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By using the JDH api track: https://journalofdigitalhistory.org/api/articles/
import requests
# API URL
api_url = "https://journalofdigitalhistory.org/api/articles"
# Make the API call
response = requests.get(api_url)
# Check if the request was successful
if response.status_code == 200:
# Parse the JSON response
response_data = response.json()
# Extract the list of articles from the response
results = response_data.get("results", [])
if results:
# Display details of the first article
first_article = results[0]
# Check if "abstract" and other attributes are present in the response
if 'abstract' in first_article and 'title' in first_article['abstract']:
title = first_article['abstract']['title']
status = first_article['abstract'].get('status', 'N/A')
publication_date = first_article.get('publication_date', 'N/A')
print("Article Details:")
print(f"Title: {title}")
print(f"Status: {status}")
print(f"Publication Date: {publication_date}")
else:
print("Incomplete or unexpected article structure in the response.")
else:
print("No articles found in the response.")
else:
print(f"Failed to retrieve data. Status code: {response.status_code}")import requests
import pandas as pd
import matplotlib.pyplot as plt
# API URL
api_url = "https://journalofdigitalhistory.org/api/articles/?format=json&limit=100"
# Make the API call
response = requests.get(api_url)
# Check if the request was successful
if response.status_code == 200:
# Parse the JSON response
response_data = response.json()
# Extract the list of articles from the response
results = response_data.get("results", [])
if results:
# Create a DataFrame with selected columns
df = pd.DataFrame(results, columns=['status', 'publication_date', 'repository_type','tags'])
# Extract the year using regular expressions
df['publication_year'] = df['publication_date'].str.extract(r'(\d{4})', expand=False)
# Check if 'tags' column exists before applying the filter
if 'tags' in df.columns:
# Filter out editorial articles
df = df[~df['tags'].apply(lambda x: any(tag['name'] == 'editorial' for tag in x))]
# Count the number of articles per year
articles_per_year = df['publication_year'].value_counts().sort_index()
# Plot the data
articles_per_year.plot(kind='bar', xlabel='Year', ylabel='Number of Articles', title='Articles Published per Year')
# Set y-axis ticks to integers only
plt.yticks(range(int(articles_per_year.max()) + 1))
plt.show()
else:
print("No articles found in the response.")
else:
print(f"Failed to retrieve data. Status code: {response.status_code}")import requests
import pandas as pd
import matplotlib.pyplot as plt
# API URL
api_url = "https://journalofdigitalhistory.org/api/abstracts/?format=json&limit=100"
username = "elisabeth.guerard"
password = PASSWORD
# Set up authentication
auth = (username, password)
# Make the API call with authentication
response = requests.get(api_url, auth=auth)
# Check if the request was successful
if response.status_code == 200:
# Parse the JSON response
response_data = response.json()
# Extract the list of articles from the response
results = response_data.get("results", [])
if results:
# Create a DataFrame with selected columns
df = pd.DataFrame(results, columns=['status', 'callpaper', 'submitted_date'])
# Extract the year using regular expressions
df['submitted_date'] = df['submitted_date'].str.extract(r'(\d{4})', expand=False)
# Count the number of abstracts per year
abstracts_per_year = df['submitted_date'].value_counts().sort_index()
# Plot the data as a line plot
abstracts_per_year.plot(kind='line', marker='o', xlabel='Year', ylabel='Number of Abstracts', title='Abstracts submitted per Year')
plt.show()
else:
print("No abstracts found in the response.")
else:
print(f"Failed to retrieve data. Status code: {response.status_code}")# Count the number of abstracts per year
abstracts_per_year = df['submitted_date'].value_counts().sort_index()
# Plot the data as a line plot
abstracts_per_year.plot(kind='line', marker='o', xlabel='Year', ylabel='Number of Abstracts', title='Abstracts submitted per Year')import requests
import pandas as pd
import matplotlib.pyplot as plt
# API URL
api_url = "https://journalofdigitalhistory.org/api/abstracts/?format=json&limit=100"
# Set up authentication
auth = (username, password)
# Make the API call with authentication
response = requests.get(api_url, auth=auth)
# Check if the request was successful
if response.status_code == 200:
# Parse the JSON response
response_data = response.json()
# Extract the list of articles from the response
results = response_data.get("results", [])
if results:
# Create a DataFrame with selected columns
df = pd.DataFrame(results, columns=['status', 'callpaper', 'submitted_date'])
# Extract the year using regular expressions
df['submitted_date'] = df['submitted_date'].str.extract(r'(\d{4})', expand=False)
# Handle the case where callpaper is null by replacing it with "open_issue"
df['callpaper'].fillna('open_issue', inplace=True)
# Create a pivot table to get the counts of abstracts for each callpaper type and year
pivot_table = pd.pivot_table(df, values='status', index='submitted_date', columns='callpaper', aggfunc='count', fill_value=0)
# Plot the data as a line plot
ax = pivot_table.plot(kind='line', marker='o', xlabel='Year', ylabel='Number of Abstracts', title='Abstracts submitted per Year by Callpaper Type')
# Get the handles and labels of the current axes
handles, labels = ax.get_legend_handles_labels()
# Include "open_issue" in the legend
open_issue_label = "Open Issue" # You can customize this label
handles.append(plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='w', markersize=5)) # Add a dummy point for open_issue
labels.append(open_issue_label)
# Plot the legend
ax.legend(handles, labels, title='Callpaper Type', loc='upper left')
# Set the y-axis origin to 0
ax.set_ylim(bottom=0)
plt.show()
else:
print("No abstracts found in the response.")
else:
print(f"Failed to retrieve data. Status code: {response.status_code}")pivot_table = pd.pivot_table(df, values='status', index='submitted_date', columns='callpaper', aggfunc='count', fill_value=0)
# Plot the data as a line plot
ax = pivot_table.plot(kind='line', marker='o', xlabel='Year', ylabel='Number of Abstracts', title='Abstracts submitted per Year by Callpaper Type')
# Get the handles and labels of the current axes
handles, labels = ax.get_legend_handles_labels()
# Map callpaper values to corresponding labels
callpaper_labels = {
'4.0': 'Teaching Digital History',
'3.0': 'DH China',
'2.0': 'History through the web',
'1.0': 'Digital Tools',
'open_issue': 'Open Issue' # You can customize this label
}
# Update legend labels
labels = [callpaper_labels[label] for label in labels]
# Plot the legend
ax.legend(handles, labels, title='Callpaper Type', loc='upper left')
# Set the y-axis origin to 0
ax.set_ylim(bottom=0)
plt.show()import requests
import pandas as pd
import matplotlib.pyplot as plt
# API URL
api_url = "http://10.240.4.191/api/abstracts/?format=json&limit=100"
# Set up authentication
auth = (username, password)
# Make the API call with authentication
response = requests.get(api_url, auth=auth)
# Check if the request was successful
if response.status_code == 200:
# Parse the JSON response
response_data = response.json()
# Extract the list of articles from the response
results = response_data.get("results", [])
if results:
# Create a DataFrame with selected columns
df = pd.DataFrame(results, columns=['status', 'callpaper', 'submitted_date'])
# Extract the year using regular expressions
df['submitted_date'] = df['submitted_date'].str.extract(r'(\d{4})', expand=False)
# Handle the case where callpaper is null by replacing it with "open_issue"
df['callpaper'].fillna('open_issue', inplace=True)
# Convert submitted_date to numeric (to handle nulls)
df['submitted_date'] = pd.to_numeric(df['submitted_date'], errors='coerce')
# Determine the minimum start year for each callpaper type
min_start_years = df.groupby('callpaper')['submitted_date'].min()
# Filter out abstracts submitted before the minimum start year for each callpaper type
df = df[df['submitted_date'] >= df['callpaper'].map(min_start_years)]
# Create a line plot for each year
unique_years = df['submitted_date'].unique()
for year in unique_years:
# Filter the data for the current year
df_year = df[df['submitted_date'] == year]
# Create a pivot table for the current year
pivot_table = pd.pivot_table(df_year, values='status', index='callpaper', aggfunc='count', fill_value=0)
# Plot the data as a line plot
pivot_table.plot(kind='line', marker='o', legend=True, title=f'Abstracts submitted in {year} by Callpaper Type')
plt.xlabel('Callpaper Type')
plt.ylabel('Number of Abstracts')
plt.show()
else:
print("No abstracts found in the response.")
else:
print(f"Failed to retrieve data. Status code: {response.status_code}")Need to track the Github issue of the repository https://github.com/C2DH/jdh-notebook
from IPython.display import Image
display(Image("stats/technical_review_stats.png"))On the 35 closed:
- 27 closed in 2023
- 6 closed in 2022
- 2 closed in 2021
Average 2 per month
Let's retrive this information by API.
ACCESS_TOKEN = ""import requests
import json
import matplotlib.pyplot as plt
headers = {
'Authorization': ACCESS_TOKEN ,
}
params = {
'state': 'closed', # or 'closed' for closed issues
}
repository_url = 'https://api.github.com/repos/C2DH/jdh-notebook/issues'
response = requests.get(repository_url, params=params, headers=headers)
issues = response.json()
# Filter issues with the title containing 'Technical review'
filtered_issues = [issue for issue in issues if 'Technical review' in issue.get('title', '')]
# Extract relevant information and create a DataFrame
data = [{'title': issue['title'], 'status': issue['state']} for issue in filtered_issues]
df = pd.DataFrame(data)
# Display the DataFrame
df.head()