<jupyter_start><jupyter_text>Variables Descriptionfrdwdm(公司名); year(年份); youbian(邮编); setup_year(开业年); setup_month(开业月); totaloutput(工业总产值); newproduct_output(工业总产值-新产品产值); total_sale(工业销售产值); export(工业销售产值-出口交货值); aemployment(全部职工); valueadded(工业增加值); longrun_invest(长期投资); totalfixed_asset(固定资产合计); fixed_asset_op(固定资产-生产经营用); depreciate(累计折旧); depreciate_tyear(累计折旧-本年折旧); fixedasset_net(固定资产净值年平均余额); total_asset(资产总计); owner_rights(所有者权益合计); paicl_up_capital(实收资本); so_capital(国家资本金); co_capital(集体资本金); lp_capital(法人资本金); p_capital(个人资本金); gat_capital(港澳台资本金); foreign_capital(外商资本金); mainbusiness_revenue(主营业务收入); mainbusiness_cost(主营业务成本); finance_expense(财务费用); fe_interest(财务费用-利息支出); total_profit(利润总额); advertise_expense(广告费); wage_payable(本年应付工资总额); wp_mainbusiness(本年应付福利费总额); totalintermediate_input(工业中间投入合计); drop variables: id(公司代码); frdm(法人代码); fddbr(法人); quhao(区号); dianhua(固定电话); fenjihao(分机号); chuanzhen(传真); email; web; registertype(登记注册类型); lishu(隶属关系); totalasset(资产总计含缺省值); e_state(!=so_capital); e_collective(=co_capital); e_individual(=p_capital); e_legal_person(=lp_capital); e_hmt(=gat_capital); e_foreign(=foreign_capital); e_total(=paicl_up_capital); soe_sh, collect_sh, lp_sh, p_sh, hmt_sh, foreign_sh(shares); state, collective, private, hmt, foreign(dummies); frdmid; firmid; newid(=frdmid-1);unclear: edu(职工教育费), rd_expense(研究开发费), code_cnty, cic_adj, BR_deflator, I, cic2(first two digits of cic_adj), lny, lnl, lnk, lnm, lnk_net, investment_net(投资收益), lninvest_net(=ln(investment_net)), lninvest, 'ownership', 'ownerships',<jupyter_code># single plot: plot the number of firms in processing trade industry

fig = plt.figure(figsize=(12,5))

ax1 = ts.plot(color='blue', grid=True, label='Number')

h1, l1 = ax1.get_legend_handles_labels()

plt.legend(h1, l1, loc=3, prop={'size': 17})
plt.show()
fig.savefig('plot1.png')
# Fixed Effect logit model on export
temp_X4 = pd.concat([temp_y8, temp_X1], axis=1)
FE1 = FixedEffectPanelModel()
FE1.fit(temp_X4, 'ex', verbose=True)
FE1.summary()
# FE.beta
# FE.beta_se

# Fixed Effect logit model on import
temp_X5 = pd.concat([temp_y9, temp_X1], axis=1)
FE2 = FixedEffectPanelModel()
FE2.fit(temp_X5, 'im', verbose=True)
FE2.summary()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15202853321444493, 'fraction_numerical': 0.013820775746767721, 'mean_word_length': 10.810526315789474, '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': '411014', 'license': 'MIT', 'n_tokens_mistral': 1070, 'n_tokens_neox': 1149, 'n_words': 153, 'path': 'EDA_and_Regression.ipynb', 'repo_name': 'jiahui-z/Graduation-Thesis', 'size': '2243'}