<jupyter_start><jupyter_text>Imports<jupyter_code>import pandas as pd
import requests
import matplotlib.pyplot as plt
%matplotlib inline<jupyter_output><empty_output><jupyter_text>Request Data<jupyter_code>url = 'https://apex.oracle.com/pls/apex/superdataworld/book_view/'
r = requests.get(url)
json = r.json()
df = pd.DataFrame(json['items'])
df.info()
df.head()<jupyter_output><class 'pandas.core.frame.DataFrame'>
RangeIndex: 25 entries, 0 to 24
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   entity  25 non-null     object
 1   year    25 non-null     int64 
dtypes: int64(1), object(1)
memory usage: 528.0+ bytes<jupyter_text>Refine Data and Graph<jupyter_code>df = df[['entity','year','books_per_million']]
df = df[df['entity'] == 'Austria']
df.head()

df.plot(x = 'year', y= 'books_per_million')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16890380313199105, 'fraction_numerical': 0.024608501118568233, 'mean_word_length': 4.5246913580246915, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '284532', 'license': 'MIT', 'n_tokens_mistral': 357, 'n_tokens_neox': 336, 'n_words': 67, 'path': 'Apex_Connect.ipynb', 'repo_name': 'SuperDataWorld/Python', 'size': '894'}