<jupyter_start><jupyter_text>Make a statistical overview about 25 conference in 7 fields.We save the result in `json` format to provide data for `D3.js`<jupyter_code>%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import scipy as sp
import matplotlib.pyplot as plt
import seaborn as sns
import gensim
import networkx as nx
df = pd.read_csv('seven_topconf_papers.csv')
res = []
for field in ['DM', 'DP', 'ED', 'ML', 'NC', 'NL', 'OS']:
    field_df = df[df.field == field]
    publications = np.unique(field_df.publication)
    for pub in publications:
        tmp_dic = {}
        tmp_dic['name'] = field + ' - ' + pub
        articles = []
        tmp = field_df[field_df.publication==pub]
        years = np.unique(tmp.time.values)
        for year in years:
            paper_num = tmp[tmp.time == year].shape[0]
            articles.append([year, paper_num])
        tmp_dic['articles'] = articles
        res.append(tmp_dic)
import json
f = open('journals_dbs.json', 'w')
f.write(json.dumps(res))
f.close()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12579762989972654, 'fraction_numerical': 0.004557885141294439, 'mean_word_length': 3.4634146341463414, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '18755', 'license': 'MIT', 'n_tokens_mistral': 378, 'n_tokens_neox': 374, 'n_words': 101, 'path': 'conf_year_stat.ipynb', 'repo_name': 'TianxiaoHu/Polar', 'size': '1097'}