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Air pressure analysis in python
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| # Introduction | |
| This analysis helps in comparing cities on air pressure variations. | |
| I calculate the mean variation in air-pressure for a given city using the wunderground data. | |
| # Overall yearly variance output: | |
| - BOS 0.08 | |
| - JFK 0.24 | |
| - SFO 0.13 | |
| - DTW 0.24 | |
| - SJU 0.09 | |
| # Monthly variation for cities for 2017 | |
| **city**|**1**|**2**|**3**|**4**|**5**|**6** | |
| :-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----: | |
| BOS|0.09|0.08|0.09|0.09|0.07|0.07 | |
| DTW|0.27|0.31|0.25|0.26|0.18|0.13 | |
| JFK|0.26|0.29|0.29|0.22|0.2|0.14 | |
| SFO|0.18|0.18|0.12|0.12|0.1|0.07 | |
| SJU|0.1|0.1|0.09|0.09|0.09|0.08 | |
| # Conclusion | |
| It seems New York and Michigan are not so good places to live for a person who has air pressure related headaches or migraines. | |
| # Notes | |
| - Used http://jakebathman.github.io/Markdown-Table-Generator/ for converting csv to markdown tables. | |
| - http://dillinger.io to preview markdown |
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import sys\n", | |
| "\n", | |
| "from firebase import firebase\n", | |
| "from datetime import datetime\n", | |
| "import dateutil.parser as dt\n", | |
| "import pandas as pd\n", | |
| "import json\n", | |
| "import copy\n", | |
| "\n", | |
| "import seaborn as sns\n", | |
| "\n", | |
| "%matplotlib inline\n", | |
| "\n", | |
| "import matplotlib\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "plt.style.use('ggplot')\n", | |
| "\n", | |
| "from scipy.stats import ttest_ind\n", | |
| "\n", | |
| "import requests\n", | |
| "\n", | |
| "alldata = {}" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 97, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "BOS\n", | |
| "JFK\n", | |
| "SFO\n", | |
| "DTW\n", | |
| "SJU\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "cities = ['BOS', 'JFK', 'SFO', 'DTW', 'SJU']\n", | |
| "\n", | |
| "for c in cities:\n", | |
| " if c in alldata:\n", | |
| " continue\n", | |
| " city = c\n", | |
| " print city\n", | |
| " r = requests.get(\"https://www.wunderground.com/history/airport/%s/2017/1/1/CustomHistory.html?dayend=16&monthend=6&yearend=2017&req_city=NA&req_state=NA&req_statename=NA&format=1\"%city)\n", | |
| " alldata[city] = r.text\n", | |
| " d = alldata[city]\n", | |
| " rows = d.split('<br />\\n')\n", | |
| " cols = rows[0].split(',')\n", | |
| " d = d.replace('<br />\\n','\\n')\n", | |
| " path = 'data/%s.csv'%city\n", | |
| " f = file(path,'w')\n", | |
| " f.write(d)\n", | |
| " f.close()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 201, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def get_mean_p_variance(pdf):\n", | |
| " pdf['ddiff'] = pdf.maxp - pdf.minp\n", | |
| " return pdf.ddiff.mean()\n", | |
| "\n", | |
| "def getmonth(x):\n", | |
| " return x.split('-')[1]\n", | |
| "\n", | |
| "def get_pressures_df(city):\n", | |
| " path = 'data/%s.csv'%city\n", | |
| " df = pd.read_csv(path)\n", | |
| " df['date'] = df[df.columns[0]]\n", | |
| " df['month'] = df.date.apply(getmonth)\n", | |
| " pdf = df[[' Min Sea Level PressureIn', ' Max Sea Level PressureIn', 'date', 'month']].reset_index()\n", | |
| " pdf.columns = ['id','minp','maxp', 'date', 'month']\n", | |
| " pdf['city'] = city\n", | |
| " return pdf" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 202, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "BOS 0.08\n", | |
| "JFK 0.24\n", | |
| "SFO 0.13\n", | |
| "DTW 0.24\n", | |
| "SJU 0.09\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for city in cities:\n", | |
| " print city,\n", | |
| " df = get_pressures_df(city)\n", | |
| " print \"%.2f\"%get_mean_p_variance(df)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 226, | |
| "metadata": { | |
| "scrolled": true | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "BOS 0.08\n", | |
| "JFK 0.24\n", | |
| "SFO 0.13\n", | |
| "DTW 0.24\n", | |
| "SJU 0.09\n" | |
| ] | |
| }, | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x121c75090>" | |
| ] | |
| }, | |
| "execution_count": 226, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
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5faWn6O7uZu/evajValavXn3VWV728U5qTjjBKdEd4mLFF0zETPa8GKFKpSIj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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x121eb2e90>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "#last 7 days\n", | |
| "dfs = []\n", | |
| "for city in cities:\n", | |
| " print city,\n", | |
| " df = get_pressures_df(city)\n", | |
| " print \"%.2f\"%get_mean_p_variance(df)\n", | |
| " dfs.append(df[-7:][['date','ddiff','city']])\n", | |
| "fdf = pd.concat(dfs)\n", | |
| "ffdf = fdf.pivot(index='city', columns='date').round(2)\n", | |
| "ffdf.columns = ffdf.columns.get_level_values(1)\n", | |
| "ffdf.transpose().plot()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 216, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "/Users/gparuthi/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", | |
| "A value is trying to be set on a copy of a slice from a DataFrame.\n", | |
| "Try using .loc[row_indexer,col_indexer] = value instead\n", | |
| "\n", | |
| "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", | |
| " from ipykernel import kernelapp as app\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# monthly\n", | |
| "tups = []\n", | |
| "for city in cities:\n", | |
| " df = get_pressures_df(city)\n", | |
| " for month in sorted(df.month.value_counts().index):\n", | |
| " tups.append((city, month, get_mean_p_variance(df[df.month==month])))\n", | |
| "\n", | |
| "fdf = pd.DataFrame(tups, columns=['city', 'month', 'mean_daily_variance'])\n", | |
| "ffdf = fdf.pivot(index='city', columns='month').round(2)\n", | |
| "ffdf.columns = ffdf.columns.get_level_values(1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 179, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "ffdf.to_clipboard()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 217, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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S02abyMzR09EeZPO6Dpobzn5B1mCZlmLhZ/NTUFX4748r2V3ZEdHxCJElrowV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\nTgK9EELkOAn0QgiR4yTQCyFEjvt/st6UBMR/Zn0AAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x1211cdcd0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "ax = ffdf.transpose().plot()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python [default]", | |
| "language": "python", | |
| "name": "python2" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 2 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython2", | |
| "version": "2.7.13" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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