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Covers the EK plot and anaylsis and N Korea
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Eastern Kazakhstan and North Korea "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Tony McDonald](https://twitter.com/ScienceTony)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'26-07-2019'"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datetime import datetime\n",
"datetime.today().strftime('%d-%m-%Y')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"License: MIT License with Copyright (c) 2019 Socratica https://github.com/socratica/data/blob/master/LICENSE\n",
"<p>The license allows me to freely use, manipulate and distribute the dataset.</p> "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Task\n",
"<ul>\n",
" <li>Find out the data range of Eastern Kazakhstan occurrences </li>\n",
" <li>Find the nuke explosion before N Korea.</li>\n",
"</ul>"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from pandas import DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from pandasql import sqldf\n",
"\n",
"# Then create a simple wrapper function to allow us to supply the query 'q' without \n",
"# the surrounding syntax.\n",
"pysqldf = lambda q: sqldf(q, globals())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data-master from YT/songs.csv\r\n",
"data-master from YT/earthquake.csv\r\n"
]
}
],
"source": [
"!find 'data-master from YT' -name '*csv'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>earthquake_id</th>\n",
" <th>occurred_on</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>depth</th>\n",
" <th>magnitude</th>\n",
" <th>calculation_method</th>\n",
" <th>network_id</th>\n",
" <th>place</th>\n",
" <th>cause</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1969-01-01 9:07:06</td>\n",
" <td>51.096</td>\n",
" <td>-179.392</td>\n",
" <td>45.0</td>\n",
" <td>5.6</td>\n",
" <td>mw</td>\n",
" <td>iscgem812771</td>\n",
" <td>Andreanof Islands, Aleutian Islands, Alaska</td>\n",
" <td>earthquake</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1969-01-02 17:50:48</td>\n",
" <td>-56.096</td>\n",
" <td>-27.842</td>\n",
" <td>80.1</td>\n",
" <td>6.0</td>\n",
" <td>mw</td>\n",
" <td>iscgemsup812819</td>\n",
" <td>South Sandwich Islands region</td>\n",
" <td>earthquake</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1969-01-03 3:16:40</td>\n",
" <td>37.140</td>\n",
" <td>57.899</td>\n",
" <td>10.0</td>\n",
" <td>5.5</td>\n",
" <td>mw</td>\n",
" <td>iscgem812826</td>\n",
" <td>Turkmenistan-Iran border region</td>\n",
" <td>earthquake</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1969-01-03 13:28:12</td>\n",
" <td>51.132</td>\n",
" <td>-179.306</td>\n",
" <td>15.0</td>\n",
" <td>5.9</td>\n",
" <td>mw</td>\n",
" <td>iscgem812841</td>\n",
" <td>Andreanof Islands, Aleutian Islands, Alaska</td>\n",
" <td>earthquake</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1969-01-04 22:36:48</td>\n",
" <td>-6.850</td>\n",
" <td>129.821</td>\n",
" <td>105.0</td>\n",
" <td>5.8</td>\n",
" <td>mw</td>\n",
" <td>iscgem812879</td>\n",
" <td>Banda Sea</td>\n",
" <td>earthquake</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" earthquake_id occurred_on latitude longitude depth magnitude \\\n",
"0 1 1969-01-01 9:07:06 51.096 -179.392 45.0 5.6 \n",
"1 2 1969-01-02 17:50:48 -56.096 -27.842 80.1 6.0 \n",
"2 3 1969-01-03 3:16:40 37.140 57.899 10.0 5.5 \n",
"3 4 1969-01-03 13:28:12 51.132 -179.306 15.0 5.9 \n",
"4 5 1969-01-04 22:36:48 -6.850 129.821 105.0 5.8 \n",
"\n",
" calculation_method network_id \\\n",
"0 mw iscgem812771 \n",
"1 mw iscgemsup812819 \n",
"2 mw iscgem812826 \n",
"3 mw iscgem812841 \n",
"4 mw iscgem812879 \n",
"\n",
" place cause \n",
"0 Andreanof Islands, Aleutian Islands, Alaska earthquake \n",
"1 South Sandwich Islands region earthquake \n",
"2 Turkmenistan-Iran border region earthquake \n",
"3 Andreanof Islands, Aleutian Islands, Alaska earthquake \n",
"4 Banda Sea earthquake "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"earthquake_data = pd.read_csv('data-master from YT/earthquake.csv')\n",
"earthquake_data.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find the date range for eastern Kazakhstan occurrences \n",
"The conditions: must come from eastern Kazakhstan, must be nuke or explosion, must be between 1970 - 1980"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"ekaz_date_range_query = \"\"\"\n",
" SELECT occurred_on, magnitude\n",
" FROM earthquake_data\n",
" WHERE place = \"eastern Kazakhstan\" AND (cause = 'nuclear explosion' OR 'explosion') \n",
" \n",
" ;\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>occurred_on</th>\n",
" <th>magnitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1973-07-23 1:22:58</td>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1973-12-14 7:46:57</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1974-05-31 3:26:57</td>\n",
" <td>5.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" occurred_on magnitude\n",
"0 1973-07-23 1:22:58 6.3\n",
"1 1973-12-14 7:46:57 6.0\n",
"2 1974-05-31 3:26:57 5.9"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pysqldf(ekaz_date_range_query)[:3]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>occurred_on</th>\n",
" <th>magnitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1973-07-23 1:22:58</td>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1973-12-14 7:46:57</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" occurred_on magnitude\n",
"0 1973-07-23 1:22:58 6.3\n",
"1 1973-12-14 7:46:57 6.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"easternKazakhstan = pysqldf(ekaz_date_range_query)\n",
"easternKazakhstan[:2]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"occurred_on object\n",
"magnitude float64\n",
"dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"easternKazakhstan.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1973-07-23 1:22:58\n",
"1 1973-12-14 7:46:57\n",
"2 1974-05-31 3:26:57\n",
"3 1974-10-16 6:32:58\n",
"Name: occurred_on, dtype: object"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"easternKazakhstan['occurred_on'].astype(str)[:4]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1973'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"easternKazakhstan['occurred_on'].str[:4].min()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1989'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"easternKazakhstan['occurred_on'].str[:4].max()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The occences for eastern Kazakhstan stared in 1973 and continued until 1989 .\n"
]
}
],
"source": [
"print (\"The occences for eastern Kazakhstan stared in\", easternKazakhstan['occurred_on'].str[:4].min(), \"and continued until\",easternKazakhstan['occurred_on'].str[:4].max(), \".\" )"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"ekaz_date_range_plot_query = \"\"\"\n",
" SELECT occurred_on, magnitude\n",
" FROM earthquake_data\n",
" WHERE place = \"eastern Kazakhstan\" AND (cause = 'nuclear explosion' OR 'explosion') \n",
" \n",
" ;\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1973-07-23 1:22:58\n",
"1 1973-12-14 7:46:57\n",
"2 1974-05-31 3:26:57\n",
"Name: occurred_on, dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pysqldf(ekaz_date_range_plot_query)['occurred_on'].astype(str)[:3]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"ek_plot = pysqldf(ekaz_date_range_plot_query)['occurred_on'].str[:4]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1973\n",
"1 1973\n",
"2 1974\n",
"3 1974\n",
"4 1974\n",
"Name: occurred_on, dtype: object"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ek_plot.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"ek_plot_df = DataFrame(ek_plot.value_counts())\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>occurred_on</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1984</th>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1987</th>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1980</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1978</th>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" occurred_on\n",
"1984 11\n",
"1987 10\n",
"1980 8\n",
"1978 8"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ek_plot_df[:4]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"ek_plot_df = ek_plot_df.reset_index()\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"ek_plot_df = ek_plot_df.rename(columns={\"index\": \"Year\", \"occurred_on\": \"Nuclear Explosions\"})"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>Year</th>\n",
" <th>Nuclear Explosions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1973</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1974</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1975</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1976</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Nuclear Explosions\n",
"15 1973 2\n",
"14 1974 3\n",
"7 1975 5\n",
"12 1976 4"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ek_plot_df = ek_plot_df.sort_values(by=\"Year\")\n",
"ek_plot_df[:4]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['1973',\n",
" '1974',\n",
" '1975',\n",
" '1976',\n",
" '1977',\n",
" '1978',\n",
" '1979',\n",
" '1980',\n",
" '1981',\n",
" '1982',\n",
" '1983',\n",
" '1984',\n",
" '1985',\n",
" '1987',\n",
" '1988',\n",
" '1989']"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"years = ek_plot_df['Year'].tolist()\n",
"years"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## I have one missing year in the list above: 1986"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# I will insert the year (1986) and Nuke explosions (0) seperatly. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Insert year"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"# Note. Once this is run, don't run it again or it will insert another 1986\n",
"years.insert(13, '1986')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['1985', '1986', '1987']"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"years[12:15]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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>Years</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1973</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1974</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1975</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Years\n",
"0 1973\n",
"1 1974\n",
"2 1975"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataYears = { 'Years' : years}\n",
"dataYears = DataFrame(dataYears)\n",
"dataYears[:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Insert 0 for number of blasts"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 3, 5, 4, 5, 8, 7, 8, 6, 4, 5, 11, 5, 10, 5, 4]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nuke_blasts = ek_plot_df['Nuclear Explosions'].tolist()\n",
"nuke_blasts"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"nuke_blasts.insert(13, 0)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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>Nuclear Explosions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Nuclear Explosions\n",
"12 5\n",
"13 0\n",
"14 10"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nuke_blasts_df = { 'Nuclear Explosions' : nuke_blasts}\n",
"nuke_blasts_df = DataFrame(nuke_blasts_df)\n",
"nuke_blasts_df[12:15]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merge the 2 df"
]
},
{
"cell_type": "code",
"execution_count": 31,
"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>Years</th>\n",
" <th>Nuclear Explosions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1973</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1974</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1975</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1976</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1977</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1978</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1979</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1980</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1981</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1982</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1983</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1984</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1985</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1986</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1987</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1988</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1989</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Years Nuclear Explosions\n",
"0 1973 2\n",
"1 1974 3\n",
"2 1975 5\n",
"3 1976 4\n",
"4 1977 5\n",
"5 1978 8\n",
"6 1979 7\n",
"7 1980 8\n",
"8 1981 6\n",
"9 1982 4\n",
"10 1983 5\n",
"11 1984 11\n",
"12 1985 5\n",
"13 1986 0\n",
"14 1987 10\n",
"15 1988 5\n",
"16 1989 4"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_plot_df = pd.merge(dataYears, nuke_blasts_df, left_index=True, right_index=True)\n",
"to_plot_df"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1c966f60>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"to_plot_df.plot()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"#plt.plot(yearList, ek_plot_df, color='#a3c1ad', label='eastern Kazakhstan')\n",
"to_plot_df2 = to_plot_df.set_index('Years')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"to_plot_df2.plot()\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# plt.figure(figsize=(12,6)) needs to come fisrt.\n",
"plt.figure(figsize=(12,4))\n",
"plt.plot(years, to_plot_df2, color='m')\n",
"\n",
"plt.ylabel('\\nNumber of Nuclear Explosions')\n",
"plt.xlabel('\\nYear')\n",
"plt.title('Number of Nuclear Explosions for eastern Kazakhstan 1973 - 1989\\n')\n",
"\n",
"plt.yticks(np.arange(0, 14, step=2))\n",
"\n",
"\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 59,
"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>Nuclear Explosions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>17.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>5.411765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.762671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>7.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>11.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Nuclear Explosions\n",
"count 17.000000\n",
"mean 5.411765\n",
"std 2.762671\n",
"min 0.000000\n",
"25% 4.000000\n",
"50% 5.000000\n",
"75% 7.000000\n",
"max 11.000000"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_plot_df2.describe()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The average nuclear explosions for eastern Kazakhstan between 1973 - 1989 is 5.4\n"
]
}
],
"source": [
"print('The average nuclear explosions for eastern Kazakhstan between 1973 - 1989 is', round(to_plot_df2['Nuclear Explosions'].mean(), 1))"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Nuclear Explosions 5.4\n",
"dtype: float64"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"round(to_plot_df2.mean(), 1)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"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>Years</th>\n",
" <th>Nuclear Explosions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1984</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1987</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1978</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1980</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1979</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1981</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1975</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1988</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1977</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1985</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1983</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1989</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1982</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1976</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1974</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1973</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1986</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Years Nuclear Explosions\n",
"11 1984 11\n",
"14 1987 10\n",
"5 1978 8\n",
"7 1980 8\n",
"6 1979 7\n",
"8 1981 6\n",
"2 1975 5\n",
"15 1988 5\n",
"4 1977 5\n",
"12 1985 5\n",
"10 1983 5\n",
"16 1989 4\n",
"9 1982 4\n",
"3 1976 4\n",
"1 1974 3\n",
"0 1973 2\n",
"13 1986 0"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_plot_df.sort_values(by='Nuclear Explosions', ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Find the last occurrence before N Korea"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"korea_query = \"\"\"\n",
" SELECT occurred_on, magnitude, place\n",
" FROM earthquake_data\n",
" WHERE occurred_on LIKE '199%' AND (cause = 'nuclear explosion' OR 'explosion') \n",
" \n",
" ;\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"occurred_on 12\n",
"magnitude 12\n",
"place 12\n",
"dtype: int64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pysqldf(korea_query).count()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"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>occurred_on</th>\n",
" <th>magnitude</th>\n",
" <th>place</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1996-06-08 2:55:58</td>\n",
" <td>5.9</td>\n",
" <td>southern Xinjiang, China</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1995-08-17 0:59:58</td>\n",
" <td>6.0</td>\n",
" <td>southern Xinjiang, China</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" occurred_on magnitude place\n",
"11 1996-06-08 2:55:58 5.9 southern Xinjiang, China\n",
"10 1995-08-17 0:59:58 6.0 southern Xinjiang, China"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pysqldf(korea_query).sort_values(by='occurred_on', ascending=False)[:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# What i learned \n",
"plot() works a bit different and i found out that plt.figure(figsize=(x,y)) needs to come before the plot function. <br>\n",
"Inserting into a dataframe was a lot more difficult than whith SQL. I had to turn the columns into lists using toList() and then use insert(index, value) then combin the to after tuning them back into dataframes. There must be an easier way i thought! "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[View Full Report](https://www.sciencetony.com/2019/07/nuclear-test-map-for-1969-to-2017.html)<br>\n",
"[Sciencetony.com](https://www.sciencetony.com/)<br>\n",
"[View My LinkedIn CV](https://www.linkedin.com/in/tony-mcdonald-sciencetony/)<br>\n",
"[My Twitter](https://twitter.com/ScienceTony)<br>\n",
"[My YouTube](https://www.youtube.com/Dreamazium)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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