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evanbiederstedt/discordant_methylation_project_April25_2016.ipynb

Created April 26, 2016 20:30
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Beginning plots for discordant methylation mini-project
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discordant_methylation_project_April25_2016.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"os.chdir('/Users/evanbiederstedt/downloads/cellsamples') \n",
"# files at http://www.broadinstitute.org/~kendell/seq/scRRBS/anno/"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"pd.set_option('display.max_columns', 50) # print all rows"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df1 = pd.read_table('RRBS_NormalBCD19_CD27_cell1_22_CGAGGCTG.ACCGCG.reads.anno') # example mammary cell, GD27"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>chr</th>\n",
" <th>start</th>\n",
" <th>cpgMeth</th>\n",
" <th>cpgUnmeth</th>\n",
" <th>readMeth</th>\n",
" <th>readUnmeth</th>\n",
" <th>readMixed</th>\n",
" <th>avCpgCount</th>\n",
" <th>epiProb</th>\n",
" <th>discProb</th>\n",
" <th>expDisc</th>\n",
" <th>epiReadTypeCount</th>\n",
" <th>tss</th>\n",
" <th>tssDistance</th>\n",
" <th>genesDistance</th>\n",
" <th>exonsDistance</th>\n",
" <th>intronsDistance</th>\n",
" <th>promoterDistance</th>\n",
" <th>cgi</th>\n",
" <th>cgiDistance</th>\n",
" <th>geneDensity</th>\n",
" <th>ctcfDistance</th>\n",
" <th>ctcfUpDistance</th>\n",
" <th>ctcfDownDistance</th>\n",
" <th>ctcfDensity</th>\n",
" <th>geneDistalRegulatoryModulesDistance</th>\n",
" <th>3PrimeUTRDistance</th>\n",
" <th>5PrimeUTRDistance</th>\n",
" <th>firstExonDistance</th>\n",
" <th>hypoInHues64Distance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>chr1</td>\n",
" <td>523390</td>\n",
" <td>16</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>3.0</td>\n",
" <td>0.48</td>\n",
" <td>0.053860</td>\n",
" <td>7.466667</td>\n",
" <td>2</td>\n",
" <td>OR4F16</td>\n",
" <td>98644</td>\n",
" <td>97705</td>\n",
" <td>97705</td>\n",
" <td>97705</td>\n",
" <td>97644.0</td>\n",
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" <td>40969</td>\n",
" <td>55</td>\n",
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" <td>1741.0</td>\n",
" <td>22611.0</td>\n",
" <td>44</td>\n",
" <td>299510</td>\n",
" <td>356143</td>\n",
" <td>337730</td>\n",
" <td>97705</td>\n",
" <td>9884412.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>chr1</td>\n",
" <td>852026</td>\n",
" <td>22</td>\n",
" <td>33</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>5.0</td>\n",
" <td>0.00</td>\n",
" <td>0.363031</td>\n",
" <td>10.032000</td>\n",
" <td>1</td>\n",
" <td>LOC100130417</td>\n",
" <td>2791</td>\n",
" <td>926</td>\n",
" <td>926</td>\n",
" <td>926</td>\n",
" <td>1791.0</td>\n",
" <td>8</td>\n",
" <td>5762</td>\n",
" <td>67</td>\n",
" <td>3472</td>\n",
" <td>3472.0</td>\n",
" <td>4420.0</td>\n",
" <td>62</td>\n",
" <td>17626</td>\n",
" <td>27507</td>\n",
" <td>9094</td>\n",
" <td>2688</td>\n",
" <td>9555776.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>chr1</td>\n",
" <td>895127</td>\n",
" <td>24</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>9.0</td>\n",
" <td>0.00</td>\n",
" <td>0.899753</td>\n",
" <td>3.895748</td>\n",
" <td>1</td>\n",
" <td>NOC2L</td>\n",
" <td>448</td>\n",
" <td>448</td>\n",
" <td>448</td>\n",
" <td>533</td>\n",
" <td>-1448.0</td>\n",
" <td>12</td>\n",
" <td>-970</td>\n",
" <td>68</td>\n",
" <td>8072</td>\n",
" <td>8072.0</td>\n",
" <td>16292.0</td>\n",
" <td>65</td>\n",
" <td>60727</td>\n",
" <td>5444</td>\n",
" <td>448</td>\n",
" <td>448</td>\n",
" <td>9512675.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>chr1</td>\n",
" <td>917687</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>8.0</td>\n",
" <td>0.00</td>\n",
" <td>0.903064</td>\n",
" <td>12.898438</td>\n",
" <td>1</td>\n",
" <td>C1orf170</td>\n",
" <td>214</td>\n",
" <td>214</td>\n",
" <td>214</td>\n",
" <td>243</td>\n",
" <td>-1214.0</td>\n",
" <td>16</td>\n",
" <td>5770</td>\n",
" <td>68</td>\n",
" <td>1809</td>\n",
" <td>5936.0</td>\n",
" <td>1809.0</td>\n",
" <td>65</td>\n",
" <td>83287</td>\n",
" <td>7203</td>\n",
" <td>15776</td>\n",
" <td>214</td>\n",
" <td>9490115.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>chr1</td>\n",
" <td>986063</td>\n",
" <td>42</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>9.0</td>\n",
" <td>0.00</td>\n",
" <td>0.516868</td>\n",
" <td>5.375034</td>\n",
" <td>1</td>\n",
" <td>RNF223</td>\n",
" <td>23624</td>\n",
" <td>-30561</td>\n",
" <td>42</td>\n",
" <td>-92</td>\n",
" <td>22624.0</td>\n",
" <td>21</td>\n",
" <td>8226</td>\n",
" <td>70</td>\n",
" <td>513</td>\n",
" <td>9785.0</td>\n",
" <td>513.0</td>\n",
" <td>68</td>\n",
" <td>103437</td>\n",
" <td>4298</td>\n",
" <td>21883</td>\n",
" <td>23332</td>\n",
" <td>9421739.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" chr start cpgMeth cpgUnmeth readMeth readUnmeth readMixed \\\n",
"0 chr1 523390 16 14 0 0 10 \n",
"1 chr1 852026 22 33 0 0 11 \n",
"2 chr1 895127 24 12 0 0 4 \n",
"3 chr1 917687 52 52 0 0 13 \n",
"4 chr1 986063 42 12 0 0 6 \n",
"\n",
" avCpgCount epiProb discProb expDisc epiReadTypeCount tss \\\n",
"0 3.0 0.48 0.053860 7.466667 2 OR4F16 \n",
"1 5.0 0.00 0.363031 10.032000 1 LOC100130417 \n",
"2 9.0 0.00 0.899753 3.895748 1 NOC2L \n",
"3 8.0 0.00 0.903064 12.898438 1 C1orf170 \n",
"4 9.0 0.00 0.516868 5.375034 1 RNF223 \n",
"\n",
" tssDistance genesDistance exonsDistance intronsDistance \\\n",
"0 98644 97705 97705 97705 \n",
"1 2791 926 926 926 \n",
"2 448 448 448 533 \n",
"3 214 214 214 243 \n",
"4 23624 -30561 42 -92 \n",
"\n",
" promoterDistance cgi cgiDistance geneDensity ctcfDistance \\\n",
"0 97644.0 2 40969 55 1741 \n",
"1 1791.0 8 5762 67 3472 \n",
"2 -1448.0 12 -970 68 8072 \n",
"3 -1214.0 16 5770 68 1809 \n",
"4 22624.0 21 8226 70 513 \n",
"\n",
" ctcfUpDistance ctcfDownDistance ctcfDensity \\\n",
"0 1741.0 22611.0 44 \n",
"1 3472.0 4420.0 62 \n",
"2 8072.0 16292.0 65 \n",
"3 5936.0 1809.0 65 \n",
"4 9785.0 513.0 68 \n",
"\n",
" geneDistalRegulatoryModulesDistance 3PrimeUTRDistance 5PrimeUTRDistance \\\n",
"0 299510 356143 337730 \n",
"1 17626 27507 9094 \n",
"2 60727 5444 448 \n",
"3 83287 7203 15776 \n",
"4 103437 4298 21883 \n",
"\n",
" firstExonDistance hypoInHues64Distance \n",
"0 97705 9884412.0 \n",
"1 2688 9555776.0 \n",
"2 448 9512675.0 \n",
"3 214 9490115.0 \n",
"4 23332 9421739.0 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5114, 30)\n",
"There are 5114 number of rows\n"
]
}
],
"source": [
"print(df1.shape)\n",
"print('There are ' + str(df1.shape[0])+ ' number of rows')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cols = list(range(8,30)) # define list of columns to drop\n",
"df1 = df1.drop(df1.columns[cols],axis=1) # drop columns"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>chr</th>\n",
" <th>start</th>\n",
" <th>cpgMeth</th>\n",
" <th>cpgUnmeth</th>\n",
" <th>readMeth</th>\n",
" <th>readUnmeth</th>\n",
" <th>readMixed</th>\n",
" <th>avCpgCount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>chr1</td>\n",
" <td>523390</td>\n",
" <td>16</td>\n",
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" <td>0</td>\n",
" <td>10</td>\n",
" <td>3.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>chr1</td>\n",
" <td>852026</td>\n",
" <td>22</td>\n",
" <td>33</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>chr1</td>\n",
" <td>895127</td>\n",
" <td>24</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>chr1</td>\n",
" <td>917687</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>chr1</td>\n",
" <td>986063</td>\n",
" <td>42</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>chr1</td>\n",
" <td>992710</td>\n",
" <td>23</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>chr1</td>\n",
" <td>1023246</td>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>chr1</td>\n",
" <td>1052599</td>\n",
" <td>6</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>chr1</td>\n",
" <td>1057756</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>chr1</td>\n",
" <td>1094887</td>\n",
" <td>24</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" chr start cpgMeth cpgUnmeth readMeth readUnmeth readMixed \\\n",
"0 chr1 523390 16 14 0 0 10 \n",
"1 chr1 852026 22 33 0 0 11 \n",
"2 chr1 895127 24 12 0 0 4 \n",
"3 chr1 917687 52 52 0 0 13 \n",
"4 chr1 986063 42 12 0 0 6 \n",
"5 chr1 992710 23 23 0 0 23 \n",
"6 chr1 1023246 16 2 0 0 2 \n",
"7 chr1 1052599 6 24 0 0 6 \n",
"8 chr1 1057756 6 12 0 0 6 \n",
"9 chr1 1094887 24 6 0 0 6 \n",
"\n",
" avCpgCount \n",
"0 3.0 \n",
"1 5.0 \n",
"2 9.0 \n",
"3 8.0 \n",
"4 9.0 \n",
"5 2.0 \n",
"6 9.0 \n",
"7 5.0 \n",
"8 3.0 \n",
"9 5.0 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df1['total_reads'] = df1[[\"readMeth\", \"readUnmeth\", \"readMixed\"]].sum(axis=1) # sum total reads"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df1['PDR'] = df1.apply(lambda row: row['readMixed']/row['total_reads'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>chr</th>\n",
" <th>start</th>\n",
" <th>cpgMeth</th>\n",
" <th>cpgUnmeth</th>\n",
" <th>readMeth</th>\n",
" <th>readUnmeth</th>\n",
" <th>readMixed</th>\n",
" <th>avCpgCount</th>\n",
" <th>total_reads</th>\n",
" <th>PDR</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>1.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>chr1</td>\n",
" <td>852026</td>\n",
" <td>22</td>\n",
" <td>33</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>5.0</td>\n",
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" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>chr1</td>\n",
" <td>895127</td>\n",
" <td>24</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>9.0</td>\n",
" <td>4</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>chr1</td>\n",
" <td>917687</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>8.0</td>\n",
" <td>13</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>chr1</td>\n",
" <td>986063</td>\n",
" <td>42</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>9.0</td>\n",
" <td>6</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>chr1</td>\n",
" <td>992710</td>\n",
" <td>23</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>2.0</td>\n",
" <td>23</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>chr1</td>\n",
" <td>1023246</td>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>9.0</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>chr1</td>\n",
" <td>1052599</td>\n",
" <td>6</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
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" <td>5.0</td>\n",
" <td>6</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>chr1</td>\n",
" <td>1057756</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>3.0</td>\n",
" <td>6</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>chr1</td>\n",
" <td>1094887</td>\n",
" <td>24</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>5.0</td>\n",
" <td>6</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" chr start cpgMeth cpgUnmeth readMeth readUnmeth readMixed \\\n",
"0 chr1 523390 16 14 0 0 10 \n",
"1 chr1 852026 22 33 0 0 11 \n",
"2 chr1 895127 24 12 0 0 4 \n",
"3 chr1 917687 52 52 0 0 13 \n",
"4 chr1 986063 42 12 0 0 6 \n",
"5 chr1 992710 23 23 0 0 23 \n",
"6 chr1 1023246 16 2 0 0 2 \n",
"7 chr1 1052599 6 24 0 0 6 \n",
"8 chr1 1057756 6 12 0 0 6 \n",
"9 chr1 1094887 24 6 0 0 6 \n",
"\n",
" avCpgCount total_reads PDR \n",
"0 3.0 10 1.0 \n",
"1 5.0 11 1.0 \n",
"2 9.0 4 1.0 \n",
"3 8.0 13 1.0 \n",
"4 9.0 6 1.0 \n",
"5 2.0 23 1.0 \n",
"6 9.0 2 1.0 \n",
"7 5.0 6 1.0 \n",
"8 3.0 6 1.0 \n",
"9 5.0 6 1.0 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 4719 total number of PDR values == 1.0\n"
]
},
{
"data": {
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BAUXC/tzKgN1uLzKtQoUKbr1nSEhg6YNwxeiz+eix+eix+Typx1lZAWVdwjVj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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1150a7a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.title('Histogram: Frequency of PDR, 5114 rows')\n",
"plt.xlabel('PDR values')\n",
"plt.ylabel('Frequency')\n",
"df1['PDR'].plot(kind='hist')\n",
"# Note that the vast majority of PDR values are 1.0\n",
"print('There are ' + str(len(df1[df1['PDR']==1])) + ' total number of PDR values == 1.0')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"PDR_lessthan1 = df1[df1['PDR'] < 1.0] # simply remove all 1.0 values"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 395 total number of samples with PDR values < 1.0\n"
]
},
{
"data": {
"image/png": 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OVevWra/5ZA8AAPA/pp4UN2XKFD366KOqXbu2DMPQnj17nJdrtGnTRtu3bzdz8QAAVBim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\nOTm68cYb9eqrrzrvqHYtduz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"text/plain": [
"<matplotlib.figure.Figure at 0x115439400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.title('Histogram: Frequency of PDR s.t. PDR < 1.0')\n",
"plt.xlabel('PDR values')\n",
"plt.ylabel('Frequency')\n",
"xticks = [x * 0.05 for x in list(range(0, 20))] # set xticks 0.0, 0.05, 0.1, 0.15, etc.\n",
"PDR_lessthan1['PDR'].plot(kind='hist', bins = xticks)\n",
"print('There are ' + str(len(PDR_lessthan1['PDR'])) + ' total number of samples with PDR values < 1.0')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/local/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11553dcc0>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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h3G2t8OvhAuQXm/Htjmz0aSu/aWdFUURRmRWn8yuQU2SCwynWul+lFKAQBJy9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\nEEII0cJIcxdCCCFaGGnuQgghRAvz/8Yk5vaAd4DkAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115418fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(PDR_lessthan1['PDR'], bins = xticks) # Flexibly plot a univariate distribution against observations.\n",
"plt.title('Distribution fitted to frequency of PDR values s.t. PDR < 1.0')\n",
"plt.xlabel('PDR values')\n",
"plt.ylabel('Frequency')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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