{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Temperatures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: for this demo to work properly, you'll need to install [statsmodels](http://http://statsmodels.sourceforge.net/)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import glob\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import hypertools as hyp\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "import statsmodels\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read in data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "results=pd.read_csv('data/temperatures.csv')\n", "locs=pd.read_csv('data/temperature_locs.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Temperature dataframe" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", " | Unnamed: 0 | \n", "Year | \n", "Month | \n", "Bangkok_anomaly | \n", "Bangkok | \n", "Bombay_anomaly | \n", "Bombay | \n", "Cairo_anomaly | \n", "Cairo | \n", "Cape_Town_anomaly | \n", "... | \n", "Seoul_anomaly | \n", "Seoul | \n", "Shanghai_anomaly | \n", "Shanghai | \n", "Somalia_anomaly | \n", "Somalia | \n", "Sydney_anomaly | \n", "Sydney | \n", "Tokyo_anomaly | \n", "Tokyo | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "0 | \n", "1850 | \n", "1 | \n", "-1.144 | \n", "23.896 | \n", "-1.590 | \n", "22.780 | \n", "-1.041 | \n", "12.079 | \n", "NaN | \n", "... | \n", "-0.937 | \n", "-4.797 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "
1 | \n", "1 | \n", "1850 | \n", "2 | \n", "-0.599 | \n", "26.431 | \n", "-0.697 | \n", "23.713 | \n", "-1.371 | \n", "13.399 | \n", "NaN | \n", "... | \n", "-0.594 | \n", "-2.284 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "
2 | \n", "2 | \n", "1850 | \n", "3 | \n", "-0.753 | \n", "28.097 | \n", "-0.719 | \n", "25.301 | \n", "-1.499 | \n", "16.041 | \n", "NaN | \n", "... | \n", "0.119 | \n", "3.549 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "
3 | \n", "3 | \n", "1850 | \n", "4 | \n", "-1.297 | \n", "28.633 | \n", "-1.194 | \n", "26.426 | \n", "-1.418 | \n", "19.862 | \n", "NaN | \n", "... | \n", "-2.896 | \n", "7.184 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "
4 | \n", "4 | \n", "1850 | \n", "5 | \n", "-1.446 | \n", "27.994 | \n", "-1.250 | \n", "27.850 | \n", "-2.427 | \n", "22.613 | \n", "NaN | \n", "... | \n", "-1.209 | \n", "14.811 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "
5 rows × 43 columns
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