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Luiz-N/gist:96477b1e327169d010c6

Created July 16, 2014 02:27
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weatherData
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gistfile1.json
{
"metadata": {
"name": "",
"signature": "sha256:1b7c6bc58dae0b00aeb80a4a33ab6337f35b23893bcf8b3e3f4e19f962772377"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from dateutil.parser import parse\n",
"from datetime import datetime, date, time\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 701
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# df = pd.read_csv(\"data/Data.csv\")\n",
"df.Date = pd.to_datetime(df.Date,utc='GMT')\n",
"#df = df.set_index('GMT')\n",
"print df.head()\n",
"df.tail()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Date Temp H_Pcnt C_Pcnt Temp_Feels Precip Radiation \\\n",
"0 2004-01-01 05:00:00 39.3 73 2 33.2 0 0 \n",
"1 2004-01-01 06:00:00 40.3 71 9 34.6 0 0 \n",
"2 2004-01-01 07:00:00 40.7 74 13 34.9 0 0 \n",
"3 2004-01-01 08:00:00 41.2 77 19 35.1 0 0 \n",
"4 2004-01-01 09:00:00 40.5 81 18 34.2 0 0 \n",
"\n",
" Wind \n",
"0 9 \n",
"1 8 \n",
"2 9 \n",
"3 10 \n",
"4 10 \n",
"\n",
"[5 rows x 8 columns]\n"
]
},
{
"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>Date</th>\n",
" <th>Temp</th>\n",
" <th>H_Pcnt</th>\n",
" <th>C_Pcnt</th>\n",
" <th>Temp_Feels</th>\n",
" <th>Precip</th>\n",
" <th>Radiation</th>\n",
" <th>Wind</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>87668</th>\n",
" <td>2014-01-01 01:00:00</td>\n",
" <td> 30.1</td>\n",
" <td> 61</td>\n",
" <td> 0</td>\n",
" <td> 22.9</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87669</th>\n",
" <td>2014-01-01 02:00:00</td>\n",
" <td> 29.3</td>\n",
" <td> 61</td>\n",
" <td> 0</td>\n",
" <td> 22.8</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87670</th>\n",
" <td>2014-01-01 03:00:00</td>\n",
" <td> 28.7</td>\n",
" <td> 61</td>\n",
" <td> 1</td>\n",
" <td> 23.6</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87671</th>\n",
" <td>2014-01-01 04:00:00</td>\n",
" <td> 28.3</td>\n",
" <td> 60</td>\n",
" <td> 1</td>\n",
" <td> 24.8</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87672</th>\n",
" <td>2014-01-01 05:00:00</td>\n",
" <td> 28.2</td>\n",
" <td> 59</td>\n",
" <td> 1</td>\n",
" <td> 25.3</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 8 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 712,
"text": [
" Date Temp H_Pcnt C_Pcnt Temp_Feels Precip \\\n",
"87668 2014-01-01 01:00:00 30.1 61 0 22.9 0 \n",
"87669 2014-01-01 02:00:00 29.3 61 0 22.8 0 \n",
"87670 2014-01-01 03:00:00 28.7 61 1 23.6 0 \n",
"87671 2014-01-01 04:00:00 28.3 60 1 24.8 0 \n",
"87672 2014-01-01 05:00:00 28.2 59 1 25.3 0 \n",
"\n",
" Radiation Wind \n",
"87668 0 7 \n",
"87669 0 6 \n",
"87670 0 5 \n",
"87671 0 3 \n",
"87672 0 3 \n",
"\n",
"[5 rows x 8 columns]"
]
}
],
"prompt_number": 712
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# create a column which is only the month, day and hour of day. \n",
"# This is used to find the average for each respective hour later\n",
"df['hour'] = df['Date'].apply(lambda t: \"%d-%d-%d\" % (t.month, t.day,t.hour))\n",
"#find average for each hour... This makes sense for all the columns except precip\n",
"g = df.groupby('hour').mean().reset_index()\n",
"# g = df.groupby('hour').agg({'Temp' : np.mean,'H_Pcnt' : np.mean,'C_Pcnt' : np.mean,'Temp_Feels' : np.mean,'Precip' : np.sum,'Radiation' : np.mean,'Wind' : np.mean}).reset_index()\n",
"g"
],
"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>hour</th>\n",
" <th>Temp</th>\n",
" <th>H_Pcnt</th>\n",
" <th>C_Pcnt</th>\n",
" <th>Temp_Feels</th>\n",
" <th>Precip</th>\n",
" <th>Radiation</th>\n",
" <th>Wind</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 1-1-0</td>\n",
" <td> 38.230000</td>\n",
" <td> 82.000000</td>\n",
" <td> 54.6</td>\n",
" <td> 33.180000</td>\n",
" <td> 0.003000</td>\n",
" <td> 0.0</td>\n",
" <td> 8.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 1-1-1</td>\n",
" <td> 38.260000</td>\n",
" <td> 82.600000</td>\n",
" <td> 51.8</td>\n",
" <td> 32.700000</td>\n",
" <td> 0.007000</td>\n",
" <td> 0.0</td>\n",
" <td> 8.700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 1-1-10</td>\n",
" <td> 38.930000</td>\n",
" <td> 86.200000</td>\n",
" <td> 65.6</td>\n",
" <td> 32.920000</td>\n",
" <td> 0.012000</td>\n",
" <td> 0.0</td>\n",
" <td> 9.400000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 1-1-11</td>\n",
" <td> 38.740000</td>\n",
" <td> 86.900000</td>\n",
" <td> 64.8</td>\n",
" <td> 32.600000</td>\n",
" <td> 0.009000</td>\n",
" <td> 0.0</td>\n",
" <td> 9.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 1-1-12</td>\n",
" <td> 38.840000</td>\n",
" <td> 87.900000</td>\n",
" <td> 53.6</td>\n",
" <td> 34.080000</td>\n",
" <td> 0.009000</td>\n",
" <td> 0.0</td>\n",
" <td> 7.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 1-1-13</td>\n",
" <td> 41.440000</td>\n",
" <td> 83.500000</td>\n",
" <td> 53.5</td>\n",
" <td> 36.550000</td>\n",
" <td> 0.011000</td>\n",
" <td> 4.5</td>\n",
" <td> 8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 1-1-14</td>\n",
" <td> 44.000000</td>\n",
" <td> 78.000000</td>\n",
" <td> 56.0</td>\n",
" <td> 39.080000</td>\n",
" <td> 0.005000</td>\n",
" <td> 78.4</td>\n",
" <td> 9.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 1-1-15</td>\n",
" <td> 46.070000</td>\n",
" <td> 72.300000</td>\n",
" <td> 55.9</td>\n",
" <td> 41.250000</td>\n",
" <td> 0.007000</td>\n",
" <td> 188.1</td>\n",
" <td> 10.400000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 1-1-16</td>\n",
" <td> 47.120000</td>\n",
" <td> 69.300000</td>\n",
" <td> 55.4</td>\n",
" <td> 42.470000</td>\n",
" <td> 0.007000</td>\n",
" <td> 266.0</td>\n",
" <td> 10.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 1-1-17</td>\n",
" <td> 47.660000</td>\n",
" <td> 67.500000</td>\n",
" <td> 55.5</td>\n",
" <td> 43.230000</td>\n",
" <td> 0.004000</td>\n",
" <td> 302.9</td>\n",
" <td> 10.700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 1-1-18</td>\n",
" <td> 49.160000</td>\n",
" <td> 66.900000</td>\n",
" <td> 55.5</td>\n",
" <td> 46.040000</td>\n",
" <td> 0.004000</td>\n",
" <td> 299.9</td>\n",
" <td> 8.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 1-1-19</td>\n",
" <td> 48.300000</td>\n",
" <td> 69.600000</td>\n",
" <td> 56.7</td>\n",
" <td> 45.450000</td>\n",
" <td> 0.001000</td>\n",
" <td> 255.8</td>\n",
" <td> 7.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 1-1-2</td>\n",
" <td> 38.290000</td>\n",
" <td> 82.900000</td>\n",
" <td> 52.9</td>\n",
" <td> 32.520000</td>\n",
" <td> 0.006000</td>\n",
" <td> 0.0</td>\n",
" <td> 9.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 1-1-20</td>\n",
" <td> 46.320000</td>\n",
" <td> 74.500000</td>\n",
" <td> 57.3</td>\n",
" <td> 43.460000</td>\n",
" <td> 0.001000</td>\n",
" <td> 177.2</td>\n",
" <td> 7.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 1-1-21</td>\n",
" <td> 42.680000</td>\n",
" <td> 79.600000</td>\n",
" <td> 57.9</td>\n",
" <td> 38.660000</td>\n",
" <td> 0.002000</td>\n",
" <td> 85.9</td>\n",
" <td> 8.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 1-1-22</td>\n",
" <td> 41.830000</td>\n",
" <td> 80.200000</td>\n",
" <td> 58.2</td>\n",
" <td> 36.940000</td>\n",
" <td> 0.003000</td>\n",
" <td> 0.0</td>\n",
" <td> 9.400000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 1-1-23</td>\n",
" <td> 41.070000</td>\n",
" <td> 80.200000</td>\n",
" <td> 58.3</td>\n",
" <td> 35.540000</td>\n",
" <td> 0.002000</td>\n",
" <td> 0.0</td>\n",
" <td> 10.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 1-1-3</td>\n",
" <td> 38.340000</td>\n",
" <td> 83.100000</td>\n",
" <td> 54.9</td>\n",
" <td> 32.670000</td>\n",
" <td> 0.007000</td>\n",
" <td> 0.0</td>\n",
" <td> 9.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 1-1-4</td>\n",
" <td> 38.280000</td>\n",
" <td> 83.300000</td>\n",
" <td> 54.3</td>\n",
" <td> 32.520000</td>\n",
" <td> 0.008000</td>\n",
" <td> 0.0</td>\n",
" <td> 9.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> 1-1-5</td>\n",
" <td> 38.272727</td>\n",
" <td> 82.454545</td>\n",
" <td> 49.0</td>\n",
" <td> 32.472727</td>\n",
" <td> 0.004545</td>\n",
" <td> 0.0</td>\n",
" <td> 9.181818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 1-1-6</td>\n",
" <td> 38.900000</td>\n",
" <td> 85.500000</td>\n",
" <td> 57.1</td>\n",
" <td> 33.570000</td>\n",
" <td> 0.005000</td>\n",
" <td> 0.0</td>\n",
" <td> 8.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> 1-1-7</td>\n",
" <td> 39.080000</td>\n",
" <td> 85.200000</td>\n",
" <td> 62.8</td>\n",
" <td> 33.470000</td>\n",
" <td> 0.009000</td>\n",
" <td> 0.0</td>\n",
" <td> 9.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 1-1-8</td>\n",
" <td> 39.310000</td>\n",
" <td> 85.100000</td>\n",
" <td> 65.4</td>\n",
" <td> 33.760000</td>\n",
" <td> 0.009000</td>\n",
" <td> 0.0</td>\n",
" <td> 9.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> 1-1-9</td>\n",
" <td> 39.190000</td>\n",
" <td> 85.600000</td>\n",
" <td> 66.4</td>\n",
" <td> 33.610000</td>\n",
" <td> 0.012000</td>\n",
" <td> 0.0</td>\n",
" <td> 9.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> 1-10-0</td>\n",
" <td> 36.190000</td>\n",
" <td> 73.000000</td>\n",
" <td> 57.3</td>\n",
" <td> 29.870000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.0</td>\n",
" <td> 8.900000</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8784 rows \u00d7 8 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 709,
"text": [
" hour Temp H_Pcnt C_Pcnt Temp_Feels Precip Radiation \\\n",
"0 1-1-0 38.230000 82.000000 54.6 33.180000 0.003000 0.0 \n",
"1 1-1-1 38.260000 82.600000 51.8 32.700000 0.007000 0.0 \n",
"2 1-1-10 38.930000 86.200000 65.6 32.920000 0.012000 0.0 \n",
"3 1-1-11 38.740000 86.900000 64.8 32.600000 0.009000 0.0 \n",
"4 1-1-12 38.840000 87.900000 53.6 34.080000 0.009000 0.0 \n",
"5 1-1-13 41.440000 83.500000 53.5 36.550000 0.011000 4.5 \n",
"6 1-1-14 44.000000 78.000000 56.0 39.080000 0.005000 78.4 \n",
"7 1-1-15 46.070000 72.300000 55.9 41.250000 0.007000 188.1 \n",
"8 1-1-16 47.120000 69.300000 55.4 42.470000 0.007000 266.0 \n",
"9 1-1-17 47.660000 67.500000 55.5 43.230000 0.004000 302.9 \n",
"10 1-1-18 49.160000 66.900000 55.5 46.040000 0.004000 299.9 \n",
"11 1-1-19 48.300000 69.600000 56.7 45.450000 0.001000 255.8 \n",
"12 1-1-2 38.290000 82.900000 52.9 32.520000 0.006000 0.0 \n",
"13 1-1-20 46.320000 74.500000 57.3 43.460000 0.001000 177.2 \n",
"14 1-1-21 42.680000 79.600000 57.9 38.660000 0.002000 85.9 \n",
"15 1-1-22 41.830000 80.200000 58.2 36.940000 0.003000 0.0 \n",
"16 1-1-23 41.070000 80.200000 58.3 35.540000 0.002000 0.0 \n",
"17 1-1-3 38.340000 83.100000 54.9 32.670000 0.007000 0.0 \n",
"18 1-1-4 38.280000 83.300000 54.3 32.520000 0.008000 0.0 \n",
"19 1-1-5 38.272727 82.454545 49.0 32.472727 0.004545 0.0 \n",
"20 1-1-6 38.900000 85.500000 57.1 33.570000 0.005000 0.0 \n",
"21 1-1-7 39.080000 85.200000 62.8 33.470000 0.009000 0.0 \n",
"22 1-1-8 39.310000 85.100000 65.4 33.760000 0.009000 0.0 \n",
"23 1-1-9 39.190000 85.600000 66.4 33.610000 0.012000 0.0 \n",
"24 1-10-0 36.190000 73.000000 57.3 29.870000 0.000000 0.0 \n",
" ... ... ... ... ... ... ... \n",
"\n",
" Wind \n",
"0 8.100000 \n",
"1 8.700000 \n",
"2 9.400000 \n",
"3 9.300000 \n",
"4 7.000000 \n",
"5 8.000000 \n",
"6 9.500000 \n",
"7 10.400000 \n",
"8 10.800000 \n",
"9 10.700000 \n",
"10 8.200000 \n",
"11 7.600000 \n",
"12 9.100000 \n",
"13 7.300000 \n",
"14 8.300000 \n",
"15 9.400000 \n",
"16 10.000000 \n",
"17 9.200000 \n",
"18 9.200000 \n",
"19 9.181818 \n",
"20 8.300000 \n",
"21 9.000000 \n",
"22 9.000000 \n",
"23 9.300000 \n",
"24 8.900000 \n",
" ... \n",
"\n",
"[8784 rows x 8 columns]"
]
}
],
"prompt_number": 709
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Do a left merge with the original dataset\n",
"new_df = pd.merge(left=df, right=g, on='hour', suffixes=('','_avg') )\n",
"new_df = new_df.set_index('Date')\n",
"new_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>Temp</th>\n",
" <th>H_Pcnt</th>\n",
" <th>C_Pcnt</th>\n",
" <th>Temp_Feels</th>\n",
" <th>Precip</th>\n",
" <th>Radiation</th>\n",
" <th>Wind</th>\n",
" <th>hour</th>\n",
" <th>Temp_avg</th>\n",
" <th>H_Pcnt_avg</th>\n",
" <th>C_Pcnt_avg</th>\n",
" <th>Temp_Feels_avg</th>\n",
" <th>Precip_avg</th>\n",
" <th>Radiation_avg</th>\n",
" <th>Wind_avg</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2004-01-01 05:00:00</th>\n",
" <td> 39.3</td>\n",
" <td> 73</td>\n",
" <td> 2</td>\n",
" <td> 33.2</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 9</td>\n",
" <td> 1-1-5</td>\n",
" <td> 38.272727</td>\n",
" <td> 82.454545</td>\n",
" <td> 49</td>\n",
" <td> 32.472727</td>\n",
" <td> 0.004545</td>\n",
" <td> 0</td>\n",
" <td> 9.181818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-01-01 05:00:00</th>\n",
" <td> 49.4</td>\n",
" <td> 94</td>\n",
" <td> 51</td>\n",
" <td> 46.0</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 8</td>\n",
" <td> 1-1-5</td>\n",
" <td> 38.272727</td>\n",
" <td> 82.454545</td>\n",
" <td> 49</td>\n",
" <td> 32.472727</td>\n",
" <td> 0.004545</td>\n",
" <td> 0</td>\n",
" <td> 9.181818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2006-01-01 05:00:00</th>\n",
" <td> 40.7</td>\n",
" <td> 85</td>\n",
" <td> 56</td>\n",
" <td> 35.1</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 9</td>\n",
" <td> 1-1-5</td>\n",
" <td> 38.272727</td>\n",
" <td> 82.454545</td>\n",
" <td> 49</td>\n",
" <td> 32.472727</td>\n",
" <td> 0.004545</td>\n",
" <td> 0</td>\n",
" <td> 9.181818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2007-01-01 05:00:00</th>\n",
" <td> 50.1</td>\n",
" <td> 96</td>\n",
" <td> 99</td>\n",
" <td> 44.4</td>\n",
" <td> 0.05</td>\n",
" <td> 0</td>\n",
" <td> 17</td>\n",
" <td> 1-1-5</td>\n",
" <td> 38.272727</td>\n",
" <td> 82.454545</td>\n",
" <td> 49</td>\n",
" <td> 32.472727</td>\n",
" <td> 0.004545</td>\n",
" <td> 0</td>\n",
" <td> 9.181818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2008-01-01 05:00:00</th>\n",
" <td> 39.7</td>\n",
" <td> 97</td>\n",
" <td> 5</td>\n",
" <td> 32.6</td>\n",
" <td> 0.00</td>\n",
" <td> 0</td>\n",
" <td> 12</td>\n",
" <td> 1-1-5</td>\n",
" <td> 38.272727</td>\n",
" <td> 82.454545</td>\n",
" <td> 49</td>\n",
" <td> 32.472727</td>\n",
" <td> 0.004545</td>\n",
" <td> 0</td>\n",
" <td> 9.181818</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 15 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 704,
"text": [
" Temp H_Pcnt C_Pcnt Temp_Feels Precip Radiation \\\n",
"Date \n",
"2004-01-01 05:00:00 39.3 73 2 33.2 0.00 0 \n",
"2005-01-01 05:00:00 49.4 94 51 46.0 0.00 0 \n",
"2006-01-01 05:00:00 40.7 85 56 35.1 0.00 0 \n",
"2007-01-01 05:00:00 50.1 96 99 44.4 0.05 0 \n",
"2008-01-01 05:00:00 39.7 97 5 32.6 0.00 0 \n",
"\n",
" Wind hour Temp_avg H_Pcnt_avg C_Pcnt_avg \\\n",
"Date \n",
"2004-01-01 05:00:00 9 1-1-5 38.272727 82.454545 49 \n",
"2005-01-01 05:00:00 8 1-1-5 38.272727 82.454545 49 \n",
"2006-01-01 05:00:00 9 1-1-5 38.272727 82.454545 49 \n",
"2007-01-01 05:00:00 17 1-1-5 38.272727 82.454545 49 \n",
"2008-01-01 05:00:00 12 1-1-5 38.272727 82.454545 49 \n",
"\n",
" Temp_Feels_avg Precip_avg Radiation_avg Wind_avg \n",
"Date \n",
"2004-01-01 05:00:00 32.472727 0.004545 0 9.181818 \n",
"2005-01-01 05:00:00 32.472727 0.004545 0 9.181818 \n",
"2006-01-01 05:00:00 32.472727 0.004545 0 9.181818 \n",
"2007-01-01 05:00:00 32.472727 0.004545 0 9.181818 \n",
"2008-01-01 05:00:00 32.472727 0.004545 0 9.181818 \n",
"\n",
"[5 rows x 15 columns]"
]
}
],
"prompt_number": 704
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# I don't need the dates before 2009 so I remove them here.\n",
"# I kept them till now in order to calculate the averages\n",
"test = []\n",
"\n",
"def drop_dates(indexes):\n",
" for index in indexes:\n",
" if index < datetime(2009,5,1):\n",
" test.append(index)\n",
" return test\n",
"trimmed = new_df.drop(drop_dates(new_df.index)).sort_index().drop('hour',axis=1)\n",
"#trimmed = trimmed.drop('date', 1)\n",
"print trimmed.head()\n",
"\n",
"trimmed.Precip.plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Temp H_Pcnt C_Pcnt Temp_Feels Precip Radiation \\\n",
"Date \n",
"2009-05-01 00:00:00 57.0 89 100 55.6 0 0 \n",
"2009-05-01 01:00:00 56.5 88 92 54.5 0 0 \n",
"2009-05-01 02:00:00 56.0 88 88 53.8 0 0 \n",
"2009-05-01 03:00:00 56.8 86 88 54.5 0 0 \n",
"2009-05-01 04:00:00 57.1 85 90 54.7 0 0 \n",
"\n",
" Wind Temp_avg H_Pcnt_avg C_Pcnt_avg Temp_Feels_avg \\\n",
"Date \n",
"2009-05-01 00:00:00 7 59.32 73.5 75.2 59.23 \n",
"2009-05-01 01:00:00 8 58.33 73.2 71.7 57.30 \n",
"2009-05-01 02:00:00 9 56.90 74.4 67.9 55.53 \n",
"2009-05-01 03:00:00 10 56.26 74.2 66.1 54.63 \n",
"2009-05-01 04:00:00 10 55.46 74.1 64.6 53.57 \n",
"\n",
" Precip_avg Radiation_avg Wind_avg \n",
"Date \n",
"2009-05-01 00:00:00 0 0 6.9 \n",
"2009-05-01 01:00:00 0 0 7.5 \n",
"2009-05-01 02:00:00 0 0 7.8 \n",
"2009-05-01 03:00:00 0 0 8.5 \n",
"2009-05-01 04:00:00 0 0 8.5 \n",
"\n",
"[5 rows x 14 columns]\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 705,
"text": [
"<matplotlib.axes.AxesSubplot at 0x158a0b790>"
]
},
{
"metadata": {},
"output_type": "display_data",
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dnZLB6HrmzgUuv9zZubz9TgmCneyQcjF7p125RIbH82ppVnFX0emRGXN3qL5X\n6GcIdTMu7fYXoN2Gem163r3U9iMXqVgYyW9qcnb9at7HjlmnbWkxPretDfjmG2P9eN1M7TpB0bKj\n1fPIkUS9tfpp/1vR0aFMdw4o9lbvlYyavdFMun6R8mEcURk/+5nzc7WYFerjvVAtefVV4LTT7OfL\n08PJcbfylaFH0GtMarnZtElZByEZrK711FONBxDJtJHZc3DnncDJJ9uXYTeM4+S8xx8HrruOL0fU\n3zzxBNCzp/L71FOVGVt56UV1tEPahnGC8jDbCeMYxRW1PVn+93/F8jQaROMGdrodyrgfZnFXv9tl\nRHAaCtTX6s367Uej0aRs7KTM2Ol+aFZL37bNfr5OMHu2rDAb9GTX2dfVxW+rk/aSs5eI/k3upKFI\nRqu9DFl2PtN5+BGz9+rle8klwDPPmB9/8UXj1ZemTgXKy+3nJ2rL1tbEfSKNtUGptADKteblAQsW\nGB+PRJTQhwhWLwq7bRwi/OUv9kfF1tcDH3yQuD/ZCobahqGXM2lSfFtVkO6/CL6GcfSIFkYR2U5q\nNDys4ort7eKygoab/ey1sj/+mC9jxQrz1ZeWL3emlwhmsV9ZFBcXOw5n2KG2lt9H/uhRsfxEvgpk\nVpSKi4uxcqW1PKN9RmXKbs1edP/8+fEhNXL2AhisbOgYu4VMm17fePL118BPfuJMD6Oa/cMPA+++\na33u4sXOaq487BREJ4VWOyiJR7LtIsnIscJObxyrhs7ycuCvfzWWx9Nd1PYjRthzvkb7veqlIuue\nJ5u/Vo877lC6DNvJ28swjturVAE+OXu1j7bf8Vx9LHTtWqUB1Qg1rmh2g42c/YwZYi+2qVOBKVOs\n09nB7VqHvleUjH72snWWKU/trWHGww8Dd98dv08t33Zj9kZpV68WO5f3AnNzZGkyvXGSadPgXaf2\n2OzZwCuvGMtwmrebz1jKT5egj69ZLQjuNl9/Hb+dzIRbMsM4tbXKWpk8/PqE9KI3jhk7d1qHgrTo\nHUF7e3yZU+e/MXIYq1ZZd5HVX5tVV8Bk4fVWkTmITi/ryy/Fv+SA+JGlbvcakllh1Df4OqnZb9xo\nbxoWL/HU2d9zT/y2aN90HsncbP20x3/6U2IabV9ps2NAcg20+kJz5ZXAxRc7l2ckU1ZaM9zoZ6+/\ntz/6kdLI65Q1awDtUgw//any3+jebd4MPPKIsS5m9lLLs17v73+/2JaeTsItboa5xo1TZv4Uzc9J\nP3u3wzhSRaFVAAAUs0lEQVQiFBQYyzHbNtJ58GA5awu4cT8DNajKizeiTCPu3Rtr9DKr2Uci9ufS\ndzKyV8vXX4s3OsrqeikDKz1Uu5g1NJrR3MwfnCRSJhobjXvtGGGn26Nb/dCNzhG9fr1s/XGrOWFk\njq1obQV27zY/TySMoywPGY/I86GVbfRMOY31+0WgnL2TWptXhjWK2a9YAbz3nvKb56DPPls8HxnX\n06sXf4UgPTJ6isicG8eoZqZ9Kd16qz05I0YA+fn2nZA2/7feitX09f3orUi2n72RPlYO2Wo/L43+\nZWW3wVxmzP6hh4BzzrHOk6eb0cC3++6L/RZx2r16ieUdZCxXqkpVgvp2NSIoNetUQR30Isr27cqw\n/2RrYvoapoz7ZiRDJB5vlbed+LZdZ29Vs3cSszdLx6vVA+INtHq0laEg9MbxQnagavZ2KS+PDed2\n+ta3YvhwpRagxqTtFox33uHLj0SsC7SbXH65vzF77Tq0VrVVu43gIjVUo3xU9L217NboY18WsZh0\nJGLdjdNMH62tli41TtPWltwXjJY1a4z3y56qgxezT+allmyFz09n7wYp7ew/+kg8jpoMXi3mYIdU\nHEFrhFEjvZktRSb14smx6jarP66P9cq6x3ZfWkb5fvKJcVoZnR6S7ekTicizlZOyKeLsZQy6JGfv\nAk88oaxPqaLG27xysJGId/PZa6/phhtibQJ+MX688lJVMSvgbtjnqadiv7V2+eKLxLTPPBPfe8YI\nM91VeVa9L/Rz+Iuij0lrewQ5xUnM3unzYhTG4dlC6+xF8jSL2Ys4U638/Pz4sS319cCQIfZkiOy3\no1+QSImYvbYxBVCc/+9+J28+GivsFjpZLFoEdO8OXH21fNlaeNf32mtAbi5wxRXu6mDEr35lvN+o\n8fm3v1Vq4r/9beIxq/CL0fwq2vNU9JPdOe3eql283KnDMIubJzvK1gijmv7WrWK6iT4XMhzn1q3K\n4DaVjRuBDRus8/BqZLHfpETN3gz9zdi+3XqgltHCyCKF3SpmDyhhhqef5suyi1VjmNkQcDtYfbaL\nfJYXFxfjtdeUgU92ef55pXukmu///I/yXx+ia2oylyGyRJyd0c+AdU3fCvXLZMSIYtP4txkdHfFf\nNnrcHKyUrGy7YRzRmD0vPv/88+bHjNAeMwsFq2k2bbLWTWS/36SVs7/7butPZHXUJE+OHtGa/fbt\nytQHMrEqULfcIjc/szybm+O3hw9PTDN+vNJVzi6lpfFD2c3mCeJ1J+XdI9GGVdHGQNGHWbsClN05\nlw4cAKZNM8/P7OUmGt7hYfXyN8pbm8ZuGMcoD3Wf6D0pLTU/ZoV+imP9+ddea3w8qE7djLRy9qLH\n9FiFgz7/HHjjjWhSMuywb1/s9/bt/NVunNSk9YjU7PX79P2O1Zh9SwtQU2Muy4xDh/iD6qzup37q\nC5W6utggLKvPeLs1edFrW7UqmrDPbC52I9lGX29WXzJG5VF7PZ9/bn7d+uvSTyNidN1ffBH/MpQR\nsxdh+3ZzW+obq62+EvS4HcbZulVOg7ooaevsRVBXjLKSc+QI8OtfJ5eXEWYOSst778XHIVXUgiuj\n26aTft1mNlu0COjXzzqdnl/8Qhm45ASjBluV730v9lvE2Wvj8smGcXioQ/PNdNI683nzEo+b3Q/1\nvzqLopnOBQXKlBC8NHZZty6mmwyZ+kqGkcw5cxKnOVDRr51gVyer5QmTrdnn5xuHld0icM7eTg1Z\nO3zZSeFSnZLI2/Xbb4sByBsi3d4u/lbnxaq1+Rt1TRTp5qe9JqP0RjV7/fW6uQatEVo9zexot+tl\nezt/KganDmz48GLTYx0dilx9uefV3Hn3VD1mtnasFrNYtdUzaGZH9Xm0W7N3q+zoQ4926ehQ7Km/\nBnW/3fEWRhjdJzUP2QTO2Yuu5QoA//hHcnmpN+Gkk6zT2l1Fx4qMDOtPbd4+PS++CGRmJu4/4QR7\nenXvDjzwQPw+rbNX/4sWRllxTb0NTj/dvgyrF/WjjyZO+MXTwUyedvCT0Xlaxo5VJgi8/XYx2YBS\nSXnhBeNj6mArO/Mr6fWzCjFZ9QTq0kVezF7ltdfE5BjpYyRPNIxz2mmJbUWlpcD554uXbaPGYys9\n7Ex3IkrgnP369c7PtVvzspc+KjVvQNxhiqQz6wpndyBSW1vs816L3jHor9frNWi188u7NVLS6ee7\n3tm//36Um37DhlgIRCQPXthKRcTZOn0RW7007YZxeDF7df/27XKfbxFZHR3GXwcbNijTPovaj7c+\ntZfdOwPn7I0cjV1Ee6j07p18XipOajBG5xg1Ui5erNQAeai1rR49zNPcdhvwhz/wddLKMjquvni0\nL6Df/Ab485/5+rlJ//7JnW9276y6ZPIe9rVrreVrj+tl2Zk8DzBf/Fw7GFGvhzrg6Lnn7OWllXXu\nuYnHeGGcyy4zXr3NyJaMKTF5pyTrSM18kfoFLerstSOxr71WWW/XDwLn7GUwf75Yv+sf/MCO1GLu\nUaMH1iydimjNvrk5cQ1WfUFWr5e3aPvcucbdGvV6d+1qLsPoIX7sMeDll4sN0wepe5pX/ewZQ9ya\nqiNGFNvWLZn1mIHYNS1ZkpwcI7RhGqPKCc/Zr1uXWJbN+tm7WesVkW0W87fr7LXrKr/5ptKJwY4e\nsgi0s1+3DliwQCzt7t3xxlcdFq8xS0YfZCPeegt4/XXrdI8/bj9/I7ZtEy94Zg2wWoxq9vqHV/1v\nNspVxcvCbIXdxnW9rZx2c/XDBm408DU0KP+TDePoj+kbKdXj2mv45S/trVImkq9T9M5euwi5dr+K\n/vq01+Xkq8opgXb2U6YAN98slla/dqzq7A8cMD/HrZj9j34EXHedcTptQZg9207+5jz5pNiXDGDs\nBOzU7PXd0WJr7EbFFPARu87eqr2D94LVHlu9OsqVI/pVaAenDf08tOEHq2u300D7/PPxMXuzc8zW\nkBVFlrPPOD7JjKrzz3/OT6/vTafVw2xAlxsEdm6cDz+0d3P0tYnaWuV/c7P5NMNOaj8iDsPuSkqi\ntLUZ97jp6EjO2ev15cXs1ev86COxbp1Hj8pxZqIN921tShsHYL/rpR6zNYn1jal6GIvPWyRmL9pW\nJVq2jPL89FPglFOA737X+ByRLr6AdW8c7apgjCn35OOPYy9PbVgDSOzaK9IALYLekWq3k3H8W7Yk\n7tu/33zcjOqLZOSdDIGs2Tc3A8OG2TOK/oFV58h54AHgX//V+Bx7Ri/mHvXiBr74ovH+9vbknP1/\n/Vf8tr5mr32Rqg9iU5MSf4xRbJqndtZMp4g6ovnzgRtvND4ma4zEtm3Kf+1IZx68fvaA2OA6FW2D\nKw+j+3zNNcC//7v5OXfeyZdpFaZR87zjjnhn/9JLSsPssGHKvr//Pf68QYPiY/Z5eXw9nGL2/NhF\nvV9anW+9FbjwQrHzvJrAUU8gnb3ap533EOpXKzp2zLhBRdtFT49MB93aKlbr4uljhbYmrdX94EHx\na2luTqyRm9XstTJV22pDG6LdOr0cEq51nHrnbuZUnTx8VmuYavO2uudffimer+hAIbNr4jXgaxuF\neVN0mOmhzVN73O7APi+Q8exr7SWypq36XCXb+O4US2dfUVGB/Px85OXlYVYsQBvH1KlTkZeXh8LC\nQlRVVSWtlDqwhfcQ5uTEbx87BqxenZhO3/Kvxd5DHuUe3b8fKCqylqIfsGQHs9r7kiXA9OliMg4f\nBv7jP+L3GS3IDMQv6pGVpfw2e+Hw7ONlTUZrX/0DPXKkfXlmjmrYMODMM8Vk5OREucftvAx/9zux\ndKKNzmbndO9unq5LF/1XXfz5o0fHumQyxm8DAoCqKudz4/iJdoZdq2vUpnn/fXf0sYLr7Nvb2zF5\n8mRUVFSguroaixYtwhZdwGrZsmWora1FTU0N5syZg0mTJklTzs6AIJE3qx57b3dlnlM/C6VoqMaK\nzz7jHzcbWQnwHLfJPLDcc4KPWRncsCG2JKaeRLuZ28YtzGyeTO80q/EFap7aWSJFnH1Njff2kY0d\nZ+8XXPdRWVmJvn37Ijc3F5mZmZgwYQKW6ha/fP3113Hz8S4zQ4cORVNTE/bs2SNFOSunpMXJp5E9\nZ98kNOWqm0QiSkx64MDk5JiFm9RrU7sYqg1aWgehrfHFv2DNg+qjRrnj8L/4gn8/ROsdvK+/iy82\nP3bRRcb7b7xRP3GeYIODRMwatF97zXxqAqu1IKxQu+HedVds3+jR1k7uqaeaEtqN3CbZqVb0ZFh0\ndYlE4qdf98OPcJ19Q0MDcjTxkuzsbDSonW05aer1AXVHRG2lNo9Fmstx4oBE5tHhE3V8ZkuL0rNo\n0yagstK5nHhbmctRh3lra7c9e8Z+m03iZITYy9hcFyN0RdGxHHOinY2xdpAxCtyYaCDkxBxVvJxP\nP01Mu3u3jGdGlKhvcoxfaPFyVq1yoos8uM4+Ivj6Yboqsuh5fKK2Uv/pT/blzJ9vJ4c6ADIGQkUd\nnzljBvDyy8rvhx4Sl/PP/xz/aR1f6MzlqL0ztEPWtTXWGTO0qeu4Oowfzz1sqYsR99wjR445suTU\nSZIT9VWOer9jZUBMjr6NKJE6J+oYEPVNjnakcmWl+rzJ0kcSjMOaNWtYSUlJ5/ajjz7KZs6cGZem\nrKyMLVq0qHP7ggsuYLt3706Qdf755zMA9Ed/9Ed/9Gfjr3fv3jw3LQw30jR48GDU1NSgrq4OvXv3\nxuLFi7FIO7EDgHHjxqG8vBwTJkzA2rVr0bNnT/TSL2MEoFY/soAgCILwDK6zz8jIQHl5OUpKStDe\n3o7S0lL0798fs4+P8y8rK8PYsWOxbNky9O3bFyeddBLmGS2rQxAEQfhKhLEgTVVFEARBuEEgR9B6\nwa5du3DVVVfhoosuQkFBAZ566ikAwIEDBzBq1Cj069cP//RP/4Sm4+P0Dxw4gKuuugonn3wypkyZ\nEidr48aNGDBgAPLy8jBt2jTPr8UNZNpn+vTpOPfcc3GyWcf0FEOWbY4ePYprrrkG/fv3R0FBAX7t\nxkLHPiCz7IwePRpFRUW46KKLUFpaijYvh2O7hEz7qIwbNw4DBgzgZywl8p+CNDY2sqqqKsYYY4cO\nHWL9+vVj1dXV7L777mOzZs1ijDE2c+ZMdv/99zPGGDt8+DD74IMP2LPPPssmT54cJ2vIkCFs3bp1\njDHGxowZw5YvX+7hlbiDTPusW7eONTY2sh49enh7ES4hyzZHjhxh0WiUMcZYa2srGz58OJUdXdk5\ndOhQ5+/x48ezhQsXenQV7iHTPowx9uqrr7IbbriBDRgwgJtvaJ29nuuuu4698847cb2JGhsb2QUX\nXBCXbt68eXEG/+qrr1h+fn7n9qJFi1hZWZk3SnuIU/toSRdnr0eGbRhjbNq0aWzu3Lmu6uoHMuzT\n2trKrr322rR4GepJxj6HDh1iw4YNY9XV1aygoICbT2jDOFrq6upQVVWFoUOHYs+ePZ29iXr16pUw\nGlg/hqChoQHZ2dmd21lZWQkDz1KdZOyT7siyTVNTE9544w2MdDKBT4CRYZ+SkhL06tUL3bt3x+jR\no13X2UuStc+DDz6Ie++9FyeeeKJlXqF39t988w3Gjx+PJ598MiGmHIlEQue89JB9zJFlm2PHjuH6\n66/HtGnTkJub64Km/iDLPm+99RYaGxvR0tKCF154wQ1VfSFZ+2zatAlffPEFrrvuuoSBrUaE2tm3\ntbVh/PjxuPHGG/HDH/4QgPJG3b17NwCgsbERZ511FldGVlZW3PQQ9fX1yFKniExxZNgnXZFpm9tv\nvx0XXHABpk6d6pq+XiO77HTr1g3jx4/HetFVbAKODPusXbsWGzZswPe+9z0MHz4c27Ztw9VXX22a\nPrTOnjGG0tJSXHjhhbj77rs7948bN66z9vDCCy903gjteVrOOeccfPe738W6devAGMPChQsTzklF\nZNknHZFpmwceeADNzc34wx/+4K7SHiLLPocPH0ZjYyMA5evnzTffxMBkZwEMALLsc8cdd6ChoQE7\nduzABx98gH79+uG9997jZhxKVq9ezSKRCCssLGRFRUWsqKiILV++nO3fv5+NHDmS5eXlsVGjRrGD\nBw92nnPeeeex0047jfXo0YNlZ2ezLVu2MMYY27BhAysoKGDnn38+mzJlil+XJBWZ9rnvvvtYdnY2\n69q1K8vOzmYPP/ywX5clBVm22bVrF4tEIuzCCy/slPPcc8/5eGVykGWfPXv2sCFDhrCLL76YDRgw\ngN17772so6PDxyuTQ7L2ycnJ6Xy2VHbs2GHZG4cGVREEQYSA0IZxCIIgwgQ5e4IgiBBAzp4gCCIE\nkLMnCIIIAeTsCYIgQgA5e4IgiBBAzp5IS7p27YqBAweioKAARUVF+O///m/LAV9ffvllwkpsBJEu\nkLMn0pITTzwRVVVV+Oyzz/DOO+9g+fLlePjhh7nn7NixAy+rK7oTRJpBzp5Ie84880zMmTMH5eXl\nAJSZBkeMGIFLLrkEl1xyCdasWQMA+NWvfoXVq1dj4MCBePLJJ9HR0YH77rsPl156KQoLCzFnzhw/\nL4MgkoJG0BJpycknn4xDhw7F7Tv11FOxbds29OjRA126dEG3bt1QU1ODG264AevXr8eqVavwxBNP\n4I033gAAzJkzB3v37sX06dPR0tKCYcOG4ZVXXkmrmSmJ8MBdcJwg0pHW1lZMnjwZmzdvRteuXVFT\nUwMgcaKpt99+G59++imWLFkCAGhubkZtbS05eyIlIWdPhIIvvvgCXbt2xZlnnokZM2bgnHPOwcKF\nC9He3o4TTjjB9Lzy8nKMGjXKQ00Jwh0oZk+kPXv37sUdd9zRuVhzc3Mzzj77bADAggUL0N7eDiAx\n9FNSUoJnnnkGx44dAwBs27YNR44c8Vh7gpAD1eyJtOTo0aMYOHAg2trakJGRgZtuugm/+MUvAAB3\n3nknxo8fjwULFmD06NHo0aMHAKCwsBBdu3ZFUVERbrnlFkydOhV1dXUYNGgQGGM466yz8Le//c3P\nyyIIx1ADLUEQRAigMA5BEEQIIGdPEAQRAsjZEwRBhABy9gRBECGAnD1BEEQIIGdPEAQRAsjZEwRB\nhABy9gRBECHg/wGu3ubA1CeqBwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x158a27d90>"
]
}
],
"prompt_number": 705
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#pd.write_csv(\"data/aggedWeather.csv\")\n",
"trimmed.to_csv(\"aggedWeather.csv\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 706
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 706
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# print trimmed.columns\n",
"# trimmed.drop()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Index([u'Temp', u'H_Pcnt', u'C_Pcnt', u'Temp_Feels', u'Precip', u'Radiation', u'Wind', u'Temp_avg', u'H_Pcnt_avg', u'C_Pcnt_avg', u'Temp_Feels_avg', u'Precip_avg', u'Radiation_avg', u'Wind_avg'], dtype='object')\n"
]
},
{
"ename": "TypeError",
"evalue": "drop() takes at least 2 arguments (1 given)",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-707-f146ea3104eb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mtrimmed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrimmed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: drop() takes at least 2 arguments (1 given)"
]
}
],
"prompt_number": 707
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"# grouped = df.groupby(\"Date\")\n",
"# #grouped['Temp'].agg([np.sum, np.mean, np.std])\n",
"# agged = grouped.agg({'Temp' : np.mean,\n",
"# 'Humidity_Pcnt' : np.mean,\n",
"# 'Clouds_Pcnt' : np.mean,\n",
"# 'Temp_Feels' : np.mean,\n",
"# 'Precip' : np.sum,\n",
"# 'Radiation' : np.mean,\n",
"# 'Wind' : np.mean})\n",
"\n",
"# agged = np.round(agged,1)\n",
"# agged['Year'] = \"holder\"\n",
"# agged['Month'] = \"holder\"\n",
"# agged['Day'] = \"holder\"\n",
"# agged['Weekday'] = \"holder\"\n",
"\n",
"# #slimmed = agged.drop(lambda x: \"2004\" is x)\n",
"# #slimmed\n",
"\n",
"# dates = []\n",
"# def dropDates():\n",
"# for dateString in agged.index:\n",
"# date = parse(dateString)\n",
"# if date < parse(\"2009-05-01\"): \n",
"# dates.append(dateString)\n",
"# else:\n",
"# agged.Year[dateString] = date.year\n",
"# agged.Month[dateString] = date.month\n",
"# agged.Weekday[dateString] = date.weekday()\n",
"# agged.Day[dateString] = date.day\n",
" \n",
" \n",
"\n",
"\n",
"\n",
"# dropDates()\n",
"# agged = agged.drop(dates)\n",
"\n",
"# agged.head()\n"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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