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Created March 17, 2014 23:41
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gistfile1.txt
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting and IO Practice.ipynb oak_weather_2013.csv\r\n",
"Untitled0.ipynb\r\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!head oak_weather_2013.csv"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"PST,Max TemperatureF,Mean TemperatureF,Min TemperatureF,Max Dew PointF,MeanDew PointF,Min DewpointF,Max Humidity, Mean Humidity, Min Humidity, Max Sea Level PressureIn, Mean Sea Level PressureIn, Min Sea Level PressureIn, Max VisibilityMiles, Mean VisibilityMiles, Min VisibilityMiles, Max Wind SpeedMPH, Mean Wind SpeedMPH, Max Gust SpeedMPH,PrecipitationIn, CloudCover, Events, WindDirDegrees\r\n",
"2013-1-1,56,47,38,38,35,33,86,66,46,30.29,30.26,30.23,10,10,10,23,9,29,0.00,3,,59\r\n",
"2013-1-2,53,45,36,41,36,30,85,70,54,30.26,30.21,30.16,10,10,10,9,3,12,0.00,0,,158\r\n",
"2013-1-3,55,45,34,45,39,34,92,74,55,30.29,30.24,30.19,10,10,10,9,3,25,0.00,2,,336\r\n",
"2013-1-4,54,46,37,42,38,34,92,76,59,30.31,30.25,30.20,10,10,9,7,2,8,0.00,2,,210\r\n",
"2013-1-5,56,47,38,51,45,37,100,86,72,30.28,30.16,30.09,10,8,2,14,3,26,0.15,7,Rain,119\r\n",
"2013-1-6,53,47,40,48,44,39,100,86,71,30.21,30.16,30.08,10,10,8,18,5,26,0.02,5,Rain,18\r\n",
"2013-1-7,57,46,35,50,43,36,100,86,72,30.29,30.22,30.18,10,8,0,8,1,24,0.00,4,Fog,137\r\n",
"2013-1-8,59,50,41,50,46,41,100,84,67,30.26,30.20,30.15,10,8,1,9,3,12,0.00,1,Fog,286\r\n",
"2013-1-9,54,48,42,54,46,38,100,81,61,30.16,30.09,30.02,10,6,1,25,8,31,T,6,Rain,225\r\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!wc -l oak_weather_2013.csv"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 366 oak_weather_2013.csv\r\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.read_csv('oak_weather_2013.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PST</th>\n",
" <th>Max TemperatureF</th>\n",
" <th>Mean TemperatureF</th>\n",
" <th>Min TemperatureF</th>\n",
" <th>Max Dew PointF</th>\n",
" <th>MeanDew PointF</th>\n",
" <th>Min DewpointF</th>\n",
" <th>Max Humidity</th>\n",
" <th> Mean Humidity</th>\n",
" <th> Min Humidity</th>\n",
" <th> Max Sea Level PressureIn</th>\n",
" <th> Mean Sea Level PressureIn</th>\n",
" <th> Min Sea Level PressureIn</th>\n",
" <th> Max VisibilityMiles</th>\n",
" <th> Mean VisibilityMiles</th>\n",
" <th> Min VisibilityMiles</th>\n",
" <th> Max Wind SpeedMPH</th>\n",
" <th> Mean Wind SpeedMPH</th>\n",
" <th> Max Gust SpeedMPH</th>\n",
" <th>PrecipitationIn</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 2013-1-1</td>\n",
" <td> 56</td>\n",
" <td> 47</td>\n",
" <td> 38</td>\n",
" <td> 38</td>\n",
" <td> 35</td>\n",
" <td> 33</td>\n",
" <td> 86</td>\n",
" <td> 66</td>\n",
" <td> 46</td>\n",
" <td> 30.29</td>\n",
" <td> 30.26</td>\n",
" <td> 30.23</td>\n",
" <td> 10</td>\n",
" <td> 10</td>\n",
" <td> 10</td>\n",
" <td> 23</td>\n",
" <td> 9</td>\n",
" <td> 29</td>\n",
" <td> 0.00</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2013-1-2</td>\n",
" <td> 53</td>\n",
" <td> 45</td>\n",
" <td> 36</td>\n",
" <td> 41</td>\n",
" <td> 36</td>\n",
" <td> 30</td>\n",
" <td> 85</td>\n",
" <td> 70</td>\n",
" <td> 54</td>\n",
" <td> 30.26</td>\n",
" <td> 30.21</td>\n",
" <td> 30.16</td>\n",
" <td> 10</td>\n",
" <td> 10</td>\n",
" <td> 10</td>\n",
" <td> 9</td>\n",
" <td> 3</td>\n",
" <td> 12</td>\n",
" <td> 0.00</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 2013-1-3</td>\n",
" <td> 55</td>\n",
" <td> 45</td>\n",
" <td> 34</td>\n",
" <td> 45</td>\n",
" <td> 39</td>\n",
" <td> 34</td>\n",
" <td> 92</td>\n",
" <td> 74</td>\n",
" <td> 55</td>\n",
" <td> 30.29</td>\n",
" <td> 30.24</td>\n",
" <td> 30.19</td>\n",
" <td> 10</td>\n",
" <td> 10</td>\n",
" <td> 10</td>\n",
" <td> 9</td>\n",
" <td> 3</td>\n",
" <td> 25</td>\n",
" <td> 0.00</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 2013-1-4</td>\n",
" <td> 54</td>\n",
" <td> 46</td>\n",
" <td> 37</td>\n",
" <td> 42</td>\n",
" <td> 38</td>\n",
" <td> 34</td>\n",
" <td> 92</td>\n",
" <td> 76</td>\n",
" <td> 59</td>\n",
" <td> 30.31</td>\n",
" <td> 30.25</td>\n",
" <td> 30.20</td>\n",
" <td> 10</td>\n",
" <td> 10</td>\n",
" <td> 9</td>\n",
" <td> 7</td>\n",
" <td> 2</td>\n",
" <td> 8</td>\n",
" <td> 0.00</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 2013-1-5</td>\n",
" <td> 56</td>\n",
" <td> 47</td>\n",
" <td> 38</td>\n",
" <td> 51</td>\n",
" <td> 45</td>\n",
" <td> 37</td>\n",
" <td> 100</td>\n",
" <td> 86</td>\n",
" <td> 72</td>\n",
" <td> 30.28</td>\n",
" <td> 30.16</td>\n",
" <td> 30.09</td>\n",
" <td> 10</td>\n",
" <td> 8</td>\n",
" <td> 2</td>\n",
" <td> 14</td>\n",
" <td> 3</td>\n",
" <td> 26</td>\n",
" <td> 0.15</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 23 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
" PST Max TemperatureF Mean TemperatureF Min TemperatureF \\\n",
"0 2013-1-1 56 47 38 \n",
"1 2013-1-2 53 45 36 \n",
"2 2013-1-3 55 45 34 \n",
"3 2013-1-4 54 46 37 \n",
"4 2013-1-5 56 47 38 \n",
"\n",
" Max Dew PointF MeanDew PointF Min DewpointF Max Humidity \\\n",
"0 38 35 33 86 \n",
"1 41 36 30 85 \n",
"2 45 39 34 92 \n",
"3 42 38 34 92 \n",
"4 51 45 37 100 \n",
"\n",
" Mean Humidity Min Humidity Max Sea Level PressureIn \\\n",
"0 66 46 30.29 \n",
"1 70 54 30.26 \n",
"2 74 55 30.29 \n",
"3 76 59 30.31 \n",
"4 86 72 30.28 \n",
"\n",
" Mean Sea Level PressureIn Min Sea Level PressureIn \\\n",
"0 30.26 30.23 \n",
"1 30.21 30.16 \n",
"2 30.24 30.19 \n",
"3 30.25 30.20 \n",
"4 30.16 30.09 \n",
"\n",
" Max VisibilityMiles Mean VisibilityMiles Min VisibilityMiles \\\n",
"0 10 10 10 \n",
"1 10 10 10 \n",
"2 10 10 10 \n",
"3 10 10 9 \n",
"4 10 8 2 \n",
"\n",
" Max Wind SpeedMPH Mean Wind SpeedMPH Max Gust SpeedMPH \\\n",
"0 23 9 29 \n",
"1 9 3 12 \n",
"2 9 3 25 \n",
"3 7 2 8 \n",
"4 14 3 26 \n",
"\n",
" PrecipitationIn \n",
"0 0.00 ... \n",
"1 0.00 ... \n",
"2 0.00 ... \n",
"3 0.00 ... \n",
"4 0.15 ... \n",
"\n",
"[5 rows x 23 columns]"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Max TemperatureF</th>\n",
" <th>Mean TemperatureF</th>\n",
" <th>Min TemperatureF</th>\n",
" <th>Max Dew PointF</th>\n",
" <th>MeanDew PointF</th>\n",
" <th>Min DewpointF</th>\n",
" <th>Max Humidity</th>\n",
" <th> Mean Humidity</th>\n",
" <th> Min Humidity</th>\n",
" <th> Max Sea Level PressureIn</th>\n",
" <th> Mean Sea Level PressureIn</th>\n",
" <th> Min Sea Level PressureIn</th>\n",
" <th> Max VisibilityMiles</th>\n",
" <th> Mean VisibilityMiles</th>\n",
" <th> Min VisibilityMiles</th>\n",
" <th> Max Wind SpeedMPH</th>\n",
" <th> Mean Wind SpeedMPH</th>\n",
" <th> Max Gust SpeedMPH</th>\n",
" <th> CloudCover</th>\n",
" <th> WindDirDegrees</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 360.000000</td>\n",
" <td> 365.000000</td>\n",
" <td> 365.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 67.652055</td>\n",
" <td> 58.632877</td>\n",
" <td> 49.093151</td>\n",
" <td> 54.550685</td>\n",
" <td> 49.682192</td>\n",
" <td> 44.495890</td>\n",
" <td> 95.123288</td>\n",
" <td> 74.235616</td>\n",
" <td> 52.832877</td>\n",
" <td> 30.100356</td>\n",
" <td> 30.046110</td>\n",
" <td> 29.990849</td>\n",
" <td> 9.997260</td>\n",
" <td> 9.600000</td>\n",
" <td> 8.057534</td>\n",
" <td> 17.909589</td>\n",
" <td> 7.482192</td>\n",
" <td> 24.105556</td>\n",
" <td> 3.263014</td>\n",
" <td> 265.967123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 7.846538</td>\n",
" <td> 7.330278</td>\n",
" <td> 8.110642</td>\n",
" <td> 7.874411</td>\n",
" <td> 9.289770</td>\n",
" <td> 11.200869</td>\n",
" <td> 9.743334</td>\n",
" <td> 11.942598</td>\n",
" <td> 17.013473</td>\n",
" <td> 0.141860</td>\n",
" <td> 0.140116</td>\n",
" <td> 0.142525</td>\n",
" <td> 0.052342</td>\n",
" <td> 0.951575</td>\n",
" <td> 2.713313</td>\n",
" <td> 5.645677</td>\n",
" <td> 3.322060</td>\n",
" <td> 7.785063</td>\n",
" <td> 2.268241</td>\n",
" <td> 57.551305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 48.000000</td>\n",
" <td> 39.000000</td>\n",
" <td> 28.000000</td>\n",
" <td> 23.000000</td>\n",
" <td> 14.000000</td>\n",
" <td> 7.000000</td>\n",
" <td> 24.000000</td>\n",
" <td> 17.000000</td>\n",
" <td> 9.000000</td>\n",
" <td> 29.770000</td>\n",
" <td> 29.680000</td>\n",
" <td> 29.590000</td>\n",
" <td> 9.000000</td>\n",
" <td> 5.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 7.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 7.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 12.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 62.000000</td>\n",
" <td> 54.000000</td>\n",
" <td> 43.000000</td>\n",
" <td> 51.000000</td>\n",
" <td> 44.000000</td>\n",
" <td> 38.000000</td>\n",
" <td> 93.000000</td>\n",
" <td> 68.000000</td>\n",
" <td> 42.000000</td>\n",
" <td> 30.000000</td>\n",
" <td> 29.950000</td>\n",
" <td> 29.890000</td>\n",
" <td> 10.000000</td>\n",
" <td> 10.000000</td>\n",
" <td> 7.000000</td>\n",
" <td> 14.000000</td>\n",
" <td> 5.000000</td>\n",
" <td> 20.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 260.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 68.000000</td>\n",
" <td> 60.000000</td>\n",
" <td> 51.000000</td>\n",
" <td> 56.000000</td>\n",
" <td> 51.000000</td>\n",
" <td> 46.000000</td>\n",
" <td> 100.000000</td>\n",
" <td> 77.000000</td>\n",
" <td> 55.000000</td>\n",
" <td> 30.090000</td>\n",
" <td> 30.040000</td>\n",
" <td> 29.980000</td>\n",
" <td> 10.000000</td>\n",
" <td> 10.000000</td>\n",
" <td> 10.000000</td>\n",
" <td> 18.000000</td>\n",
" <td> 7.000000</td>\n",
" <td> 24.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 278.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 72.000000</td>\n",
" <td> 64.000000</td>\n",
" <td> 55.000000</td>\n",
" <td> 60.000000</td>\n",
" <td> 57.000000</td>\n",
" <td> 54.000000</td>\n",
" <td> 100.000000</td>\n",
" <td> 83.000000</td>\n",
" <td> 66.000000</td>\n",
" <td> 30.200000</td>\n",
" <td> 30.140000</td>\n",
" <td> 30.090000</td>\n",
" <td> 10.000000</td>\n",
" <td> 10.000000</td>\n",
" <td> 10.000000</td>\n",
" <td> 21.000000</td>\n",
" <td> 10.000000</td>\n",
" <td> 29.000000</td>\n",
" <td> 5.000000</td>\n",
" <td> 299.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 92.000000</td>\n",
" <td> 74.000000</td>\n",
" <td> 64.000000</td>\n",
" <td> 70.000000</td>\n",
" <td> 65.000000</td>\n",
" <td> 63.000000</td>\n",
" <td> 100.000000</td>\n",
" <td> 97.000000</td>\n",
" <td> 93.000000</td>\n",
" <td> 30.500000</td>\n",
" <td> 30.450000</td>\n",
" <td> 30.420000</td>\n",
" <td> 10.000000</td>\n",
" <td> 10.000000</td>\n",
" <td> 10.000000</td>\n",
" <td> 44.000000</td>\n",
" <td> 20.000000</td>\n",
" <td> 66.000000</td>\n",
" <td> 8.000000</td>\n",
" <td> 355.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows \u00d7 20 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
" Max TemperatureF Mean TemperatureF Min TemperatureF Max Dew PointF \\\n",
"count 365.000000 365.000000 365.000000 365.000000 \n",
"mean 67.652055 58.632877 49.093151 54.550685 \n",
"std 7.846538 7.330278 8.110642 7.874411 \n",
"min 48.000000 39.000000 28.000000 23.000000 \n",
"25% 62.000000 54.000000 43.000000 51.000000 \n",
"50% 68.000000 60.000000 51.000000 56.000000 \n",
"75% 72.000000 64.000000 55.000000 60.000000 \n",
"max 92.000000 74.000000 64.000000 70.000000 \n",
"\n",
" MeanDew PointF Min DewpointF Max Humidity Mean Humidity \\\n",
"count 365.000000 365.000000 365.000000 365.000000 \n",
"mean 49.682192 44.495890 95.123288 74.235616 \n",
"std 9.289770 11.200869 9.743334 11.942598 \n",
"min 14.000000 7.000000 24.000000 17.000000 \n",
"25% 44.000000 38.000000 93.000000 68.000000 \n",
"50% 51.000000 46.000000 100.000000 77.000000 \n",
"75% 57.000000 54.000000 100.000000 83.000000 \n",
"max 65.000000 63.000000 100.000000 97.000000 \n",
"\n",
" Min Humidity Max Sea Level PressureIn Mean Sea Level PressureIn \\\n",
"count 365.000000 365.000000 365.000000 \n",
"mean 52.832877 30.100356 30.046110 \n",
"std 17.013473 0.141860 0.140116 \n",
"min 9.000000 29.770000 29.680000 \n",
"25% 42.000000 30.000000 29.950000 \n",
"50% 55.000000 30.090000 30.040000 \n",
"75% 66.000000 30.200000 30.140000 \n",
"max 93.000000 30.500000 30.450000 \n",
"\n",
" Min Sea Level PressureIn Max VisibilityMiles Mean VisibilityMiles \\\n",
"count 365.000000 365.000000 365.000000 \n",
"mean 29.990849 9.997260 9.600000 \n",
"std 0.142525 0.052342 0.951575 \n",
"min 29.590000 9.000000 5.000000 \n",
"25% 29.890000 10.000000 10.000000 \n",
"50% 29.980000 10.000000 10.000000 \n",
"75% 30.090000 10.000000 10.000000 \n",
"max 30.420000 10.000000 10.000000 \n",
"\n",
" Min VisibilityMiles Max Wind SpeedMPH Mean Wind SpeedMPH \\\n",
"count 365.000000 365.000000 365.000000 \n",
"mean 8.057534 17.909589 7.482192 \n",
"std 2.713313 5.645677 3.322060 \n",
"min 0.000000 7.000000 1.000000 \n",
"25% 7.000000 14.000000 5.000000 \n",
"50% 10.000000 18.000000 7.000000 \n",
"75% 10.000000 21.000000 10.000000 \n",
"max 10.000000 44.000000 20.000000 \n",
"\n",
" Max Gust SpeedMPH CloudCover WindDirDegrees \n",
"count 360.000000 365.000000 365.000000 \n",
"mean 24.105556 3.263014 265.967123 \n",
"std 7.785063 2.268241 57.551305 \n",
"min 7.000000 0.000000 12.000000 \n",
"25% 20.000000 1.000000 260.000000 \n",
"50% 24.000000 3.000000 278.000000 \n",
"75% 29.000000 5.000000 299.000000 \n",
"max 66.000000 8.000000 355.000000 \n",
"\n",
"[8 rows x 20 columns]"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"Index([u'PST', u'Max TemperatureF', u'Mean TemperatureF', u'Min TemperatureF', u'Max Dew PointF', u'MeanDew PointF', u'Min DewpointF', u'Max Humidity', u' Mean Humidity', u' Min Humidity', u' Max Sea Level PressureIn', u' Mean Sea Level PressureIn', u' Min Sea Level PressureIn', u' Max VisibilityMiles', u' Mean VisibilityMiles', u' Min VisibilityMiles', u' Max Wind SpeedMPH', u' Mean Wind SpeedMPH', u' Max Gust SpeedMPH', u'PrecipitationIn', u' CloudCover', u' Events', u' WindDirDegrees'], dtype='object')"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['PST']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"0 2013-1-1\n",
"1 2013-1-2\n",
"2 2013-1-3\n",
"3 2013-1-4\n",
"4 2013-1-5\n",
"5 2013-1-6\n",
"6 2013-1-7\n",
"7 2013-1-8\n",
"8 2013-1-9\n",
"9 2013-1-10\n",
"10 2013-1-11\n",
"11 2013-1-12\n",
"12 2013-1-13\n",
"13 2013-1-14\n",
"14 2013-1-15\n",
"...\n",
"350 2013-12-17\n",
"351 2013-12-18\n",
"352 2013-12-19\n",
"353 2013-12-20\n",
"354 2013-12-21\n",
"355 2013-12-22\n",
"356 2013-12-23\n",
"357 2013-12-24\n",
"358 2013-12-25\n",
"359 2013-12-26\n",
"360 2013-12-27\n",
"361 2013-12-28\n",
"362 2013-12-29\n",
"363 2013-12-30\n",
"364 2013-12-31\n",
"Name: PST, Length: 365, dtype: object"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.read_csv('oak_weather_2013.csv', parse_dates=[0])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['PST']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"0 2013-01-01\n",
"1 2013-01-02\n",
"2 2013-01-03\n",
"3 2013-01-04\n",
"4 2013-01-05\n",
"5 2013-01-06\n",
"6 2013-01-07\n",
"7 2013-01-08\n",
"8 2013-01-09\n",
"9 2013-01-10\n",
"10 2013-01-11\n",
"11 2013-01-12\n",
"12 2013-01-13\n",
"13 2013-01-14\n",
"14 2013-01-15\n",
"...\n",
"350 2013-12-17\n",
"351 2013-12-18\n",
"352 2013-12-19\n",
"353 2013-12-20\n",
"354 2013-12-21\n",
"355 2013-12-22\n",
"356 2013-12-23\n",
"357 2013-12-24\n",
"358 2013-12-25\n",
"359 2013-12-26\n",
"360 2013-12-27\n",
"361 2013-12-28\n",
"362 2013-12-29\n",
"363 2013-12-30\n",
"364 2013-12-31\n",
"Name: PST, Length: 365, dtype: datetime64[ns]"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(df['Mean TemperatureF'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"[<matplotlib.lines.Line2D at 0x1068ed2d0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
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No7z3nnvUGm2OSLS43ZRD82pSJimJVko+/ri1MjptSeOECVQ18vWvk5j/5CeU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\nAsSIjAz1HykIQuwRTPzCxRqnT3tvYcm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446FKnkjQ0tIydFm7EC1S41QUBStWrMDs2bNx\n1113DV0fba+n2Tij7fVsb28fiix4AWJVVVXUvZ5m42ShBKLj9XzkkUdw/PhxNDc344UXXsBll12G\n5557LnSvZzhmdXfu3KmUlZUppaWlyiOPPBKOpwiKo0ePKpWVlUplZaUyZ86cobHZ7XZlwYIFyowZ\nM5Samhrl9OnTIz62m266SSkqKlKSkpKU4uJi5dlnn/U5rocfflgpLS1VysvLlV27dkVsnBs3blSW\nL1+uVFRUKOeff75yzTXXKK2trREd5/79+xWbzaZUVlYq8+bNU+bNm6e88sorUfd6Go1z586dUfd6\nHj58WKmqqlIqKyuViooK5Wc/+5miKL4/N9E0zmh7PbU0NDQMVb+E6vWUxUeCIAhxhGxnJwiCEEeI\nqAuCIMQRIuqCIAhxhIi6IAhCHCGiLgiCEEeIqAuCIMQRIuqCIAhxhIi6IAhCHPH/AaX9Dsp9J0r+\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1068bf0d0>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['Mean TemperatureF'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"<matplotlib.axes.AxesSubplot at 0x1068fa910>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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LKnaUF0kte0U9EFg86tStytSFpy4wREsLTS0WCdx+AUjcuIcbqacOxJapc/vF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\nAkEvQoi6QCAQ9CKEqAsEAkEv4v8D2fFdKKjVtRMAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1068fdd90>"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.index"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], dtype='int64')"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2 = df.set_index(df['PST'], drop=False)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2['Mean TemperatureF'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"<matplotlib.axes.AxesSubplot at 0x1068dbe50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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7MGwYebS8aZuah925M4377e0M25VrtG1besDZwkdK5A+IqCjguuuUj61rVxMs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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x106daa8d0>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mt = df2['Mean TemperatureF']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mt.plot(label='Temp', kind='line', legend=True, marker='o')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"<matplotlib.axes.AxesSubplot at 0x106934410>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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2AB1KS82IjhbTa2xk1999Z4bZ7H18586x9q68Uji+9nY9gJMdNLM96LPEA97G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\nDUGIC8NTDQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x106da0310>"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mt.hist(bins=30)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"<matplotlib.axes.AxesSubplot at 0x107242290>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x107204310>"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"Index([u'PST', u'Max TemperatureF', u'Mean TemperatureF', u'Min TemperatureF', u'Max Dew PointF', u'MeanDew PointF', u'Min DewpointF', u'Max Humidity', u' Mean Humidity', u' Min Humidity', u' Max Sea Level PressureIn', u' Mean Sea Level PressureIn', u' Min Sea Level PressureIn', u' Max VisibilityMiles', u' Mean VisibilityMiles', u' Min VisibilityMiles', u' Max Wind SpeedMPH', u' Mean Wind SpeedMPH', u' Max Gust SpeedMPH', u'PrecipitationIn', u' CloudCover', u' Events', u' WindDirDegrees'], dtype='object')"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2 = df2.rename(columns={' Mean Humidity': 'MeanHumidity'})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"Index([u'PST', u'Max TemperatureF', u'Mean TemperatureF', u'Min TemperatureF', u'Max Dew PointF', u'MeanDew PointF', u'Min DewpointF', u'Max Humidity', u'MeanHumidity', u' Min Humidity', u' Max Sea Level PressureIn', u' Mean Sea Level PressureIn', u' Min Sea Level PressureIn', u' Max VisibilityMiles', u' Mean VisibilityMiles', u' Min VisibilityMiles', u' Max Wind SpeedMPH', u' Mean Wind SpeedMPH', u' Max Gust SpeedMPH', u'PrecipitationIn', u' CloudCover', u' Events', u' WindDirDegrees'], dtype='object')"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def no_spaces(s):\n",
" return s.replace(' ', '')\n",
"no_spaces('a b c ')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
"'abc'"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2 = df2.rename(columns=no_spaces)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"Index([u'PST', u'MaxTemperatureF', u'MeanTemperatureF', u'MinTemperatureF', u'MaxDewPointF', u'MeanDewPointF', u'MinDewpointF', u'MaxHumidity', u'MeanHumidity', u'MinHumidity', u'MaxSeaLevelPressureIn', u'MeanSeaLevelPressureIn', u'MinSeaLevelPressureIn', u'MaxVisibilityMiles', u'MeanVisibilityMiles', u'MinVisibilityMiles', u'MaxWindSpeedMPH', u'MeanWindSpeedMPH', u'MaxGustSpeedMPH', u'PrecipitationIn', u'CloudCover', u'Events', u'WindDirDegrees'], dtype='object')"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.scatter(df2.MeanTemperatureF, df2.MeanHumidity, marker='s', color='m')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": [
"<matplotlib.collections.PathCollection at 0x108078a50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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/EO3jM3AXQzSAA9gYD+k4X8EDxYAqHOn4quCHhoZUV1enZcuWSZJOnDih+vp6zZ07V0uX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\nJHgAcBQJHgAcRYIHAEeR4AHAUf8DttQf+cQN9MIAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x107cbf910>"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.savefig('magenta.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"<matplotlib.figure.Figure at 0x108062910>"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ls"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting and IO Practice.ipynb magenta.pdf\r\n",
"Untitled0.ipynb oak_weather_2013.csv\r\n"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!open magenta.pdf"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig, ax = plt.subplots()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1083866d0>"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ax.scatter(df2.MeanTemperatureF, df2.MeanHumidity, marker='s', color='m')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 43,
"text": [
"<matplotlib.collections.PathCollection at 0x1083f1110>"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"png": 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"prompt_number": 44,
"text": [
"<matplotlib.figure.Figure at 0x1083866d0>"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig.savefig('magenta.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!open magenta.pdf"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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