Original file (SVG file, nominally 800 × 400 pixels, file size: 396 KB)
Summary
DescriptionWineHQ ratings.svg |
Deutsch: WineHQ ratings of applications for different wine versions |
Date | |
Source | Own work |
Author | Laserlicht |
Licensing
This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication. | |
The person who associated a work with this deed has dedicated the work to the public domain by waiving all of their rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.
http://creativecommons.org/publicdomain/zero/1.0/deed.enCC0Creative Commons Zero, Public Domain Dedicationfalsefalse |
Code to create chart
Execute in Jupyter Notebook. There is sequential execute possible. If something aborts it's possible to resume. Data is written as pickle file for further analysis. Script needs very long to execute (> 5h).
Needs pip librarys: beautifulsoup plotly pandas numpy natsort
import urllib.request
import re
from bs4 import BeautifulSoup
import plotly.express as px
import pandas as pd
import numpy as np
import pickle
import os
from natsort import natsorted, natsort_keygen, ns
url = "https://appdb.winehq.org/objectManager.php?bIsQueue=false&bIsRejected=false&sClass=application&sTitle=Browse+Applications&iItemsPerPage=200&sOrderBy=appName&bAscending=true&sOrderBy=appId&bAscending=true&iPage="
url_version = "https://appdb.winehq.org/objectManager.php?sClass=application&iId="
#
# get pages
#
req = urllib.request.Request(
url + "1",
data=None,
headers={
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'
}
)
f = urllib.request.urlopen(req)
html = f.read().decode('utf-8')
pages = int(re.search(r'of <b>(\d*)<\/b>', html, re.IGNORECASE).group(1))
pages
#
# get applications
#
applications = None
for i in range(pages):
req = urllib.request.Request(
url + str(i+1),
data=None,
headers={
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'
}
)
f = urllib.request.urlopen(req)
html = f.read().decode('utf-8')
soup = BeautifulSoup(html, "html.parser")
table = soup.find("table", {"class": "whq-table"})
if isinstance(applications, pd.DataFrame):
applications = pd.concat([applications, pd.read_html(table.prettify())[0]])
else:
applications = pd.read_html(table.prettify())[0]
pickle.dump(applications, open("wine_applications.pickle", "wb"))
applications
if os.path.isfile("wine_applications.pickle"):
applications = pickle.load(open("wine_applications.pickle", "rb"))
applications_to_process = applications.iloc[:, 1].values.tolist()
versions = None
#
# get versions
#
if os.path.isfile("wine_versions.pickle"):
applications_to_process, versions = pickle.load(open("wine_versions.pickle", "rb"))
while len(applications_to_process) > 0:
req = urllib.request.Request(
url_version + str(applications_to_process[0]),
data=None,
headers={
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'
}
)
f = urllib.request.urlopen(req)
html = f.read().decode('utf-8')
soup = BeautifulSoup(html, "html.parser")
table = soup.find("table", {"class": "whq-table"})
if table != None:
df = pd.read_html(table.prettify(), extract_links="body")[0]
df["AppId"] = applications_to_process[0]
if isinstance(versions, pd.DataFrame):
versions = pd.concat([versions, df])
else:
versions = df
applications_to_process.remove(applications_to_process[0])
pickle.dump((applications_to_process, versions), open("wine_versions.pickle", "wb"))
print("remain: " + str(len(applications_to_process)))
versions_to_process = []
for i in range(versions.shape[0]):
versions_to_process.append((versions["Version"].tolist()[i][0], versions["Version"].tolist()[i][1], versions["AppId"].tolist()[i]))
tests = None
#
# get tests
#
if os.path.isfile("wine_tests.pickle"):
versions_to_process, tests = pickle.load(open("wine_tests.pickle", "rb"))
while len(versions_to_process) > 0:
req = urllib.request.Request(
versions_to_process[0][1] + "&bShowAll=true",
data=None,
headers={
'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.47 Safari/537.36'
}
)
f = urllib.request.urlopen(req)
html = f.read().decode('utf-8')
soup = BeautifulSoup(html, "html.parser")
table = soup.find("div", id="collapse-tests").find("table", {"class": "whq-table"})
if table != None:
df = pd.read_html(table.prettify(), extract_links="body")[0]
df["Ver"] = versions_to_process[0][0]
df["AppId"] = versions_to_process[0][2]
if isinstance(tests, pd.DataFrame):
tests = pd.concat([tests, df])
else:
tests = df
versions_to_process.remove(versions_to_process[0])
pickle.dump((versions_to_process, tests), open("wine_tests.pickle", "wb"))
print("remain: " + str(len(versions_to_process)))
if os.path.isfile("wine_applications.pickle"):
applications = pickle.load(open("wine_applications.pickle", "rb"))
if os.path.isfile("wine_versions.pickle"):
applications_to_process, versions = pickle.load(open("wine_versions.pickle", "rb"))
if os.path.isfile("wine_tests.pickle"):
versions_to_process, tests = pickle.load(open("wine_tests.pickle", "rb"))
version_no = natsorted([x for x, y in tests["Wine version"].drop_duplicates().tolist() if not "staging" in x and not "rc" in x], alg=ns.IGNORECASE)
version_no
df = pd.DataFrame({"version": version_no})
df
tests_edit = tests.copy()
tests_edit["Wine version"] = [x for x, y in tests_edit["Wine version"]]
tests_edit["Test date"] = [x for x, y in tests_edit["Test date"]]
tests_edit["Rating"] = [x for x, y in tests_edit["Rating"]]
tests_edit
df1 = pd.merge(df, tests_edit, how="left", left_on="version", right_on="Wine version")
df1 = df1[["version", "Test date", "Rating"]]
df1
df2 = pd.pivot_table(df1, index="version", columns="Rating", values="Rating", aggfunc="count").reset_index()
df2 = df2.fillna(0)
df2["Sum"] = df2["Bronze"] + df2["Garbage"] + df2["Gold"] + df2["Platinum"] + df2["Silver"]
df2["Bronze %"] = df2["Bronze"] / df2["Sum"]
df2["Garbage %"] = df2["Garbage"] / df2["Sum"]
df2["Gold %"] = df2["Gold"] / df2["Sum"]
df2["Platinum %"] = df2["Platinum"] / df2["Sum"]
df2["Silver %"] = df2["Silver"] / df2["Sum"]
df2 = df2.replace([np.inf, -np.inf], 0)
df2
df3 = df2.copy()
df3 = df3[["version", "Bronze %", "Garbage %", "Gold %", "Platinum %", "Silver %"]]
df3 = pd.melt(df3, id_vars="version", value_vars=list(df3.columns[1:]))
df3['Rating'] = df3['Rating'].str.replace(' %','')
df3['order'] = df3['Rating'].replace({'Garbage':0, 'Bronze':1, 'Silver':2, 'Gold':3, 'Platinum':4})
df3
fig = px.bar(df3.sort_values(["version", "order"], key=natsort_keygen()), width=800, height=400, x="version", y="value", color="Rating", color_discrete_map={"Garbage": 'rgb(255, 0, 0)', "Bronze": 'rgb(255, 128, 2)', "Silver": 'rgb(255, 255, 0)', "Gold": 'rgb(128, 192, 0)', "Platinum": 'rgb(0, 128, 0)'})
fig.update_layout(bargap=0)
fig.update_layout({
'plot_bgcolor': 'rgba(0, 0, 0, 0)',
'paper_bgcolor': 'rgba(255, 255, 255, 255)',
'title': dict(text = 'WineHQ ratings of applications for different wine versions', y=0.955),
'margin': dict( l = 10, r = 10, b = 10, t = 30)
})
fig.update_traces(marker_line_width=0)
fig.update_yaxes(visible=False, showticklabels=False)
fig.update_xaxes(title='Version')
fig.write_image("wine.svg")
fig.show()
Items portrayed in this file
depicts
creator
some value
copyright status
copyrighted, dedicated to the public domain by copyright holder
copyright license
Creative Commons CC0 License
source of file
original creation by uploader
inception
25 November 2023
File history
Click on a date/time to view the file as it appeared at that time.
Date/Time | Thumbnail | Dimensions | User | Comment | |
---|---|---|---|---|---|
current | 13:26, 25 November 2023 | 800 × 400 (396 KB) | Laserlicht | Uploaded own work with UploadWizard |
File usage
Global file usage
The following other wikis use this file:
- Usage on de.wikipedia.org
Metadata
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If the file has been modified from its original state, some details may not fully reflect the modified file.
Width | 800 |
---|---|
Height | 400 |
Well, that’s interesting to know that Psilotum nudum are known as whisk ferns. Psilotum nudum is the commoner species of the two. While the P. flaccidum is a rare species and is found in the tropical islands. Both the species are usually epiphytic in habit and grow upon tree ferns. These species may also be terrestrial and grow in humus or in the crevices of the rocks.
View the detailed Guide of Psilotum nudum: Detailed Study Of Psilotum Nudum (Whisk Fern), Classification, Anatomy, Reproduction