Merge remote-tracking branch 'origin/main'

Author: mikeckennedy

code/code_with_me/podcast/program.py

@@ -5,27 +5,23 @@ def main():
     show_header()
 
     service.download_info()
-    print("Working with total of {} episodes".format(service.get_latest_show_id()))
+    print('Working with total of {} episodes'.format(service.get_latest_show_id()))
     display_results()
 
 
 def show_header():
-    print("Welcome to the talk python info downloader.")
-    print("Code with me version.")
+    print('Welcome to the talk python info downloader.')
+    print('Code with me version.')
     print()
 
 
 def display_results():
-    # This is updated since the video recording.
-    # We had to trim back the episode list, so I changed
-    # this code to use the moving numbers from the RSS feed
-    # as they change over time.
-    start = service.get_min_show_id()
     end = service.get_latest_show_id()
+    start = max(1, end - 20)
 
     for show_id in range(start, end):
         info = service.get_episode(show_id)
-        print("{}. {}".format(info.show_id, info.title))
+        print('{}. {}'.format(info.show_id, info.title))
 
 
 if __name__ == '__main__':

code/code_with_me/podcast/service.py

@@ -8,22 +8,23 @@
 
 
 def download_info():
-    url = 'https://pythonbytes.fm/episodes/rss'
+    url = 'https://talkpython.fm/episodes/rss'
 
     resp = requests.get(url)
     resp.raise_for_status()
 
     dom = ElementTree.fromstring(resp.text)
 
     items = dom.findall('channel/item')
+    # Probably right here, smell, smell
     episode_count = len(items)
 
     for idx, item in enumerate(items):
         episode = Episode(
             item.find('title').text,
             item.find('link').text,
             item.find('pubDate').text,
-            episode_count - idx
+            episode_count - idx + 1
         )
         episode_data[episode.show_id] = episode
 

demos/adv_analysis_project/adv-moto-data.csv

@@ -0,0 +1,50 @@
+Manufacturer,Model,Engine (cc),Category
+BMW,R1150GS,1130,Heavyweight
+BMW,R1200GS,1170,Heavyweight
+BMW,R1250GS,1254,Heavyweight
+BMW,F650GS/G650GS,652,Middleweight
+BMW,F800GS,798,Middleweight
+BMW,F850GS,853,Middleweight
+BMW,G310GS,313,Lightweight
+Kawasaki,KLR650,652,Lightweight
+Kawasaki,Versys 650,649,Middleweight
+Kawasaki,Versys 1000,1043,Heavyweight
+Kawasaki,Versys-X 300,296,Lightweight
+Suzuki,V-Strom 1000,996,Heavyweight
+Suzuki,V-Strom 650,645,Middleweight
+Suzuki,V-Strom 800DE,776,Middleweight
+Honda,XR650L,644,Lightweight
+Honda,Africa Twin 1000 (CRF1000L),998,Heavyweight
+Honda,Africa Twin 1100 (CRF1100L),1084,Heavyweight
+Honda,CB500X,471,Lightweight
+Honda,NC700X,670,Middleweight
+Honda,XL750 Transalp,755,Middleweight
+Honda,CRF300L Rally,286,Lightweight
+Yamaha,Super Tenere 1200,1199,Heavyweight
+Yamaha,Tenere,689,Middleweight
+Triumph,Tiger 955i,955,Heavyweight
+Triumph,Tiger 1050,1050,Heavyweight
+Triumph,Tiger 800,799,Middleweight
+Triumph,Tiger 900,888,Middleweight
+Triumph,Tiger 1200,1215,Heavyweight
+Ducati,Multistrada 1100,1078,Heavyweight
+Ducati,Multistrada 1260,1262,Heavyweight
+Ducati,Multistrada V4,1158,Heavyweight
+Ducati,Multistrada 950/V2,937,Middleweight
+Ducati,DesertX,937,Middleweight
+KTM,640 Adventure,625,Lightweight
+KTM,950 Adventure,942,Heavyweight
+KTM,990 Adventure,999,Heavyweight
+KTM,1190 Adventure,1195,Heavyweight
+KTM,1290 Super Adventure,1301,Heavyweight
+KTM,790 Adventure,799,Middleweight
+KTM,890 Adventure,889,Middleweight
+KTM,390 Adventure,373,Lightweight
+Aprilia,Caponord 1000,998,Heavyweight
+Aprilia,Caponord 1200,1197,Heavyweight
+Aprilia,Tuareg 660,659,Middleweight
+Moto Guzzi,Stelvio 1200,1151,Heavyweight
+Moto Guzzi,V85 TT,853,Middleweight
+Buell/Harley,Buell Ulysses XB12X,1203,Heavyweight
+Harley-Davidson,Pan America 1250,1252,Heavyweight
+Zero,DSR/X (electric),0,Heavyweight

demos/adv_analysis_project/adv_notebook.ipynb

@@ -0,0 +1,66 @@
+#%% md
+# # Adventure Motorcycle Segment
+#%%
+import pandas as pd
+import matplotlib.pyplot as plt
+#%%
+df = pd.read_csv('adv-moto-data.csv')
+# df
+#%%
+df.groupby('Category')['Engine (cc)'].mean().astype(int)
+#%% md
+# ## Let's visualize our data
+#%% md
+# ### Plotting engine size
+#%%
+# Map categories to colors
+colors = {'Heavyweight': 'red', 'Middleweight': 'blue', 'Lightweight': 'green'}
+df['Color'] = df['Category'].map(colors)
+
+plt.figure(figsize=(12, 8))
+
+# Use df.index for the x-axis and 'Engine (cc)' for the y-axis.
+# The marker size is proportional to engine size, scaled down by a factor.
+scaling_factor = 0.5
+plt.scatter(
+    df.index,
+    df['Engine (cc)'],
+    s=df['Engine (cc)'] * scaling_factor,
+    color=df['Color'],
+    alpha=0.6
+)
+
+# Annotate each point with the model name, offset slightly above the marker
+for i, row in df.iterrows():
+    plt.annotate(
+        row['Model'],
+        (i, row['Engine (cc)']),
+        textcoords="offset points",
+        xytext=(0, -14),  # Offset label by 10 points below the marker
+        ha='center',
+        va='top',  # Align text so that its top is at the offset position
+        fontsize=8
+    )
+
+plt.xlabel('Model Index')
+plt.ylabel('Engine (cc)')
+plt.title('Engine Size by Model with Category Color')
+plt.show()
+#%% md
+# ## Visualizing Engine Size by Category
+#%%
+# Compute the average engine size by category
+avg_engine_size = df.groupby('Category')['Engine (cc)'].mean()
+
+# Define colors for each category
+colors = {'Heavyweight': 'red', 'Middleweight': 'blue', 'Lightweight': 'green'}
+
+# Create the bar plot
+plt.figure(figsize=(10, 3))
+plt.bar(avg_engine_size.index, avg_engine_size.values,
+        color=[colors[cat] for cat in avg_engine_size.index])
+plt.xlabel('Category')
+plt.ylabel('Average Engine Size (cc)')
+plt.title('Average Engine Size by Category')
+plt.show()
+#%%

demos/adv_analysis_project/adv_notebook.py

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Talk Python
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

demos/data-driven-web-apps-with-flask/LICENSE

@@ -0,0 +1,47 @@
+# Data-driven web apps with 
+
+Course demo code and other hand-out materials for our data driven web apps in Flask course. Visit the full course page at **[training.talkpython.fm](https://training.talkpython.fm/courses/explore_flask/building-data-driven-web-applications-in-python-with-flask-sqlalchemy-and-bootstrap)**
+
+[![](readme_materials/flask-data-web-apps.jpg)](https://training.talkpython.fm/courses/explore_flask/building-data-driven-web-applications-in-python-with-flask-sqlalchemy-and-bootstrap)
+
+## Course Summary
+
+One of the areas Python really shines is in building clean and powerful web applications. Once you know the language basics, this course will teach you everything you need to build data-driven, modern web applications in Python with the Flask web framework.
+
+## What's this course about and how is it different?
+
+**Full stack web development is exactly what you need to build true data-driven web applications in Python**. Yet these courses can be confusing and overwhelming due to the many technologies involved (Python, SQL, CSS, etc).
+
+We have taken great care to build a course that is **just real enough to give you what you need without anything extra**. We build a replica of a popular web application, Python's own packaging index over at pypi.org.
+
+While building our replica PyPI, you will learn:
+
+* Learn about the different major Python web frameworks
+* Create a Flask-based website from scratch using the CLI and PyCharm
+* Work with dynamic HTML templates
+* Map URLs to view methods using routing
+* Make advanced use of routing to build a full custom CMS in 8 minutes
+* Take advantage of bootstrap to build well designed sites
+* Map data to and from Python using classes with SQLAlchemy
+* Learn how Alembic can help our database evolve as our models change
+* Accept user input with HTML forms
+* Add client and server-side validation
+* Overcome the special challenges of testing web apps (databases, frameworks, etc)
+* Deploy our web application to a fresh Linux machine (virtual, cloud-based)
+* Leverage our design patterns to convert our app to another data model (MongoDB edition)
+
+## Who is this course for?
+
+This course is for **anyone who wants to create Python-based web applications using the Flask web framework**. In fact, you'll learn a lot of web skills that will translate across frameworks as well.
+
+We do assume that you have basic Python language skills and can read HTML and CSS. But most advanced uses of the language or CSS are explained in the course.
+
+## Is this course based on Python 3 or Python 2?
+
+This course is **based upon Python 3**. With Python 2 going unsupported in 2020, we believe that it would be ill-advised to teach or learn Python 2.
+
+## The time to act is now
+
+This **data-driven web apps with Python and Flask** course is just what you need to build that fast, custom web application that can help you create your next great idea or just solve a business problem by deploying it behind your firewall.
+
+Take the course over at **[training.talkpython.fm](https://training.talkpython.fm/courses/explore_flask/building-data-driven-web-applications-in-python-with-flask-sqlalchemy-and-bootstrap)**.

demos/data-driven-web-apps-with-flask/README.md

@@ -0,0 +1,74 @@
+# A generic, single database configuration.
+
+[alembic]
+# path to migration scripts
+script_location = alembic
+
+# template used to generate migration files
+# file_template = %%(rev)s_%%(slug)s
+
+# timezone to use when rendering the date
+# within the migration file as well as the filename.
+# string value is passed to dateutil.tz.gettz()
+# leave blank for localtime
+# timezone =
+
+# max length of characters to apply to the
+# "slug" field
+# truncate_slug_length = 40
+
+# set to 'true' to run the environment during
+# the 'revision' command, regardless of autogenerate
+# revision_environment = false
+
+# set to 'true' to allow .pyc and .pyo files without
+# a source .py file to be detected as revisions in the
+# versions/ directory
+# sourceless = false
+
+# version location specification; this defaults
+# to alembic/versions.  When using multiple version
+# directories, initial revisions must be specified with --version-path
+# version_locations = %(here)s/bar %(here)s/bat alembic/versions
+
+# the output encoding used when revision files
+# are written from script.py.mako
+# output_encoding = utf-8
+
+sqlalchemy.url = sqlite:///./pypi_org/db/pypi.sqlite
+
+
+# Logging configuration
+[loggers]
+keys = root,sqlalchemy,alembic
+
+[handlers]
+keys = console
+
+[formatters]
+keys = generic
+
+[logger_root]
+level = WARN
+handlers = console
+qualname =
+
+[logger_sqlalchemy]
+level = WARN
+handlers =
+qualname = sqlalchemy.engine
+
+[logger_alembic]
+level = INFO
+handlers =
+qualname = alembic
+
+[handler_console]
+class = StreamHandler
+args = (sys.stderr,)
+level = NOTSET
+formatter = generic
+
+[formatter_generic]
+format = %(levelname)-5.5s [%(name)s] %(message)s
+datefmt = %H:%M:%S

demos/data-driven-web-apps-with-flask/app/ch15_deploy/final/alembic.ini

@@ -0,0 +1 @@
+Generic single-database configuration.

demos/data-driven-web-apps-with-flask/app/ch15_deploy/final/alembic/README

@@ -0,0 +1,15 @@
+from alembic import op
+from sqlalchemy import engine_from_config
+from sqlalchemy.engine import reflection
+
+
+def table_has_column(table, column):
+    config = op.get_context().config
+    engine = engine_from_config(config.get_section(config.config_ini_section), prefix='sqlalchemy.')
+    insp = reflection.Inspector.from_engine(engine)
+    has_column = False
+    for col in insp.get_columns(table):
+        if column not in col['name']:
+            continue
+        has_column = True
+    return has_column