Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly
Appearance settings

Commit 5686095

Browse filesBrowse files
rueedlingerrueedlinger
committed
updated examples
1 parent a86dede commit 5686095
Copy full SHA for 5686095

File tree

Expand file treeCollapse file tree

7 files changed

+129
-194
lines changed
Filter options
Expand file treeCollapse file tree

7 files changed

+129
-194
lines changed

‎README.md

Copy file name to clipboard
+12-17Lines changed: 12 additions & 17 deletions
Original file line numberDiff line numberDiff line change
@@ -1,36 +1,31 @@
11
# Python Snippets
22

3-
These __Python Snippets__ are tested with Python 3.5.x. All snippets are
3+
These __Python Snippets__ are tested with Python 3.6.x. All snippets are
44
available as Jupyter notebooks (http://jupyter.org/).
55

66

77
## Get started...
88
To get started create a virtual environment and install the required packages.
99

10-
### Virtualenv
11-
The following example shows how to create an environment with
12-
_"virtualenv"_ (https://virtualenv.pypa.io/)
13-
and Python 3.5 with the required packages.
10+
- Juypter Notebook
11+
- pandas
12+
- geojson
13+
- beautifulsoup4
14+
- feedparser
15+
- scikit-image
16+
- matplotlib
1417

15-
```bash
16-
virtualenv --python=/usr/bin/python3.5 py35-ps
17-
18-
source py35-ps/bin/activate
19-
20-
pip install -r requirements.txt
21-
```
2218

2319
### Conda
2420
The following example shows how to create an environment with _"conda"_
25-
(http://conda.pydata.org/) and Python 3.5 with
21+
(http://conda.pydata.org/) and Python 3.6 with
2622
the required packages.
2723

2824
```bash
29-
conda create -n py35-ps python=3.5
25+
conda create -n py36-ps python=3.6
3026

31-
source activate py35-ps
27+
source activate py36-ps
3228

33-
pip install -r requirements.txt
3429
```
3530

3631
## The Python Snippets
@@ -47,4 +42,4 @@ __HTTP__
4742
- [Extract the text from a HTML document with Beautiful Soup](http/beautifulsoup4.ipynb)
4843

4944
__Image__
50-
- tbd
45+
- [Image diff with scikit-image](image/image_diff.ipynb)

‎geojson/geojson_stations.ipynb

Copy file name to clipboardExpand all lines: geojson/geojson_stations.ipynb
+24-20Lines changed: 24 additions & 20 deletions
Original file line numberDiff line numberDiff line change
@@ -42,15 +42,26 @@
4242
},
4343
{
4444
"cell_type": "code",
45-
"execution_count": 1,
46-
"metadata": {
47-
"collapsed": false
48-
},
45+
"execution_count": 2,
46+
"metadata": {},
4947
"outputs": [
5048
{
5149
"data": {
5250
"text/html": [
5351
"<div>\n",
52+
"<style scoped>\n",
53+
" .dataframe tbody tr th:only-of-type {\n",
54+
" vertical-align: middle;\n",
55+
" }\n",
56+
"\n",
57+
" .dataframe tbody tr th {\n",
58+
" vertical-align: top;\n",
59+
" }\n",
60+
"\n",
61+
" .dataframe thead th {\n",
62+
" text-align: right;\n",
63+
" }\n",
64+
"</style>\n",
5465
"<table border=\"1\" class=\"dataframe\">\n",
5566
" <thead>\n",
5667
" <tr style=\"text-align: right;\">\n",
@@ -116,7 +127,7 @@
116127
"4 22.0 7.589551 47.547405 277.0 Basel SBB"
117128
]
118129
},
119-
"execution_count": 1,
130+
"execution_count": 2,
120131
"metadata": {},
121132
"output_type": "execute_result"
122133
}
@@ -137,9 +148,8 @@
137148
},
138149
{
139150
"cell_type": "code",
140-
"execution_count": 2,
151+
"execution_count": 3,
141152
"metadata": {
142-
"collapsed": false,
143153
"scrolled": true
144154
},
145155
"outputs": [],
@@ -161,10 +171,8 @@
161171
},
162172
{
163173
"cell_type": "code",
164-
"execution_count": 3,
165-
"metadata": {
166-
"collapsed": false
167-
},
174+
"execution_count": 4,
175+
"metadata": {},
168176
"outputs": [
169177
{
170178
"name": "stdout",
@@ -195,18 +203,16 @@
195203
},
196204
{
197205
"cell_type": "code",
198-
"execution_count": 4,
199-
"metadata": {
200-
"collapsed": false
201-
},
206+
"execution_count": 5,
207+
"metadata": {},
202208
"outputs": [
203209
{
204210
"data": {
205211
"text/plain": [
206212
"\"\\nwith open('stations.geojson', 'w') as file:\\n file.write(dump)\\n\""
207213
]
208214
},
209-
"execution_count": 4,
215+
"execution_count": 5,
210216
"metadata": {},
211217
"output_type": "execute_result"
212218
}
@@ -232,9 +238,7 @@
232238
{
233239
"cell_type": "code",
234240
"execution_count": null,
235-
"metadata": {
236-
"collapsed": true
237-
},
241+
"metadata": {},
238242
"outputs": [],
239243
"source": []
240244
}
@@ -255,7 +259,7 @@
255259
"name": "python",
256260
"nbconvert_exporter": "python",
257261
"pygments_lexer": "ipython3",
258-
"version": "3.5.2"
262+
"version": "3.6.7"
259263
}
260264
},
261265
"nbformat": 4,

‎http/beautifulsoup4.ipynb

Copy file name to clipboardExpand all lines: http/beautifulsoup4.ipynb
+13-23Lines changed: 13 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -11,10 +11,8 @@
1111
},
1212
{
1313
"cell_type": "code",
14-
"execution_count": null,
15-
"metadata": {
16-
"collapsed": true
17-
},
14+
"execution_count": 1,
15+
"metadata": {},
1816
"outputs": [],
1917
"source": [
2018
"html_doc = \"\"\"\n",
@@ -41,10 +39,8 @@
4139
},
4240
{
4341
"cell_type": "code",
44-
"execution_count": null,
45-
"metadata": {
46-
"collapsed": false
47-
},
42+
"execution_count": 2,
43+
"metadata": {},
4844
"outputs": [],
4945
"source": [
5046
"from bs4 import BeautifulSoup\n",
@@ -80,18 +76,16 @@
8076
},
8177
{
8278
"cell_type": "code",
83-
"execution_count": 13,
84-
"metadata": {
85-
"collapsed": false
86-
},
79+
"execution_count": 3,
80+
"metadata": {},
8781
"outputs": [
8882
{
8983
"data": {
9084
"text/plain": [
9185
"'My new page'"
9286
]
9387
},
94-
"execution_count": 13,
88+
"execution_count": 3,
9589
"metadata": {},
9690
"output_type": "execute_result"
9791
}
@@ -109,10 +103,8 @@
109103
},
110104
{
111105
"cell_type": "code",
112-
"execution_count": 14,
113-
"metadata": {
114-
"collapsed": false
115-
},
106+
"execution_count": 4,
107+
"metadata": {},
116108
"outputs": [
117109
{
118110
"name": "stdout",
@@ -138,18 +130,16 @@
138130
},
139131
{
140132
"cell_type": "code",
141-
"execution_count": 15,
142-
"metadata": {
143-
"collapsed": false
144-
},
133+
"execution_count": 5,
134+
"metadata": {},
145135
"outputs": [
146136
{
147137
"data": {
148138
"text/plain": [
149139
"'\\nMy new page\\n\\nCool my new page\\nI have written the following articles:\\nA1,\\nA2\\nA3;\\n\\n...\\n'"
150140
]
151141
},
152-
"execution_count": 15,
142+
"execution_count": 5,
153143
"metadata": {},
154144
"output_type": "execute_result"
155145
}
@@ -175,7 +165,7 @@
175165
"name": "python",
176166
"nbconvert_exporter": "python",
177167
"pygments_lexer": "ipython3",
178-
"version": "3.5.2"
168+
"version": "3.6.7"
179169
}
180170
},
181171
"nbformat": 4,

‎http/urlib.ipynb

Copy file name to clipboardExpand all lines: http/urlib.ipynb
+21-24Lines changed: 21 additions & 24 deletions
Original file line numberDiff line numberDiff line change
@@ -11,10 +11,8 @@
1111
},
1212
{
1313
"cell_type": "code",
14-
"execution_count": 21,
15-
"metadata": {
16-
"collapsed": false
17-
},
14+
"execution_count": 1,
15+
"metadata": {},
1816
"outputs": [],
1917
"source": [
2018
"import urllib.request"
@@ -30,10 +28,8 @@
3028
},
3129
{
3230
"cell_type": "code",
33-
"execution_count": 22,
34-
"metadata": {
35-
"collapsed": false
36-
},
31+
"execution_count": 2,
32+
"metadata": {},
3733
"outputs": [],
3834
"source": [
3935
"url = 'https://medium.com/tag/machine-learning'\n",
@@ -50,10 +46,8 @@
5046
},
5147
{
5248
"cell_type": "code",
53-
"execution_count": 23,
54-
"metadata": {
55-
"collapsed": false
56-
},
49+
"execution_count": 3,
50+
"metadata": {},
5751
"outputs": [
5852
{
5953
"name": "stdout",
@@ -76,18 +70,16 @@
7670
},
7771
{
7872
"cell_type": "code",
79-
"execution_count": 24,
80-
"metadata": {
81-
"collapsed": false
82-
},
73+
"execution_count": 4,
74+
"metadata": {},
8375
"outputs": [
8476
{
8577
"data": {
8678
"text/plain": [
8779
"'text/html; charset=utf-8'"
8880
]
8981
},
90-
"execution_count": 24,
82+
"execution_count": 4,
9183
"metadata": {},
9284
"output_type": "execute_result"
9385
}
@@ -105,18 +97,16 @@
10597
},
10698
{
10799
"cell_type": "code",
108-
"execution_count": 25,
109-
"metadata": {
110-
"collapsed": false
111-
},
100+
"execution_count": 5,
101+
"metadata": {},
112102
"outputs": [
113103
{
114104
"data": {
115105
"text/plain": [
116-
"b'<!DOCTYPE html><html xmlns:cc=\"http://creativecommons.org/ns#\"><head prefix=\"og: http://ogp.me/ns# fb: http://ogp.me/ns/fb# medium-com: http://ogp.me/ns/fb/medium-com#\"><meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\"><meta name=\"viewport\" content=\"width=device-width, initial-scale=1\"><title>Machine Learning \\xe2\\x80\\x93 Medium</title><link rel=\"canonical\" href=\"https://medium.com/tag/machine-learning\"><link id=\"feedLink\" rel=\"alternate\" type=\"application/rss+xml\" title=\"RSS\" href=\"/fee'"
106+
"b'<!DOCTYPE html><html xmlns:cc=\"http://creativecommons.org/ns#\"><head prefix=\"og: http://ogp.me/ns# fb: http://ogp.me/ns/fb# medium-com: http://ogp.me/ns/fb/medium-com#\"><meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\"><meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0, viewport-fit=contain\"><title>The most insightful stories about Machine Learning \\xe2\\x80\\x93 Medium</title><link rel=\"canonical\" href=\"https://medium.com/tag/machine-learning\"><link id=\"feedLink\" rel=\"al'"
117107
]
118108
},
119-
"execution_count": 25,
109+
"execution_count": 5,
120110
"metadata": {},
121111
"output_type": "execute_result"
122112
}
@@ -125,6 +115,13 @@
125115
"text = con.read()\n",
126116
"text[:500]"
127117
]
118+
},
119+
{
120+
"cell_type": "code",
121+
"execution_count": null,
122+
"metadata": {},
123+
"outputs": [],
124+
"source": []
128125
}
129126
],
130127
"metadata": {
@@ -143,7 +140,7 @@
143140
"name": "python",
144141
"nbconvert_exporter": "python",
145142
"pygments_lexer": "ipython3",
146-
"version": "3.5.2"
143+
"version": "3.6.7"
147144
}
148145
},
149146
"nbformat": 4,

0 commit comments

Comments
0 (0)
Morty Proxy This is a proxified and sanitized view of the page, visit original site.