Skip to content

Instantly share code, notes, and snippets.

@JJnotJimmyJohn

JJnotJimmyJohn/Test for wrodpress.ipynb

Created March 14, 2017 22:40
Show Gist options
  • Save JJnotJimmyJohn/4f16fb4a32ee519dd938f117ccb42a15 to your computer and use it in GitHub Desktop.
Save JJnotJimmyJohn/4f16fb4a32ee519dd938f117ccb42a15 to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
Test for wrodpress.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Assignment 2\n",
"\n",
"Before working on this assignment please read these instructions fully. In the submission area, you will notice that you can click the link to **Preview the Grading** for each step of the assignment. This is the criteria that will be used for peer grading. Please familiarize yourself with the criteria before beginning the assignment.\n",
"\n",
"An NOAA dataset has been stored in the file `data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv`. The data for this assignment comes from a subset of The National Centers for Environmental Information (NCEI) [Daily Global Historical Climatology Network](https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/readme.txt) (GHCN-Daily). The GHCN-Daily is comprised of daily climate records from thousands of land surface stations across the globe.\n",
"\n",
"Each row in the assignment datafile corresponds to a single observation.\n",
"\n",
"The following variables are provided to you:\n",
"\n",
"* **id** : station identification code\n",
"* **date** : date in YYYY-MM-DD format (e.g. 2012-01-24 = January 24, 2012)\n",
"* **element** : indicator of element type\n",
" * TMAX : Maximum temperature (tenths of degrees C)\n",
" * TMIN : Minimum temperature (tenths of degrees C)\n",
"* **value** : data value for element (tenths of degrees C)\n",
"\n",
"For this assignment, you must:\n",
"\n",
"1. Read the documentation and familiarize yourself with the dataset, then write some python code which returns a line graph of the record high and record low temperatures by day of the year over the period 2005-2014. The area between the record high and record low temperatures for each day should be shaded.\n",
"2. Overlay a scatter of the 2015 data for any points (highs and lows) for which the ten year record (2005-2014) record high or record low was broken in 2015.\n",
"3. Watch out for leap days (i.e. February 29th), it is reasonable to remove these points from the dataset for the purpose of this visualization.\n",
"4. Make the visual nice! Leverage principles from the first module in this course when developing your solution. Consider issues such as legends, labels, and chart junk.\n",
"\n",
"The data you have been given is near **Ann Arbor, Michigan, United States**, and the stations the data comes from are shown on the map below."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe src=\"data:text/html;base64,<head>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js"></script>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css" />
  <style>
    #map2ac57eb6e9b849c8a858f49726e0c206 {
      height:100%;
    }
  </style> 
</head>
<body>
  <div id="map2ac57eb6e9b849c8a858f49726e0c206"></div>
<script text="text/javascript">
var map = L.map('map2ac57eb6e9b849c8a858f49726e0c206');
L.tileLayer(
  "http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
  {maxZoom:19, attribution: '<a href="https://github.com/jwass/mplleaflet">mplleaflet</a> | Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'}).addTo(map);
var gjData = {"type": "FeatureCollection", "features": [{"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-84.0158, 41.9164], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.7611, 42.2875], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.6933, 42.2417], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.7108, 42.2947], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.8608, 41.84], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.4358, 42.0636], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-84.0133, 42.3264], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.6489, 41.9553], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.9858, 42.4344], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-84.0236, 42.1508], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.6186, 42.0664], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.6769, 42.0811], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.4158, 41.9069], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.28, 41.9497], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.7819, 42.1611], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.82, 42.1236], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.5831, 41.8069], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.9336, 42.0028], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-84.1108, 42.0283], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.7831, 42.4356], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.4764, 41.5631], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-84.4667, 42.2667], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.5333, 42.2333], "type": "Point"}}, {"type": "Feature", "properties": {"anchor_y": 9.0, "html": "<svg width=\"18px\" height=\"18px\" viewBox=\"-9.0 -9.0 18.0 18.0\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">  <path d=\"M 0.0 7.07106781187 C 1.87526910402 7.07106781187 3.6739845 6.3260155 5.0 5.0 C 6.3260155 3.6739845 7.07106781187 1.87526910402 7.07106781187 -0.0 C 7.07106781187 -1.87526910402 6.3260155 -3.6739845 5.0 -5.0 C 3.6739845 -6.3260155 1.87526910402 -7.07106781187 0.0 -7.07106781187 C -1.87526910402 -7.07106781187 -3.6739845 -6.3260155 -5.0 -5.0 C -6.3260155 -3.6739845 -7.07106781187 -1.87526910402 -7.07106781187 -0.0 C -7.07106781187 1.87526910402 -6.3260155 3.6739845 -5.0 5.0 C -3.6739845 6.3260155 -1.87526910402 7.07106781187 0.0 7.07106781187 Z\" fill=\"#FF0000\" stroke-width=\"1.0\" stroke-opacity=\"0.7\" fill-opacity=\"0.7\" stroke=\"#FF0000\" /></svg>", "anchor_x": 9.0}, "geometry": {"coordinates": [-83.7444, 42.2228], "type": "Point"}}]};

if (gjData.features.length != 0) {
  var gj = L.geoJson(gjData, {
    style: function (feature) {
      return feature.properties;
    },
    pointToLayer: function (feature, latlng) {
      var icon = L.divIcon({'html': feature.properties.html, 
        iconAnchor: [feature.properties.anchor_x, 
                     feature.properties.anchor_y], 
          className: 'empty'});  // What can I do about empty?
      return L.marker(latlng, {icon: icon});
    }
  });
  gj.addTo(map);
  
  map.fitBounds(gj.getBounds());
} else {
  map.setView([0, 0], 1);
}
</script>
</body>\" width=\"100%\" height=\"480\"></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import mplleaflet\n",
"import pandas as pd\n",
"\n",
"def leaflet_plot_stations(binsize, hashid):\n",
"\n",
" df = pd.read_csv('data/C2A2_data/BinSize_d{}.csv'.format(binsize))\n",
"\n",
" station_locations_by_hash = df[df['hash'] == hashid]\n",
"\n",
" lons = station_locations_by_hash['LONGITUDE'].tolist()\n",
" lats = station_locations_by_hash['LATITUDE'].tolist()\n",
"\n",
" plt.figure(figsize=(8,8))\n",
"\n",
" plt.scatter(lons, lats, c='r', alpha=0.7, s=200)\n",
"\n",
" return mplleaflet.display()\n",
"\n",
"leaflet_plot_stations(400,'fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Read Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Date</th>\n",
" <th>Element</th>\n",
" <th>Data_Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>USW00094889</td>\n",
" <td>2014-11-12</td>\n",
" <td>TMAX</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>USC00208972</td>\n",
" <td>2009-04-29</td>\n",
" <td>TMIN</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>USC00200032</td>\n",
" <td>2008-05-26</td>\n",
" <td>TMAX</td>\n",
" <td>278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>USC00205563</td>\n",
" <td>2005-11-11</td>\n",
" <td>TMAX</td>\n",
" <td>139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>USC00200230</td>\n",
" <td>2014-02-27</td>\n",
" <td>TMAX</td>\n",
" <td>-106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>USW00014833</td>\n",
" <td>2010-10-01</td>\n",
" <td>TMAX</td>\n",
" <td>194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>USC00207308</td>\n",
" <td>2010-06-29</td>\n",
" <td>TMIN</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>USC00203712</td>\n",
" <td>2005-10-04</td>\n",
" <td>TMAX</td>\n",
" <td>289</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>USW00004848</td>\n",
" <td>2007-12-14</td>\n",
" <td>TMIN</td>\n",
" <td>-16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>USC00200220</td>\n",
" <td>2011-04-21</td>\n",
" <td>TMAX</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>USC00205822</td>\n",
" <td>2013-01-16</td>\n",
" <td>TMAX</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>USC00205822</td>\n",
" <td>2008-05-29</td>\n",
" <td>TMIN</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>USC00203712</td>\n",
" <td>2008-10-17</td>\n",
" <td>TMIN</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>USC00205563</td>\n",
" <td>2006-05-14</td>\n",
" <td>TMAX</td>\n",
" <td>183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>USC00200842</td>\n",
" <td>2006-05-14</td>\n",
" <td>TMAX</td>\n",
" <td>122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>USC00205563</td>\n",
" <td>2014-12-07</td>\n",
" <td>TMAX</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>USC00205822</td>\n",
" <td>2008-09-07</td>\n",
" <td>TMAX</td>\n",
" <td>250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>USC00205450</td>\n",
" <td>2006-04-22</td>\n",
" <td>TMIN</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>USC00203712</td>\n",
" <td>2008-02-22</td>\n",
" <td>TMAX</td>\n",
" <td>-44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>USC00205563</td>\n",
" <td>2015-01-03</td>\n",
" <td>TMIN</td>\n",
" <td>-39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>USC00201250</td>\n",
" <td>2011-03-28</td>\n",
" <td>TMIN</td>\n",
" <td>-78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>USC00200230</td>\n",
" <td>2008-02-10</td>\n",
" <td>TMAX</td>\n",
" <td>-6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>USC00207320</td>\n",
" <td>2008-02-03</td>\n",
" <td>TMIN</td>\n",
" <td>-39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>USC00200228</td>\n",
" <td>2008-02-23</td>\n",
" <td>TMAX</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>USC00201250</td>\n",
" <td>2012-03-20</td>\n",
" <td>TMIN</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>USC00205822</td>\n",
" <td>2006-03-29</td>\n",
" <td>TMIN</td>\n",
" <td>-17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>USC00202308</td>\n",
" <td>2006-05-11</td>\n",
" <td>TMAX</td>\n",
" <td>233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>USW00014833</td>\n",
" <td>2012-03-31</td>\n",
" <td>TMAX</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>USW00014853</td>\n",
" <td>2010-07-25</td>\n",
" <td>TMAX</td>\n",
" <td>283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>USC00205822</td>\n",
" <td>2014-12-09</td>\n",
" <td>TMIN</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165055</th>\n",
" <td>USC00202308</td>\n",
" <td>2009-10-09</td>\n",
" <td>TMAX</td>\n",
" <td>111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165056</th>\n",
" <td>USC00200228</td>\n",
" <td>2015-02-23</td>\n",
" <td>TMAX</td>\n",
" <td>-11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165057</th>\n",
" <td>USC00208080</td>\n",
" <td>2009-11-24</td>\n",
" <td>TMAX</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165058</th>\n",
" <td>USC00208972</td>\n",
" <td>2010-03-22</td>\n",
" <td>TMAX</td>\n",
" <td>94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165059</th>\n",
" <td>USC00207312</td>\n",
" <td>2015-06-23</td>\n",
" <td>TMIN</td>\n",
" <td>172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165060</th>\n",
" <td>USC00200230</td>\n",
" <td>2010-05-23</td>\n",
" <td>TMAX</td>\n",
" <td>283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165061</th>\n",
" <td>USW00004848</td>\n",
" <td>2012-12-26</td>\n",
" <td>TMIN</td>\n",
" <td>-32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165062</th>\n",
" <td>USC00205822</td>\n",
" <td>2014-02-06</td>\n",
" <td>TMAX</td>\n",
" <td>-11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165063</th>\n",
" <td>USC00200230</td>\n",
" <td>2010-05-23</td>\n",
" <td>TMIN</td>\n",
" <td>133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165064</th>\n",
" <td>USC00207312</td>\n",
" <td>2008-08-04</td>\n",
" <td>TMIN</td>\n",
" <td>172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165065</th>\n",
" <td>USC00200032</td>\n",
" <td>2006-03-01</td>\n",
" <td>TMAX</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165066</th>\n",
" <td>USC00207312</td>\n",
" <td>2008-08-04</td>\n",
" <td>TMAX</td>\n",
" <td>306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165067</th>\n",
" <td>USC00207312</td>\n",
" <td>2005-12-31</td>\n",
" <td>TMAX</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165068</th>\n",
" <td>USW00094889</td>\n",
" <td>2005-12-20</td>\n",
" <td>TMAX</td>\n",
" <td>-39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165069</th>\n",
" <td>USW00004848</td>\n",
" <td>2011-03-18</td>\n",
" <td>TMIN</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165070</th>\n",
" <td>USC00208202</td>\n",
" <td>2011-11-26</td>\n",
" <td>TMIN</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165071</th>\n",
" <td>USC00201250</td>\n",
" <td>2010-06-19</td>\n",
" <td>TMAX</td>\n",
" <td>294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165072</th>\n",
" <td>USC00208080</td>\n",
" <td>2015-11-14</td>\n",
" <td>TMIN</td>\n",
" <td>-17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165073</th>\n",
" <td>USC00205563</td>\n",
" <td>2005-05-13</td>\n",
" <td>TMAX</td>\n",
" <td>222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165074</th>\n",
" <td>USW00094889</td>\n",
" <td>2009-07-09</td>\n",
" <td>TMAX</td>\n",
" <td>261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165075</th>\n",
" <td>USC00205451</td>\n",
" <td>2014-10-03</td>\n",
" <td>TMIN</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165076</th>\n",
" <td>USC00205050</td>\n",
" <td>2013-09-29</td>\n",
" <td>TMAX</td>\n",
" <td>261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165077</th>\n",
" <td>USC00205050</td>\n",
" <td>2014-07-14</td>\n",
" <td>TMIN</td>\n",
" <td>172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165078</th>\n",
" <td>USC00200032</td>\n",
" <td>2011-06-27</td>\n",
" <td>TMIN</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165079</th>\n",
" <td>USC00202308</td>\n",
" <td>2005-03-02</td>\n",
" <td>TMIN</td>\n",
" <td>-67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165080</th>\n",
" <td>USC00205822</td>\n",
" <td>2015-06-09</td>\n",
" <td>TMAX</td>\n",
" <td>256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165081</th>\n",
" <td>USC00205822</td>\n",
" <td>2009-10-06</td>\n",
" <td>TMAX</td>\n",
" <td>167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165082</th>\n",
" <td>USC00205050</td>\n",
" <td>2014-07-14</td>\n",
" <td>TMAX</td>\n",
" <td>283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165083</th>\n",
" <td>USC00200230</td>\n",
" <td>2006-11-29</td>\n",
" <td>TMIN</td>\n",
" <td>117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165084</th>\n",
" <td>USC00207312</td>\n",
" <td>2006-09-04</td>\n",
" <td>TMIN</td>\n",
" <td>111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>165085 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" ID Date Element Data_Value\n",
"0 USW00094889 2014-11-12 TMAX 22\n",
"1 USC00208972 2009-04-29 TMIN 56\n",
"2 USC00200032 2008-05-26 TMAX 278\n",
"3 USC00205563 2005-11-11 TMAX 139\n",
"4 USC00200230 2014-02-27 TMAX -106\n",
"5 USW00014833 2010-10-01 TMAX 194\n",
"6 USC00207308 2010-06-29 TMIN 144\n",
"7 USC00203712 2005-10-04 TMAX 289\n",
"8 USW00004848 2007-12-14 TMIN -16\n",
"9 USC00200220 2011-04-21 TMAX 72\n",
"10 USC00205822 2013-01-16 TMAX 11\n",
"11 USC00205822 2008-05-29 TMIN 28\n",
"12 USC00203712 2008-10-17 TMIN 17\n",
"13 USC00205563 2006-05-14 TMAX 183\n",
"14 USC00200842 2006-05-14 TMAX 122\n",
"15 USC00205563 2014-12-07 TMAX 67\n",
"16 USC00205822 2008-09-07 TMAX 250\n",
"17 USC00205450 2006-04-22 TMIN 67\n",
"18 USC00203712 2008-02-22 TMAX -44\n",
"19 USC00205563 2015-01-03 TMIN -39\n",
"20 USC00201250 2011-03-28 TMIN -78\n",
"21 USC00200230 2008-02-10 TMAX -6\n",
"22 USC00207320 2008-02-03 TMIN -39\n",
"23 USC00200228 2008-02-23 TMAX 11\n",
"24 USC00201250 2012-03-20 TMIN 100\n",
"25 USC00205822 2006-03-29 TMIN -17\n",
"26 USC00202308 2006-05-11 TMAX 233\n",
"27 USW00014833 2012-03-31 TMAX 61\n",
"28 USW00014853 2010-07-25 TMAX 283\n",
"29 USC00205822 2014-12-09 TMIN 17\n",
"... ... ... ... ...\n",
"165055 USC00202308 2009-10-09 TMAX 111\n",
"165056 USC00200228 2015-02-23 TMAX -11\n",
"165057 USC00208080 2009-11-24 TMAX 100\n",
"165058 USC00208972 2010-03-22 TMAX 94\n",
"165059 USC00207312 2015-06-23 TMIN 172\n",
"165060 USC00200230 2010-05-23 TMAX 283\n",
"165061 USW00004848 2012-12-26 TMIN -32\n",
"165062 USC00205822 2014-02-06 TMAX -11\n",
"165063 USC00200230 2010-05-23 TMIN 133\n",
"165064 USC00207312 2008-08-04 TMIN 172\n",
"165065 USC00200032 2006-03-01 TMAX 17\n",
"165066 USC00207312 2008-08-04 TMAX 306\n",
"165067 USC00207312 2005-12-31 TMAX 17\n",
"165068 USW00094889 2005-12-20 TMAX -39\n",
"165069 USW00004848 2011-03-18 TMIN 44\n",
"165070 USC00208202 2011-11-26 TMIN 28\n",
"165071 USC00201250 2010-06-19 TMAX 294\n",
"165072 USC00208080 2015-11-14 TMIN -17\n",
"165073 USC00205563 2005-05-13 TMAX 222\n",
"165074 USW00094889 2009-07-09 TMAX 261\n",
"165075 USC00205451 2014-10-03 TMIN 100\n",
"165076 USC00205050 2013-09-29 TMAX 261\n",
"165077 USC00205050 2014-07-14 TMIN 172\n",
"165078 USC00200032 2011-06-27 TMIN 144\n",
"165079 USC00202308 2005-03-02 TMIN -67\n",
"165080 USC00205822 2015-06-09 TMAX 256\n",
"165081 USC00205822 2009-10-06 TMAX 167\n",
"165082 USC00205050 2014-07-14 TMAX 283\n",
"165083 USC00200230 2006-11-29 TMIN 117\n",
"165084 USC00207312 2006-09-04 TMIN 111\n",
"\n",
"[165085 rows x 4 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(r'data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv')\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quick Look at the Data by Field"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 165085 entries, 0 to 165084\n",
"Data columns (total 4 columns):\n",
"ID 165085 non-null object\n",
"Date 165085 non-null object\n",
"Element 165085 non-null object\n",
"Data_Value 165085 non-null int64\n",
"dtypes: int64(1), object(3)\n",
"memory usage: 5.0+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"24"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['ID'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4017"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Date'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"TMAX 83063\n",
"TMIN 82022\n",
"Name: Element, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Element'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"406\n",
"-343\n"
]
}
],
"source": [
"print(df['Data_Value'].max())\n",
"print(df['Data_Value'].min())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Cleaning\n",
"### Delete Feb 29"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = df[df['Date'].str[-5:]!='02-29']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transfer temprature to 1 degree"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:1: 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",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"df['Data_Value'] = df['Data_Value']*0.1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Date</th>\n",
" <th>Element</th>\n",
" <th>Data_Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>USW00094889</td>\n",
" <td>2014-11-12</td>\n",
" <td>TMAX</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>USC00208972</td>\n",
" <td>2009-04-29</td>\n",
" <td>TMIN</td>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>USC00200032</td>\n",
" <td>2008-05-26</td>\n",
" <td>TMAX</td>\n",
" <td>27.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>USC00205563</td>\n",
" <td>2005-11-11</td>\n",
" <td>TMAX</td>\n",
" <td>13.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>USC00200230</td>\n",
" <td>2014-02-27</td>\n",
" <td>TMAX</td>\n",
" <td>-10.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>USW00014833</td>\n",
" <td>2010-10-01</td>\n",
" <td>TMAX</td>\n",
" <td>19.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>USC00207308</td>\n",
" <td>2010-06-29</td>\n",
" <td>TMIN</td>\n",
" <td>14.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>USC00203712</td>\n",
" <td>2005-10-04</td>\n",
" <td>TMAX</td>\n",
" <td>28.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>USW00004848</td>\n",
" <td>2007-12-14</td>\n",
" <td>TMIN</td>\n",
" <td>-1.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>USC00200220</td>\n",
" <td>2011-04-21</td>\n",
" <td>TMAX</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>USC00205822</td>\n",
" <td>2013-01-16</td>\n",
" <td>TMAX</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>USC00205822</td>\n",
" <td>2008-05-29</td>\n",
" <td>TMIN</td>\n",
" <td>2.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>USC00203712</td>\n",
" <td>2008-10-17</td>\n",
" <td>TMIN</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>USC00205563</td>\n",
" <td>2006-05-14</td>\n",
" <td>TMAX</td>\n",
" <td>18.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>USC00200842</td>\n",
" <td>2006-05-14</td>\n",
" <td>TMAX</td>\n",
" <td>12.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>USC00205563</td>\n",
" <td>2014-12-07</td>\n",
" <td>TMAX</td>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>USC00205822</td>\n",
" <td>2008-09-07</td>\n",
" <td>TMAX</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>USC00205450</td>\n",
" <td>2006-04-22</td>\n",
" <td>TMIN</td>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>USC00203712</td>\n",
" <td>2008-02-22</td>\n",
" <td>TMAX</td>\n",
" <td>-4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>USC00205563</td>\n",
" <td>2015-01-03</td>\n",
" <td>TMIN</td>\n",
" <td>-3.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>USC00201250</td>\n",
" <td>2011-03-28</td>\n",
" <td>TMIN</td>\n",
" <td>-7.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>USC00200230</td>\n",
" <td>2008-02-10</td>\n",
" <td>TMAX</td>\n",
" <td>-0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>USC00207320</td>\n",
" <td>2008-02-03</td>\n",
" <td>TMIN</td>\n",
" <td>-3.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>USC00200228</td>\n",
" <td>2008-02-23</td>\n",
" <td>TMAX</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>USC00201250</td>\n",
" <td>2012-03-20</td>\n",
" <td>TMIN</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>USC00205822</td>\n",
" <td>2006-03-29</td>\n",
" <td>TMIN</td>\n",
" <td>-1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>USC00202308</td>\n",
" <td>2006-05-11</td>\n",
" <td>TMAX</td>\n",
" <td>23.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>USW00014833</td>\n",
" <td>2012-03-31</td>\n",
" <td>TMAX</td>\n",
" <td>6.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>USW00014853</td>\n",
" <td>2010-07-25</td>\n",
" <td>TMAX</td>\n",
" <td>28.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>USC00205822</td>\n",
" <td>2014-12-09</td>\n",
" <td>TMIN</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165055</th>\n",
" <td>USC00202308</td>\n",
" <td>2009-10-09</td>\n",
" <td>TMAX</td>\n",
" <td>11.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165056</th>\n",
" <td>USC00200228</td>\n",
" <td>2015-02-23</td>\n",
" <td>TMAX</td>\n",
" <td>-1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165057</th>\n",
" <td>USC00208080</td>\n",
" <td>2009-11-24</td>\n",
" <td>TMAX</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165058</th>\n",
" <td>USC00208972</td>\n",
" <td>2010-03-22</td>\n",
" <td>TMAX</td>\n",
" <td>9.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165059</th>\n",
" <td>USC00207312</td>\n",
" <td>2015-06-23</td>\n",
" <td>TMIN</td>\n",
" <td>17.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165060</th>\n",
" <td>USC00200230</td>\n",
" <td>2010-05-23</td>\n",
" <td>TMAX</td>\n",
" <td>28.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165061</th>\n",
" <td>USW00004848</td>\n",
" <td>2012-12-26</td>\n",
" <td>TMIN</td>\n",
" <td>-3.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165062</th>\n",
" <td>USC00205822</td>\n",
" <td>2014-02-06</td>\n",
" <td>TMAX</td>\n",
" <td>-1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165063</th>\n",
" <td>USC00200230</td>\n",
" <td>2010-05-23</td>\n",
" <td>TMIN</td>\n",
" <td>13.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165064</th>\n",
" <td>USC00207312</td>\n",
" <td>2008-08-04</td>\n",
" <td>TMIN</td>\n",
" <td>17.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165065</th>\n",
" <td>USC00200032</td>\n",
" <td>2006-03-01</td>\n",
" <td>TMAX</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165066</th>\n",
" <td>USC00207312</td>\n",
" <td>2008-08-04</td>\n",
" <td>TMAX</td>\n",
" <td>30.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165067</th>\n",
" <td>USC00207312</td>\n",
" <td>2005-12-31</td>\n",
" <td>TMAX</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165068</th>\n",
" <td>USW00094889</td>\n",
" <td>2005-12-20</td>\n",
" <td>TMAX</td>\n",
" <td>-3.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165069</th>\n",
" <td>USW00004848</td>\n",
" <td>2011-03-18</td>\n",
" <td>TMIN</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165070</th>\n",
" <td>USC00208202</td>\n",
" <td>2011-11-26</td>\n",
" <td>TMIN</td>\n",
" <td>2.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165071</th>\n",
" <td>USC00201250</td>\n",
" <td>2010-06-19</td>\n",
" <td>TMAX</td>\n",
" <td>29.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165072</th>\n",
" <td>USC00208080</td>\n",
" <td>2015-11-14</td>\n",
" <td>TMIN</td>\n",
" <td>-1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165073</th>\n",
" <td>USC00205563</td>\n",
" <td>2005-05-13</td>\n",
" <td>TMAX</td>\n",
" <td>22.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165074</th>\n",
" <td>USW00094889</td>\n",
" <td>2009-07-09</td>\n",
" <td>TMAX</td>\n",
" <td>26.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165075</th>\n",
" <td>USC00205451</td>\n",
" <td>2014-10-03</td>\n",
" <td>TMIN</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165076</th>\n",
" <td>USC00205050</td>\n",
" <td>2013-09-29</td>\n",
" <td>TMAX</td>\n",
" <td>26.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165077</th>\n",
" <td>USC00205050</td>\n",
" <td>2014-07-14</td>\n",
" <td>TMIN</td>\n",
" <td>17.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165078</th>\n",
" <td>USC00200032</td>\n",
" <td>2011-06-27</td>\n",
" <td>TMIN</td>\n",
" <td>14.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165079</th>\n",
" <td>USC00202308</td>\n",
" <td>2005-03-02</td>\n",
" <td>TMIN</td>\n",
" <td>-6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165080</th>\n",
" <td>USC00205822</td>\n",
" <td>2015-06-09</td>\n",
" <td>TMAX</td>\n",
" <td>25.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165081</th>\n",
" <td>USC00205822</td>\n",
" <td>2009-10-06</td>\n",
" <td>TMAX</td>\n",
" <td>16.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165082</th>\n",
" <td>USC00205050</td>\n",
" <td>2014-07-14</td>\n",
" <td>TMAX</td>\n",
" <td>28.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165083</th>\n",
" <td>USC00200230</td>\n",
" <td>2006-11-29</td>\n",
" <td>TMIN</td>\n",
" <td>11.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165084</th>\n",
" <td>USC00207312</td>\n",
" <td>2006-09-04</td>\n",
" <td>TMIN</td>\n",
" <td>11.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>165002 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" ID Date Element Data_Value\n",
"0 USW00094889 2014-11-12 TMAX 2.2\n",
"1 USC00208972 2009-04-29 TMIN 5.6\n",
"2 USC00200032 2008-05-26 TMAX 27.8\n",
"3 USC00205563 2005-11-11 TMAX 13.9\n",
"4 USC00200230 2014-02-27 TMAX -10.6\n",
"5 USW00014833 2010-10-01 TMAX 19.4\n",
"6 USC00207308 2010-06-29 TMIN 14.4\n",
"7 USC00203712 2005-10-04 TMAX 28.9\n",
"8 USW00004848 2007-12-14 TMIN -1.6\n",
"9 USC00200220 2011-04-21 TMAX 7.2\n",
"10 USC00205822 2013-01-16 TMAX 1.1\n",
"11 USC00205822 2008-05-29 TMIN 2.8\n",
"12 USC00203712 2008-10-17 TMIN 1.7\n",
"13 USC00205563 2006-05-14 TMAX 18.3\n",
"14 USC00200842 2006-05-14 TMAX 12.2\n",
"15 USC00205563 2014-12-07 TMAX 6.7\n",
"16 USC00205822 2008-09-07 TMAX 25.0\n",
"17 USC00205450 2006-04-22 TMIN 6.7\n",
"18 USC00203712 2008-02-22 TMAX -4.4\n",
"19 USC00205563 2015-01-03 TMIN -3.9\n",
"20 USC00201250 2011-03-28 TMIN -7.8\n",
"21 USC00200230 2008-02-10 TMAX -0.6\n",
"22 USC00207320 2008-02-03 TMIN -3.9\n",
"23 USC00200228 2008-02-23 TMAX 1.1\n",
"24 USC00201250 2012-03-20 TMIN 10.0\n",
"25 USC00205822 2006-03-29 TMIN -1.7\n",
"26 USC00202308 2006-05-11 TMAX 23.3\n",
"27 USW00014833 2012-03-31 TMAX 6.1\n",
"28 USW00014853 2010-07-25 TMAX 28.3\n",
"29 USC00205822 2014-12-09 TMIN 1.7\n",
"... ... ... ... ...\n",
"165055 USC00202308 2009-10-09 TMAX 11.1\n",
"165056 USC00200228 2015-02-23 TMAX -1.1\n",
"165057 USC00208080 2009-11-24 TMAX 10.0\n",
"165058 USC00208972 2010-03-22 TMAX 9.4\n",
"165059 USC00207312 2015-06-23 TMIN 17.2\n",
"165060 USC00200230 2010-05-23 TMAX 28.3\n",
"165061 USW00004848 2012-12-26 TMIN -3.2\n",
"165062 USC00205822 2014-02-06 TMAX -1.1\n",
"165063 USC00200230 2010-05-23 TMIN 13.3\n",
"165064 USC00207312 2008-08-04 TMIN 17.2\n",
"165065 USC00200032 2006-03-01 TMAX 1.7\n",
"165066 USC00207312 2008-08-04 TMAX 30.6\n",
"165067 USC00207312 2005-12-31 TMAX 1.7\n",
"165068 USW00094889 2005-12-20 TMAX -3.9\n",
"165069 USW00004848 2011-03-18 TMIN 4.4\n",
"165070 USC00208202 2011-11-26 TMIN 2.8\n",
"165071 USC00201250 2010-06-19 TMAX 29.4\n",
"165072 USC00208080 2015-11-14 TMIN -1.7\n",
"165073 USC00205563 2005-05-13 TMAX 22.2\n",
"165074 USW00094889 2009-07-09 TMAX 26.1\n",
"165075 USC00205451 2014-10-03 TMIN 10.0\n",
"165076 USC00205050 2013-09-29 TMAX 26.1\n",
"165077 USC00205050 2014-07-14 TMIN 17.2\n",
"165078 USC00200032 2011-06-27 TMIN 14.4\n",
"165079 USC00202308 2005-03-02 TMIN -6.7\n",
"165080 USC00205822 2015-06-09 TMAX 25.6\n",
"165081 USC00205822 2009-10-06 TMAX 16.7\n",
"165082 USC00205050 2014-07-14 TMAX 28.3\n",
"165083 USC00200230 2006-11-29 TMIN 11.7\n",
"165084 USC00207312 2006-09-04 TMIN 11.1\n",
"\n",
"[165002 rows x 4 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Seperate data into 2 datasets: 2005-2014 and 2015 data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2005-01-01\n",
"2014-12-31\n"
]
}
],
"source": [
"df_14=df[df['Date'].str[:4]!='2015']\n",
"\n",
"print(df_14['Date'].min())\n",
"print(df_14['Date'].max())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2015-01-01\n",
"2015-12-31\n"
]
}
],
"source": [
"df_15=df[df['Date'].str[:4]=='2015']\n",
"print(df_15['Date'].min())\n",
"print(df_15['Date'].max())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delete \"Year\" from the label"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because we want to draw a line by day of the year"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:1: 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",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"df_14['Date'] = df_14['Date'].map(lambda x: x[-5:])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"365"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_14['Date'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Date</th>\n",
" <th>Element</th>\n",
" <th>Data_Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>USW00094889</td>\n",
" <td>11-12</td>\n",
" <td>TMAX</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>USC00208972</td>\n",
" <td>04-29</td>\n",
" <td>TMIN</td>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>USC00200032</td>\n",
" <td>05-26</td>\n",
" <td>TMAX</td>\n",
" <td>27.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>USC00205563</td>\n",
" <td>11-11</td>\n",
" <td>TMAX</td>\n",
" <td>13.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>USC00200230</td>\n",
" <td>02-27</td>\n",
" <td>TMAX</td>\n",
" <td>-10.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>USW00014833</td>\n",
" <td>10-01</td>\n",
" <td>TMAX</td>\n",
" <td>19.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>USC00207308</td>\n",
" <td>06-29</td>\n",
" <td>TMIN</td>\n",
" <td>14.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>USC00203712</td>\n",
" <td>10-04</td>\n",
" <td>TMAX</td>\n",
" <td>28.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>USW00004848</td>\n",
" <td>12-14</td>\n",
" <td>TMIN</td>\n",
" <td>-1.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>USC00200220</td>\n",
" <td>04-21</td>\n",
" <td>TMAX</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>USC00205822</td>\n",
" <td>01-16</td>\n",
" <td>TMAX</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>USC00205822</td>\n",
" <td>05-29</td>\n",
" <td>TMIN</td>\n",
" <td>2.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>USC00203712</td>\n",
" <td>10-17</td>\n",
" <td>TMIN</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>USC00205563</td>\n",
" <td>05-14</td>\n",
" <td>TMAX</td>\n",
" <td>18.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>USC00200842</td>\n",
" <td>05-14</td>\n",
" <td>TMAX</td>\n",
" <td>12.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>USC00205563</td>\n",
" <td>12-07</td>\n",
" <td>TMAX</td>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>USC00205822</td>\n",
" <td>09-07</td>\n",
" <td>TMAX</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>USC00205450</td>\n",
" <td>04-22</td>\n",
" <td>TMIN</td>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>USC00203712</td>\n",
" <td>02-22</td>\n",
" <td>TMAX</td>\n",
" <td>-4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>USC00201250</td>\n",
" <td>03-28</td>\n",
" <td>TMIN</td>\n",
" <td>-7.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>USC00200230</td>\n",
" <td>02-10</td>\n",
" <td>TMAX</td>\n",
" <td>-0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>USC00207320</td>\n",
" <td>02-03</td>\n",
" <td>TMIN</td>\n",
" <td>-3.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>USC00200228</td>\n",
" <td>02-23</td>\n",
" <td>TMAX</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>USC00201250</td>\n",
" <td>03-20</td>\n",
" <td>TMIN</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>USC00205822</td>\n",
" <td>03-29</td>\n",
" <td>TMIN</td>\n",
" <td>-1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>USC00202308</td>\n",
" <td>05-11</td>\n",
" <td>TMAX</td>\n",
" <td>23.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>USW00014833</td>\n",
" <td>03-31</td>\n",
" <td>TMAX</td>\n",
" <td>6.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>USW00014853</td>\n",
" <td>07-25</td>\n",
" <td>TMAX</td>\n",
" <td>28.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>USC00205822</td>\n",
" <td>12-09</td>\n",
" <td>TMIN</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>USC00205050</td>\n",
" <td>03-20</td>\n",
" <td>TMIN</td>\n",
" <td>9.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165050</th>\n",
" <td>USC00200842</td>\n",
" <td>12-08</td>\n",
" <td>TMAX</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165051</th>\n",
" <td>USC00205451</td>\n",
" <td>09-18</td>\n",
" <td>TMIN</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165052</th>\n",
" <td>USC00208080</td>\n",
" <td>11-03</td>\n",
" <td>TMIN</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165053</th>\n",
" <td>USC00200032</td>\n",
" <td>06-27</td>\n",
" <td>TMAX</td>\n",
" <td>28.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165055</th>\n",
" <td>USC00202308</td>\n",
" <td>10-09</td>\n",
" <td>TMAX</td>\n",
" <td>11.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165057</th>\n",
" <td>USC00208080</td>\n",
" <td>11-24</td>\n",
" <td>TMAX</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165058</th>\n",
" <td>USC00208972</td>\n",
" <td>03-22</td>\n",
" <td>TMAX</td>\n",
" <td>9.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165060</th>\n",
" <td>USC00200230</td>\n",
" <td>05-23</td>\n",
" <td>TMAX</td>\n",
" <td>28.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165061</th>\n",
" <td>USW00004848</td>\n",
" <td>12-26</td>\n",
" <td>TMIN</td>\n",
" <td>-3.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165062</th>\n",
" <td>USC00205822</td>\n",
" <td>02-06</td>\n",
" <td>TMAX</td>\n",
" <td>-1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165063</th>\n",
" <td>USC00200230</td>\n",
" <td>05-23</td>\n",
" <td>TMIN</td>\n",
" <td>13.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165064</th>\n",
" <td>USC00207312</td>\n",
" <td>08-04</td>\n",
" <td>TMIN</td>\n",
" <td>17.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165065</th>\n",
" <td>USC00200032</td>\n",
" <td>03-01</td>\n",
" <td>TMAX</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165066</th>\n",
" <td>USC00207312</td>\n",
" <td>08-04</td>\n",
" <td>TMAX</td>\n",
" <td>30.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165067</th>\n",
" <td>USC00207312</td>\n",
" <td>12-31</td>\n",
" <td>TMAX</td>\n",
" <td>1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165068</th>\n",
" <td>USW00094889</td>\n",
" <td>12-20</td>\n",
" <td>TMAX</td>\n",
" <td>-3.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165069</th>\n",
" <td>USW00004848</td>\n",
" <td>03-18</td>\n",
" <td>TMIN</td>\n",
" <td>4.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165070</th>\n",
" <td>USC00208202</td>\n",
" <td>11-26</td>\n",
" <td>TMIN</td>\n",
" <td>2.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165071</th>\n",
" <td>USC00201250</td>\n",
" <td>06-19</td>\n",
" <td>TMAX</td>\n",
" <td>29.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165073</th>\n",
" <td>USC00205563</td>\n",
" <td>05-13</td>\n",
" <td>TMAX</td>\n",
" <td>22.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165074</th>\n",
" <td>USW00094889</td>\n",
" <td>07-09</td>\n",
" <td>TMAX</td>\n",
" <td>26.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165075</th>\n",
" <td>USC00205451</td>\n",
" <td>10-03</td>\n",
" <td>TMIN</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165076</th>\n",
" <td>USC00205050</td>\n",
" <td>09-29</td>\n",
" <td>TMAX</td>\n",
" <td>26.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165077</th>\n",
" <td>USC00205050</td>\n",
" <td>07-14</td>\n",
" <td>TMIN</td>\n",
" <td>17.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165078</th>\n",
" <td>USC00200032</td>\n",
" <td>06-27</td>\n",
" <td>TMIN</td>\n",
" <td>14.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165079</th>\n",
" <td>USC00202308</td>\n",
" <td>03-02</td>\n",
" <td>TMIN</td>\n",
" <td>-6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165081</th>\n",
" <td>USC00205822</td>\n",
" <td>10-06</td>\n",
" <td>TMAX</td>\n",
" <td>16.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165082</th>\n",
" <td>USC00205050</td>\n",
" <td>07-14</td>\n",
" <td>TMAX</td>\n",
" <td>28.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165083</th>\n",
" <td>USC00200230</td>\n",
" <td>11-29</td>\n",
" <td>TMIN</td>\n",
" <td>11.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165084</th>\n",
" <td>USC00207312</td>\n",
" <td>09-04</td>\n",
" <td>TMIN</td>\n",
" <td>11.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>151245 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" ID Date Element Data_Value\n",
"0 USW00094889 11-12 TMAX 2.2\n",
"1 USC00208972 04-29 TMIN 5.6\n",
"2 USC00200032 05-26 TMAX 27.8\n",
"3 USC00205563 11-11 TMAX 13.9\n",
"4 USC00200230 02-27 TMAX -10.6\n",
"5 USW00014833 10-01 TMAX 19.4\n",
"6 USC00207308 06-29 TMIN 14.4\n",
"7 USC00203712 10-04 TMAX 28.9\n",
"8 USW00004848 12-14 TMIN -1.6\n",
"9 USC00200220 04-21 TMAX 7.2\n",
"10 USC00205822 01-16 TMAX 1.1\n",
"11 USC00205822 05-29 TMIN 2.8\n",
"12 USC00203712 10-17 TMIN 1.7\n",
"13 USC00205563 05-14 TMAX 18.3\n",
"14 USC00200842 05-14 TMAX 12.2\n",
"15 USC00205563 12-07 TMAX 6.7\n",
"16 USC00205822 09-07 TMAX 25.0\n",
"17 USC00205450 04-22 TMIN 6.7\n",
"18 USC00203712 02-22 TMAX -4.4\n",
"20 USC00201250 03-28 TMIN -7.8\n",
"21 USC00200230 02-10 TMAX -0.6\n",
"22 USC00207320 02-03 TMIN -3.9\n",
"23 USC00200228 02-23 TMAX 1.1\n",
"24 USC00201250 03-20 TMIN 10.0\n",
"25 USC00205822 03-29 TMIN -1.7\n",
"26 USC00202308 05-11 TMAX 23.3\n",
"27 USW00014833 03-31 TMAX 6.1\n",
"28 USW00014853 07-25 TMAX 28.3\n",
"29 USC00205822 12-09 TMIN 1.7\n",
"31 USC00205050 03-20 TMIN 9.4\n",
"... ... ... ... ...\n",
"165050 USC00200842 12-08 TMAX 1.7\n",
"165051 USC00205451 09-18 TMIN 10.0\n",
"165052 USC00208080 11-03 TMIN 5.0\n",
"165053 USC00200032 06-27 TMAX 28.3\n",
"165055 USC00202308 10-09 TMAX 11.1\n",
"165057 USC00208080 11-24 TMAX 10.0\n",
"165058 USC00208972 03-22 TMAX 9.4\n",
"165060 USC00200230 05-23 TMAX 28.3\n",
"165061 USW00004848 12-26 TMIN -3.2\n",
"165062 USC00205822 02-06 TMAX -1.1\n",
"165063 USC00200230 05-23 TMIN 13.3\n",
"165064 USC00207312 08-04 TMIN 17.2\n",
"165065 USC00200032 03-01 TMAX 1.7\n",
"165066 USC00207312 08-04 TMAX 30.6\n",
"165067 USC00207312 12-31 TMAX 1.7\n",
"165068 USW00094889 12-20 TMAX -3.9\n",
"165069 USW00004848 03-18 TMIN 4.4\n",
"165070 USC00208202 11-26 TMIN 2.8\n",
"165071 USC00201250 06-19 TMAX 29.4\n",
"165073 USC00205563 05-13 TMAX 22.2\n",
"165074 USW00094889 07-09 TMAX 26.1\n",
"165075 USC00205451 10-03 TMIN 10.0\n",
"165076 USC00205050 09-29 TMAX 26.1\n",
"165077 USC00205050 07-14 TMIN 17.2\n",
"165078 USC00200032 06-27 TMIN 14.4\n",
"165079 USC00202308 03-02 TMIN -6.7\n",
"165081 USC00205822 10-06 TMAX 16.7\n",
"165082 USC00205050 07-14 TMAX 28.3\n",
"165083 USC00200230 11-29 TMIN 11.7\n",
"165084 USC00207312 09-04 TMIN 11.1\n",
"\n",
"[151245 rows x 4 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_14"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:1: 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",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"df_15['Date'] = df_15['Date'].map(lambda x: x[-5:])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"365"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_15['Date'].nunique()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Date</th>\n",
" <th>Element</th>\n",
" <th>Data_Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>USC00205563</td>\n",
" <td>01-03</td>\n",
" <td>TMIN</td>\n",
" <td>-3.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>USC00203712</td>\n",
" <td>03-17</td>\n",
" <td>TMAX</td>\n",
" <td>18.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>USC00200032</td>\n",
" <td>06-06</td>\n",
" <td>TMIN</td>\n",
" <td>12.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>USW00014833</td>\n",
" <td>08-30</td>\n",
" <td>TMIN</td>\n",
" <td>17.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>USC00202308</td>\n",
" <td>08-30</td>\n",
" <td>TMIN</td>\n",
" <td>15.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>USC00205563</td>\n",
" <td>01-03</td>\n",
" <td>TMAX</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>USC00202308</td>\n",
" <td>08-30</td>\n",
" <td>TMAX</td>\n",
" <td>26.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>USC00200230</td>\n",
" <td>04-01</td>\n",
" <td>TMIN</td>\n",
" <td>-1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td>USC00200032</td>\n",
" <td>06-06</td>\n",
" <td>TMAX</td>\n",
" <td>23.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td>USW00014853</td>\n",
" <td>05-17</td>\n",
" <td>TMIN</td>\n",
" <td>18.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>USC00208972</td>\n",
" <td>04-09</td>\n",
" <td>TMAX</td>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>155</th>\n",
" <td>USC00205050</td>\n",
" <td>01-05</td>\n",
" <td>TMIN</td>\n",
" <td>-13.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>157</th>\n",
" <td>USC00200230</td>\n",
" <td>04-01</td>\n",
" <td>TMAX</td>\n",
" <td>18.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>USC00203712</td>\n",
" <td>03-17</td>\n",
" <td>TMIN</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179</th>\n",
" <td>USC00208972</td>\n",
" <td>05-27</td>\n",
" <td>TMAX</td>\n",
" <td>27.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td>USC00201250</td>\n",
" <td>05-14</td>\n",
" <td>TMIN</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>USC00205451</td>\n",
" <td>04-01</td>\n",
" <td>TMAX</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>225</th>\n",
" <td>USC00200230</td>\n",
" <td>11-24</td>\n",
" <td>TMAX</td>\n",
" <td>6.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>279</th>\n",
" <td>USC00205563</td>\n",
" <td>08-27</td>\n",
" <td>TMAX</td>\n",
" <td>18.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>284</th>\n",
" <td>USC00205822</td>\n",
" <td>09-10</td>\n",
" <td>TMAX</td>\n",
" <td>27.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>294</th>\n",
" <td>USC00205822</td>\n",
" <td>09-10</td>\n",
" <td>TMIN</td>\n",
" <td>11.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>329</th>\n",
" <td>USC00205563</td>\n",
" <td>08-27</td>\n",
" <td>TMIN</td>\n",
" <td>10.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>332</th>\n",
" <td>USC00208972</td>\n",
" <td>04-09</td>\n",
" <td>TMIN</td>\n",
" <td>2.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>338</th>\n",
" <td>USW00014853</td>\n",
" <td>05-17</td>\n",
" <td>TMAX</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>340</th>\n",
" <td>USC00200228</td>\n",
" <td>05-17</td>\n",
" <td>TMAX</td>\n",
" <td>25.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>347</th>\n",
" <td>USC00203712</td>\n",
" <td>11-30</td>\n",
" <td>TMAX</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405</th>\n",
" <td>USC00208972</td>\n",
" <td>05-27</td>\n",
" <td>TMIN</td>\n",
" <td>16.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>406</th>\n",
" <td>USC00205451</td>\n",
" <td>04-01</td>\n",
" <td>TMIN</td>\n",
" <td>-1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>409</th>\n",
" <td>USC00200228</td>\n",
" <td>08-26</td>\n",
" <td>TMAX</td>\n",
" <td>18.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>417</th>\n",
" <td>USC00200228</td>\n",
" <td>08-26</td>\n",
" <td>TMIN</td>\n",
" <td>11.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164732</th>\n",
" <td>USC00205050</td>\n",
" <td>07-22</td>\n",
" <td>TMAX</td>\n",
" <td>26.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164737</th>\n",
" <td>USC00201250</td>\n",
" <td>06-08</td>\n",
" <td>TMAX</td>\n",
" <td>25.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164763</th>\n",
" <td>USC00207308</td>\n",
" <td>07-10</td>\n",
" <td>TMIN</td>\n",
" <td>11.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164774</th>\n",
" <td>USC00203712</td>\n",
" <td>10-05</td>\n",
" <td>TMIN</td>\n",
" <td>11.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164780</th>\n",
" <td>USC00203712</td>\n",
" <td>10-05</td>\n",
" <td>TMAX</td>\n",
" <td>15.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164781</th>\n",
" <td>USC00208202</td>\n",
" <td>01-19</td>\n",
" <td>TMAX</td>\n",
" <td>2.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164801</th>\n",
" <td>USC00200230</td>\n",
" <td>03-10</td>\n",
" <td>TMIN</td>\n",
" <td>-1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164821</th>\n",
" <td>USC00207308</td>\n",
" <td>07-10</td>\n",
" <td>TMAX</td>\n",
" <td>22.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164827</th>\n",
" <td>USC00208080</td>\n",
" <td>11-14</td>\n",
" <td>TMAX</td>\n",
" <td>9.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164849</th>\n",
" <td>USC00205822</td>\n",
" <td>06-10</td>\n",
" <td>TMAX</td>\n",
" <td>26.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164862</th>\n",
" <td>USC00200230</td>\n",
" <td>10-06</td>\n",
" <td>TMAX</td>\n",
" <td>20.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164895</th>\n",
" <td>USC00205822</td>\n",
" <td>06-10</td>\n",
" <td>TMIN</td>\n",
" <td>15.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164920</th>\n",
" <td>USC00207312</td>\n",
" <td>07-10</td>\n",
" <td>TMAX</td>\n",
" <td>22.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164938</th>\n",
" <td>USC00205451</td>\n",
" <td>08-08</td>\n",
" <td>TMIN</td>\n",
" <td>16.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164964</th>\n",
" <td>USW00094889</td>\n",
" <td>04-21</td>\n",
" <td>TMIN</td>\n",
" <td>2.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164966</th>\n",
" <td>USC00205822</td>\n",
" <td>06-09</td>\n",
" <td>TMIN</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164971</th>\n",
" <td>USC00200228</td>\n",
" <td>02-16</td>\n",
" <td>TMIN</td>\n",
" <td>-26.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164975</th>\n",
" <td>USC00200228</td>\n",
" <td>02-23</td>\n",
" <td>TMIN</td>\n",
" <td>-22.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165000</th>\n",
" <td>USC00200228</td>\n",
" <td>02-16</td>\n",
" <td>TMAX</td>\n",
" <td>-13.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165007</th>\n",
" <td>USC00205451</td>\n",
" <td>08-08</td>\n",
" <td>TMAX</td>\n",
" <td>25.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165020</th>\n",
" <td>USC00205451</td>\n",
" <td>10-02</td>\n",
" <td>TMAX</td>\n",
" <td>18.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165023</th>\n",
" <td>USC00205822</td>\n",
" <td>01-16</td>\n",
" <td>TMIN</td>\n",
" <td>-15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165032</th>\n",
" <td>USC00205451</td>\n",
" <td>10-02</td>\n",
" <td>TMIN</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165038</th>\n",
" <td>USC00207312</td>\n",
" <td>07-10</td>\n",
" <td>TMIN</td>\n",
" <td>13.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165045</th>\n",
" <td>USC00200230</td>\n",
" <td>03-10</td>\n",
" <td>TMAX</td>\n",
" <td>9.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165054</th>\n",
" <td>USC00208080</td>\n",
" <td>02-14</td>\n",
" <td>TMIN</td>\n",
" <td>-21.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165056</th>\n",
" <td>USC00200228</td>\n",
" <td>02-23</td>\n",
" <td>TMAX</td>\n",
" <td>-1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165059</th>\n",
" <td>USC00207312</td>\n",
" <td>06-23</td>\n",
" <td>TMIN</td>\n",
" <td>17.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165072</th>\n",
" <td>USC00208080</td>\n",
" <td>11-14</td>\n",
" <td>TMIN</td>\n",
" <td>-1.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165080</th>\n",
" <td>USC00205822</td>\n",
" <td>06-09</td>\n",
" <td>TMAX</td>\n",
" <td>25.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>13757 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" ID Date Element Data_Value\n",
"19 USC00205563 01-03 TMIN -3.9\n",
"30 USC00203712 03-17 TMAX 18.9\n",
"34 USC00200032 06-06 TMIN 12.8\n",
"46 USW00014833 08-30 TMIN 17.8\n",
"50 USC00202308 08-30 TMIN 15.6\n",
"51 USC00205563 01-03 TMAX 2.2\n",
"59 USC00202308 08-30 TMAX 26.1\n",
"72 USC00200230 04-01 TMIN -1.7\n",
"126 USC00200032 06-06 TMAX 23.3\n",
"139 USW00014853 05-17 TMIN 18.3\n",
"146 USC00208972 04-09 TMAX 6.7\n",
"155 USC00205050 01-05 TMIN -13.9\n",
"157 USC00200230 04-01 TMAX 18.3\n",
"170 USC00203712 03-17 TMIN 1.1\n",
"179 USC00208972 05-27 TMAX 27.8\n",
"188 USC00201250 05-14 TMIN 2.2\n",
"198 USC00205451 04-01 TMAX 7.2\n",
"225 USC00200230 11-24 TMAX 6.7\n",
"279 USC00205563 08-27 TMAX 18.9\n",
"284 USC00205822 09-10 TMAX 27.8\n",
"294 USC00205822 09-10 TMIN 11.7\n",
"329 USC00205563 08-27 TMIN 10.6\n",
"332 USC00208972 04-09 TMIN 2.8\n",
"338 USW00014853 05-17 TMAX 30.0\n",
"340 USC00200228 05-17 TMAX 25.6\n",
"347 USC00203712 11-30 TMAX 5.0\n",
"405 USC00208972 05-27 TMIN 16.7\n",
"406 USC00205451 04-01 TMIN -1.7\n",
"409 USC00200228 08-26 TMAX 18.9\n",
"417 USC00200228 08-26 TMIN 11.7\n",
"... ... ... ... ...\n",
"164732 USC00205050 07-22 TMAX 26.7\n",
"164737 USC00201250 06-08 TMAX 25.6\n",
"164763 USC00207308 07-10 TMIN 11.1\n",
"164774 USC00203712 10-05 TMIN 11.1\n",
"164780 USC00203712 10-05 TMAX 15.6\n",
"164781 USC00208202 01-19 TMAX 2.8\n",
"164801 USC00200230 03-10 TMIN -1.1\n",
"164821 USC00207308 07-10 TMAX 22.8\n",
"164827 USC00208080 11-14 TMAX 9.4\n",
"164849 USC00205822 06-10 TMAX 26.7\n",
"164862 USC00200230 10-06 TMAX 20.6\n",
"164895 USC00205822 06-10 TMIN 15.6\n",
"164920 USC00207312 07-10 TMAX 22.8\n",
"164938 USC00205451 08-08 TMIN 16.1\n",
"164964 USW00094889 04-21 TMIN 2.2\n",
"164966 USC00205822 06-09 TMIN 15.0\n",
"164971 USC00200228 02-16 TMIN -26.1\n",
"164975 USC00200228 02-23 TMIN -22.8\n",
"165000 USC00200228 02-16 TMAX -13.9\n",
"165007 USC00205451 08-08 TMAX 25.6\n",
"165020 USC00205451 10-02 TMAX 18.9\n",
"165023 USC00205822 01-16 TMIN -15.0\n",
"165032 USC00205451 10-02 TMIN 7.2\n",
"165038 USC00207312 07-10 TMIN 13.3\n",
"165045 USC00200230 03-10 TMAX 9.4\n",
"165054 USC00208080 02-14 TMIN -21.1\n",
"165056 USC00200228 02-23 TMAX -1.1\n",
"165059 USC00207312 06-23 TMIN 17.2\n",
"165072 USC00208080 11-14 TMIN -1.7\n",
"165080 USC00205822 06-09 TMAX 25.6\n",
"\n",
"[13757 rows x 4 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_15"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get record high/Low temp by day\n",
"###### 2005 - 2014"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">Data_Value</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th>Element</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">01-01</th>\n",
" <th>TMAX</th>\n",
" <td>-9.3</td>\n",
" <td>15.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TMIN</th>\n",
" <td>-16.0</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">01-02</th>\n",
" <th>TMAX</th>\n",
" <td>-10.0</td>\n",
" <td>13.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TMIN</th>\n",
" <td>-26.7</td>\n",
" <td>2.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>01-03</th>\n",
" <th>TMAX</th>\n",
" <td>-11.7</td>\n",
" <td>13.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Data_Value \n",
" min max\n",
"Date Element \n",
"01-01 TMAX -9.3 15.6\n",
" TMIN -16.0 7.2\n",
"01-02 TMAX -10.0 13.9\n",
" TMIN -26.7 2.8\n",
"01-03 TMAX -11.7 13.3"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gb_14 = df_14.groupby(['Date','Element'])\n",
"df_early = gb_14.agg({'Data_Value' : [min, max]})\n",
"df_early.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"15.600000000000001"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_early.loc[('01-01','TMAX'),('Data_Value','max')]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[15.600000000000001,\n",
" 13.9,\n",
" 13.300000000000001,\n",
" 10.600000000000001,\n",
" 12.800000000000001,\n",
" 18.900000000000002,\n",
" 21.700000000000003,\n",
" 19.400000000000002,\n",
" 17.800000000000001,\n",
" 10.0,\n",
" 15.600000000000001,\n",
" 16.100000000000001,\n",
" 16.699999999999999,\n",
" 15.0,\n",
" 6.7000000000000002,\n",
" 9.4000000000000004,\n",
" 13.300000000000001,\n",
" 12.200000000000001,\n",
" 10.600000000000001,\n",
" 13.300000000000001,\n",
" 13.300000000000001,\n",
" 11.700000000000001,\n",
" 12.800000000000001,\n",
" 11.700000000000001,\n",
" 10.0,\n",
" 8.9000000000000004,\n",
" 7.8000000000000007,\n",
" 12.200000000000001,\n",
" 17.800000000000001,\n",
" 18.300000000000001,\n",
" 14.4,\n",
" 15.0,\n",
" 10.600000000000001,\n",
" 8.9000000000000004,\n",
" 11.700000000000001,\n",
" 12.200000000000001,\n",
" 11.700000000000001,\n",
" 11.700000000000001,\n",
" 12.800000000000001,\n",
" 7.8000000000000007,\n",
" 16.100000000000001,\n",
" 16.100000000000001,\n",
" 19.400000000000002,\n",
" 9.4000000000000004,\n",
" 10.600000000000001,\n",
" 11.700000000000001,\n",
" 14.4,\n",
" 15.600000000000001,\n",
" 14.4,\n",
" 12.800000000000001,\n",
" 10.600000000000001,\n",
" 10.0,\n",
" 10.600000000000001,\n",
" 12.200000000000001,\n",
" 11.100000000000001,\n",
" 9.4000000000000004,\n",
" 11.100000000000001,\n",
" 12.200000000000001,\n",
" 11.700000000000001,\n",
" 11.700000000000001,\n",
" 15.600000000000001,\n",
" 16.100000000000001,\n",
" 15.600000000000001,\n",
" 15.600000000000001,\n",
" 20.600000000000001,\n",
" 21.100000000000001,\n",
" 20.0,\n",
" 18.900000000000002,\n",
" 18.900000000000002,\n",
" 20.0,\n",
" 20.600000000000001,\n",
" 22.800000000000001,\n",
" 26.700000000000003,\n",
" 26.700000000000003,\n",
" 26.100000000000001,\n",
" 26.100000000000001,\n",
" 25.0,\n",
" 26.700000000000003,\n",
" 30.0,\n",
" 30.600000000000001,\n",
" 31.700000000000003,\n",
" 30.600000000000001,\n",
" 28.300000000000001,\n",
" 23.300000000000001,\n",
" 27.200000000000003,\n",
" 26.700000000000003,\n",
" 27.200000000000003,\n",
" 22.200000000000003,\n",
" 23.900000000000002,\n",
" 23.300000000000001,\n",
" 27.200000000000003,\n",
" 27.800000000000001,\n",
" 27.200000000000003,\n",
" 25.600000000000001,\n",
" 25.600000000000001,\n",
" 28.300000000000001,\n",
" 27.800000000000001,\n",
" 22.800000000000001,\n",
" 21.700000000000003,\n",
" 30.0,\n",
" 28.900000000000002,\n",
" 24.400000000000002,\n",
" 27.200000000000003,\n",
" 27.800000000000001,\n",
" 28.900000000000002,\n",
" 28.900000000000002,\n",
" 24.400000000000002,\n",
" 28.300000000000001,\n",
" 28.900000000000002,\n",
" 28.900000000000002,\n",
" 27.800000000000001,\n",
" 27.200000000000003,\n",
" 27.800000000000001,\n",
" 30.0,\n",
" 30.600000000000001,\n",
" 29.400000000000002,\n",
" 29.400000000000002,\n",
" 29.400000000000002,\n",
" 27.200000000000003,\n",
" 27.800000000000001,\n",
" 28.300000000000001,\n",
" 30.600000000000001,\n",
" 33.300000000000004,\n",
" 32.200000000000003,\n",
" 27.800000000000001,\n",
" 28.300000000000001,\n",
" 25.600000000000001,\n",
" 31.700000000000003,\n",
" 31.700000000000003,\n",
" 29.400000000000002,\n",
" 29.400000000000002,\n",
" 31.700000000000003,\n",
" 32.200000000000003,\n",
" 30.600000000000001,\n",
" 32.800000000000004,\n",
" 31.700000000000003,\n",
" 30.0,\n",
" 28.900000000000002,\n",
" 31.100000000000001,\n",
" 33.899999999999999,\n",
" 32.800000000000004,\n",
" 31.700000000000003,\n",
" 32.800000000000004,\n",
" 32.800000000000004,\n",
" 33.300000000000004,\n",
" 33.300000000000004,\n",
" 32.200000000000003,\n",
" 36.700000000000003,\n",
" 35.0,\n",
" 35.0,\n",
" 34.399999999999999,\n",
" 35.0,\n",
" 32.200000000000003,\n",
" 32.200000000000003,\n",
" 36.100000000000001,\n",
" 35.0,\n",
" 34.399999999999999,\n",
" 37.200000000000003,\n",
" 38.300000000000004,\n",
" 36.700000000000003,\n",
" 35.0,\n",
" 35.0,\n",
" 32.200000000000003,\n",
" 32.800000000000004,\n",
" 32.800000000000004,\n",
" 32.800000000000004,\n",
" 33.899999999999999,\n",
" 35.0,\n",
" 35.600000000000001,\n",
" 36.700000000000003,\n",
" 36.100000000000001,\n",
" 36.700000000000003,\n",
" 34.399999999999999,\n",
" 33.899999999999999,\n",
" 36.700000000000003,\n",
" 37.200000000000003,\n",
" 37.800000000000004,\n",
" 36.100000000000001,\n",
" 40.600000000000001,\n",
" 38.900000000000006,\n",
" 36.700000000000003,\n",
" 36.700000000000003,\n",
" 38.300000000000004,\n",
" 36.100000000000001,\n",
" 40.0,\n",
" 38.900000000000006,\n",
" 38.300000000000004,\n",
" 39.400000000000006,\n",
" 39.400000000000006,\n",
" 35.600000000000001,\n",
" 36.100000000000001,\n",
" 35.600000000000001,\n",
" 35.0,\n",
" 34.399999999999999,\n",
" 33.899999999999999,\n",
" 36.100000000000001,\n",
" 37.200000000000003,\n",
" 40.600000000000001,\n",
" 38.900000000000006,\n",
" 36.100000000000001,\n",
" 37.200000000000003,\n",
" 40.600000000000001,\n",
" 38.300000000000004,\n",
" 38.300000000000004,\n",
" 36.100000000000001,\n",
" 34.399999999999999,\n",
" 34.399999999999999,\n",
" 33.300000000000004,\n",
" 36.100000000000001,\n",
" 33.899999999999999,\n",
" 34.399999999999999,\n",
" 35.0,\n",
" 35.600000000000001,\n",
" 36.700000000000003,\n",
" 37.200000000000003,\n",
" 36.700000000000003,\n",
" 33.899999999999999,\n",
" 32.800000000000004,\n",
" 36.100000000000001,\n",
" 33.899999999999999,\n",
" 34.399999999999999,\n",
" 34.399999999999999,\n",
" 36.100000000000001,\n",
" 32.800000000000004,\n",
" 33.300000000000004,\n",
" 31.700000000000003,\n",
" 33.300000000000004,\n",
" 33.300000000000004,\n",
" 33.899999999999999,\n",
" 33.899999999999999,\n",
" 32.200000000000003,\n",
" 32.200000000000003,\n",
" 32.200000000000003,\n",
" 33.300000000000004,\n",
" 34.399999999999999,\n",
" 34.399999999999999,\n",
" 35.600000000000001,\n",
" 35.0,\n",
" 33.899999999999999,\n",
" 32.200000000000003,\n",
" 34.399999999999999,\n",
" 34.399999999999999,\n",
" 36.100000000000001,\n",
" 35.0,\n",
" 37.200000000000003,\n",
" 36.700000000000003,\n",
" 36.100000000000001,\n",
" 35.600000000000001,\n",
" 34.399999999999999,\n",
" 34.399999999999999,\n",
" 33.300000000000004,\n",
" 33.300000000000004,\n",
" 36.100000000000001,\n",
" 36.700000000000003,\n",
" 33.899999999999999,\n",
" 33.300000000000004,\n",
" 33.300000000000004,\n",
" 31.100000000000001,\n",
" 27.800000000000001,\n",
" 28.900000000000002,\n",
" 29.400000000000002,\n",
" 31.100000000000001,\n",
" 30.600000000000001,\n",
" 32.800000000000004,\n",
" 32.800000000000004,\n",
" 33.300000000000004,\n",
" 33.899999999999999,\n",
" 32.800000000000004,\n",
" 31.700000000000003,\n",
" 27.800000000000001,\n",
" 28.300000000000001,\n",
" 27.800000000000001,\n",
" 27.800000000000001,\n",
" 27.200000000000003,\n",
" 28.900000000000002,\n",
" 31.100000000000001,\n",
" 31.100000000000001,\n",
" 32.200000000000003,\n",
" 31.700000000000003,\n",
" 32.800000000000004,\n",
" 32.800000000000004,\n",
" 32.800000000000004,\n",
" 30.600000000000001,\n",
" 31.100000000000001,\n",
" 29.400000000000002,\n",
" 28.900000000000002,\n",
" 28.300000000000001,\n",
" 24.400000000000002,\n",
" 23.300000000000001,\n",
" 25.600000000000001,\n",
" 26.700000000000003,\n",
" 25.600000000000001,\n",
" 22.200000000000003,\n",
" 27.800000000000001,\n",
" 27.200000000000003,\n",
" 26.700000000000003,\n",
" 26.700000000000003,\n",
" 27.200000000000003,\n",
" 25.600000000000001,\n",
" 26.700000000000003,\n",
" 25.600000000000001,\n",
" 21.100000000000001,\n",
" 25.0,\n",
" 25.0,\n",
" 22.200000000000003,\n",
" 21.100000000000001,\n",
" 22.800000000000001,\n",
" 25.0,\n",
" 23.900000000000002,\n",
" 23.300000000000001,\n",
" 22.800000000000001,\n",
" 22.800000000000001,\n",
" 25.600000000000001,\n",
" 22.200000000000003,\n",
" 22.200000000000003,\n",
" 21.100000000000001,\n",
" 22.800000000000001,\n",
" 21.100000000000001,\n",
" 20.600000000000001,\n",
" 19.400000000000002,\n",
" 19.400000000000002,\n",
" 19.400000000000002,\n",
" 17.800000000000001,\n",
" 16.699999999999999,\n",
" 17.199999999999999,\n",
" 21.700000000000003,\n",
" 18.300000000000001,\n",
" 17.199999999999999,\n",
" 19.400000000000002,\n",
" 19.400000000000002,\n",
" 18.300000000000001,\n",
" 20.0,\n",
" 20.600000000000001,\n",
" 18.900000000000002,\n",
" 18.300000000000001,\n",
" 15.600000000000001,\n",
" 18.300000000000001,\n",
" 18.300000000000001,\n",
" 17.199999999999999,\n",
" 12.800000000000001,\n",
" 8.3000000000000007,\n",
" 7.2000000000000002,\n",
" 8.3000000000000007,\n",
" 11.100000000000001,\n",
" 12.800000000000001,\n",
" 13.300000000000001,\n",
" 11.100000000000001,\n",
" 13.9,\n",
" 15.0,\n",
" 13.9,\n",
" 14.4,\n",
" 15.600000000000001,\n",
" 12.200000000000001,\n",
" 13.300000000000001,\n",
" 15.600000000000001,\n",
" 13.300000000000001,\n",
" 13.300000000000001,\n",
" 13.9,\n",
" 10.0,\n",
" 10.600000000000001,\n",
" 18.900000000000002,\n",
" 19.400000000000002,\n",
" 12.800000000000001,\n",
" 11.700000000000001,\n",
" 13.9]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"high_14=[]\n",
"for x in list(df_early.xs('TMAX',level='Element').xs('max',level=1,axis=1).values):\n",
" high_14.extend(x)\n",
"high_14"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[-16.0,\n",
" -26.700000000000003,\n",
" -26.700000000000003,\n",
" -26.100000000000001,\n",
" -15.0,\n",
" -26.600000000000001,\n",
" -30.600000000000001,\n",
" -29.400000000000002,\n",
" -27.800000000000001,\n",
" -25.600000000000001,\n",
" -18.300000000000001,\n",
" -19.300000000000001,\n",
" -25.0,\n",
" -26.600000000000001,\n",
" -27.200000000000003,\n",
" -29.400000000000002,\n",
" -29.400000000000002,\n",
" -28.900000000000002,\n",
" -30.0,\n",
" -23.900000000000002,\n",
" -26.0,\n",
" -27.700000000000003,\n",
" -25.0,\n",
" -26.700000000000003,\n",
" -24.300000000000001,\n",
" -23.800000000000001,\n",
" -23.900000000000002,\n",
" -29.400000000000002,\n",
" -27.800000000000001,\n",
" -23.300000000000001,\n",
" -19.400000000000002,\n",
" -21.100000000000001,\n",
" -21.100000000000001,\n",
" -23.200000000000003,\n",
" -26.100000000000001,\n",
" -28.200000000000003,\n",
" -26.100000000000001,\n",
" -23.300000000000001,\n",
" -22.800000000000001,\n",
" -21.0,\n",
" -25.600000000000001,\n",
" -25.5,\n",
" -28.800000000000001,\n",
" -27.200000000000003,\n",
" -21.700000000000003,\n",
" -25.600000000000001,\n",
" -22.200000000000003,\n",
" -24.300000000000001,\n",
" -22.200000000000003,\n",
" -18.800000000000001,\n",
" -17.800000000000001,\n",
" -17.800000000000001,\n",
" -17.199999999999999,\n",
" -22.800000000000001,\n",
" -21.100000000000001,\n",
" -17.199999999999999,\n",
" -20.0,\n",
" -23.800000000000001,\n",
" -27.100000000000001,\n",
" -26.700000000000003,\n",
" -18.199999999999999,\n",
" -24.400000000000002,\n",
" -23.900000000000002,\n",
" -18.900000000000002,\n",
" -17.800000000000001,\n",
" -17.800000000000001,\n",
" -16.699999999999999,\n",
" -14.4,\n",
" -15.600000000000001,\n",
" -12.200000000000001,\n",
" -14.4,\n",
" -22.200000000000003,\n",
" -20.600000000000001,\n",
" -10.600000000000001,\n",
" -13.9,\n",
" -17.800000000000001,\n",
" -16.100000000000001,\n",
" -10.0,\n",
" -10.600000000000001,\n",
" -13.300000000000001,\n",
" -12.200000000000001,\n",
" -13.300000000000001,\n",
" -15.0,\n",
" -13.9,\n",
" -15.600000000000001,\n",
" -15.600000000000001,\n",
" -10.600000000000001,\n",
" -10.0,\n",
" -9.4000000000000004,\n",
" -8.3000000000000007,\n",
" -8.3000000000000007,\n",
" -7.8000000000000007,\n",
" -8.9000000000000004,\n",
" -8.3000000000000007,\n",
" -7.2000000000000002,\n",
" -9.4000000000000004,\n",
" -8.3000000000000007,\n",
" -8.9000000000000004,\n",
" -6.7000000000000002,\n",
" -8.3000000000000007,\n",
" -6.7000000000000002,\n",
" -7.2000000000000002,\n",
" -5.0,\n",
" -6.7000000000000002,\n",
" -8.3000000000000007,\n",
" -11.700000000000001,\n",
" -8.9000000000000004,\n",
" -2.8000000000000003,\n",
" -3.9000000000000004,\n",
" -4.4000000000000004,\n",
" -7.2000000000000002,\n",
" -5.0,\n",
" -5.6000000000000005,\n",
" -3.3000000000000003,\n",
" -3.9000000000000004,\n",
" -6.6000000000000005,\n",
" -3.9000000000000004,\n",
" -3.9000000000000004,\n",
" -5.0,\n",
" -4.9000000000000004,\n",
" -3.3000000000000003,\n",
" -2.8000000000000003,\n",
" -4.4000000000000004,\n",
" -6.1000000000000005,\n",
" -3.3000000000000003,\n",
" -1.7000000000000002,\n",
" -2.8000000000000003,\n",
" 0.0,\n",
" -1.7000000000000002,\n",
" -1.7000000000000002,\n",
" -1.1000000000000001,\n",
" -3.8000000000000003,\n",
" -6.0,\n",
" -2.8000000000000003,\n",
" 2.2000000000000002,\n",
" -3.3000000000000003,\n",
" -1.0,\n",
" -2.7000000000000002,\n",
" -1.6000000000000001,\n",
" 1.7000000000000002,\n",
" 2.2000000000000002,\n",
" 0.60000000000000009,\n",
" -2.2000000000000002,\n",
" 0.0,\n",
" -1.7000000000000002,\n",
" -1.0,\n",
" 0.0,\n",
" -1.1000000000000001,\n",
" 0.0,\n",
" 3.3000000000000003,\n",
" 2.2000000000000002,\n",
" 4.4000000000000004,\n",
" 3.3000000000000003,\n",
" 1.7000000000000002,\n",
" 3.3000000000000003,\n",
" 1.1000000000000001,\n",
" 2.8000000000000003,\n",
" 1.7000000000000002,\n",
" 6.1000000000000005,\n",
" 6.7000000000000002,\n",
" 5.0,\n",
" 2.8000000000000003,\n",
" 3.3000000000000003,\n",
" 5.0,\n",
" 2.8000000000000003,\n",
" 3.9000000000000004,\n",
" 5.6000000000000005,\n",
" 6.1000000000000005,\n",
" 6.1000000000000005,\n",
" 4.4000000000000004,\n",
" 4.4000000000000004,\n",
" 8.3000000000000007,\n",
" 5.0,\n",
" 3.9000000000000004,\n",
" 6.7000000000000002,\n",
" 7.8000000000000007,\n",
" 5.6000000000000005,\n",
" 6.1000000000000005,\n",
" 10.0,\n",
" 7.8000000000000007,\n",
" 6.1000000000000005,\n",
" 6.1000000000000005,\n",
" 4.4000000000000004,\n",
" 7.8000000000000007,\n",
" 6.7000000000000002,\n",
" 5.0,\n",
" 6.7000000000000002,\n",
" 7.8000000000000007,\n",
" 5.0,\n",
" 9.4000000000000004,\n",
" 9.4000000000000004,\n",
" 8.9000000000000004,\n",
" 8.9000000000000004,\n",
" 3.9000000000000004,\n",
" 5.6000000000000005,\n",
" 7.2000000000000002,\n",
" 7.8000000000000007,\n",
" 8.3000000000000007,\n",
" 7.2000000000000002,\n",
" 7.2000000000000002,\n",
" 8.3000000000000007,\n",
" 5.0,\n",
" 7.2000000000000002,\n",
" 8.9000000000000004,\n",
" 8.9000000000000004,\n",
" 6.1000000000000005,\n",
" 8.9000000000000004,\n",
" 10.0,\n",
" 6.1000000000000005,\n",
" 5.0,\n",
" 4.4000000000000004,\n",
" 6.7000000000000002,\n",
" 7.2000000000000002,\n",
" 7.8000000000000007,\n",
" 8.3000000000000007,\n",
" 7.2000000000000002,\n",
" 7.2000000000000002,\n",
" 6.7000000000000002,\n",
" 8.9000000000000004,\n",
" 8.9000000000000004,\n",
" 8.3000000000000007,\n",
" 6.7000000000000002,\n",
" 8.9000000000000004,\n",
" 6.7000000000000002,\n",
" 7.8000000000000007,\n",
" 5.0,\n",
" 3.9000000000000004,\n",
" 5.6000000000000005,\n",
" 7.2000000000000002,\n",
" 4.4000000000000004,\n",
" 7.2000000000000002,\n",
" 6.1000000000000005,\n",
" 6.1000000000000005,\n",
" 7.8000000000000007,\n",
" 3.9000000000000004,\n",
" 7.2000000000000002,\n",
" 5.0,\n",
" 6.7000000000000002,\n",
" 5.0,\n",
" 6.7000000000000002,\n",
" 7.8000000000000007,\n",
" 6.7000000000000002,\n",
" 3.3000000000000003,\n",
" 4.4000000000000004,\n",
" 5.6000000000000005,\n",
" 6.7000000000000002,\n",
" 6.7000000000000002,\n",
" 2.8000000000000003,\n",
" 4.4000000000000004,\n",
" 4.4000000000000004,\n",
" 3.9000000000000004,\n",
" 5.0,\n",
" 2.2000000000000002,\n",
" 4.4000000000000004,\n",
" 3.9000000000000004,\n",
" 1.7000000000000002,\n",
" -0.60000000000000009,\n",
" 0.60000000000000009,\n",
" -1.0,\n",
" -0.60000000000000009,\n",
" 1.1000000000000001,\n",
" -1.0,\n",
" 1.1000000000000001,\n",
" -0.5,\n",
" 0.0,\n",
" 1.1000000000000001,\n",
" -2.1000000000000001,\n",
" 0.0,\n",
" 0.60000000000000009,\n",
" 3.3000000000000003,\n",
" 0.60000000000000009,\n",
" -2.1000000000000001,\n",
" -1.1000000000000001,\n",
" -3.2000000000000002,\n",
" -1.7000000000000002,\n",
" -0.60000000000000009,\n",
" -2.7000000000000002,\n",
" -2.2000000000000002,\n",
" -1.1000000000000001,\n",
" -0.60000000000000009,\n",
" -6.1000000000000005,\n",
" -5.6000000000000005,\n",
" -4.4000000000000004,\n",
" -6.1000000000000005,\n",
" -3.3000000000000003,\n",
" -5.6000000000000005,\n",
" -4.4000000000000004,\n",
" -6.1000000000000005,\n",
" -3.3000000000000003,\n",
" -4.4000000000000004,\n",
" -4.9000000000000004,\n",
" -4.2999999999999998,\n",
" -2.7000000000000002,\n",
" -3.2000000000000002,\n",
" -5.6000000000000005,\n",
" -5.6000000000000005,\n",
" -5.0,\n",
" -5.0,\n",
" -5.0,\n",
" -6.1000000000000005,\n",
" -5.0,\n",
" -7.2000000000000002,\n",
" -8.2000000000000011,\n",
" -6.1000000000000005,\n",
" -7.1000000000000005,\n",
" -9.9000000000000004,\n",
" -8.9000000000000004,\n",
" -9.3000000000000007,\n",
" -7.8000000000000007,\n",
" -7.8000000000000007,\n",
" -6.7000000000000002,\n",
" -8.2000000000000011,\n",
" -6.1000000000000005,\n",
" -3.9000000000000004,\n",
" -7.7000000000000002,\n",
" -9.4000000000000004,\n",
" -11.100000000000001,\n",
" -9.4000000000000004,\n",
" -9.4000000000000004,\n",
" -7.8000000000000007,\n",
" -12.200000000000001,\n",
" -14.4,\n",
" -12.800000000000001,\n",
" -12.200000000000001,\n",
" -18.199999999999999,\n",
" -17.199999999999999,\n",
" -13.800000000000001,\n",
" -15.0,\n",
" -15.600000000000001,\n",
" -12.200000000000001,\n",
" -11.100000000000001,\n",
" -13.300000000000001,\n",
" -12.100000000000001,\n",
" -10.600000000000001,\n",
" -13.200000000000001,\n",
" -13.300000000000001,\n",
" -10.0,\n",
" -12.200000000000001,\n",
" -15.5,\n",
" -18.300000000000001,\n",
" -19.400000000000002,\n",
" -20.0,\n",
" -18.900000000000002,\n",
" -17.199999999999999,\n",
" -16.699999999999999,\n",
" -21.0,\n",
" -17.800000000000001,\n",
" -16.100000000000001,\n",
" -16.600000000000001,\n",
" -22.800000000000001,\n",
" -22.200000000000003,\n",
" -19.400000000000002,\n",
" -16.100000000000001,\n",
" -16.699999999999999,\n",
" -19.400000000000002,\n",
" -20.0,\n",
" -20.0,\n",
" -16.699999999999999,\n",
" -16.699999999999999,\n",
" -15.600000000000001,\n",
" -13.800000000000001,\n",
" -16.600000000000001,\n",
" -15.0,\n",
" -14.4,\n",
" -15.0]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"low_14=[]\n",
"for x in list(df_early.xs('TMIN',level='Element').xs('min',level=1,axis=1).values):\n",
" low_14.extend(x)\n",
"low_14"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"date = list(df_early.index.levels[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2015"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">Data_Value</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th>Element</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">01-01</th>\n",
" <th>TMAX</th>\n",
" <td>-6.7</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TMIN</th>\n",
" <td>-13.3</td>\n",
" <td>-7.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">01-02</th>\n",
" <th>TMAX</th>\n",
" <td>-2.2</td>\n",
" <td>3.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TMIN</th>\n",
" <td>-12.2</td>\n",
" <td>-2.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>01-03</th>\n",
" <th>TMAX</th>\n",
" <td>0.0</td>\n",
" <td>3.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Data_Value \n",
" min max\n",
"Date Element \n",
"01-01 TMAX -6.7 1.1\n",
" TMIN -13.3 -7.1\n",
"01-02 TMAX -2.2 3.9\n",
" TMIN -12.2 -2.8\n",
"01-03 TMAX 0.0 3.9"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gb_15 = df_15.groupby(['Date','Element'])\n",
"df_late = gb_15.agg({'Data_Value' : [min, max]})\n",
"df_late.head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1.1000000000000001,\n",
" 3.9000000000000004,\n",
" 3.9000000000000004,\n",
" 4.4000000000000004,\n",
" 2.8000000000000003,\n",
" 3.3000000000000003,\n",
" -6.7000000000000002,\n",
" -8.2000000000000011,\n",
" -6.6000000000000005,\n",
" -7.1000000000000005,\n",
" 0.60000000000000009,\n",
" 0.60000000000000009,\n",
" 0.0,\n",
" -5.6000000000000005,\n",
" -0.5,\n",
" 0.60000000000000009,\n",
" 7.8000000000000007,\n",
" 8.3000000000000007,\n",
" 6.7000000000000002,\n",
" 3.9000000000000004,\n",
" 2.2000000000000002,\n",
" -0.5,\n",
" 1.1000000000000001,\n",
" 4.4000000000000004,\n",
" 3.3000000000000003,\n",
" 2.8000000000000003,\n",
" -0.5,\n",
" 0.60000000000000009,\n",
" 3.9000000000000004,\n",
" 2.8000000000000003,\n",
" 2.8000000000000003,\n",
" 3.9000000000000004,\n",
" -2.2000000000000002,\n",
" -2.1000000000000001,\n",
" 1.1000000000000001,\n",
" 1.1000000000000001,\n",
" 0.0,\n",
" 5.6000000000000005,\n",
" 8.3000000000000007,\n",
" 8.3000000000000007,\n",
" 0.0,\n",
" 3.3000000000000003,\n",
" 1.1000000000000001,\n",
" -3.2000000000000002,\n",
" -2.7000000000000002,\n",
" -3.9000000000000004,\n",
" -12.100000000000001,\n",
" -5.5,\n",
" -3.9000000000000004,\n",
" -5.6000000000000005,\n",
" -8.8000000000000007,\n",
" -1.0,\n",
" 0.0,\n",
" 2.2000000000000002,\n",
" -3.8000000000000003,\n",
" -3.8000000000000003,\n",
" -3.3000000000000003,\n",
" -5.0,\n",
" -3.9000000000000004,\n",
" -1.7000000000000002,\n",
" 1.7000000000000002,\n",
" 4.4000000000000004,\n",
" 3.3000000000000003,\n",
" 3.9000000000000004,\n",
" -3.9000000000000004,\n",
" 7.8000000000000007,\n",
" 8.3000000000000007,\n",
" 9.4000000000000004,\n",
" 10.0,\n",
" 13.300000000000001,\n",
" 12.800000000000001,\n",
" 15.0,\n",
" 12.800000000000001,\n",
" 13.9,\n",
" 20.600000000000001,\n",
" 19.400000000000002,\n",
" 11.700000000000001,\n",
" 10.600000000000001,\n",
" 11.700000000000001,\n",
" 12.800000000000001,\n",
" 11.700000000000001,\n",
" 11.100000000000001,\n",
" 7.2000000000000002,\n",
" 12.800000000000001,\n",
" 11.100000000000001,\n",
" 12.800000000000001,\n",
" 4.4000000000000004,\n",
" 8.9000000000000004,\n",
" 16.699999999999999,\n",
" 15.0,\n",
" 20.600000000000001,\n",
" 19.400000000000002,\n",
" 17.199999999999999,\n",
" 15.600000000000001,\n",
" 19.400000000000002,\n",
" 18.300000000000001,\n",
" 17.199999999999999,\n",
" 16.699999999999999,\n",
" 21.100000000000001,\n",
" 21.700000000000003,\n",
" 18.900000000000002,\n",
" 21.100000000000001,\n",
" 24.400000000000002,\n",
" 23.900000000000002,\n",
" 20.0,\n",
" 18.900000000000002,\n",
" 27.800000000000001,\n",
" 26.700000000000003,\n",
" 25.600000000000001,\n",
" 19.400000000000002,\n",
" 19.400000000000002,\n",
" 12.800000000000001,\n",
" 7.8000000000000007,\n",
" 16.699999999999999,\n",
" 16.100000000000001,\n",
" 16.699999999999999,\n",
" 16.100000000000001,\n",
" 20.0,\n",
" 21.100000000000001,\n",
" 20.0,\n",
" 25.600000000000001,\n",
" 26.700000000000003,\n",
" 28.900000000000002,\n",
" 26.700000000000003,\n",
" 25.600000000000001,\n",
" 24.400000000000002,\n",
" 30.600000000000001,\n",
" 33.300000000000004,\n",
" 31.100000000000001,\n",
" 29.400000000000002,\n",
" 30.600000000000001,\n",
" 30.0,\n",
" 17.800000000000001,\n",
" 18.900000000000002,\n",
" 26.700000000000003,\n",
" 27.200000000000003,\n",
" 30.0,\n",
" 31.700000000000003,\n",
" 28.900000000000002,\n",
" 17.199999999999999,\n",
" 20.0,\n",
" 21.700000000000003,\n",
" 26.700000000000003,\n",
" 28.300000000000001,\n",
" 30.600000000000001,\n",
" 31.700000000000003,\n",
" 30.0,\n",
" 26.700000000000003,\n",
" 30.0,\n",
" 30.0,\n",
" 28.900000000000002,\n",
" 20.0,\n",
" 23.900000000000002,\n",
" 26.100000000000001,\n",
" 28.900000000000002,\n",
" 28.900000000000002,\n",
" 28.300000000000001,\n",
" 28.900000000000002,\n",
" 28.300000000000001,\n",
" 28.900000000000002,\n",
" 33.899999999999999,\n",
" 32.200000000000003,\n",
" 28.900000000000002,\n",
" 28.300000000000001,\n",
" 30.600000000000001,\n",
" 30.600000000000001,\n",
" 30.0,\n",
" 30.0,\n",
" 30.0,\n",
" 29.400000000000002,\n",
" 26.100000000000001,\n",
" 30.600000000000001,\n",
" 30.600000000000001,\n",
" 29.400000000000002,\n",
" 28.300000000000001,\n",
" 27.200000000000003,\n",
" 26.100000000000001,\n",
" 25.0,\n",
" 26.700000000000003,\n",
" 26.700000000000003,\n",
" 26.100000000000001,\n",
" 25.600000000000001,\n",
" 26.100000000000001,\n",
" 26.700000000000003,\n",
" 28.900000000000002,\n",
" 29.400000000000002,\n",
" 30.600000000000001,\n",
" 30.600000000000001,\n",
" 28.300000000000001,\n",
" 25.600000000000001,\n",
" 28.300000000000001,\n",
" 29.400000000000002,\n",
" 28.300000000000001,\n",
" 28.300000000000001,\n",
" 28.300000000000001,\n",
" 27.200000000000003,\n",
" 27.200000000000003,\n",
" 32.200000000000003,\n",
" 33.300000000000004,\n",
" 33.300000000000004,\n",
" 32.200000000000003,\n",
" 30.0,\n",
" 28.900000000000002,\n",
" 31.100000000000001,\n",
" 32.800000000000004,\n",
" 33.300000000000004,\n",
" 32.800000000000004,\n",
" 33.899999999999999,\n",
" 35.0,\n",
" 36.100000000000001,\n",
" 33.300000000000004,\n",
" 32.800000000000004,\n",
" 31.100000000000001,\n",
" 35.600000000000001,\n",
" 32.800000000000004,\n",
" 32.200000000000003,\n",
" 32.200000000000003,\n",
" 27.800000000000001,\n",
" 28.900000000000002,\n",
" 27.800000000000001,\n",
" 29.400000000000002,\n",
" 28.900000000000002,\n",
" 28.900000000000002,\n",
" 29.400000000000002,\n",
" 30.600000000000001,\n",
" 31.700000000000003,\n",
" 31.100000000000001,\n",
" 32.800000000000004,\n",
" 32.800000000000004,\n",
" 31.700000000000003,\n",
" 32.800000000000004,\n",
" 30.600000000000001,\n",
" 27.200000000000003,\n",
" 28.300000000000001,\n",
" 29.400000000000002,\n",
" 28.300000000000001,\n",
" 24.400000000000002,\n",
" 21.100000000000001,\n",
" 23.300000000000001,\n",
" 26.100000000000001,\n",
" 27.200000000000003,\n",
" 30.600000000000001,\n",
" 31.100000000000001,\n",
" 33.300000000000004,\n",
" 33.899999999999999,\n",
" 33.899999999999999,\n",
" 33.300000000000004,\n",
" 31.700000000000003,\n",
" 33.300000000000004,\n",
" 35.0,\n",
" 33.899999999999999,\n",
" 32.800000000000004,\n",
" 28.300000000000001,\n",
" 27.800000000000001,\n",
" 26.100000000000001,\n",
" 22.200000000000003,\n",
" 27.800000000000001,\n",
" 30.0,\n",
" 31.100000000000001,\n",
" 31.700000000000003,\n",
" 30.0,\n",
" 28.300000000000001,\n",
" 27.800000000000001,\n",
" 25.0,\n",
" 27.800000000000001,\n",
" 28.300000000000001,\n",
" 29.400000000000002,\n",
" 29.400000000000002,\n",
" 27.800000000000001,\n",
" 27.800000000000001,\n",
" 29.400000000000002,\n",
" 28.300000000000001,\n",
" 25.600000000000001,\n",
" 21.700000000000003,\n",
" 18.900000000000002,\n",
" 17.199999999999999,\n",
" 18.300000000000001,\n",
" 25.0,\n",
" 22.800000000000001,\n",
" 26.100000000000001,\n",
" 26.100000000000001,\n",
" 26.100000000000001,\n",
" 20.600000000000001,\n",
" 26.100000000000001,\n",
" 27.200000000000003,\n",
" 25.600000000000001,\n",
" 18.900000000000002,\n",
" 20.600000000000001,\n",
" 18.900000000000002,\n",
" 14.4,\n",
" 13.300000000000001,\n",
" 21.700000000000003,\n",
" 27.200000000000003,\n",
" 25.600000000000001,\n",
" 25.0,\n",
" 21.700000000000003,\n",
" 22.800000000000001,\n",
" 21.100000000000001,\n",
" 20.600000000000001,\n",
" 20.600000000000001,\n",
" 20.600000000000001,\n",
" 20.600000000000001,\n",
" 20.600000000000001,\n",
" 12.800000000000001,\n",
" 20.600000000000001,\n",
" 22.800000000000001,\n",
" 26.100000000000001,\n",
" 26.100000000000001,\n",
" 25.600000000000001,\n",
" 23.900000000000002,\n",
" 22.200000000000003,\n",
" 15.600000000000001,\n",
" 15.0,\n",
" 15.0,\n",
" 15.600000000000001,\n",
" 15.0,\n",
" 13.9,\n",
" 12.200000000000001,\n",
" 20.0,\n",
" 19.400000000000002,\n",
" 18.900000000000002,\n",
" 20.0,\n",
" 17.800000000000001,\n",
" 15.0,\n",
" 11.100000000000001,\n",
" 2.2000000000000002,\n",
" 2.8000000000000003,\n",
" 8.3000000000000007,\n",
" 15.600000000000001,\n",
" 17.800000000000001,\n",
" 17.199999999999999,\n",
" 12.800000000000001,\n",
" 12.200000000000001,\n",
" 10.0,\n",
" 11.100000000000001,\n",
" 10.0,\n",
" 9.4000000000000004,\n",
" 9.4000000000000004,\n",
" 9.4000000000000004,\n",
" 10.0,\n",
" 10.0,\n",
" 10.0,\n",
" 12.200000000000001,\n",
" 15.0,\n",
" 15.600000000000001,\n",
" 20.0,\n",
" 19.400000000000002,\n",
" 18.300000000000001,\n",
" 16.100000000000001,\n",
" 15.0,\n",
" 12.200000000000001,\n",
" 3.3000000000000003,\n",
" 1.1000000000000001,\n",
" 8.3000000000000007,\n",
" 12.200000000000001,\n",
" 13.300000000000001,\n",
" 18.300000000000001,\n",
" 17.199999999999999,\n",
" 11.100000000000001,\n",
" 11.700000000000001,\n",
" 8.3000000000000007,\n",
" 6.1000000000000005,\n",
" 10.0,\n",
" 6.7000000000000002,\n",
" 1.7000000000000002]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"high_15=[]\n",
"for x in list(df_late.xs('TMAX',level='Element').xs('max',level=1,axis=1).values):\n",
" high_15.extend(x)\n",
"high_15"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[-13.300000000000001,\n",
" -12.200000000000001,\n",
" -6.7000000000000002,\n",
" -8.8000000000000007,\n",
" -15.5,\n",
" -18.199999999999999,\n",
" -18.199999999999999,\n",
" -21.100000000000001,\n",
" -20.600000000000001,\n",
" -20.600000000000001,\n",
" -20.0,\n",
" -11.700000000000001,\n",
" -21.600000000000001,\n",
" -24.400000000000002,\n",
" -20.0,\n",
" -16.699999999999999,\n",
" -11.700000000000001,\n",
" -10.0,\n",
" -1.7000000000000002,\n",
" -3.3000000000000003,\n",
" -6.1000000000000005,\n",
" -6.7000000000000002,\n",
" -10.0,\n",
" -6.1000000000000005,\n",
" -8.8000000000000007,\n",
" -15.0,\n",
" -16.100000000000001,\n",
" -17.199999999999999,\n",
" -16.699999999999999,\n",
" -14.300000000000001,\n",
" -15.600000000000001,\n",
" -12.200000000000001,\n",
" -19.300000000000001,\n",
" -23.800000000000001,\n",
" -21.100000000000001,\n",
" -27.700000000000003,\n",
" -25.0,\n",
" -12.200000000000001,\n",
" -5.6000000000000005,\n",
" -11.600000000000001,\n",
" -17.100000000000001,\n",
" -15.0,\n",
" -21.100000000000001,\n",
" -26.600000000000001,\n",
" -23.900000000000002,\n",
" -26.0,\n",
" -29.400000000000002,\n",
" -27.200000000000003,\n",
" -21.700000000000003,\n",
" -26.0,\n",
" -34.300000000000004,\n",
" -32.200000000000003,\n",
" -16.0,\n",
" -26.700000000000003,\n",
" -27.200000000000003,\n",
" -21.700000000000003,\n",
" -21.600000000000001,\n",
" -28.800000000000001,\n",
" -27.200000000000003,\n",
" -24.400000000000002,\n",
" -14.4,\n",
" -12.200000000000001,\n",
" -12.200000000000001,\n",
" -22.100000000000001,\n",
" -25.5,\n",
" -22.200000000000003,\n",
" -10.600000000000001,\n",
" -8.9000000000000004,\n",
" -6.7000000000000002,\n",
" -3.8000000000000003,\n",
" -6.7000000000000002,\n",
" -5.0,\n",
" -3.3000000000000003,\n",
" -2.2000000000000002,\n",
" -6.7000000000000002,\n",
" -5.5,\n",
" -7.8000000000000007,\n",
" -6.7000000000000002,\n",
" -5.0,\n",
" -2.2000000000000002,\n",
" -7.2000000000000002,\n",
" -9.4000000000000004,\n",
" -11.100000000000001,\n",
" -11.100000000000001,\n",
" -2.8000000000000003,\n",
" -11.700000000000001,\n",
" -12.200000000000001,\n",
" -11.100000000000001,\n",
" -12.200000000000001,\n",
" -6.7000000000000002,\n",
" -4.2999999999999998,\n",
" -3.3000000000000003,\n",
" -0.5,\n",
" -5.6000000000000005,\n",
" -5.6000000000000005,\n",
" -2.2000000000000002,\n",
" -1.7000000000000002,\n",
" 1.1000000000000001,\n",
" 1.7000000000000002,\n",
" 1.7000000000000002,\n",
" -1.6000000000000001,\n",
" -2.1000000000000001,\n",
" -0.60000000000000009,\n",
" 0.60000000000000009,\n",
" -1.0,\n",
" 0.0,\n",
" 4.4000000000000004,\n",
" 4.4000000000000004,\n",
" 4.4000000000000004,\n",
" 3.3000000000000003,\n",
" 2.2000000000000002,\n",
" 0.0,\n",
" -4.2999999999999998,\n",
" -7.1000000000000005,\n",
" -5.0,\n",
" -2.2000000000000002,\n",
" -2.1000000000000001,\n",
" -3.2000000000000002,\n",
" -1.1000000000000001,\n",
" 2.2000000000000002,\n",
" 4.4000000000000004,\n",
" 4.4000000000000004,\n",
" 4.4000000000000004,\n",
" 7.2000000000000002,\n",
" 8.3000000000000007,\n",
" 8.3000000000000007,\n",
" 8.3000000000000007,\n",
" 8.9000000000000004,\n",
" 10.600000000000001,\n",
" 13.9,\n",
" 13.300000000000001,\n",
" 8.3000000000000007,\n",
" 2.8000000000000003,\n",
" 0.60000000000000009,\n",
" 2.2000000000000002,\n",
" 8.3000000000000007,\n",
" 13.300000000000001,\n",
" 12.800000000000001,\n",
" 2.8000000000000003,\n",
" 0.0,\n",
" 3.3000000000000003,\n",
" 1.1000000000000001,\n",
" 0.0,\n",
" 4.4000000000000004,\n",
" 10.0,\n",
" 8.9000000000000004,\n",
" 14.4,\n",
" 8.3000000000000007,\n",
" 11.700000000000001,\n",
" 10.0,\n",
" 7.8000000000000007,\n",
" 5.0,\n",
" 3.3000000000000003,\n",
" 4.4000000000000004,\n",
" 5.6000000000000005,\n",
" 8.3000000000000007,\n",
" 10.0,\n",
" 5.6000000000000005,\n",
" 8.3000000000000007,\n",
" 12.800000000000001,\n",
" 13.9,\n",
" 13.9,\n",
" 13.9,\n",
" 12.200000000000001,\n",
" 13.300000000000001,\n",
" 17.199999999999999,\n",
" 16.699999999999999,\n",
" 13.9,\n",
" 13.9,\n",
" 13.300000000000001,\n",
" 10.600000000000001,\n",
" 10.600000000000001,\n",
" 13.300000000000001,\n",
" 11.700000000000001,\n",
" 10.0,\n",
" 11.100000000000001,\n",
" 11.700000000000001,\n",
" 11.100000000000001,\n",
" 10.600000000000001,\n",
" 11.700000000000001,\n",
" 12.200000000000001,\n",
" 11.700000000000001,\n",
" 9.4000000000000004,\n",
" 7.2000000000000002,\n",
" 7.8000000000000007,\n",
" 10.600000000000001,\n",
" 10.0,\n",
" 12.200000000000001,\n",
" 11.100000000000001,\n",
" 12.200000000000001,\n",
" 10.0,\n",
" 10.600000000000001,\n",
" 14.4,\n",
" 10.0,\n",
" 14.4,\n",
" 11.100000000000001,\n",
" 8.3000000000000007,\n",
" 10.0,\n",
" 17.199999999999999,\n",
" 15.600000000000001,\n",
" 12.200000000000001,\n",
" 11.700000000000001,\n",
" 10.600000000000001,\n",
" 11.100000000000001,\n",
" 12.800000000000001,\n",
" 13.300000000000001,\n",
" 16.699999999999999,\n",
" 12.800000000000001,\n",
" 13.9,\n",
" 12.800000000000001,\n",
" 13.300000000000001,\n",
" 13.9,\n",
" 13.300000000000001,\n",
" 12.200000000000001,\n",
" 8.9000000000000004,\n",
" 8.3000000000000007,\n",
" 8.3000000000000007,\n",
" 11.100000000000001,\n",
" 10.0,\n",
" 10.0,\n",
" 13.300000000000001,\n",
" 16.699999999999999,\n",
" 15.0,\n",
" 12.200000000000001,\n",
" 11.100000000000001,\n",
" 12.800000000000001,\n",
" 15.0,\n",
" 14.4,\n",
" 16.100000000000001,\n",
" 16.699999999999999,\n",
" 17.199999999999999,\n",
" 13.9,\n",
" 9.4000000000000004,\n",
" 10.0,\n",
" 10.0,\n",
" 8.9000000000000004,\n",
" 10.600000000000001,\n",
" 10.600000000000001,\n",
" 7.2000000000000002,\n",
" 5.6000000000000005,\n",
" 7.8000000000000007,\n",
" 7.2000000000000002,\n",
" 14.4,\n",
" 13.9,\n",
" 15.600000000000001,\n",
" 17.199999999999999,\n",
" 16.100000000000001,\n",
" 16.699999999999999,\n",
" 15.0,\n",
" 15.600000000000001,\n",
" 17.800000000000001,\n",
" 11.100000000000001,\n",
" 7.2000000000000002,\n",
" 8.3000000000000007,\n",
" 4.4000000000000004,\n",
" 3.3000000000000003,\n",
" 1.7000000000000002,\n",
" 5.6000000000000005,\n",
" 6.7000000000000002,\n",
" 7.8000000000000007,\n",
" 10.600000000000001,\n",
" 9.4000000000000004,\n",
" 3.9000000000000004,\n",
" 5.6000000000000005,\n",
" 1.7000000000000002,\n",
" 5.6000000000000005,\n",
" 5.6000000000000005,\n",
" 7.8000000000000007,\n",
" 11.100000000000001,\n",
" 10.600000000000001,\n",
" 11.700000000000001,\n",
" 11.100000000000001,\n",
" 6.1000000000000005,\n",
" 4.4000000000000004,\n",
" 5.0,\n",
" 4.4000000000000004,\n",
" 6.1000000000000005,\n",
" 9.4000000000000004,\n",
" 9.4000000000000004,\n",
" 6.7000000000000002,\n",
" 5.0,\n",
" 2.8000000000000003,\n",
" 0.0,\n",
" 2.8000000000000003,\n",
" 2.8000000000000003,\n",
" 7.2000000000000002,\n",
" -1.0,\n",
" -1.6000000000000001,\n",
" -1.0,\n",
" -5.5,\n",
" -5.5,\n",
" -6.1000000000000005,\n",
" -3.9000000000000004,\n",
" -2.8000000000000003,\n",
" 1.1000000000000001,\n",
" 1.7000000000000002,\n",
" 2.2000000000000002,\n",
" 2.8000000000000003,\n",
" -1.0,\n",
" -1.1000000000000001,\n",
" 2.2000000000000002,\n",
" 0.60000000000000009,\n",
" -6.1000000000000005,\n",
" 0.60000000000000009,\n",
" 0.60000000000000009,\n",
" -1.0,\n",
" 1.1000000000000001,\n",
" 3.3000000000000003,\n",
" 3.9000000000000004,\n",
" 7.8000000000000007,\n",
" -0.5,\n",
" -5.5,\n",
" -6.1000000000000005,\n",
" -4.4000000000000004,\n",
" -3.3000000000000003,\n",
" -1.7000000000000002,\n",
" 0.0,\n",
" -2.8000000000000003,\n",
" -2.2000000000000002,\n",
" -3.2000000000000002,\n",
" -2.2000000000000002,\n",
" 2.8000000000000003,\n",
" 2.2000000000000002,\n",
" -1.7000000000000002,\n",
" -3.3000000000000003,\n",
" -11.600000000000001,\n",
" -11.100000000000001,\n",
" -11.0,\n",
" -9.4000000000000004,\n",
" -8.9000000000000004,\n",
" 0.60000000000000009,\n",
" -3.9000000000000004,\n",
" -9.3000000000000007,\n",
" -8.9000000000000004,\n",
" -2.8000000000000003,\n",
" -6.1000000000000005,\n",
" -7.8000000000000007,\n",
" -4.2999999999999998,\n",
" -5.0,\n",
" -5.6000000000000005,\n",
" -6.7000000000000002,\n",
" -6.7000000000000002,\n",
" -3.3000000000000003,\n",
" -4.4000000000000004,\n",
" 0.0,\n",
" 2.8000000000000003,\n",
" 6.7000000000000002,\n",
" 6.1000000000000005,\n",
" 3.9000000000000004,\n",
" 0.60000000000000009,\n",
" -1.1000000000000001,\n",
" -5.0,\n",
" -6.7000000000000002,\n",
" -9.4000000000000004,\n",
" -8.3000000000000007,\n",
" 0.60000000000000009,\n",
" 0.0,\n",
" 0.0,\n",
" -3.2000000000000002,\n",
" -3.9000000000000004,\n",
" -0.60000000000000009,\n",
" -3.9000000000000004,\n",
" -3.9000000000000004,\n",
" -2.2000000000000002,\n",
" -5.6000000000000005]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"low_15=[]\n",
"for x in list(df_late.xs('TMIN',level='Element').xs('min',level=1,axis=1).values):\n",
" low_15.extend(x)\n",
"low_15"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import datetime as dt\n",
"import seaborn as sns\n",
"import matplotlib.dates as mdates"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_color_codes()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sns.set_style('whitegrid')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"date_1=[dt.datetime.strptime(d,'%m-%d').date() for d in date]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[datetime.date(1900, 1, 1),\n",
" datetime.date(1900, 1, 2),\n",
" datetime.date(1900, 1, 3),\n",
" datetime.date(1900, 1, 4),\n",
" datetime.date(1900, 1, 5),\n",
" datetime.date(1900, 1, 6),\n",
" datetime.date(1900, 1, 7),\n",
" datetime.date(1900, 1, 8),\n",
" datetime.date(1900, 1, 9),\n",
" datetime.date(1900, 1, 10),\n",
" datetime.date(1900, 1, 11),\n",
" datetime.date(1900, 1, 12),\n",
" datetime.date(1900, 1, 13),\n",
" datetime.date(1900, 1, 14),\n",
" datetime.date(1900, 1, 15),\n",
" datetime.date(1900, 1, 16),\n",
" datetime.date(1900, 1, 17),\n",
" datetime.date(1900, 1, 18),\n",
" datetime.date(1900, 1, 19),\n",
" datetime.date(1900, 1, 20),\n",
" datetime.date(1900, 1, 21),\n",
" datetime.date(1900, 1, 22),\n",
" datetime.date(1900, 1, 23),\n",
" datetime.date(1900, 1, 24),\n",
" datetime.date(1900, 1, 25),\n",
" datetime.date(1900, 1, 26),\n",
" datetime.date(1900, 1, 27),\n",
" datetime.date(1900, 1, 28),\n",
" datetime.date(1900, 1, 29),\n",
" datetime.date(1900, 1, 30),\n",
" datetime.date(1900, 1, 31),\n",
" datetime.date(1900, 2, 1),\n",
" datetime.date(1900, 2, 2),\n",
" datetime.date(1900, 2, 3),\n",
" datetime.date(1900, 2, 4),\n",
" datetime.date(1900, 2, 5),\n",
" datetime.date(1900, 2, 6),\n",
" datetime.date(1900, 2, 7),\n",
" datetime.date(1900, 2, 8),\n",
" datetime.date(1900, 2, 9),\n",
" datetime.date(1900, 2, 10),\n",
" datetime.date(1900, 2, 11),\n",
" datetime.date(1900, 2, 12),\n",
" datetime.date(1900, 2, 13),\n",
" datetime.date(1900, 2, 14),\n",
" datetime.date(1900, 2, 15),\n",
" datetime.date(1900, 2, 16),\n",
" datetime.date(1900, 2, 17),\n",
" datetime.date(1900, 2, 18),\n",
" datetime.date(1900, 2, 19),\n",
" datetime.date(1900, 2, 20),\n",
" datetime.date(1900, 2, 21),\n",
" datetime.date(1900, 2, 22),\n",
" datetime.date(1900, 2, 23),\n",
" datetime.date(1900, 2, 24),\n",
" datetime.date(1900, 2, 25),\n",
" datetime.date(1900, 2, 26),\n",
" datetime.date(1900, 2, 27),\n",
" datetime.date(1900, 2, 28),\n",
" datetime.date(1900, 3, 1),\n",
" datetime.date(1900, 3, 2),\n",
" datetime.date(1900, 3, 3),\n",
" datetime.date(1900, 3, 4),\n",
" datetime.date(1900, 3, 5),\n",
" datetime.date(1900, 3, 6),\n",
" datetime.date(1900, 3, 7),\n",
" datetime.date(1900, 3, 8),\n",
" datetime.date(1900, 3, 9),\n",
" datetime.date(1900, 3, 10),\n",
" datetime.date(1900, 3, 11),\n",
" datetime.date(1900, 3, 12),\n",
" datetime.date(1900, 3, 13),\n",
" datetime.date(1900, 3, 14),\n",
" datetime.date(1900, 3, 15),\n",
" datetime.date(1900, 3, 16),\n",
" datetime.date(1900, 3, 17),\n",
" datetime.date(1900, 3, 18),\n",
" datetime.date(1900, 3, 19),\n",
" datetime.date(1900, 3, 20),\n",
" datetime.date(1900, 3, 21),\n",
" datetime.date(1900, 3, 22),\n",
" datetime.date(1900, 3, 23),\n",
" datetime.date(1900, 3, 24),\n",
" datetime.date(1900, 3, 25),\n",
" datetime.date(1900, 3, 26),\n",
" datetime.date(1900, 3, 27),\n",
" datetime.date(1900, 3, 28),\n",
" datetime.date(1900, 3, 29),\n",
" datetime.date(1900, 3, 30),\n",
" datetime.date(1900, 3, 31),\n",
" datetime.date(1900, 4, 1),\n",
" datetime.date(1900, 4, 2),\n",
" datetime.date(1900, 4, 3),\n",
" datetime.date(1900, 4, 4),\n",
" datetime.date(1900, 4, 5),\n",
" datetime.date(1900, 4, 6),\n",
" datetime.date(1900, 4, 7),\n",
" datetime.date(1900, 4, 8),\n",
" datetime.date(1900, 4, 9),\n",
" datetime.date(1900, 4, 10),\n",
" datetime.date(1900, 4, 11),\n",
" datetime.date(1900, 4, 12),\n",
" datetime.date(1900, 4, 13),\n",
" datetime.date(1900, 4, 14),\n",
" datetime.date(1900, 4, 15),\n",
" datetime.date(1900, 4, 16),\n",
" datetime.date(1900, 4, 17),\n",
" datetime.date(1900, 4, 18),\n",
" datetime.date(1900, 4, 19),\n",
" datetime.date(1900, 4, 20),\n",
" datetime.date(1900, 4, 21),\n",
" datetime.date(1900, 4, 22),\n",
" datetime.date(1900, 4, 23),\n",
" datetime.date(1900, 4, 24),\n",
" datetime.date(1900, 4, 25),\n",
" datetime.date(1900, 4, 26),\n",
" datetime.date(1900, 4, 27),\n",
" datetime.date(1900, 4, 28),\n",
" datetime.date(1900, 4, 29),\n",
" datetime.date(1900, 4, 30),\n",
" datetime.date(1900, 5, 1),\n",
" datetime.date(1900, 5, 2),\n",
" datetime.date(1900, 5, 3),\n",
" datetime.date(1900, 5, 4),\n",
" datetime.date(1900, 5, 5),\n",
" datetime.date(1900, 5, 6),\n",
" datetime.date(1900, 5, 7),\n",
" datetime.date(1900, 5, 8),\n",
" datetime.date(1900, 5, 9),\n",
" datetime.date(1900, 5, 10),\n",
" datetime.date(1900, 5, 11),\n",
" datetime.date(1900, 5, 12),\n",
" datetime.date(1900, 5, 13),\n",
" datetime.date(1900, 5, 14),\n",
" datetime.date(1900, 5, 15),\n",
" datetime.date(1900, 5, 16),\n",
" datetime.date(1900, 5, 17),\n",
" datetime.date(1900, 5, 18),\n",
" datetime.date(1900, 5, 19),\n",
" datetime.date(1900, 5, 20),\n",
" datetime.date(1900, 5, 21),\n",
" datetime.date(1900, 5, 22),\n",
" datetime.date(1900, 5, 23),\n",
" datetime.date(1900, 5, 24),\n",
" datetime.date(1900, 5, 25),\n",
" datetime.date(1900, 5, 26),\n",
" datetime.date(1900, 5, 27),\n",
" datetime.date(1900, 5, 28),\n",
" datetime.date(1900, 5, 29),\n",
" datetime.date(1900, 5, 30),\n",
" datetime.date(1900, 5, 31),\n",
" datetime.date(1900, 6, 1),\n",
" datetime.date(1900, 6, 2),\n",
" datetime.date(1900, 6, 3),\n",
" datetime.date(1900, 6, 4),\n",
" datetime.date(1900, 6, 5),\n",
" datetime.date(1900, 6, 6),\n",
" datetime.date(1900, 6, 7),\n",
" datetime.date(1900, 6, 8),\n",
" datetime.date(1900, 6, 9),\n",
" datetime.date(1900, 6, 10),\n",
" datetime.date(1900, 6, 11),\n",
" datetime.date(1900, 6, 12),\n",
" datetime.date(1900, 6, 13),\n",
" datetime.date(1900, 6, 14),\n",
" datetime.date(1900, 6, 15),\n",
" datetime.date(1900, 6, 16),\n",
" datetime.date(1900, 6, 17),\n",
" datetime.date(1900, 6, 18),\n",
" datetime.date(1900, 6, 19),\n",
" datetime.date(1900, 6, 20),\n",
" datetime.date(1900, 6, 21),\n",
" datetime.date(1900, 6, 22),\n",
" datetime.date(1900, 6, 23),\n",
" datetime.date(1900, 6, 24),\n",
" datetime.date(1900, 6, 25),\n",
" datetime.date(1900, 6, 26),\n",
" datetime.date(1900, 6, 27),\n",
" datetime.date(1900, 6, 28),\n",
" datetime.date(1900, 6, 29),\n",
" datetime.date(1900, 6, 30),\n",
" datetime.date(1900, 7, 1),\n",
" datetime.date(1900, 7, 2),\n",
" datetime.date(1900, 7, 3),\n",
" datetime.date(1900, 7, 4),\n",
" datetime.date(1900, 7, 5),\n",
" datetime.date(1900, 7, 6),\n",
" datetime.date(1900, 7, 7),\n",
" datetime.date(1900, 7, 8),\n",
" datetime.date(1900, 7, 9),\n",
" datetime.date(1900, 7, 10),\n",
" datetime.date(1900, 7, 11),\n",
" datetime.date(1900, 7, 12),\n",
" datetime.date(1900, 7, 13),\n",
" datetime.date(1900, 7, 14),\n",
" datetime.date(1900, 7, 15),\n",
" datetime.date(1900, 7, 16),\n",
" datetime.date(1900, 7, 17),\n",
" datetime.date(1900, 7, 18),\n",
" datetime.date(1900, 7, 19),\n",
" datetime.date(1900, 7, 20),\n",
" datetime.date(1900, 7, 21),\n",
" datetime.date(1900, 7, 22),\n",
" datetime.date(1900, 7, 23),\n",
" datetime.date(1900, 7, 24),\n",
" datetime.date(1900, 7, 25),\n",
" datetime.date(1900, 7, 26),\n",
" datetime.date(1900, 7, 27),\n",
" datetime.date(1900, 7, 28),\n",
" datetime.date(1900, 7, 29),\n",
" datetime.date(1900, 7, 30),\n",
" datetime.date(1900, 7, 31),\n",
" datetime.date(1900, 8, 1),\n",
" datetime.date(1900, 8, 2),\n",
" datetime.date(1900, 8, 3),\n",
" datetime.date(1900, 8, 4),\n",
" datetime.date(1900, 8, 5),\n",
" datetime.date(1900, 8, 6),\n",
" datetime.date(1900, 8, 7),\n",
" datetime.date(1900, 8, 8),\n",
" datetime.date(1900, 8, 9),\n",
" datetime.date(1900, 8, 10),\n",
" datetime.date(1900, 8, 11),\n",
" datetime.date(1900, 8, 12),\n",
" datetime.date(1900, 8, 13),\n",
" datetime.date(1900, 8, 14),\n",
" datetime.date(1900, 8, 15),\n",
" datetime.date(1900, 8, 16),\n",
" datetime.date(1900, 8, 17),\n",
" datetime.date(1900, 8, 18),\n",
" datetime.date(1900, 8, 19),\n",
" datetime.date(1900, 8, 20),\n",
" datetime.date(1900, 8, 21),\n",
" datetime.date(1900, 8, 22),\n",
" datetime.date(1900, 8, 23),\n",
" datetime.date(1900, 8, 24),\n",
" datetime.date(1900, 8, 25),\n",
" datetime.date(1900, 8, 26),\n",
" datetime.date(1900, 8, 27),\n",
" datetime.date(1900, 8, 28),\n",
" datetime.date(1900, 8, 29),\n",
" datetime.date(1900, 8, 30),\n",
" datetime.date(1900, 8, 31),\n",
" datetime.date(1900, 9, 1),\n",
" datetime.date(1900, 9, 2),\n",
" datetime.date(1900, 9, 3),\n",
" datetime.date(1900, 9, 4),\n",
" datetime.date(1900, 9, 5),\n",
" datetime.date(1900, 9, 6),\n",
" datetime.date(1900, 9, 7),\n",
" datetime.date(1900, 9, 8),\n",
" datetime.date(1900, 9, 9),\n",
" datetime.date(1900, 9, 10),\n",
" datetime.date(1900, 9, 11),\n",
" datetime.date(1900, 9, 12),\n",
" datetime.date(1900, 9, 13),\n",
" datetime.date(1900, 9, 14),\n",
" datetime.date(1900, 9, 15),\n",
" datetime.date(1900, 9, 16),\n",
" datetime.date(1900, 9, 17),\n",
" datetime.date(1900, 9, 18),\n",
" datetime.date(1900, 9, 19),\n",
" datetime.date(1900, 9, 20),\n",
" datetime.date(1900, 9, 21),\n",
" datetime.date(1900, 9, 22),\n",
" datetime.date(1900, 9, 23),\n",
" datetime.date(1900, 9, 24),\n",
" datetime.date(1900, 9, 25),\n",
" datetime.date(1900, 9, 26),\n",
" datetime.date(1900, 9, 27),\n",
" datetime.date(1900, 9, 28),\n",
" datetime.date(1900, 9, 29),\n",
" datetime.date(1900, 9, 30),\n",
" datetime.date(1900, 10, 1),\n",
" datetime.date(1900, 10, 2),\n",
" datetime.date(1900, 10, 3),\n",
" datetime.date(1900, 10, 4),\n",
" datetime.date(1900, 10, 5),\n",
" datetime.date(1900, 10, 6),\n",
" datetime.date(1900, 10, 7),\n",
" datetime.date(1900, 10, 8),\n",
" datetime.date(1900, 10, 9),\n",
" datetime.date(1900, 10, 10),\n",
" datetime.date(1900, 10, 11),\n",
" datetime.date(1900, 10, 12),\n",
" datetime.date(1900, 10, 13),\n",
" datetime.date(1900, 10, 14),\n",
" datetime.date(1900, 10, 15),\n",
" datetime.date(1900, 10, 16),\n",
" datetime.date(1900, 10, 17),\n",
" datetime.date(1900, 10, 18),\n",
" datetime.date(1900, 10, 19),\n",
" datetime.date(1900, 10, 20),\n",
" datetime.date(1900, 10, 21),\n",
" datetime.date(1900, 10, 22),\n",
" datetime.date(1900, 10, 23),\n",
" datetime.date(1900, 10, 24),\n",
" datetime.date(1900, 10, 25),\n",
" datetime.date(1900, 10, 26),\n",
" datetime.date(1900, 10, 27),\n",
" datetime.date(1900, 10, 28),\n",
" datetime.date(1900, 10, 29),\n",
" datetime.date(1900, 10, 30),\n",
" datetime.date(1900, 10, 31),\n",
" datetime.date(1900, 11, 1),\n",
" datetime.date(1900, 11, 2),\n",
" datetime.date(1900, 11, 3),\n",
" datetime.date(1900, 11, 4),\n",
" datetime.date(1900, 11, 5),\n",
" datetime.date(1900, 11, 6),\n",
" datetime.date(1900, 11, 7),\n",
" datetime.date(1900, 11, 8),\n",
" datetime.date(1900, 11, 9),\n",
" datetime.date(1900, 11, 10),\n",
" datetime.date(1900, 11, 11),\n",
" datetime.date(1900, 11, 12),\n",
" datetime.date(1900, 11, 13),\n",
" datetime.date(1900, 11, 14),\n",
" datetime.date(1900, 11, 15),\n",
" datetime.date(1900, 11, 16),\n",
" datetime.date(1900, 11, 17),\n",
" datetime.date(1900, 11, 18),\n",
" datetime.date(1900, 11, 19),\n",
" datetime.date(1900, 11, 20),\n",
" datetime.date(1900, 11, 21),\n",
" datetime.date(1900, 11, 22),\n",
" datetime.date(1900, 11, 23),\n",
" datetime.date(1900, 11, 24),\n",
" datetime.date(1900, 11, 25),\n",
" datetime.date(1900, 11, 26),\n",
" datetime.date(1900, 11, 27),\n",
" datetime.date(1900, 11, 28),\n",
" datetime.date(1900, 11, 29),\n",
" datetime.date(1900, 11, 30),\n",
" datetime.date(1900, 12, 1),\n",
" datetime.date(1900, 12, 2),\n",
" datetime.date(1900, 12, 3),\n",
" datetime.date(1900, 12, 4),\n",
" datetime.date(1900, 12, 5),\n",
" datetime.date(1900, 12, 6),\n",
" datetime.date(1900, 12, 7),\n",
" datetime.date(1900, 12, 8),\n",
" datetime.date(1900, 12, 9),\n",
" datetime.date(1900, 12, 10),\n",
" datetime.date(1900, 12, 11),\n",
" datetime.date(1900, 12, 12),\n",
" datetime.date(1900, 12, 13),\n",
" datetime.date(1900, 12, 14),\n",
" datetime.date(1900, 12, 15),\n",
" datetime.date(1900, 12, 16),\n",
" datetime.date(1900, 12, 17),\n",
" datetime.date(1900, 12, 18),\n",
" datetime.date(1900, 12, 19),\n",
" datetime.date(1900, 12, 20),\n",
" datetime.date(1900, 12, 21),\n",
" datetime.date(1900, 12, 22),\n",
" datetime.date(1900, 12, 23),\n",
" datetime.date(1900, 12, 24),\n",
" datetime.date(1900, 12, 25),\n",
" datetime.date(1900, 12, 26),\n",
" datetime.date(1900, 12, 27),\n",
" datetime.date(1900, 12, 28),\n",
" datetime.date(1900, 12, 29),\n",
" datetime.date(1900, 12, 30),\n",
" datetime.date(1900, 12, 31)]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"date_1"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support.' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" this.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option)\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width, fig.canvas.height);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>')\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img src=\"data:image/png;base64,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\" width=\"800\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b'))\n",
"plt.gca().xaxis.set_major_locator(mdates.MonthLocator())\n",
"plt.plot(date_1,low_14,'b-',alpha=0.7,label='2005-2014 Record Low')\n",
"plt.plot(date_1,high_14,'r-',alpha=0.7,label='2005-2014 Record High')\n",
"plt.gca().fill_between(date_1, \n",
" low_14, high_14, \n",
" facecolor='yellow', \n",
" alpha=0.25)\n",
"#plt.xticks(range(12), ['Jan','f','f','f','f','f','f','f','f','f','f','f'])\n",
"#plt.xticks(np.arange(12), ('Jan','f','f','f','f','f','f','f','f','f','f','f'))\n",
"plt.plot(date_1,low_15,'bo',label='2015 Low',markersize=3)\n",
"plt.plot(date_1,high_15,'ro',label='2015 High',markersize=3)\n",
"plt.xlabel('Month')\n",
"plt.ylabel('Temprature (Celsius)')\n",
"plt.title('2005 - 2014 Record vs. 2015 Record Temprature \\n(Ann Arbor, Michigan, United States)')\n",
"plt.legend()\n",
"plt.gcf().autofmt_xdate()"
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment