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INSTRUCTIONS.md 0 → 100644
View file @ 2e2bb3fc
Uploading assignment solutions for review
=========================================
Some assignments need additional data or come with some example startup code
for you to use. These are stored in a Git repository. Git can also be used to
share your solutions for review.
Start by configuring Git (only need to do this once per computer). In a
terminal, type:
git config --global user.name "John Doe"
git config --global user.email johndoe@example.com
Clone the assignments repository
--------------------------------
Use your webbrowser to go to:
https://git.lumc.nl/humgen/programming-course-assignments
Sign in using your LUMC account or the account you were emailed (notify a
teacher if you cannot login). Click the *Fork repository* button on the
right.
The resulting page is your fork of the repository. Bookmark this page in your
browser.
On your fork, click the *HTTPS* button on the top and copy the resulting URL,
e.g.,
`https://git.lumc.nl/johndoe/programming-course-assignments.git`.
Use this URL to clone the repository on your local computer. In a terminal,
type:
git clone https://git.lumc.nl/johndoe/programming-course-assignments.git
You now have a copy of the material on your computer. Change directory to it:
cd programming-course-assignments
Do an assignment
----------------
You'll have to figure this out yourself. Just make sure you do your work
within the directory we just created.
Commit and publish your solution
--------------------------------
For any files you created as part of your solution, run `git add`. For
example:
git add solution_1.py
Commit your changes to Git:
git commit -am 'Solution for assignment 1'
Push your changes to the server:
git push
You can repeat this section later when you did another assignment.
Share your solution
-------------------
Go to your project page (the one you bookmarked) where you should now see what
you pushed earlier.
You can of course share your solution simply by sharing a link to your project
page. However, there is a nice feature in GitLab called *merge requests* which
you can use to directly submit you solution to the teachers.
On your project page, click the green square button on the right (with three
horizontal white stripes) an choose *New merge request*. Make sure the *Source
branch* is set to *master* on your fork and the *Target branch* is set to
*master* on the original (*humgen*).
Click *Compare branches*. Enter an appropriate title and perhaps a description
before clicking *Submit merge request*. The resulting page can be used by you
and the teachers to discuss your solution.
PaintingPandas_assignment.ipynb 0 → 100644
View file @ 2e2bb3fc
{
"metadata": {
"name": "",
"signature": "sha256:cedf215df94e96ca29ce550848dcc31d28ad2a71ca8d94a1fcacba400c12b017"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Enable inline plotting\n",
"%matplotlib inline\n",
"%pylab inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import seaborn as sns\n",
"import numpy as np\n",
"sns.set(font=\"Bitstream Vera Sans\")\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.read_csv('data/iris.data', \n",
" header=None, \n",
" names=['sepal_length','sepal_width','petal_length','petal_width', 'class'], \n",
" keep_default_na=False,\n",
" engine='c')\n",
"\n",
"df"
],
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 5.1</td>\n",
" <td> 3.5</td>\n",
" <td> 1.4</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 4.9</td>\n",
" <td> 3.0</td>\n",
" <td> 1.4</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 4.7</td>\n",
" <td> 3.2</td>\n",
" <td> 1.3</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 4.6</td>\n",
" <td> 3.1</td>\n",
" <td> 1.5</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 5.0</td>\n",
" <td> 3.6</td>\n",
" <td> 1.4</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 5.4</td>\n",
" <td> 3.9</td>\n",
" <td> 1.7</td>\n",
" <td> 0.4</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 4.6</td>\n",
" <td> 3.4</td>\n",
" <td> 1.4</td>\n",
" <td> 0.3</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 5.0</td>\n",
" <td> 3.4</td>\n",
" <td> 1.5</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 4.4</td>\n",
" <td> 2.9</td>\n",
" <td> 1.4</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 4.9</td>\n",
" <td> 3.1</td>\n",
" <td> 1.5</td>\n",
" <td> 0.1</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10 </th>\n",
" <td> 5.4</td>\n",
" <td> 3.7</td>\n",
" <td> 1.5</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 </th>\n",
" <td> 4.8</td>\n",
" <td> 3.4</td>\n",
" <td> 1.6</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 </th>\n",
" <td> 4.8</td>\n",
" <td> 3.0</td>\n",
" <td> 1.4</td>\n",
" <td> 0.1</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> 4.3</td>\n",
" <td> 3.0</td>\n",
" <td> 1.1</td>\n",
" <td> 0.1</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> 5.8</td>\n",
" <td> 4.0</td>\n",
" <td> 1.2</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> 5.7</td>\n",
" <td> 4.4</td>\n",
" <td> 1.5</td>\n",
" <td> 0.4</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> 5.4</td>\n",
" <td> 3.9</td>\n",
" <td> 1.3</td>\n",
" <td> 0.4</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> 5.1</td>\n",
" <td> 3.5</td>\n",
" <td> 1.4</td>\n",
" <td> 0.3</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18 </th>\n",
" <td> 5.7</td>\n",
" <td> 3.8</td>\n",
" <td> 1.7</td>\n",
" <td> 0.3</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> 5.1</td>\n",
" <td> 3.8</td>\n",
" <td> 1.5</td>\n",
" <td> 0.3</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> 5.4</td>\n",
" <td> 3.4</td>\n",
" <td> 1.7</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> 5.1</td>\n",
" <td> 3.7</td>\n",
" <td> 1.5</td>\n",
" <td> 0.4</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> 4.6</td>\n",
" <td> 3.6</td>\n",
" <td> 1.0</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> 5.1</td>\n",
" <td> 3.3</td>\n",
" <td> 1.7</td>\n",
" <td> 0.5</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> 4.8</td>\n",
" <td> 3.4</td>\n",
" <td> 1.9</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> 5.0</td>\n",
" <td> 3.0</td>\n",
" <td> 1.6</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> 5.0</td>\n",
" <td> 3.4</td>\n",
" <td> 1.6</td>\n",
" <td> 0.4</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> 5.2</td>\n",
" <td> 3.5</td>\n",
" <td> 1.5</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> 5.2</td>\n",
" <td> 3.4</td>\n",
" <td> 1.4</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> 4.7</td>\n",
" <td> 3.2</td>\n",
" <td> 1.6</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td> 5.6</td>\n",
" <td> 2.8</td>\n",
" <td> 4.9</td>\n",
" <td> 2.0</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td> 7.7</td>\n",
" <td> 2.8</td>\n",
" <td> 6.7</td>\n",
" <td> 2.0</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td> 6.3</td>\n",
" <td> 2.7</td>\n",
" <td> 4.9</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td> 6.7</td>\n",
" <td> 3.3</td>\n",
" <td> 5.7</td>\n",
" <td> 2.1</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td> 7.2</td>\n",
" <td> 3.2</td>\n",
" <td> 6.0</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td> 6.2</td>\n",
" <td> 2.8</td>\n",
" <td> 4.8</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td> 6.1</td>\n",
" <td> 3.0</td>\n",
" <td> 4.9</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td> 6.4</td>\n",
" <td> 2.8</td>\n",
" <td> 5.6</td>\n",
" <td> 2.1</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td> 7.2</td>\n",
" <td> 3.0</td>\n",
" <td> 5.8</td>\n",
" <td> 1.6</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td> 7.4</td>\n",
" <td> 2.8</td>\n",
" <td> 6.1</td>\n",
" <td> 1.9</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td> 7.9</td>\n",
" <td> 3.8</td>\n",
" <td> 6.4</td>\n",
" <td> 2.0</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td> 6.4</td>\n",
" <td> 2.8</td>\n",
" <td> 5.6</td>\n",
" <td> 2.2</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td> 6.3</td>\n",
" <td> 2.8</td>\n",
" <td> 5.1</td>\n",
" <td> 1.5</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td> 6.1</td>\n",
" <td> 2.6</td>\n",
" <td> 5.6</td>\n",
" <td> 1.4</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td> 7.7</td>\n",
" <td> 3.0</td>\n",
" <td> 6.1</td>\n",
" <td> 2.3</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td> 6.3</td>\n",
" <td> 3.4</td>\n",
" <td> 5.6</td>\n",
" <td> 2.4</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td> 6.4</td>\n",
" <td> 3.1</td>\n",
" <td> 5.5</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td> 6.0</td>\n",
" <td> 3.0</td>\n",
" <td> 4.8</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td> 6.9</td>\n",
" <td> 3.1</td>\n",
" <td> 5.4</td>\n",
" <td> 2.1</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td> 6.7</td>\n",
" <td> 3.1</td>\n",
" <td> 5.6</td>\n",
" <td> 2.4</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td> 6.9</td>\n",
" <td> 3.1</td>\n",
" <td> 5.1</td>\n",
" <td> 2.3</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142</th>\n",
" <td> 5.8</td>\n",
" <td> 2.7</td>\n",
" <td> 5.1</td>\n",
" <td> 1.9</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td> 6.8</td>\n",
" <td> 3.2</td>\n",
" <td> 5.9</td>\n",
" <td> 2.3</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td> 6.7</td>\n",
" <td> 3.3</td>\n",
" <td> 5.7</td>\n",
" <td> 2.5</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td> 6.7</td>\n",
" <td> 3.0</td>\n",
" <td> 5.2</td>\n",
" <td> 2.3</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td> 6.3</td>\n",
" <td> 2.5</td>\n",
" <td> 5.0</td>\n",
" <td> 1.9</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td> 6.5</td>\n",
" <td> 3.0</td>\n",
" <td> 5.2</td>\n",
" <td> 2.0</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td> 6.2</td>\n",
" <td> 3.4</td>\n",
" <td> 5.4</td>\n",
" <td> 2.3</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td> 5.9</td>\n",
" <td> 3.0</td>\n",
" <td> 5.1</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td> </td>\n",
" <td> </td>\n",
" <td> </td>\n",
" <td> </td>\n",
" <td> </td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>151 rows \u00d7 5 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
" sepal_length sepal_width petal_length petal_width class\n",
"0 5.1 3.5 1.4 0.2 Iris-setosa\n",
"1 4.9 3.0 1.4 0.2 Iris-setosa\n",
"2 4.7 3.2 1.3 0.2 Iris-setosa\n",
"3 4.6 3.1 1.5 0.2 Iris-setosa\n",
"4 5.0 3.6 1.4 0.2 Iris-setosa\n",
"5 5.4 3.9 1.7 0.4 Iris-setosa\n",
"6 4.6 3.4 1.4 0.3 Iris-setosa\n",
"7 5.0 3.4 1.5 0.2 Iris-setosa\n",
"8 4.4 2.9 1.4 0.2 Iris-setosa\n",
"9 4.9 3.1 1.5 0.1 Iris-setosa\n",
"10 5.4 3.7 1.5 0.2 Iris-setosa\n",
"11 4.8 3.4 1.6 0.2 Iris-setosa\n",
"12 4.8 3.0 1.4 0.1 Iris-setosa\n",
"13 4.3 3.0 1.1 0.1 Iris-setosa\n",
"14 5.8 4.0 1.2 0.2 Iris-setosa\n",
"15 5.7 4.4 1.5 0.4 Iris-setosa\n",
"16 5.4 3.9 1.3 0.4 Iris-setosa\n",
"17 5.1 3.5 1.4 0.3 Iris-setosa\n",
"18 5.7 3.8 1.7 0.3 Iris-setosa\n",
"19 5.1 3.8 1.5 0.3 Iris-setosa\n",
"20 5.4 3.4 1.7 0.2 Iris-setosa\n",
"21 5.1 3.7 1.5 0.4 Iris-setosa\n",
"22 4.6 3.6 1.0 0.2 Iris-setosa\n",
"23 5.1 3.3 1.7 0.5 Iris-setosa\n",
"24 4.8 3.4 1.9 0.2 Iris-setosa\n",
"25 5.0 3.0 1.6 0.2 Iris-setosa\n",
"26 5.0 3.4 1.6 0.4 Iris-setosa\n",
"27 5.2 3.5 1.5 0.2 Iris-setosa\n",
"28 5.2 3.4 1.4 0.2 Iris-setosa\n",
"29 4.7 3.2 1.6 0.2 Iris-setosa\n",
".. ... ... ... ... ...\n",
"121 5.6 2.8 4.9 2.0 Iris-virginica\n",
"122 7.7 2.8 6.7 2.0 Iris-virginica\n",
"123 6.3 2.7 4.9 1.8 Iris-virginica\n",
"124 6.7 3.3 5.7 2.1 Iris-virginica\n",
"125 7.2 3.2 6.0 1.8 Iris-virginica\n",
"126 6.2 2.8 4.8 1.8 Iris-virginica\n",
"127 6.1 3.0 4.9 1.8 Iris-virginica\n",
"128 6.4 2.8 5.6 2.1 Iris-virginica\n",
"129 7.2 3.0 5.8 1.6 Iris-virginica\n",
"130 7.4 2.8 6.1 1.9 Iris-virginica\n",
"131 7.9 3.8 6.4 2.0 Iris-virginica\n",
"132 6.4 2.8 5.6 2.2 Iris-virginica\n",
"133 6.3 2.8 5.1 1.5 Iris-virginica\n",
"134 6.1 2.6 5.6 1.4 Iris-virginica\n",
"135 7.7 3.0 6.1 2.3 Iris-virginica\n",
"136 6.3 3.4 5.6 2.4 Iris-virginica\n",
"137 6.4 3.1 5.5 1.8 Iris-virginica\n",
"138 6.0 3.0 4.8 1.8 Iris-virginica\n",
"139 6.9 3.1 5.4 2.1 Iris-virginica\n",
"140 6.7 3.1 5.6 2.4 Iris-virginica\n",
"141 6.9 3.1 5.1 2.3 Iris-virginica\n",
"142 5.8 2.7 5.1 1.9 Iris-virginica\n",
"143 6.8 3.2 5.9 2.3 Iris-virginica\n",
"144 6.7 3.3 5.7 2.5 Iris-virginica\n",
"145 6.7 3.0 5.2 2.3 Iris-virginica\n",
"146 6.3 2.5 5.0 1.9 Iris-virginica\n",
"147 6.5 3.0 5.2 2.0 Iris-virginica\n",
"148 6.2 3.4 5.4 2.3 Iris-virginica\n",
"149 5.9 3.0 5.1 1.8 Iris-virginica\n",
"150 \n",
"\n",
"[151 rows x 5 columns]"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['sepal_length'] = df['sepal_length'].convert_objects(convert_numeric=True)\n",
"df['sepal_width'] = df['sepal_width'].convert_objects(convert_numeric=True)\n",
"df['petal_length'] = df['petal_length'].convert_objects(convert_numeric=True)\n",
"df['petal_width'] = df['petal_width'].convert_objects(convert_numeric=True)\n",
"df"
],
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 5.1</td>\n",
" <td> 3.5</td>\n",
" <td> 1.4</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 4.9</td>\n",
" <td> 3.0</td>\n",
" <td> 1.4</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 4.7</td>\n",
" <td> 3.2</td>\n",
" <td> 1.3</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 4.6</td>\n",
" <td> 3.1</td>\n",
" <td> 1.5</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 5.0</td>\n",
" <td> 3.6</td>\n",
" <td> 1.4</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 5.4</td>\n",
" <td> 3.9</td>\n",
" <td> 1.7</td>\n",
" <td> 0.4</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 4.6</td>\n",
" <td> 3.4</td>\n",
" <td> 1.4</td>\n",
" <td> 0.3</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 5.0</td>\n",
" <td> 3.4</td>\n",
" <td> 1.5</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 4.4</td>\n",
" <td> 2.9</td>\n",
" <td> 1.4</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 4.9</td>\n",
" <td> 3.1</td>\n",
" <td> 1.5</td>\n",
" <td> 0.1</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10 </th>\n",
" <td> 5.4</td>\n",
" <td> 3.7</td>\n",
" <td> 1.5</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 </th>\n",
" <td> 4.8</td>\n",
" <td> 3.4</td>\n",
" <td> 1.6</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 </th>\n",
" <td> 4.8</td>\n",
" <td> 3.0</td>\n",
" <td> 1.4</td>\n",
" <td> 0.1</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> 4.3</td>\n",
" <td> 3.0</td>\n",
" <td> 1.1</td>\n",
" <td> 0.1</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> 5.8</td>\n",
" <td> 4.0</td>\n",
" <td> 1.2</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> 5.7</td>\n",
" <td> 4.4</td>\n",
" <td> 1.5</td>\n",
" <td> 0.4</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> 5.4</td>\n",
" <td> 3.9</td>\n",
" <td> 1.3</td>\n",
" <td> 0.4</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> 5.1</td>\n",
" <td> 3.5</td>\n",
" <td> 1.4</td>\n",
" <td> 0.3</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18 </th>\n",
" <td> 5.7</td>\n",
" <td> 3.8</td>\n",
" <td> 1.7</td>\n",
" <td> 0.3</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> 5.1</td>\n",
" <td> 3.8</td>\n",
" <td> 1.5</td>\n",
" <td> 0.3</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> 5.4</td>\n",
" <td> 3.4</td>\n",
" <td> 1.7</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> 5.1</td>\n",
" <td> 3.7</td>\n",
" <td> 1.5</td>\n",
" <td> 0.4</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> 4.6</td>\n",
" <td> 3.6</td>\n",
" <td> 1.0</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> 5.1</td>\n",
" <td> 3.3</td>\n",
" <td> 1.7</td>\n",
" <td> 0.5</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> 4.8</td>\n",
" <td> 3.4</td>\n",
" <td> 1.9</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> 5.0</td>\n",
" <td> 3.0</td>\n",
" <td> 1.6</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> 5.0</td>\n",
" <td> 3.4</td>\n",
" <td> 1.6</td>\n",
" <td> 0.4</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> 5.2</td>\n",
" <td> 3.5</td>\n",
" <td> 1.5</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> 5.2</td>\n",
" <td> 3.4</td>\n",
" <td> 1.4</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> 4.7</td>\n",
" <td> 3.2</td>\n",
" <td> 1.6</td>\n",
" <td> 0.2</td>\n",
" <td> Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td> 5.6</td>\n",
" <td> 2.8</td>\n",
" <td> 4.9</td>\n",
" <td> 2.0</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td> 7.7</td>\n",
" <td> 2.8</td>\n",
" <td> 6.7</td>\n",
" <td> 2.0</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td> 6.3</td>\n",
" <td> 2.7</td>\n",
" <td> 4.9</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td> 6.7</td>\n",
" <td> 3.3</td>\n",
" <td> 5.7</td>\n",
" <td> 2.1</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td> 7.2</td>\n",
" <td> 3.2</td>\n",
" <td> 6.0</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td> 6.2</td>\n",
" <td> 2.8</td>\n",
" <td> 4.8</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td> 6.1</td>\n",
" <td> 3.0</td>\n",
" <td> 4.9</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td> 6.4</td>\n",
" <td> 2.8</td>\n",
" <td> 5.6</td>\n",
" <td> 2.1</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td> 7.2</td>\n",
" <td> 3.0</td>\n",
" <td> 5.8</td>\n",
" <td> 1.6</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td> 7.4</td>\n",
" <td> 2.8</td>\n",
" <td> 6.1</td>\n",
" <td> 1.9</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td> 7.9</td>\n",
" <td> 3.8</td>\n",
" <td> 6.4</td>\n",
" <td> 2.0</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td> 6.4</td>\n",
" <td> 2.8</td>\n",
" <td> 5.6</td>\n",
" <td> 2.2</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td> 6.3</td>\n",
" <td> 2.8</td>\n",
" <td> 5.1</td>\n",
" <td> 1.5</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td> 6.1</td>\n",
" <td> 2.6</td>\n",
" <td> 5.6</td>\n",
" <td> 1.4</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <td> 7.7</td>\n",
" <td> 3.0</td>\n",
" <td> 6.1</td>\n",
" <td> 2.3</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td> 6.3</td>\n",
" <td> 3.4</td>\n",
" <td> 5.6</td>\n",
" <td> 2.4</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td> 6.4</td>\n",
" <td> 3.1</td>\n",
" <td> 5.5</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td> 6.0</td>\n",
" <td> 3.0</td>\n",
" <td> 4.8</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>139</th>\n",
" <td> 6.9</td>\n",
" <td> 3.1</td>\n",
" <td> 5.4</td>\n",
" <td> 2.1</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td> 6.7</td>\n",
" <td> 3.1</td>\n",
" <td> 5.6</td>\n",
" <td> 2.4</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td> 6.9</td>\n",
" <td> 3.1</td>\n",
" <td> 5.1</td>\n",
" <td> 2.3</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142</th>\n",
" <td> 5.8</td>\n",
" <td> 2.7</td>\n",
" <td> 5.1</td>\n",
" <td> 1.9</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>143</th>\n",
" <td> 6.8</td>\n",
" <td> 3.2</td>\n",
" <td> 5.9</td>\n",
" <td> 2.3</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>144</th>\n",
" <td> 6.7</td>\n",
" <td> 3.3</td>\n",
" <td> 5.7</td>\n",
" <td> 2.5</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>145</th>\n",
" <td> 6.7</td>\n",
" <td> 3.0</td>\n",
" <td> 5.2</td>\n",
" <td> 2.3</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td> 6.3</td>\n",
" <td> 2.5</td>\n",
" <td> 5.0</td>\n",
" <td> 1.9</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td> 6.5</td>\n",
" <td> 3.0</td>\n",
" <td> 5.2</td>\n",
" <td> 2.0</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td> 6.2</td>\n",
" <td> 3.4</td>\n",
" <td> 5.4</td>\n",
" <td> 2.3</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td> 5.9</td>\n",
" <td> 3.0</td>\n",
" <td> 5.1</td>\n",
" <td> 1.8</td>\n",
" <td> Iris-virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> </td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>151 rows \u00d7 5 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
" sepal_length sepal_width petal_length petal_width class\n",
"0 5.1 3.5 1.4 0.2 Iris-setosa\n",
"1 4.9 3.0 1.4 0.2 Iris-setosa\n",
"2 4.7 3.2 1.3 0.2 Iris-setosa\n",
"3 4.6 3.1 1.5 0.2 Iris-setosa\n",
"4 5.0 3.6 1.4 0.2 Iris-setosa\n",
"5 5.4 3.9 1.7 0.4 Iris-setosa\n",
"6 4.6 3.4 1.4 0.3 Iris-setosa\n",
"7 5.0 3.4 1.5 0.2 Iris-setosa\n",
"8 4.4 2.9 1.4 0.2 Iris-setosa\n",
"9 4.9 3.1 1.5 0.1 Iris-setosa\n",
"10 5.4 3.7 1.5 0.2 Iris-setosa\n",
"11 4.8 3.4 1.6 0.2 Iris-setosa\n",
"12 4.8 3.0 1.4 0.1 Iris-setosa\n",
"13 4.3 3.0 1.1 0.1 Iris-setosa\n",
"14 5.8 4.0 1.2 0.2 Iris-setosa\n",
"15 5.7 4.4 1.5 0.4 Iris-setosa\n",
"16 5.4 3.9 1.3 0.4 Iris-setosa\n",
"17 5.1 3.5 1.4 0.3 Iris-setosa\n",
"18 5.7 3.8 1.7 0.3 Iris-setosa\n",
"19 5.1 3.8 1.5 0.3 Iris-setosa\n",
"20 5.4 3.4 1.7 0.2 Iris-setosa\n",
"21 5.1 3.7 1.5 0.4 Iris-setosa\n",
"22 4.6 3.6 1.0 0.2 Iris-setosa\n",
"23 5.1 3.3 1.7 0.5 Iris-setosa\n",
"24 4.8 3.4 1.9 0.2 Iris-setosa\n",
"25 5.0 3.0 1.6 0.2 Iris-setosa\n",
"26 5.0 3.4 1.6 0.4 Iris-setosa\n",
"27 5.2 3.5 1.5 0.2 Iris-setosa\n",
"28 5.2 3.4 1.4 0.2 Iris-setosa\n",
"29 4.7 3.2 1.6 0.2 Iris-setosa\n",
".. ... ... ... ... ...\n",
"121 5.6 2.8 4.9 2.0 Iris-virginica\n",
"122 7.7 2.8 6.7 2.0 Iris-virginica\n",
"123 6.3 2.7 4.9 1.8 Iris-virginica\n",
"124 6.7 3.3 5.7 2.1 Iris-virginica\n",
"125 7.2 3.2 6.0 1.8 Iris-virginica\n",
"126 6.2 2.8 4.8 1.8 Iris-virginica\n",
"127 6.1 3.0 4.9 1.8 Iris-virginica\n",
"128 6.4 2.8 5.6 2.1 Iris-virginica\n",
"129 7.2 3.0 5.8 1.6 Iris-virginica\n",
"130 7.4 2.8 6.1 1.9 Iris-virginica\n",
"131 7.9 3.8 6.4 2.0 Iris-virginica\n",
"132 6.4 2.8 5.6 2.2 Iris-virginica\n",
"133 6.3 2.8 5.1 1.5 Iris-virginica\n",
"134 6.1 2.6 5.6 1.4 Iris-virginica\n",
"135 7.7 3.0 6.1 2.3 Iris-virginica\n",
"136 6.3 3.4 5.6 2.4 Iris-virginica\n",
"137 6.4 3.1 5.5 1.8 Iris-virginica\n",
"138 6.0 3.0 4.8 1.8 Iris-virginica\n",
"139 6.9 3.1 5.4 2.1 Iris-virginica\n",
"140 6.7 3.1 5.6 2.4 Iris-virginica\n",
"141 6.9 3.1 5.1 2.3 Iris-virginica\n",
"142 5.8 2.7 5.1 1.9 Iris-virginica\n",
"143 6.8 3.2 5.9 2.3 Iris-virginica\n",
"144 6.7 3.3 5.7 2.5 Iris-virginica\n",
"145 6.7 3.0 5.2 2.3 Iris-virginica\n",
"146 6.3 2.5 5.0 1.9 Iris-virginica\n",
"147 6.5 3.0 5.2 2.0 Iris-virginica\n",
"148 6.2 3.4 5.4 2.3 Iris-virginica\n",
"149 5.9 3.0 5.1 1.8 Iris-virginica\n",
"150 NaN NaN NaN NaN \n",
"\n",
"[151 rows x 5 columns]"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"summary = df.describe()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"summary"
],
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 150.000000</td>\n",
" <td> 150.000000</td>\n",
" <td> 150.000000</td>\n",
" <td> 150.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 5.843333</td>\n",
" <td> 3.054000</td>\n",
" <td> 3.758667</td>\n",
" <td> 1.198667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 0.828066</td>\n",
" <td> 0.433594</td>\n",
" <td> 1.764420</td>\n",
" <td> 0.763161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 4.300000</td>\n",
" <td> 2.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 5.100000</td>\n",
" <td> 2.800000</td>\n",
" <td> 1.600000</td>\n",
" <td> 0.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 5.800000</td>\n",
" <td> 3.000000</td>\n",
" <td> 4.350000</td>\n",
" <td> 1.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 6.400000</td>\n",
" <td> 3.300000</td>\n",
" <td> 5.100000</td>\n",
" <td> 1.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 7.900000</td>\n",
" <td> 4.400000</td>\n",
" <td> 6.900000</td>\n",
" <td> 2.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
" sepal_length sepal_width petal_length petal_width\n",
"count 150.000000 150.000000 150.000000 150.000000\n",
"mean 5.843333 3.054000 3.758667 1.198667\n",
"std 0.828066 0.433594 1.764420 0.763161\n",
"min 4.300000 2.000000 1.000000 0.100000\n",
"25% 5.100000 2.800000 1.600000 0.300000\n",
"50% 5.800000 3.000000 4.350000 1.300000\n",
"75% 6.400000 3.300000 5.100000 1.800000\n",
"max 7.900000 4.400000 6.900000 2.500000"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.plot(style='o')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"<matplotlib.axes.AxesSubplot at 0x3b08750>"
]
},
{
"metadata": {},
"output_type": "display_data",
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rrHkU9v/5z4LdQxbJsbTDqV/GjRrgPCYe/Cwa35M/b3+yhE8i396e8aPA42j9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\ndJUGUmkrGWOVABYB+CXn/HdO5zY366XJqIb8Swd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"text": [
"<matplotlib.figure.Figure at 0x3b08fd0>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"iris_classes = df.groupby('class')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.boxplot(column=['sepal_length'], by=['class'])\n",
"sns.factorplot(\"sepal_length\", hue=\"class\", data=df, kind=\"box\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/home/wyleung/.virtualenvs/bio/local/lib/python2.7/site-packages/numpy/core/_methods.py:55: RuntimeWarning: Mean of empty slice.\n",
" warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"<seaborn.axisgrid.FacetGrid at 0x3c948d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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lSaoog7gkSRVlEJckqaIcnS4Nw7nTJU1UBnFNKosWnczSpSNbLff+Neup1Wp8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\n",
"text": [
"<matplotlib.figure.Figure at 0x3c9ea50>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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PIg0YfUp+37qkMVb3UJqaqDY3XZ+kWjXmDpK2yeMydpFmp5ia/982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\nmZmZmZmZmZmZmZmZmZmZmVXH/wfJIrbPDseLBgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x3f20e90>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.pivot_table(index=['class'])\n",
"df?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
\ No newline at end of file
README deleted 100644 → 0
View file @ 2438a631
Programming course assignments
==============================
This repository contains some assignment material for the first installment
(August 2013) of a programming course for scientists organised by the
department of Human Genetics of the Leiden University Medical Center.
See the [Trac Wiki](https://humgenprojects.lumc.nl/trac/programming-course)
for more information.
README.md 0 → 100644
View file @ 2e2bb3fc
Programming course assignments
==============================
This repository contains some assignment material for the second installment
(July 2014) of a programming course for scientists organised by the department
of Human Genetics of the Leiden University Medical Center.
See the
[course website](https://humgenprojects.lumc.nl/trac/humgenprojects/wiki/ProgrammingCourse)
for more information.
Also see the [instructions on using this repository](INSTRUCTIONS.md).
calculator/calculator.py 0 → 100644
View file @ 2e2bb3fc
#!/usr/bin/env python
class Stack(list):
def push(self, element):
self.append(element)
def __str__(self):
return str(self[-1])
class Calculator(Stack):
def add(self):
self.push(self.pop() + self.pop())
def sub(self):
temp = self.pop()
self.push(self.pop() - temp)
def mul(self):
self.push(self.pop() * self.pop())
def div(self):
temp = self.pop()
self.push(self.pop() / temp)
data/easy.fastq 0 → 100644
View file @ 2e2bb3fc
@HWI-ST1019:196:D121WACXX:5:1101:1538:2300/1
CCGCGACCTCTGTTCTGCAGCCCCTTCCCTTCCCCGCCTCCTGCTCTGCCGGGACTACGCACCGGCCTGATTGGTTACCCCCGGGGTGTCCTCGGTCACCA
+
1+++4)<@<A<+2A9A2++:3C8:)1?BDBDBBDC@::6@(.8..7)777:<?@@##############################################
data/iris.data 0 → 100644
View file @ 2e2bb3fc
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica
data/iris.names 0 → 100644
View file @ 2e2bb3fc
1. Title: Iris Plants Database
Updated Sept 21 by C.Blake - Added discrepency information
2. Sources:
(a) Creator: R.A. Fisher
(b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
(c) Date: July, 1988
3. Past Usage:
- Publications: too many to mention!!! Here are a few.
1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
to Mathematical Statistics" (John Wiley, NY, 1950).
2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
Structure and Classification Rule for Recognition in Partially Exposed
Environments". IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-2, No. 1, 67-71.
-- Results:
-- very low misclassification rates (0% for the setosa class)
4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
Transactions on Information Theory, May 1972, 431-433.
-- Results:
-- very low misclassification rates again
5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
conceptual clustering system finds 3 classes in the data.
4. Relevant Information:
--- This is perhaps the best known database to be found in the pattern
recognition literature. Fisher's paper is a classic in the field
and is referenced frequently to this day. (See Duda & Hart, for
example.) The data set contains 3 classes of 50 instances each,
where each class refers to a type of iris plant. One class is
linearly separable from the other 2; the latter are NOT linearly
separable from each other.
--- Predicted attribute: class of iris plant.
--- This is an exceedingly simple domain.
--- This data differs from the data presented in Fishers article
(identified by Steve Chadwick, spchadwick@espeedaz.net )
The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
where the error is in the fourth feature.
The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
where the errors are in the second and third features.
5. Number of Instances: 150 (50 in each of three classes)
6. Number of Attributes: 4 numeric, predictive attributes and the class
7. Attribute Information:
1. sepal length in cm
2. sepal width in cm
3. petal length in cm
4. petal width in cm
5. class:
-- Iris Setosa
-- Iris Versicolour
-- Iris Virginica
8. Missing Attribute Values: None
Summary Statistics:
Min Max Mean SD Class Correlation
sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
9. Class Distribution: 33.3% for each of 3 classes.
data/mangled.fq 0 → 100755
View file @ 2e2bb3fc
@IRIS:7:1:17:394#0/1
GTCAGGACAAGAAAGACAANTCCAATTNACATTATG
+IRIS:7:1:17:394#0/1
aaabaa`]baaaaa_aab]D^^`b`aYDW]abaa`^
@IRIS:7:1:17:800#0/2
GGAAACACTACTTAGGCTTATAAGATCNGGTTGCGG
+IRIS:7:1:17:800#0/2
ababbaaabaaaaa`]`ba`]`aaaaYD\\_a``XT
@IRIS:7:1:17:1757#0/1
TTTTCTCGACGATTTCCACTCCTGGTCNACGAATCC
+IRIS:7:1:17:1757#0/1
aaaaaa``aaa`aaaa_^a```]][Z[DY^XYV^_Y
@IRIS:7:1:17:1479#0/2
CATATTGTAGGGTGGATCTCGAAAGATATGAAAGAT
+IRIS:7:1:17:1479#0/2
abaaaaa`a```^aaaaa`_]aaa`aaa__a_X]``
@IRIS:7:1:17:150#0/1
TGATGTACTATGCATATGAACTTGTATGCAAAGTGG
+IRIS:7:1:17:150#0/1
abaabaa`aaaaaaa^ba_]]aaa^aaaaa_^][aa
@IRIS:7:1:18:443#0/2
CGATTCCACGTGATCAAAGAAACTAGAGTGGGTCTG
+IRIS:7:1:18:443#0/2
abbbbbababababababbaaaababababa``aaa
@IRIS:7:1:18:622#0/1
TGGGCTCCGGGAGGGGGGGATGTAAGAAAAATTACA
+IRIS:7:1:18:622#0/1
ababaaaabaa_``aa``]^aa_`aa_Z_aaaaa^b
@IRIS:7:1:18:628#0/2
AAAACAATTAGATGATGATAAAATTAACAAAGATTG
+IRIS:7:1:18:628#0/2
aaaaaaaaaaaaaaaaaa`^```a`aaa`^`a`a^a
@IRIS:7:1:18:1642#0/1
AATAAGTTTTAAACAGCTAACCTTGTTTACTTCTTT
+IRIS:7:1:18:1642#0/1
aaaaaa_aaababba_baa\aaaaa`a`baaaaa`a
@IRIS:7:1:18:1260#0/2
AGATGTACAAATTTGTATTTCAAGACAGATTTATAA
+IRIS:7:1:18:1260#0/2
abaab`bab`abaab`bbaaab_babb]aaaabba^
@IRIS:7:1:18:16#0/1
AGGTTCGTGTTGAGTGTTGCCTCTTTTTCTGTTAAT
+IRIS:7:1:18:16#0/1
abb`bbbabbbaWa`baba_Z``babaa[_aa`\X_
@IRIS:7:1:18:1150#0/2
AGCTAGGTAAAGTTATACATACAGATGGCCGAACTT
+IRIS:7:1:18:1150#0/2
abaaabb^bbabaabbbaaaaaaaaa`W^`a`_^``
@IRIS:7:1:18:1819#0/1
CTTACCGCGGTTTTAATAATTTTACCATCACTTCGT
+IRIS:7:1:18:1819#0/1
abbbbbbbba[aba``ba`bbba^]]``___]`_^W
@IRIS:7:1:18:1232#0/2
AAGAAATATATCGAAGAGTTAGTGCTGGACTGGCTG
+IRIS:7:1:18:1232#0/2
a^aY\Y^]^Y[WYUBBBBBBBBBBBBBBBBBBBBBB
@IRIS:7:1:19:1343#0/1
GGCATCTCCAGAGGAGGCTGTACCTGTGGAATAGCA
+IRIS:7:1:19:1343#0/1
abbbbbbbbababaaba_a\FXQXNVKO^F[RWYSQ
@IRIS:7:1:19:1885#0/2
TGAATTTCAATACGTGCAATTCCCTCCATACCAGAG
+IRIS:7:1:19:1885#0/2
abbbbbbbbbbbabaa[a_aba_^aa^_a``UZaab
@IRIS:7:1:19:691#0/1
GACATGAATTGTGTTTTAGGATAATTGATTAAAAAT
+IRIS:7:1:19:691#0/1
aaabbb`aaab`baaaaaba`aaababaaaaaaaab
@IRIS:7:1:19:1159#0/2
CAAGAATCATACTCAACATGCAAGCGATGATGAACA
+IRIS:7:1:19:1159#0/2
`a^`bbabba\_a^[_a\\]][GY]YBBBBBBBBBB
@IRIS:7:1:19:1310#0/1
GGAGATGATTTGACGATTCGCAGTCGGATCATCTGC
+IRIS:7:1:19:1310#0/1
aaabbabbabbbbbbbbbbbbbbabbbaabaa^a^Z
@IRIS:7:1:19:1108#0/2
GGATAATAAGCTCATGGGTTTGGTCTTACTTCACCG
+IRIS:7:1:19:1108#0/2
abaaaaaaaaaaaaaaa`^aaaa]]a``_a`aa^_Z
data/my_blast_output.xml 0 → 100644
View file @ 2e2bb3fc
Source diff could not be displayed: it is too large. Options to address this: view the blob.
data/parse.fastq 0 → 100644
View file @ 2e2bb3fc
@HWI-ST1019:196:D121WACXX:5:1101:1538:2300/1
CCGCGACCTCTGTTCTGCAGCCCCTTCCCTTCCCCGCCTCCTGCTCTGCCGGGACTACGCACCGGCCTGATTGGTTACCCCCGGGGTGTCCTCGGTCACCA
+
1+++4)<@<A<+2A9A2++:3C8:)1?BDBDBBDC@::6@(.8..7)777:<?@@##############################################
@HWI-ST1019:196:D121WACXX:5:1101:1538:2300/2
CCGCGACCTCTGTTCTGCAGCCCCTTCCCTTCCCCGCCTCCTGCTCTGCCGGGACTACGCACCGGCCTGATTGGTTACCCCCGGGGTGTCCTCGGTCACCA
+
1+++4)<@<A<+2A9A2++:3C8:)1?BDBDBBDC@::6@(.8..7)777:<?@@##############################################
data/roche.sff 0 → 100644
View file @ 2e2bb3fc
File added
data/sanger.ab1 0 → 100644
View file @ 2e2bb3fc
File added
data/simple.fa 0 → 100644
View file @ 2e2bb3fc
>sp|P12464|RPOE_BACSU DNA-directed RNA polymerase subunit delta OS=Bacillus subtilis (strain 168) GN=rpoE PE=1 SV=1
MGIKQYSQEELKEMALVEIAHELFEEHKKPVPFQELLNEIASLLGVKKEELGDRIAQFYT
DLNIDGRFLALSDQTWGLRSWYPYDQLDEETQPTVKAKKKKAKKAVEEDLDLDEFEEIDE
DDLDLDEVEEELDLEADDFDEEDLDEDDDDLEIEEDIIDEDDEDYDDEEEEIK