Marimo-quarto

Example of marimo in quarto

Here you can run python directly within your browser thanks to WASM to compute the salary of french civil servant depending on the “indice majoré” (IM). A big thank you to the awesome pluggin quarto-marimo.

import%20pandas%20as%20pd%0A%23%20---%20Table%20data%20---%0Adata%20%3D%20%7B%0A%20%20%20%20%22Salaire%22%3A%20%5B%0A%20%20%20%20%20%20%20%20%22Brut%20mensuel%22%2C%0A%20%20%20%20%20%20%20%20%22Net%20mensuel%22%2C%0A%20%20%20%20%20%20%20%20%22Net%20mensuel%20apr%C3%A8s%20taxes%22%2C%0A%20%20%20%20%20%20%20%20%22Brut%20annuel%22%2C%0A%20%20%20%20%20%20%20%20%22Brut%20%C3%A9quivalent%20priv%C3%A9%22%2C%0A%20%20%20%20%20%20%20%20%22taxes%20annuelles%22%2C%0A%20%20%20%20%20%20%20%20%22Pourcentage%20%C3%A0%20la%20source%22%0A%20%20%20%20%5D%2C%0A%20%20%20%20f%22Montant%20(%E2%82%AC)%20-%20IM%20%7Bstart_indice_CPJ%7D%22%3A%20%5B%0A%20%20%20%20%20%20%20%20gross_salary%2C%0A%20%20%20%20%20%20%20%20gross2annual_net(gross_salary)%2F12%2C%0A%20%20%20%20%20%20%20%20total_annual_net%2F12%2C%0A%20%20%20%20%20%20%20%2012*gross_salary%2C%0A%20%20%20%20%20%20%20%2012*gross_salary*0.8%2F0.75%2C%0A%20%20%20%20%20%20%20%20taxes(annual_taxable_net(gross_salary))%2C%0A%20%20%20%20%20%20%20%20100*taxes(annual_taxable_net(gross_salary))%2Fgross2annual_net(gross_salary)%0A%20%20%20%20%5D%2C%0A%0A%20%20%20%20f%22Montant%20(%E2%82%AC)%20-%20IM%20%7Bstart_indice_default%7D%22%3A%20%5B%0A%20%20%20%20%20%20%20%20gross_salary_default%2C%0A%20%20%20%20%20%20%20%20gross2annual_net(gross_salary_default)%2F12%2C%0A%20%20%20%20%20%20%20%20total_annual_net_default%2F12%2C%0A%20%20%20%20%20%20%20%2012*gross_salary_default%2C%0A%20%20%20%20%20%20%20%2012*gross_salary_default*0.8%2F0.75%2C%0A%20%20%20%20%20%20%20%20taxes(annual_taxable_net(gross_salary_default))%2C%0A%20%20%20%20%20%20%20%20100*taxes(annual_taxable_net(gross_salary_default))%2Fgross2annual_net(gross_salary_default)%0A%20%20%20%20%5D%0A%7D%0A%0Adf%20%3D%20pd.DataFrame(data)%0Adf%5Bf%22Montant%20(%E2%82%AC)%20-%20IM%20%7Bstart_indice_CPJ%7D%22%5D%20%3D%20df%5Bf%22Montant%20(%E2%82%AC)%20-%20IM%20%7Bstart_indice_CPJ%7D%22%5D.map(lambda%20x%3A%20f%22%E2%82%AC%7Bx%3A%2C.2f%7D%22)%0Adf%5Bf%22Montant%20(%E2%82%AC)%20-%20IM%20%7Bstart_indice_default%7D%22%5D%20%3D%20df%5Bf%22Montant%20(%E2%82%AC)%20-%20IM%20%7Bstart_indice_default%7D%22%5D.map(lambda%20x%3A%20f%22%E2%82%AC%7Bx%3A%2C.2f%7D%22)%0A%0A%23%20---%20Display%20both%20---%0A%0Amo.ui.table(df)%0A

import%20plotly.graph_objects%20as%20go%0Afig%20%3D%20go.Figure()%0Afig.add_trace(go.Scatter(x%3Dnp.cumsum(years_ENSTA)%2C%20y%3DSalaire_ENSTA_default%2C%20visible%3D'legendonly'%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3Df'%20IM%20%7Bstart_indice_default%7D%20(salaire%20seul)'%2C%20line%3Ddict(color%3D'skyblue'%2C%20dash%3D'dash'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'cross')))%0Afig.add_trace(go.Scatter(x%3Dnp.cumsum(years_ENSTA)%2C%20y%3DSalaire_ENSTA_default_prime%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3Df'%20IM%20%7Bstart_indice_default%7D%20(avec%20C1)%20'%2C%20line%3Ddict(color%3D'skyblue'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'circle')))%0Afig.add_trace(go.Scatter(x%3Dnp.cumsum(years_ENSTA)%2C%20y%3DSalaire_ENSTA_default_prime%20-%20Prime%20%2B%20Prime_mieux%2C%20visible%3D'legendonly'%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3Df'%20IM%20%7Bstart_indice_default%7D%20(avec%20C1%20%2B%20C3)%20'%2C%20line%3Ddict(color%3D'skyblue'%2C%20dash%3D'dash'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'circle')))%0Afig.add_trace(go.Scatter(x%3Dnp.cumsum(years_ENSTA)%2C%20y%3DSalaire_ENSTA_CPJ%2C%20visible%3D'legendonly'%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3Df'IM%20%7Bstart_indice_CPJ%7D%20(salaire%20seul)%20'%2C%20line%3Ddict(color%3D'blue'%2C%20dash%3D'dash'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'cross')))%0Afig.add_trace(go.Scatter(x%3Dnp.cumsum(years_ENSTA)%2C%20y%3DSalaire_ENSTA_CPJ_prime%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3Df'IM%20%7Bstart_indice_CPJ%7D%20(avec%20C1)%20'%2C%20line%3Ddict(color%3D'blue'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'circle')))%0Afig.add_trace(go.Scatter(x%3Dnp.cumsum(years_ENSTA)%2C%20y%3DSalaire_ENSTA_CPJ_prime%20-%20Prime%20%2B%20Prime_mieux%2C%20visible%3D'legendonly'%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3Df'IM%20%7Bstart_indice_CPJ%7D%20(avec%20C1%20%2B%20C3)%20'%2C%20line%3Ddict(color%3D'blue'%2C%20dash%3D'dash'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'circle')))%0A%23%20Polytechnique%0Afig.add_trace(go.Scatter(x%3Dnp.cumsum(years_mcf_ENS)%2C%20y%3DSalaire_mcf_ENS%2C%20visible%3D'legendonly'%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3D'MCF%20ENS%20(salaire%20seul)%20'%2C%20line%3Ddict(color%3D'lightcoral'%2C%20dash%3D'dash'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'cross')))%0A%23%20fig.add_trace(go.Scatter(x%3Dnp.cumsum(years_mcf_ENS)%2C%20y%3DSalaire_mcf_ENS%20%2B%20RIPEC_ENS%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3D'MCF%20ENS%20(avec%20RIPEC)%20'%2C%20line%3Ddict(color%3D'lightcoral'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'circle')))%0Afig.add_trace(go.Scatter(x%3Dnp.cumsum(years_mcf_ENS)%2C%20y%3Dres%20*%20Salaire_mcf_ENS%20%2B%20RIPEC_ENS%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3D'MCF%20ENS%20(avec%20RIPEC%20et%20res.)%20'%2C%20line%3Ddict(color%3D'lightcoral'%2C%20dash%3D'dash'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'circle')))%0Afig.add_trace(go.Scatter(x%3Dnp.cumsum(years_mcf_default)%2C%20y%3DSalaire_mcf%2C%20visible%3D'legendonly'%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3D'MCF%20(salaire%20seul)%20'%2C%20line%3Ddict(color%3D'chocolate'%2C%20dash%3D'dash'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'cross')))%0A%23%20fig.add_trace(go.Scatter(x%3Dnp.cumsum(years_mcf_default)%2C%20y%3DSalaire_mcf%20%2B%20RIPEC%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3D'MCF%20(avec%20RIPEC)%20'%2C%20line%3Ddict(color%3D'chocolate'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'circle')))%0Afig.add_trace(go.Scatter(x%3Dnp.cumsum(years_mcf_default)%2C%20y%3Dres%20*%20Salaire_mcf%20%2B%20RIPEC%2C%20visible%3D'legendonly'%2C%20mode%3D'lines%2Bmarkers'%2C%20name%3D'MCF%20(avec%20RIPEC%20et%20res.)%20'%2C%20line%3Ddict(color%3D'chocolate'%2C%20dash%3D'dash'%2C%20shape%3D'hv')%2C%20marker%3Ddict(symbol%3D'circle')))%0A%0A%23%23%23%23%23%23%23%23%23%23%23%23%23%23%20Plotly%0Afig.update_layout(title%3D'%C3%89volution%20de%20la%20r%C3%A9mun%C3%A9ration'%2C%20xaxis_title%3D'Anciennet%C3%A9%20(ann%C3%A9es)'%2C%20yaxis_title%3D'R%C3%A9mun%C3%A9ration%20mensuelle%20(%E2%82%AC)'%2C%20template%3D'plotly_white'%2C%20legend%3Ddict(title%3D'Cat%C3%A9gorie')%2C%20font%3Ddict(size%3D12)%2C%20width%3D1000%2C%20height%3D600)%0Afig.update_xaxes(showgrid%3DTrue%2C%20gridwidth%3D1%2C%20gridcolor%3D'lightgrey')%0Afig.update_yaxes(showgrid%3DTrue%2C%20gridwidth%3D1%2C%20gridcolor%3D'lightgrey')%0A