{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" value | \n",
" latitude | \n",
" longitude | \n",
"
\n",
" \n",
" \n",
" \n",
" count | \n",
" 585438.000000 | \n",
" 532969.000000 | \n",
" 532969.000000 | \n",
"
\n",
" \n",
" mean | \n",
" 105.033613 | \n",
" 34.184246 | \n",
" 22.745671 | \n",
"
\n",
" \n",
" std | \n",
" 2343.220700 | \n",
" 12.256093 | \n",
" 42.024280 | \n",
"
\n",
" \n",
" min | \n",
" -9999.000000 | \n",
" -53.158295 | \n",
" -158.088593 | \n",
"
\n",
" \n",
" 25% | \n",
" 0.002700 | \n",
" 31.757090 | \n",
" 18.834639 | \n",
"
\n",
" \n",
" 50% | \n",
" 0.044500 | \n",
" 32.178700 | \n",
" 34.896092 | \n",
"
\n",
" \n",
" 75% | \n",
" 1.230000 | \n",
" 36.179700 | \n",
" 35.098936 | \n",
"
\n",
" \n",
" max | \n",
" 183000.000000 | \n",
" 78.906690 | \n",
" 153.402400 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" value latitude longitude\n",
"count 585438.000000 532969.000000 532969.000000\n",
"mean 105.033613 34.184246 22.745671\n",
"std 2343.220700 12.256093 42.024280\n",
"min -9999.000000 -53.158295 -158.088593\n",
"25% 0.002700 31.757090 18.834639\n",
"50% 0.044500 32.178700 34.896092\n",
"75% 1.230000 36.179700 35.098936\n",
"max 183000.000000 78.906690 153.402400"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2 = pd.read_csv('https://openaq-data.s3.amazonaws.com/2018-04-06.csv')\n",
"data2.describe()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((585438, 11), 585438)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2.shape, len(data2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"location 585438\n",
"city 585438\n",
"country 585438\n",
"utc 585438\n",
"local 585438\n",
"parameter 585438\n",
"value 585438\n",
"unit 585438\n",
"latitude 532969\n",
"longitude 532969\n",
"attribution 585438\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2.count()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"955 ns ± 22.9 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
]
}
],
"source": [
"timeit data2.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"215 ms ± 11.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"timeit data2.count()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"677 ns ± 17 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
]
}
],
"source": [
"timeit len(data2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" location | \n",
" city | \n",
" country | \n",
" utc | \n",
" local | \n",
" parameter | \n",
" value | \n",
" unit | \n",
" latitude | \n",
" longitude | \n",
" attribution | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" תחנה:ניידת6 | \n",
" צפון הנגב | \n",
" IL | \n",
" 2018-04-06T10:00:00.000Z | \n",
" 2018-04-06T13:00:00+03:00 | \n",
" co | \n",
" 2.4000 | \n",
" ppm | \n",
" 31.248970 | \n",
" 35.215970 | \n",
" [{\"name\":\"Israel Ministry of Environmental Pro... | \n",
"
\n",
" \n",
" 1 | \n",
" תחנה:אום אל קוטוף | \n",
" תחנות ניידות | \n",
" IL | \n",
" 2018-04-06T00:30:00.000Z | \n",
" 2018-04-06T03:30:00+03:00 | \n",
" o3 | \n",
" 0.0289 | \n",
" ppm | \n",
" 31.987899 | \n",
" 34.757384 | \n",
" [{\"name\":\"Israel Ministry of Environmental Pro... | \n",
"
\n",
" \n",
" 2 | \n",
" תחנה:אום אל קוטוף | \n",
" תחנות ניידות | \n",
" IL | \n",
" 2018-04-06T01:30:00.000Z | \n",
" 2018-04-06T04:30:00+03:00 | \n",
" so2 | \n",
" 0.0012 | \n",
" ppm | \n",
" 31.987899 | \n",
" 34.757384 | \n",
" [{\"name\":\"Israel Ministry of Environmental Pro... | \n",
"
\n",
" \n",
" 3 | \n",
" תחנה:אום אל קוטוף | \n",
" תחנות ניידות | \n",
" IL | \n",
" 2018-04-06T01:30:00.000Z | \n",
" 2018-04-06T04:30:00+03:00 | \n",
" no2 | \n",
" 0.0093 | \n",
" ppm | \n",
" 31.987899 | \n",
" 34.757384 | \n",
" [{\"name\":\"Israel Ministry of Environmental Pro... | \n",
"
\n",
" \n",
" 4 | \n",
" תחנה:אום אל קוטוף | \n",
" תחנות ניידות | \n",
" IL | \n",
" 2018-04-06T01:15:00.000Z | \n",
" 2018-04-06T04:15:00+03:00 | \n",
" o3 | \n",
" 0.0284 | \n",
" ppm | \n",
" 31.987899 | \n",
" 34.757384 | \n",
" [{\"name\":\"Israel Ministry of Environmental Pro... | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" location city country utc \\\n",
"0 תחנה:ניידת6 צפון הנגב IL 2018-04-06T10:00:00.000Z \n",
"1 תחנה:אום אל קוטוף תחנות ניידות IL 2018-04-06T00:30:00.000Z \n",
"2 תחנה:אום אל קוטוף תחנות ניידות IL 2018-04-06T01:30:00.000Z \n",
"3 תחנה:אום אל קוטוף תחנות ניידות IL 2018-04-06T01:30:00.000Z \n",
"4 תחנה:אום אל קוטוף תחנות ניידות IL 2018-04-06T01:15:00.000Z \n",
"\n",
" local parameter value unit latitude longitude \\\n",
"0 2018-04-06T13:00:00+03:00 co 2.4000 ppm 31.248970 35.215970 \n",
"1 2018-04-06T03:30:00+03:00 o3 0.0289 ppm 31.987899 34.757384 \n",
"2 2018-04-06T04:30:00+03:00 so2 0.0012 ppm 31.987899 34.757384 \n",
"3 2018-04-06T04:30:00+03:00 no2 0.0093 ppm 31.987899 34.757384 \n",
"4 2018-04-06T04:15:00+03:00 o3 0.0284 ppm 31.987899 34.757384 \n",
"\n",
" attribution \n",
"0 [{\"name\":\"Israel Ministry of Environmental Pro... \n",
"1 [{\"name\":\"Israel Ministry of Environmental Pro... \n",
"2 [{\"name\":\"Israel Ministry of Environmental Pro... \n",
"3 [{\"name\":\"Israel Ministry of Environmental Pro... \n",
"4 [{\"name\":\"Israel Ministry of Environmental Pro... "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
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" \n",
" \n",
" | \n",
" location | \n",
" city | \n",
" country | \n",
" utc | \n",
" local | \n",
" parameter | \n",
" value | \n",
" unit | \n",
" latitude | \n",
" longitude | \n",
" attribution | \n",
"
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" count | \n",
" 585438 | \n",
" 585438 | \n",
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" 585438 | \n",
" 585438 | \n",
" 585438.000000 | \n",
" 585438 | \n",
" 532969.000000 | \n",
" 532969.000000 | \n",
" 585438 | \n",
"
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" \n",
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" 4279 | \n",
" 1418 | \n",
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" 2 | \n",
" NaN | \n",
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"
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" \n",
" top | \n",
" תחנה:עטרות א.תעשיה | \n",
" מישור החוף הדרומי | \n",
" IL | \n",
" 2018-04-06 03:00:00 | \n",
" 2018-04-06 03:00:00 | \n",
" no2 | \n",
" NaN | \n",
" ppm | \n",
" NaN | \n",
" NaN | \n",
" [{\"name\":\"Israel Ministry of Environmental Pro... | \n",
"
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" \n",
" freq | \n",
" 4329 | \n",
" 43739 | \n",
" 403131 | \n",
" 13464 | \n",
" 13464 | \n",
" 154327 | \n",
" NaN | \n",
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"
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" 2018-04-06 23:45:00 | \n",
" 2018-04-06 23:45:00 | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
"
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" \n",
" mean | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 105.033613 | \n",
" NaN | \n",
" 34.184246 | \n",
" 22.745671 | \n",
" NaN | \n",
"
\n",
" \n",
" std | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 2343.220700 | \n",
" NaN | \n",
" 12.256093 | \n",
" 42.024280 | \n",
" NaN | \n",
"
\n",
" \n",
" min | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" -9999.000000 | \n",
" NaN | \n",
" -53.158295 | \n",
" -158.088593 | \n",
" NaN | \n",
"
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" \n",
" 25% | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 0.002700 | \n",
" NaN | \n",
" 31.757090 | \n",
" 18.834639 | \n",
" NaN | \n",
"
\n",
" \n",
" 50% | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 0.044500 | \n",
" NaN | \n",
" 32.178700 | \n",
" 34.896092 | \n",
" NaN | \n",
"
\n",
" \n",
" 75% | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 1.230000 | \n",
" NaN | \n",
" 36.179700 | \n",
" 35.098936 | \n",
" NaN | \n",
"
\n",
" \n",
" max | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" NaN | \n",
" 183000.000000 | \n",
" NaN | \n",
" 78.906690 | \n",
" 153.402400 | \n",
" NaN | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" location city country utc \\\n",
"count 585438 585438 585438 585438 \n",
"unique 4279 1418 53 96 \n",
"top תחנה:עטרות א.תעשיה מישור החוף הדרומי IL 2018-04-06 03:00:00 \n",
"freq 4329 43739 403131 13464 \n",
"first NaN NaN NaN 2018-04-06 00:00:00 \n",
"last NaN NaN NaN 2018-04-06 23:45:00 \n",
"mean NaN NaN NaN NaN \n",
"std NaN NaN NaN NaN \n",
"min NaN NaN NaN NaN \n",
"25% NaN NaN NaN NaN \n",
"50% NaN NaN NaN NaN \n",
"75% NaN NaN NaN NaN \n",
"max NaN NaN NaN NaN \n",
"\n",
" local parameter value unit latitude \\\n",
"count 585438 585438 585438.000000 585438 532969.000000 \n",
"unique 96 7 NaN 2 NaN \n",
"top 2018-04-06 03:00:00 no2 NaN ppm NaN \n",
"freq 13464 154327 NaN 434896 NaN \n",
"first 2018-04-06 00:00:00 NaN NaN NaN NaN \n",
"last 2018-04-06 23:45:00 NaN NaN NaN NaN \n",
"mean NaN NaN 105.033613 NaN 34.184246 \n",
"std NaN NaN 2343.220700 NaN 12.256093 \n",
"min NaN NaN -9999.000000 NaN -53.158295 \n",
"25% NaN NaN 0.002700 NaN 31.757090 \n",
"50% NaN NaN 0.044500 NaN 32.178700 \n",
"75% NaN NaN 1.230000 NaN 36.179700 \n",
"max NaN NaN 183000.000000 NaN 78.906690 \n",
"\n",
" longitude attribution \n",
"count 532969.000000 585438 \n",
"unique NaN 208 \n",
"top NaN [{\"name\":\"Israel Ministry of Environmental Pro... \n",
"freq NaN 403131 \n",
"first NaN NaN \n",
"last NaN NaN \n",
"mean 22.745671 NaN \n",
"std 42.024280 NaN \n",
"min -158.088593 NaN \n",
"25% 18.834639 NaN \n",
"50% 34.896092 NaN \n",
"75% 35.098936 NaN \n",
"max 153.402400 NaN "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2[\"local\"]= pd.to_datetime(data2[\"local\"]); data2[\"utc\"]= pd.to_datetime(data2[\"utc\"])\n",
"data2.describe(include = 'all')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['IL', 'IE', 'IN', 'PT', 'FR', 'ZA', 'AU', 'CL', 'DK', 'CO', 'SE',\n",
" 'MN', 'TR', 'CN', 'SI', 'PE', 'BA', 'VN', 'ID', 'KW', 'XK', 'BD',\n",
" 'BH', 'HK', 'GB', 'ET', 'NO', 'TH', 'LK', 'NP', 'UG', 'AE', 'NL',\n",
" 'US', 'CA', 'MX', 'ES', 'CH', 'BE', 'GI', 'MK', 'LT', 'HR', 'MT',\n",
" 'LV', 'HU', 'FI', 'AD', 'SK', 'CZ', 'RS', 'LU', 'DE'], dtype=object)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2['country'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# We want to EDA historical data from Portugal, Serbia, Italy and the Netherlands"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4009"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ptData = data2.loc[data2[\"country\"] == \"PT\"]\n",
"len(ptData)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7867"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bmData = data2.loc[data2[\"country\"].isin([\"PT\",\"RS\",\"IT\",\"NL\"])]\n",
"len(bmData)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.set()\n",
"sns.countplot(bmData['country'], color='gray')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(bmData['parameter'], color='gray')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
"sns.countplot(x='country', hue=\"parameter\",data=data2, order=data2[\"country\"].value_counts().keys()[1:6])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"#!pip install pandas_profiling\n",
"import pandas_profiling"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data2.profile_report()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Try it yourself\n",
"\n",
"Plot ts value for each parameter. Check meaning of negative concentrations?"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='parameter',data=data2[data2[\"value\"]==0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Homework 2: Analyse last 90 days\n",
"# Add info on streaming data\n",
"#data = pd.read_csv('https://openaq-data.s3.amazonaws.com/2018-04-06.csv', iterator=True, chunksize=10000)"
]
}
],
"metadata": {
"@webio": {
"lastCommId": null,
"lastKernelId": null
},
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}