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   "source": [
    "# Visualization workflows\n",
    "\n",
    "This notebook shows the new unified `db.plot(...)` workflow on the packaged MARIO test databases.\n",
    "\n",
    "The same plotting engine supports both a guided path for quick inspection and an advanced path with explicit Plotly Express mappings, including custom palettes."
   ]
  },
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     "shell.execute_reply": "2026-05-01T06:52:50.852135Z"
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   "source": [
    "import mario"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0a722f3",
   "metadata": {},
   "source": [
    "## Load the packaged test databases\n",
    "\n",
    "These are the same small MARIO-readable tables used elsewhere in the documentation and test suite, so the examples are reproducible without external downloads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8ee3735",
   "metadata": {
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     "shell.execute_reply": "2026-05-01T06:52:51.016028Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO Parser: excel reading IOT flows from /Users/lorenzorinaldi/Documents/GitHub/MARIO/mario/test/tables/test_IOT_standard.xlsx.\n",
      "INFO Parser: state payload ready with 6 canonical blocks.\n",
      "INFO Parser: excel state ready for IOT.\n",
      "INFO Metadata: initialized.\n",
      "INFO Parser: excel reading SUT flows from /Users/lorenzorinaldi/Documents/GitHub/MARIO/mario/test/tables/test_SUT_standard.xlsx.\n",
      "INFO Parser: state payload ready with 10 canonical blocks.\n",
      "INFO Parser: excel state ready for SUT.\n",
      "INFO Metadata: initialized.\n"
     ]
    }
   ],
   "source": [
    "iot = mario.load_test(\"IOT\")\n",
    "sut = mario.load_test(\"SUT\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b211e5",
   "metadata": {},
   "source": [
    "## Quick path: plot one matrix with a preset\n",
    "\n",
    "For a first look at a matrix, pass the matrix name and let MARIO choose a useful Plotly Express mapping with a preset such as `\"overview\"`."
   ]
  },
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            "backgroundcolor": "white",
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            "gridwidth": 2,
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            "ticks": "",
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            "backgroundcolor": "white",
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            "gridwidth": 2,
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            "ticks": ""
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           "bgcolor": "white",
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   ],
   "source": [
    "fig = iot.plot(\n",
    "    matrix=\"Z\",\n",
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    "    auto_open=False,\n",
    "    path=False,\n",
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    "fig"
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  {
   "cell_type": "markdown",
   "id": "866c7bf2",
   "metadata": {},
   "source": [
    "## Inspect the dataframe actually plotted\n",
    "\n",
    "Set `return_data=True` when you want both the Plotly figure and the dataframe passed to Plotly Express after filtering, aggregation and top-n trimming."
   ]
  },
  {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sector_from</th>\n",
       "      <th>Region_to</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Reg1</td>\n",
       "      <td>7.367798e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Reg2</td>\n",
       "      <td>1.048720e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Industry</td>\n",
       "      <td>Reg1</td>\n",
       "      <td>3.487216e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Industry</td>\n",
       "      <td>Reg2</td>\n",
       "      <td>6.248561e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Services</td>\n",
       "      <td>Reg1</td>\n",
       "      <td>4.228452e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sector_from Region_to         Value\n",
       "0  Agriculture      Reg1  7.367798e+06\n",
       "1  Agriculture      Reg2  1.048720e+05\n",
       "2     Industry      Reg1  3.487216e+07\n",
       "3     Industry      Reg2  6.248561e+05\n",
       "4     Services      Reg1  4.228452e+07"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, plotted = iot.plot(\n",
    "    matrix=\"Z\",\n",
    "    preset=None,\n",
    "    kind=\"bar\",\n",
    "    x=\"Sector_from\",\n",
    "    color=\"Region_to\",\n",
    "    auto_open=False,\n",
    "    path=False,\n",
    "    return_data=True,\n",
    ")\n",
    "plotted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cec2b5a5",
   "metadata": {},
   "source": [
    "## Advanced path: explicit Plotly Express mappings\n",
    "\n",
    "More experienced users can control the chart directly through `x`, `color`, `facet_col`, `animation_frame`, `agg`, `top_n` and the other familiar mappings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bbda5325",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T06:52:51.197232Z",
     "iopub.status.busy": "2026-05-01T06:52:51.197104Z",
     "iopub.status.idle": "2026-05-01T06:52:51.232780Z",
     "shell.execute_reply": "2026-05-01T06:52:51.232511Z"
    }
   },
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = iot.plot(\n",
    "    matrix=\"Y\",\n",
    "    kind=\"bar\",\n",
    "    preset=None,\n",
    "    x=\"Sector_from\",\n",
    "    color=\"Consumption category_to\",\n",
    "    facet_col=\"Region_to\",\n",
    "    agg=\"sum\",\n",
    "    top_n=6,\n",
    "    auto_open=False,\n",
    "    path=False,\n",
    ")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f139d8",
   "metadata": {},
   "source": [
    "## Filter before plotting\n",
    "\n",
    "Use `filters={...}` to keep only the levels you want before MARIO aggregates the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29cbbad5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T06:52:51.234442Z",
     "iopub.status.busy": "2026-05-01T06:52:51.234322Z",
     "iopub.status.idle": "2026-05-01T06:52:51.266834Z",
     "shell.execute_reply": "2026-05-01T06:52:51.266522Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
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         "legendgroup": "Agriculture",
         "marker": {
          "color": "#3F8EFC",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Agriculture",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Agriculture",
          "Industry",
          "Services"
         ],
         "xaxis": "x",
         "y": {
          "bdata": "VKiuK2JoLEFZYNSBXCAxQdES5sjR5DxB",
          "dtype": "f8"
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         "yaxis": "y"
        },
        {
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         "legendgroup": "Industry",
         "marker": {
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         "name": "Industry",
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         "yaxis": "y"
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        {
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         "legendgroup": "Services",
         "marker": {
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         "name": "Services",
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          "Agriculture",
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         "y": {
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         "tracegroupgap": 0
        },
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          "table": [
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          ],
          "font": {
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          "geo": {
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           "lakecolor": "white",
           "landcolor": "white",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "#C8D4E3"
          },
          "hoverlabel": {
           "align": "left"
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          "hovermode": "closest",
          "mapbox": {
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          "plot_bgcolor": "white",
          "polar": {
           "angularaxis": {
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            "linecolor": "#EBF0F8",
            "ticks": ""
           },
           "bgcolor": "white",
           "radialaxis": {
            "gridcolor": "#EBF0F8",
            "linecolor": "#EBF0F8",
            "ticks": ""
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "white",
            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#EBF0F8"
           },
           "yaxis": {
            "backgroundcolor": "white",
            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#EBF0F8"
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           "zaxis": {
            "backgroundcolor": "white",
            "gridcolor": "#DFE8F3",
            "gridwidth": 2,
            "linecolor": "#EBF0F8",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "#EBF0F8"
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          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           },
           "bgcolor": "white",
           "caxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "#EBF0F8",
           "linecolor": "#EBF0F8",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "#EBF0F8",
           "linecolor": "#EBF0F8",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "Intersectoral transaction flows"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Sector_from"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Value"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = iot.plot(\n",
    "    matrix=\"Z\",\n",
    "    kind=\"bar\",\n",
    "    preset=None,\n",
    "    x=\"Sector_from\",\n",
    "    color=\"Sector_to\",\n",
    "    filters={\"Region_from\": [\"Reg1\"], \"Region_to\": [\"Reg1\"]},\n",
    "    top_n=4,\n",
    "    auto_open=False,\n",
    "    path=False,\n",
    ")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877b4c41",
   "metadata": {},
   "source": [
    "## SUT-specific views\n",
    "\n",
    "The same engine works on split SUT blocks such as `U` and `S`, so you can plot activity and commodity dimensions directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6c01dfe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T06:52:51.268517Z",
     "iopub.status.busy": "2026-05-01T06:52:51.268399Z",
     "iopub.status.idle": "2026-05-01T06:52:51.307390Z",
     "shell.execute_reply": "2026-05-01T06:52:51.307090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Activity_to=Manufacturing<br>Region_to=Region 1<br>Commodity_from=%{x}<br>Value=%{y}<extra></extra>",
         "legendgroup": "Manufacturing",
         "marker": {
          "color": "#3F8EFC",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Manufacturing",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Goods",
          "Services"
         ],
         "xaxis": "x",
         "y": {
          "bdata": "y4SYqlKaXkIW7CaxHoJUQg==",
          "dtype": "f8"
         },
         "yaxis": "y"
        },
        {
         "hovertemplate": "Activity_to=Manufacturing<br>Region_to=Region 2<br>Commodity_from=%{x}<br>Value=%{y}<extra></extra>",
         "legendgroup": "Manufacturing",
         "marker": {
          "color": "#3F8EFC",
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         },
         "name": "Manufacturing",
         "orientation": "v",
         "showlegend": false,
         "textposition": "auto",
         "type": "bar",
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   "source": [
    "fig = sut.plot(\n",
    "    matrix=\"U\",\n",
    "    kind=\"bar\",\n",
    "    preset=None,\n",
    "    x=\"Commodity_from\",\n",
    "    color=\"Activity_to\",\n",
    "    facet_col=\"Region_to\",\n",
    "    top_n=4,\n",
    "    auto_open=False,\n",
    "    path=False,\n",
    ")\n",
    "fig"
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  },
  {
   "cell_type": "markdown",
   "id": "6bdec663",
   "metadata": {},
   "source": [
    "## Plot region-by-region trades for one item\n",
    "\n",
    "`db.calc_trades(...)` can aggregate intermediate and final trade into one region-by-region matrix. Use `total=True` to add row and column totals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "21a8322f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Region</th>\n",
       "      <th>Reg1</th>\n",
       "      <th>Reg2</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Reg1</th>\n",
       "      <td>9.897401e+06</td>\n",
       "      <td>74768.097334</td>\n",
       "      <td>9.972169e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reg2</th>\n",
       "      <td>8.933711e+03</td>\n",
       "      <td>70915.947332</td>\n",
       "      <td>7.984966e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>9.906335e+06</td>\n",
       "      <td>145684.044667</td>\n",
       "      <td>1.005202e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Region          Reg1           Reg2         Total\n",
       "Region                                           \n",
       "Reg1    9.897401e+06   74768.097334  9.972169e+06\n",
       "Reg2    8.933711e+03   70915.947332  7.984966e+04\n",
       "Total   9.906335e+06  145684.044667  1.005202e+07"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade_matrix = iot.calc_trades(\n",
    "    \"Agriculture\",\n",
    "    total=True,\n",
    ")\n",
    "trade_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2966bd83",
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   "source": [
    "Set `aggregate=False` and `show_plot=True` to keep intermediate and final trade separate and produce a heatmap with one panel per component. The plot is displayed as a side effect; the return value remains the trade matrix."
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       "        Intermediate                       Final              \n",
       "Region          Reg1          Reg2          Reg1          Reg2\n",
       "Region                                                        \n",
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   "source": [
    "trade_components = iot.calc_trades(\n",
    "    \"Agriculture\",\n",
    "    aggregate=False,\n",
    "    show_plot=True,\n",
    "    path=False,\n",
    "    auto_open=False,\n",
    ")\n",
    "trade_components"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "567a067b",
   "metadata": {},
   "source": [
    "## Plot a prepared dataframe\n",
    "\n",
    "`db.plot(...)` can also accept a custom dataframe when the quantity to visualize is not one raw MARIO matrix. Here we reuse sectoral GDP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf68c4c0",
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     "shell.execute_reply": "2026-05-01T06:52:51.313742Z"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Sector</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Reg1</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>6.016424e+06</td>\n",
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       "      <th>1</th>\n",
       "      <td>Reg1</td>\n",
       "      <td>Industry</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Reg1</td>\n",
       "      <td>Services</td>\n",
       "      <td>6.206624e+07</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reg2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Reg2</td>\n",
       "      <td>Industry</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Reg2</td>\n",
       "      <td>Services</td>\n",
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       "  Region       Sector           GDP\n",
       "0   Reg1  Agriculture  6.016424e+06\n",
       "1   Reg1     Industry  1.497569e+07\n",
       "2   Reg1     Services  6.206624e+07\n",
       "3   Reg2  Agriculture  4.539138e+04\n",
       "4   Reg2     Industry  3.520592e+05\n",
       "5   Reg2     Services  1.428250e+06"
      ]
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   "source": [
    "gdp = iot.GDP(total=False).reset_index()\n",
    "gdp"
   ]
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   "source": [
    "fig = iot.plot(\n",
    "    data=gdp,\n",
    "    kind=\"treemap\",\n",
    "    preset=None,\n",
    "    y=\"GDP\",\n",
    "    path_columns=[\"Sector\"],\n",
    "    auto_open=False,\n",
    "    path=False,\n",
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    "fig"
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  },
  {
   "cell_type": "markdown",
   "id": "6389aa2f",
   "metadata": {},
   "source": [
    "## Change the default palette\n",
    "\n",
    "Use `mario.set_palette(...)` to change the default discrete color sequence used by `db.plot(...)`. MARIO ships built-in palettes such as `mario`, `Plotly`, `Safe`, `Vivid`, `Pastel`, `Alphabet`, and `McKinsey`."
   ]
  },
  {
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   "execution_count": 12,
   "id": "46aac2c5",
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     "shell.execute_reply": "2026-05-01T06:52:51.377380Z"
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           "bgcolor": "white",
           "caxis": {
            "gridcolor": "#DFE8F3",
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          },
          "title": {
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          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "#EBF0F8",
           "linecolor": "#EBF0F8",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "#EBF0F8",
           "linecolor": "#EBF0F8",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "Final demand"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
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         ],
         "title": {
          "text": "Sector_from"
         }
        },
        "xaxis2": {
         "anchor": "y2",
         "domain": [
          0.51,
          1
         ],
         "matches": "x",
         "title": {
          "text": "Sector_from"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
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         "title": {
          "text": "Value"
         }
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
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         "matches": "y",
         "showticklabels": false
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mario.set_palette(\"Safe\")\n",
    "\n",
    "fig = iot.plot(\n",
    "    matrix=\"Y\",\n",
    "    kind=\"bar\",\n",
    "    preset=None,\n",
    "    x=\"Sector_from\",\n",
    "    color=\"Consumption category_to\",\n",
    "    facet_col=\"Region_to\",\n",
    "    agg=\"sum\",\n",
    "    top_n=6,\n",
    "    auto_open=False,\n",
    "    path=False,\n",
    ")\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03abc260",
   "metadata": {},
   "source": [
    "You can also pass an explicit list of colors. The palette is session-wide, so reset it when you want to go back to the MARIO defaults."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e438788a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-05-01T06:52:51.379307Z",
     "iopub.status.busy": "2026-05-01T06:52:51.379181Z",
     "iopub.status.idle": "2026-05-01T06:52:51.414401Z",
     "shell.execute_reply": "2026-05-01T06:52:51.414082Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "branchvalues": "total",
         "customdata": [
          [
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          ],
          [
           "(?)"
          ],
          [
           "(?)"
          ],
          [
           "(?)"
          ],
          [
           "(?)"
          ],
          [
           "(?)"
          ],
          [
           "(?)"
          ],
          [
           "(?)"
          ]
         ],
         "domain": {
          "x": [
           0,
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          ],
          "y": [
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          ]
         },
         "hovertemplate": "labels=%{label}<br>Value=%{value}<br>parent=%{parent}<br>id=%{id}<br>Region_to=%{customdata[0]}<extra></extra>",
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          "Reg1/Agriculture",
          "Reg2/Agriculture",
          "Reg1/Industry",
          "Reg2/Industry",
          "Reg1/Services",
          "Reg2/Services",
          "Reg1",
          "Reg2"
         ],
         "labels": [
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          "Agriculture",
          "Industry",
          "Industry",
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         },
         "name": "",
         "parents": [
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          "Reg2",
          "Reg1",
          "Reg2",
          "Reg1",
          "Reg2",
          "",
          ""
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         "type": "sunburst",
         "values": {
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       "layout": {
        "font": {
         "family": "Verdana",
         "size": 15
        },
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
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             "color": "#2a3f5f"
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           "radialaxis": {
            "gridcolor": "#EBF0F8",
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          "scene": {
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            "gridwidth": 2,
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          },
          "ternary": {
           "aaxis": {
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            "ticks": ""
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           },
           "bgcolor": "white",
           "caxis": {
            "gridcolor": "#DFE8F3",
            "linecolor": "#A2B1C6",
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          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
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           "linecolor": "#EBF0F8",
           "ticks": "",
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           "zerolinecolor": "#EBF0F8",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
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           "zerolinecolor": "#EBF0F8",
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         }
        },
        "title": {
         "text": "Intersectoral transaction flows"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mario.set_palette(\"mario\")\n",
    "\n",
    "fig = iot.plot(\n",
    "    matrix=\"Z\",\n",
    "    kind=\"sunburst\",\n",
    "    preset=None,\n",
    "    path_columns=[\"Region_from\", \"Sector_from\"],\n",
    "    top_n=8,\n",
    "    auto_open=False,\n",
    "    path=False,\n",
    ")\n",
    "fig"
   ]
  }
 ],
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   "display_name": "mario",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.13"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}
