
|
||
" <td>Thursday</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2018-12-27</th>\n",
|
||
" <td>3854.69</td>\n",
|
||
" <td>3874.42</td>\n",
|
||
" <td>3645.45</td>\n",
|
||
" <td>3654.83</td>\n",
|
||
" <td>5130222366</td>\n",
|
||
" <td>Thursday</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2018-12-28</th>\n",
|
||
" <td>3653.13</td>\n",
|
||
" <td>3956.14</td>\n",
|
||
" <td>3642.63</td>\n",
|
||
" <td>3923.92</td>\n",
|
||
" <td>5631554348</td>\n",
|
||
" <td>Friday</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2018-12-29</th>\n",
|
||
" <td>3932.49</td>\n",
|
||
" <td>3963.76</td>\n",
|
||
" <td>3820.41</td>\n",
|
||
" <td>3820.41</td>\n",
|
||
" <td>4991655917</td>\n",
|
||
" <td>Saturday</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2018-12-30</th>\n",
|
||
" <td>3822.38</td>\n",
|
||
" <td>3901.91</td>\n",
|
||
" <td>3797.22</td>\n",
|
||
" <td>3865.95</td>\n",
|
||
" <td>4770578575</td>\n",
|
||
" <td>Sunday</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2018-12-31</th>\n",
|
||
" <td>3866.84</td>\n",
|
||
" <td>3868.74</td>\n",
|
||
" <td>3725.87</td>\n",
|
||
" <td>3742.70</td>\n",
|
||
" <td>4661840806</td>\n",
|
||
" <td>Monday</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>730 rows × 6 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" open high low close volume day_of_week\n",
|
||
"date \n",
|
||
"2017-01-01 963.66 1003.08 958.70 998.33 147775008 Sunday\n",
|
||
"2017-01-02 998.62 1031.39 996.70 1021.75 222184992 Monday\n",
|
||
"2017-01-03 1021.60 1044.08 1021.60 1043.84 185168000 Tuesday\n",
|
||
"2017-01-04 1044.40 1159.42 1044.40 1154.73 344945984 Wednesday\n",
|
||
"2017-01-05 1156.73 1191.10 910.42 1013.38 510199008 Thursday\n",
|
||
"... ... ... ... ... ... ...\n",
|
||
"2018-12-27 3854.69 3874.42 3645.45 3654.83 5130222366 Thursday\n",
|
||
"2018-12-28 3653.13 3956.14 3642.63 3923.92 5631554348 Friday\n",
|
||
"2018-12-29 3932.49 3963.76 3820.41 3820.41 4991655917 Saturday\n",
|
||
"2018-12-30 3822.38 3901.91 3797.22 3865.95 4770578575 Sunday\n",
|
||
"2018-12-31 3866.84 3868.74 3725.87 3742.70 4661840806 Monday\n",
|
||
"\n",
|
||
"[730 rows x 6 columns]"
|
||
]
|
||
},
|
||
"execution_count": 313,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# otworz plik bitcoin.csv, spraw aby daty były indeksem, wyciągnij dzień tygodnia, zlikwiduj market_cap\n",
|
||
"df_btc = pd.read_csv(\"data/bitcoin.csv\", index_col='date', parse_dates=['date']).drop(columns=['market_cap'])\n",
|
||
"df_btc.assign(\n",
|
||
" day_of_week = lambda x: x.index.day_name()\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 327,
|
||
"id": "d1fcf963-92f1-4619-800d-cf1e4f5f89d6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x300 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Create figure and axes\n",
|
||
"fig, ax = plt.subplots()\n",
|
||
"\n",
|
||
"# Plot both DataFrames\n",
|
||
"sns.lineplot(data=df_sp, x=df_sp.index, y='close', label='SP500')\n",
|
||
"sns.lineplot(data=df_btc, x=df_btc.index, y='close', label='Bitcoin')\n",
|
||
"\n",
|
||
"# Customize the plot (e.g., labels, legend)\n",
|
||
"ax.set_xlabel('Index')\n",
|
||
"ax.set_ylabel('Close')\n",
|
||
"ax.legend()\n",
|
||
"\n",
|
||
"# Display the plot\n",
|
||
"plt.show()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 348,
|
||
"id": "84fafaeb-21c1-4632-b106-e7c6893f977c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_sp_f = df_sp.reindex(df_btc.index).ffill().bfill()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 353,
|
||
"id": "e09ce8ea-3bec-4d16-8a38-3f0591d2d247",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"portfolio = pd.concat([df_btc, df_sp_f], sort=False).groupby(level='date').sum()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 360,
|
||
"id": "f84c2022-c673-46dd-9775-e3a4542b3c95",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x300 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Create figure and axes\n",
|
||
"fig, ax = plt.subplots()\n",
|
||
"\n",
|
||
"# Plot both DataFrames\n",
|
||
"sns.lineplot(data=portfolio, x=portfolio.index, y='close', label='SP500 + BTC', linestyle='dashdot')\n",
|
||
"sns.lineplot(data=df_btc, x=df_btc.index, y='close', label='BTC', linestyle='dotted')\n",
|
||
"sns.lineplot(data=df_sp_f, x=df_sp_f.index, y='close', label='SP500', linestyle='--')\n",
|
||
"\n",
|
||
"# Customize the plot (e.g., labels, legend)\n",
|
||
"ax.set_xlabel('Index')\n",
|
||
"ax.set_ylabel('Close')\n",
|
||
"ax.legend()\n",
|
||
"\n",
|
||
"# Display the plot\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2c31f40f-ef10-49f3-8b4d-7bfd5ed89965",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Naprawa df_long"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 371,
|
||
"id": "b44f84d3-ce86-439c-8ab8-1847cc3664f4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_long = pd.read_csv(\"data/long_data.csv\", parse_dates=['date'], usecols=['date', 'datatype', 'value'])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 372,
|
||
"id": "3fcf062f-de19-42f5-9c60-ccf6f097deef",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 93 entries, 0 to 92\n",
|
||
"Data columns (total 4 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 datatype 93 non-null object \n",
|
||
" 1 date 93 non-null datetime64[ns]\n",
|
||
" 2 temp_C 93 non-null float64 \n",
|
||
" 3 temp_F 93 non-null float64 \n",
|
||
"dtypes: datetime64[ns](1), float64(2), object(1)\n",
|
||
"memory usage: 3.0+ KB\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"df_long = df_long.assign(temp_F = lambda x: x.value * 9/5 + 32).rename(columns={'value':'temp_C'})\n",
|
||
"df_long.info()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 369,
|
||
"id": "f44206d7-4511-4a17-8e1a-a17fc6795c4e",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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>date</th>\n",
|
||
" <th>2018-10-01</th>\n",
|
||
" <th>2018-10-01</th>\n",
|
||
" <th>2018-10-01</th>\n",
|
||
" <th>2018-10-02</th>\n",
|
||
" <th>2018-10-02</th>\n",
|
||
" <th>2018-10-02</th>\n",
|
||
" <th>2018-10-03</th>\n",
|
||
" <th>2018-10-03</th>\n",
|
||
" <th>2018-10-03</th>\n",
|
||
" <th>2018-10-04</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>2018-10-28</th>\n",
|
||
" <th>2018-10-29</th>\n",
|
||
" <th>2018-10-29</th>\n",
|
||
" <th>2018-10-29</th>\n",
|
||
" <th>2018-10-30</th>\n",
|
||
" <th>2018-10-30</th>\n",
|
||
" <th>2018-10-30</th>\n",
|
||
" <th>2018-10-31</th>\n",
|
||
" <th>2018-10-31</th>\n",
|
||
" <th>2018-10-31</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>datatype</th>\n",
|
||
" <td>TMAX</td>\n",
|
||
" <td>TMIN</td>\n",
|
||
" <td>TOBS</td>\n",
|
||
" <td>TMAX</td>\n",
|
||
" <td>TMIN</td>\n",
|
||
" <td>TOBS</td>\n",
|
||
" <td>TMAX</td>\n",
|
||
" <td>TMIN</td>\n",
|
||
" <td>TOBS</td>\n",
|
||
" <td>TMAX</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>TOBS</td>\n",
|
||
" <td>TMAX</td>\n",
|
||
" <td>TMIN</td>\n",
|
||
" <td>TOBS</td>\n",
|
||
" <td>TMAX</td>\n",
|
||
" <td>TMIN</td>\n",
|
||
" <td>TOBS</td>\n",
|
||
" <td>TMAX</td>\n",
|
||
" <td>TMIN</td>\n",
|
||
" <td>TOBS</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>temp_C</th>\n",
|
||
" <td>21.1</td>\n",
|
||
" <td>8.9</td>\n",
|
||
" <td>13.9</td>\n",
|
||
" <td>23.9</td>\n",
|
||
" <td>13.9</td>\n",
|
||
" <td>17.2</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>15.6</td>\n",
|
||
" <td>16.1</td>\n",
|
||
" <td>22.8</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>7.2</td>\n",
|
||
" <td>10.6</td>\n",
|
||
" <td>6.7</td>\n",
|
||
" <td>8.3</td>\n",
|
||
" <td>13.3</td>\n",
|
||
" <td>2.2</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>12.2</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>temp_F</th>\n",
|
||
" <td>69.98</td>\n",
|
||
" <td>48.02</td>\n",
|
||
" <td>57.02</td>\n",
|
||
" <td>75.02</td>\n",
|
||
" <td>57.02</td>\n",
|
||
" <td>62.96</td>\n",
|
||
" <td>77.0</td>\n",
|
||
" <td>60.08</td>\n",
|
||
" <td>60.98</td>\n",
|
||
" <td>73.04</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>44.96</td>\n",
|
||
" <td>51.08</td>\n",
|
||
" <td>44.06</td>\n",
|
||
" <td>46.94</td>\n",
|
||
" <td>55.94</td>\n",
|
||
" <td>35.96</td>\n",
|
||
" <td>41.0</td>\n",
|
||
" <td>53.96</td>\n",
|
||
" <td>32.0</td>\n",
|
||
" <td>32.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>3 rows × 93 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
"date 2018-10-01 2018-10-01 2018-10-01 2018-10-02 2018-10-02 2018-10-02 \\\n",
|
||
"datatype TMAX TMIN TOBS TMAX TMIN TOBS \n",
|
||
"temp_C 21.1 8.9 13.9 23.9 13.9 17.2 \n",
|
||
"temp_F 69.98 48.02 57.02 75.02 57.02 62.96 \n",
|
||
"\n",
|
||
"date 2018-10-03 2018-10-03 2018-10-03 2018-10-04 ... 2018-10-28 \\\n",
|
||
"datatype TMAX TMIN TOBS TMAX ... TOBS \n",
|
||
"temp_C 25.0 15.6 16.1 22.8 ... 7.2 \n",
|
||
"temp_F 77.0 60.08 60.98 73.04 ... 44.96 \n",
|
||
"\n",
|
||
"date 2018-10-29 2018-10-29 2018-10-29 2018-10-30 2018-10-30 2018-10-30 \\\n",
|
||
"datatype TMAX TMIN TOBS TMAX TMIN TOBS \n",
|
||
"temp_C 10.6 6.7 8.3 13.3 2.2 5.0 \n",
|
||
"temp_F 51.08 44.06 46.94 55.94 35.96 41.0 \n",
|
||
"\n",
|
||
"date 2018-10-31 2018-10-31 2018-10-31 \n",
|
||
"datatype TMAX TMIN TOBS \n",
|
||
"temp_C 12.2 0.0 0.0 \n",
|
||
"temp_F 53.96 32.0 32.0 \n",
|
||
"\n",
|
||
"[3 rows x 93 columns]"
|
||
]
|
||
},
|
||
"execution_count": 369,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# transpose\n",
|
||
"df_long.set_index('date').T"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 393,
|
||
"id": "5e026fc2-5ef7-4867-9c84-d961b93776f3",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# pivot\n",
|
||
"df_long_piv = df_long.pivot(\n",
|
||
" index='date',\n",
|
||
" columns='datatype',\n",
|
||
" values=['temp_C', 'temp_F']\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 397,
|
||
"id": "a49458b0-b1ef-4cb0-b5e5-69b784b8ee5a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
" temp_C temp_F\n",
|
||
"date datatype \n",
|
||
"2018-10-01 TMAX 21.1 69.98\n",
|
||
" TMIN 8.9 48.02\n",
|
||
" TOBS 13.9 57.02\n",
|
||
"2018-10-02 TMAX 23.9 75.02\n",
|
||
" TMIN 13.9 57.02\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"True"
|
||
]
|
||
},
|
||
"execution_count": 397,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"print(df_long.set_index(['date','datatype']).head())\n",
|
||
"df_long.set_index(['date','datatype']).unstack().equals(df_long_piv)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9acff35d-4675-4f7d-92a7-81287e2f3e1a",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Dirty data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 419,
|
||
"id": "186dafe4-4c8c-41fc-9c22-e2ff8f059cd4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\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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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>date</th>\n",
|
||
" <th>station</th>\n",
|
||
" <th>PRCP</th>\n",
|
||
" <th>SNOW</th>\n",
|
||
" <th>SNWD</th>\n",
|
||
" <th>TMAX</th>\n",
|
||
" <th>TMIN</th>\n",
|
||
" <th>TOBS</th>\n",
|
||
" <th>WESF</th>\n",
|
||
" <th>inclement_weather</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>378</th>\n",
|
||
" <td>2018-06-29T00:00:00</td>\n",
|
||
" <td>?</td>\n",
|
||
" <td>0.5</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>5505.0</td>\n",
|
||
" <td>-40.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>742</th>\n",
|
||
" <td>2018-12-24T00:00:00</td>\n",
|
||
" <td>?</td>\n",
|
||
" <td>1.5</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>5505.0</td>\n",
|
||
" <td>-40.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>247</th>\n",
|
||
" <td>2018-04-30T00:00:00</td>\n",
|
||
" <td>GHCND:USC00280907</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>-inf</td>\n",
|
||
" <td>13.3</td>\n",
|
||
" <td>7.2</td>\n",
|
||
" <td>7.2</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" date station PRCP SNOW SNWD TMAX TMIN \\\n",
|
||
"378 2018-06-29T00:00:00 ? 0.5 NaN NaN 5505.0 -40.0 \n",
|
||
"742 2018-12-24T00:00:00 ? 1.5 NaN NaN 5505.0 -40.0 \n",
|
||
"247 2018-04-30T00:00:00 GHCND:USC00280907 0.0 0.0 -inf 13.3 7.2 \n",
|
||
"\n",
|
||
" TOBS WESF inclement_weather \n",
|
||
"378 NaN NaN NaN \n",
|
||
"742 NaN NaN NaN \n",
|
||
"247 7.2 NaN False "
|
||
]
|
||
},
|
||
"execution_count": 419,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df = pd.read_csv(\"data/dirty_data.csv\")\n",
|
||
"df.sample(3)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 424,
|
||
"id": "dcc25f9d-b9b2-4ffa-b8f6-34b7a4920291",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"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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|
||
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|
||
" .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>date</th>\n",
|
||
" <th>station</th>\n",
|
||
" <th>PRCP</th>\n",
|
||
" <th>SNOW</th>\n",
|
||
" <th>SNWD</th>\n",
|
||
" <th>TMAX</th>\n",
|
||
" <th>TMIN</th>\n",
|
||
" <th>TOBS</th>\n",
|
||
" <th>WESF</th>\n",
|
||
" <th>inclement_weather</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>2018-01-05T00:00:00</td>\n",
|
||
" <td>?</td>\n",
|
||
" <td>0.3</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>5505.0</td>\n",
|
||
" <td>-40.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>2018-01-05T00:00:00</td>\n",
|
||
" <td>?</td>\n",
|
||
" <td>0.3</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>5505.0</td>\n",
|
||
" <td>-40.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>21</th>\n",
|
||
" <td>2018-01-12T00:00:00</td>\n",
|
||
" <td>?</td>\n",
|
||
" <td>0.5</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>5505.0</td>\n",
|
||
" <td>-40.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>22</th>\n",
|
||
" <td>2018-01-12T00:00:00</td>\n",
|
||
" <td>?</td>\n",
|
||
" <td>0.5</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>5505.0</td>\n",
|
||
" <td>-40.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25</th>\n",
|
||
" <td>2018-01-13T00:00:00</td>\n",
|
||
" <td>?</td>\n",
|
||
" <td>17.5</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>5505.0</td>\n",
|
||
" <td>-40.0</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>754</th>\n",
|
||
" <td>2018-12-28T00:00:00</td>\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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|
||
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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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|
||
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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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|
||
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|
||
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|
||
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|
||
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|
||
" <td>2018-12-29T00:00:00</td>\n",
|
||
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|
||
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|
||
" <td>NaN</td>\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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|
||
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|
||
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|
||
" <td>2018-12-29T00:00:00</td>\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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|
||
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|
||
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|
||
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|
||
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|
||
" <th>759</th>\n",
|
||
" <td>2018-12-30T00:00:00</td>\n",
|
||
" <td>?</td>\n",
|
||
" <td>0.0</td>\n",
|
||
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|
||
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|
||
" <td>5505.0</td>\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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|
||
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|
||
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|
||
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|
||
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|
||
" date station PRCP SNOW SNWD TMAX TMIN TOBS WESF \\\n",
|
||
"9 2018-01-05T00:00:00 ? 0.3 NaN NaN 5505.0 -40.0 NaN NaN \n",
|
||
"10 2018-01-05T00:00:00 ? 0.3 NaN NaN 5505.0 -40.0 NaN NaN \n",
|
||
"21 2018-01-12T00:00:00 ? 0.5 NaN NaN 5505.0 -40.0 NaN NaN \n",
|
||
"22 2018-01-12T00:00:00 ? 0.5 NaN NaN 5505.0 -40.0 NaN NaN \n",
|
||
"25 2018-01-13T00:00:00 ? 17.5 NaN NaN 5505.0 -40.0 NaN NaN \n",
|
||
".. ... ... ... ... ... ... ... ... ... \n",
|
||
"754 2018-12-28T00:00:00 ? 11.4 NaN NaN 5505.0 -40.0 NaN NaN \n",
|
||
"756 2018-12-28T00:00:00 ? 11.4 NaN NaN 5505.0 -40.0 NaN NaN \n",
|
||
"757 2018-12-29T00:00:00 ? 21.3 NaN NaN 5505.0 -40.0 NaN NaN \n",
|
||
"758 2018-12-29T00:00:00 ? 21.3 NaN NaN 5505.0 -40.0 NaN NaN \n",
|
||
"759 2018-12-30T00:00:00 ? 0.0 NaN NaN 5505.0 -40.0 NaN NaN \n",
|
||
"\n",
|
||
" inclement_weather \n",
|
||
"9 NaN \n",
|
||
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|
||
"21 NaN \n",
|
||
"22 NaN \n",
|
||
"25 NaN \n",
|
||
".. ... \n",
|
||
"754 NaN \n",
|
||
"756 NaN \n",
|
||
"757 NaN \n",
|
||
"758 NaN \n",
|
||
"759 NaN \n",
|
||
"\n",
|
||
"[188 rows x 10 columns]"
|
||
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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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|
||
"source": [
|
||
"has_nan = df[df.PRCP.isna() | df.SNOW.isna() | df.SNWD.isna() ]\n",
|
||
"has_nan"
|
||
]
|
||
},
|
||
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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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|
||
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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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|
||
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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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|
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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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|
||
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||
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|
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|
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|
||
" date station PRCP SNOW SNWD TMAX TMIN \\\n",
|
||
"1 2018-01-01T00:00:00 ? 0.0 0.0 -inf 5505.0 -40.0 \n",
|
||
"2 2018-01-01T00:00:00 ? 0.0 0.0 -inf 5505.0 -40.0 \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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|
||
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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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|
||
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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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||
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