hotel_de_vile_stationement (1).ipynb

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        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.integrate import solve_ivp\n",
        "import matplotlib.animation as animation\n",
        "import pandas as pd\n"
      ]
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      "source": [
        "link = (\"/content/244400644_stationnements_cyclables.csv\")\n",
        "df = pd.read_csv(link,sep=';')\n",
        "df.head()"
      ],
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              "  lumiere url_info d_service                                          source  \\\n",
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        "id": "CVtYCwx_UyUM",
        "outputId": "d0ac2a03-fe8f-40f1-f7b1-a360bd41ed9e"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "geo_point_2d        0\n",
              "Geo Shape           0\n",
              "id_local            0\n",
              "id_osm            162\n",
              "code_com            0\n",
              "coordonneesxy       0\n",
              "capacite            0\n",
              "capacite_cargo    600\n",
              "type_accroche       0\n",
              "mobilier            0\n",
              "acces               0\n",
              "gratuit             0\n",
              "protection          0\n",
              "couverture         12\n",
              "surveillance      311\n",
              "lumiere           311\n",
              "url_info          591\n",
              "d_service         590\n",
              "source              0\n",
              "proprietaire      579\n",
              "gestionnaire      483\n",
              "date_maj            0\n",
              "commentaires      570\n",
              "dtype: int64"
            ],
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>geo_point_2d</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Geo Shape</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id_local</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>id_osm</th>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>code_com</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>coordonneesxy</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>capacite</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>capacite_cargo</th>\n",
              "      <td>600</td>\n",
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              "    <tr>\n",
              "      <th>type_accroche</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mobilier</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>acces</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>gratuit</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>protection</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>couverture</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>surveillance</th>\n",
              "      <td>311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lumiere</th>\n",
              "      <td>311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>url_info</th>\n",
              "      <td>591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d_service</th>\n",
              "      <td>590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>source</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>proprietaire</th>\n",
              "      <td>579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gestionnaire</th>\n",
              "      <td>483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_maj</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>commentaires</th>\n",
              "      <td>570</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.commentaires"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "BvB9ikdQVLBX",
        "outputId": "2c1a9a23-7869-4cd8-9c9b-c58a85f906db"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                                                    NaN\n",
              "1                                                    NaN\n",
              "2                                                    NaN\n",
              "3      stationnement amovible, mis à dispo par la lav...\n",
              "4                          salariés des chantiers navals\n",
              "                             ...                        \n",
              "596                                                  NaN\n",
              "597                                                  NaN\n",
              "598                                                  NaN\n",
              "599                                                  NaN\n",
              "600                                                  NaN\n",
              "Name: commentaires, Length: 601, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>commentaires</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>stationnement amovible, mis à dispo par la lav...</td>\n",
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              "      <th>4</th>\n",
              "      <td>salariés des chantiers navals</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>NaN</td>\n",
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              "      <th>597</th>\n",
              "      <td>NaN</td>\n",
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              "      <th>598</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <th>600</th>\n",
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              "<p>601 rows × 1 columns</p>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.columns"
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      "metadata": {
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          "height": 0
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        "id": "782U0m9S_5tC",
        "outputId": "cd678318-483e-4bae-8961-f3349fd25b6f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['geo_point_2d', 'Geo Shape', 'id_local', 'id_osm', 'code_com',\n",
              "       'coordonneesxy', 'capacite', 'capacite_cargo', 'type_accroche',\n",
              "       'mobilier', 'acces', 'gratuit', 'protection', 'couverture',\n",
              "       'surveillance', 'lumiere', 'url_info', 'd_service', 'source',\n",
              "       'proprietaire', 'gestionnaire', 'date_maj', 'commentaires'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Descriptive Analysis"
      ],
      "metadata": {
        "id": "yiC2MmjXHLmA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
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          "height": 300
        },
        "id": "csZl6fFZHQZj",
        "outputId": "30109b43-c10f-470b-97fe-f4bdb34313b0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id_local      code_com    capacite  capacite_cargo    d_service\n",
              "count  601.000000    601.000000  601.000000             1.0    11.000000\n",
              "mean   340.860233  44164.159734    7.628952             2.0  2022.818182\n",
              "std    194.827275     38.711296    5.392627             NaN     1.078720\n",
              "min      1.000000  44013.000000    0.000000             2.0  2021.000000\n",
              "25%    173.000000  44132.000000    4.000000             2.0  2022.000000\n",
              "50%    341.000000  44184.000000    6.000000             2.0  2023.000000\n",
              "75%    508.000000  44184.000000   10.000000             2.0  2024.000000\n",
              "max    675.000000  44210.000000   48.000000             2.0  2024.000000"
            ],
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              "      <td>2.0</td>\n",
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              "      <th>50%</th>\n",
              "      <td>341.000000</td>\n",
              "      <td>44184.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2023.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>508.000000</td>\n",
              "      <td>44184.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2024.000000</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-90ddd4ba-26cc-4495-8cd0-a3d4a9346d3c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"id_local\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 230.1591707034328,\n        \"min\": 1.0,\n        \"max\": 675.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          340.8602329450915,\n          341.0,\n          601.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"code_com\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20289.068853847293,\n        \"min\": 38.7112960593632,\n        \"max\": 44210.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          601.0,\n          44164.159733777036,\n          44184.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capacite\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 208.94295259703733,\n        \"min\": 0.0,\n        \"max\": 601.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.628951747088187,\n          6.0,\n          601.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capacite_cargo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3779644730092273,\n        \"min\": 1.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"d_service\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 933.5844570886848,\n        \"min\": 1.0787197799411874,\n        \"max\": 2024.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11.0,\n          2022.8181818181818\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "interpretation of descriptive"
      ],
      "metadata": {
        "id": "zTQPr57-IX9G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. id_local (Local ID)\n",
        "Count: 601 — This indicates that there are 601 unique parking locations in the dataset.\n",
        "Mean: 340.86 — On average, the IDs of the parking locations are around 340, but since this is just an identifier, it doesn't carry significant meaning.\n",
        "Standard Deviation (std): 194.83 — This shows how spread out the IDs are from the mean. The higher the value, the more spread the data points are from the average.\n",
        "Min: 1 — The smallest ID is 1.\n",
        "25% (1st Quartile): 173 — 25% of the parking locations have IDs less than 173.\n",
        "50% (Median/2nd Quartile): 341 — Half of the parking locations have an ID less than or equal to 341.\n",
        "75% (3rd Quartile): 508 — 75% of the parking locations have IDs less than 508.\n",
        "Max: 675 — The largest ID is 675.\n",
        "2. code_com (Municipality Code)\n",
        "Count: 601 — There are 601 municipality codes, corresponding to 601 parking locations.\n",
        "Mean: 44164.16 — The average code is 44164, but like id_local, it’s an identifier for municipalities.\n",
        "Standard Deviation (std): 38.71 — This tells us how much variation there is in the municipality codes, but the number doesn’t have much interpretive value beyond showing some spread in the codes.\n",
        "Min: 44013 — The smallest municipality code is 44013.\n",
        "25% (1st Quartile): 44132 — 25% of the parking locations have codes less than 44132.\n",
        "50% (Median/2nd Quartile): 44184 — Half of the locations have codes less than or equal to 44184.\n",
        "75% (3rd Quartile): 44184 — 75% of the locations have codes less than 44184.\n",
        "Max: 44210 — The largest municipality code is 44210.\n",
        "3. capacite (Capacity of Parking)\n",
        "Count: 601 — All 601 parking locations have data on capacity.\n",
        "Mean: 7.63 — On average, each parking location can hold around 7.6 bicycles.\n",
        "Standard Deviation (std): 5.39 — The capacity varies by about 5.39 bicycles from the mean across parking locations.\n",
        "Min: 0 — There are locations with 0 capacity, meaning no bicycles can be parked there.\n",
        "25% (1st Quartile): 4 — 25% of the locations can hold 4 or fewer bicycles.\n",
        "50% (Median/2nd Quartile): 6 — Half of the parking locations can hold 6 or fewer bicycles.\n",
        "75% (3rd Quartile): 10 — 75% of the parking locations can hold 10 or fewer bicycles.\n",
        "Max: 48 — The largest parking capacity is 48 bicycles, indicating this is a large facility.\n",
        "4. capacite_cargo (Capacity for Cargo Bikes)\n",
        "Count: 1 — Only 1 parking location in the dataset has a value for capacite_cargo, meaning almost all locations do not have capacity specifically for cargo bikes.\n",
        "Mean: 2 — The one location that has capacity for cargo bikes can hold 2 cargo bikes.\n",
        "Standard Deviation (std): NaN — There is no standard deviation because only 1 data point exists.\n",
        "Min, 25%, 50%, 75%, Max: 2 — Since there is only one data point, all these values are the same, showing the location has a capacity of 2 cargo bikes.\n",
        "5. d_service (Year of Service)\n",
        "Count: 11 — Only 11 entries have the year of service data.\n",
        "Mean: 2022.82 — On average, the year of service is around 2022.\n",
        "Standard Deviation (std): 1.08 — There is some spread in the years of service, varying by around 1 year from the mean.\n",
        "Min: 2021 — The earliest year of service recorded is 2021.\n",
        "25% (1st Quartile): 2022 — 25% of the locations have a year of service of 2022 or earlier.\n",
        "50% (Median/2nd Quartile): 2023 — Half of the parking locations with service data were serviced in or before 2023.\n",
        "75% (3rd Quartile): 2024 — 75% of the parking locations with service data were serviced by 2024.\n",
        "Max: 2024 — The most recent year of service is 2024.\n",
        "Overall Insights:\n",
        "Capacity: The majority of parking locations have a capacity of 4-10 bikes, with an average of 7.6 bikes per location.\n",
        "Cargo Capacity: Cargo bike capacity is rare, with only 1 location having capacity for cargo bikes.\n",
        "Service Year: The majority of parking locations with available data have been updated or serviced in recent years (2022-2024)."
      ],
      "metadata": {
        "id": "7MjcCdxXIdvD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Frequency distribution of a categorical column\n",
        "df['type_accroche'].value_counts()\n",
        "df['mobilier'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "wVuIXB7aIdHC",
        "outputId": "166d4841-a3c6-4c17-c967-5ac04b24536c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mobilier\n",
              "ARCEAU      585\n",
              "RATELIER     16\n",
              "Name: count, dtype: int64"
            ],
            "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",
              "        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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mobilier</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ARCEAU</th>\n",
              "      <td>585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RATELIER</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Interpretation:"
      ],
      "metadata": {
        "id": "qc6cTyGHJaES"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "ARCEAU: There are 585 bicycle parking locations equipped with an \"ARCEAU,\" which is a hoop or loop-type rack where bicycles can be secured.\n",
        "RATELIER: There are 16 bicycle parking locations equipped with a \"RATELIER,\" which is a bike rack typically used to hold multiple bikes by their wheels.\n",
        "Insights:\n",
        "The overwhelming majority (97.3%) of the parking facilities use ARCEAU as the main type of furniture, indicating that this type of rack is the preferred or most commonly installed infrastructure for bicycle parking.\n",
        "Only 16 locations (2.7%) use the RATELIER type, which is much less common.\n",
        "This suggests that the infrastructure is predominantly designed around the ARCEAU-type racks,"
      ],
      "metadata": {
        "id": "upMTG9OlJdEI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Bar plot for categorical variables\n",
        "df['type_accroche'].value_counts().plot(kind='bar', title='Distribution of Type Accroche')\n",
        "plt.show()\n",
        "\n",
        "# Histogram for numerical variables like capacity\n",
        "df['capacite'].plot(kind='hist', bins=10, title='Distribution of Capacite')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1007
        },
        "id": "-CZcubQeJk_i",
        "outputId": "30db28c2-dd90-48af-941c-1bbf821d506f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create a figure and two subplots\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12, 5))  # 1 row, 2 columns\n",
        "\n",
        "# Bar plot for 'type_accroche'\n",
        "df['type_accroche'].value_counts().plot(kind='bar', ax=axes[0], title='Distribution of Type Accroche')\n",
        "axes[0].set_title('Distribution of Type Accroche')  # Set the title for the first subplot\n",
        "axes[0].set_xlabel('Type Accroche')  # Label x-axis\n",
        "axes[0].set_ylabel('Count')  # Label y-axis\n",
        "\n",
        "# Histogram for 'capacite'\n",
        "df['capacite'].plot(kind='hist', bins=10, ax=axes[1], title='Distribution of Capacite')\n",
        "axes[1].set_title('Distribution of Capacite')  # Set the title for the second subplot\n",
        "axes[1].set_xlabel('Capacity')  # Label x-axis\n",
        "axes[1].set_ylabel('Frequency')  # Label y-axis\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()  # Automatically adjust spacing between plots\n",
        "\n",
        "# Display the plots\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "Is5nsFgxKY_l",
        "outputId": "f644b90c-4f75-4235-b570-26c4d1fd14ee"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handling Missing Data"
      ],
      "metadata": {
        "id": "dH1R6pQTLS3o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill missing values with the median for numerical columns\n",
        "df['capacite'].fillna(df['capacite'].median(), inplace=True)\n",
        "\n",
        "# Fill missing categorical data with the most frequent value\n",
        "df['type_accroche'].fillna(df['type_accroche'].mode()[0], inplace=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "1uNiZbSYLVAn",
        "outputId": "89ee0f84-b4c0-47c7-d847-7ff8d2755c85"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-16-2ce0b47b09b0>:5: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
            "\n",
            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
            "\n",
            "\n",
            "  df['type_accroche'].fillna(df['type_accroche'].mode()[0], inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop rows with missing values\n",
        "df.dropna(inplace=True)\n"
      ],
      "metadata": {
        "id": "zgA4xOIMLocT"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Detecting Outliers"
      ],
      "metadata": {
        "id": "c9Ih9NeWLvK4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Boxplot to detect outliers in capacity\n",
        "df.boxplot(column='capacite')\n",
        "plt.title('Boxplot for Capacite')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "vfEKWTwDL2mO",
        "outputId": "e779ab33-30f3-42be-d342-4ef23f362796"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the unique values in the 'capacite' column\n",
        "df['capacite'].unique()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "1PAIq_kaMZud",
        "outputId": "6e8f29bd-5333-4a2d-d3db-72c5e30ea625"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([], dtype=int64)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The output array([], dtype=int64) suggests that the capacite column contains no unique values, which likely means one of the following:\n",
        "\n",
        "The capacite column is completely empty (all values might be NaN or missing).\n",
        "The capacite column contains no valid integers (possibly it was misread as a different data type or has non-numeric data)."
      ],
      "metadata": {
        "id": "PsfsgDSjMxDg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values in 'capacite'\n",
        "df['capacite'].isnull().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ONX3rYebMyJg",
        "outputId": "80e80c13-2aec-4a8a-a862-ad9fd695d404"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the data type of 'capacite'\n",
        "print(df['capacite'].dtype)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "QbGerHoGNDJ5",
        "outputId": "d1471d29-3a24-41b7-d8ad-031da443844f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for any non-numeric values\n",
        "non_numeric_values = df[~df['capacite'].apply(lambda x: str(x).isdigit())]\n",
        "print(non_numeric_values)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "gotY2okGNNVy",
        "outputId": "b5d77b58-7ba1-4ed5-f853-af17a16783f1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Empty DataFrame\n",
            "Columns: []\n",
            "Index: []\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "# Calculate IQR and detect outliers\n",
        "Q1 = df['capacite'].quantile(0.25)\n",
        "Q3 = df['capacite'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "outliers = df[(df['capacite'] < (Q1 - 1.5 * IQR)) | (df['capacite'] > (Q3 + 1.5 * IQR))]\n",
        "print(\"Outliers in Capacite:\", outliers)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "e3xxIoCbNgK4",
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      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outliers in Capacite: Empty DataFrame\n",
            "Columns: [geo_point_2d, Geo Shape, id_local, id_osm, code_com, coordonneesxy, capacite, capacite_cargo, type_accroche, mobilier, acces, gratuit, protection, couverture, surveillance, lumiere, url_info, d_service, source, proprietaire, gestionnaire, date_maj, commentaires]\n",
            "Index: []\n",
            "\n",
            "[0 rows x 23 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "skewness = df['capacite'].skew()\n",
        "kurtosis = df['capacite'].kurtosis()\n",
        "print(f'Skewness: {skewness}, Kurtosis: {kurtosis}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "v5IeQePHO_t_",
        "outputId": "c13fb23e-bb13-43d3-8eaa-2ff912cc760b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Skewness: nan, Kurtosis: nan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###2. Geospatial Analysis"
      ],
      "metadata": {
        "id": "Hc6aa_s-QbcS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import geopandas as gpd\n",
        "import folium\n",
        "from shapely.geometry import Point\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "-Oq9EiGRQ2YA"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Understand the Geographical Data\n",
        "geo_point_2d: Likely contains the latitude and longitude in a comma-separated format.\n",
        "Geo Shape: Likely contains more detailed geographic information (such as polygons for areas).\n",
        "coordonneesxy: Could also contain XY coordinates in some format."
      ],
      "metadata": {
        "id": "m9p8J0yrRZv4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.geo_point_2d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "id": "oVKQaIFXReXk",
        "outputId": "1572d979-3335-4445-bfc3-ef9f82e5555d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Series([], Name: geo_point_2d, dtype: object)"
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>geo_point_2d</th>\n",
              "    </tr>\n",
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              "  </tbody>\n",
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              "</div><br><label><b>dtype:</b> object</label>"
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      "cell_type": "code",
      "source": [
        "# Filter out rows where 'geo_point_2d' doesn't contain a valid latitude and longitude pair\n",
        "df = df[df['geo_point_2d'].str.contains(',')]\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [geo_point_2d, Geo Shape, id_local, id_osm, code_com, coordonneesxy, capacite, capacite_cargo, type_accroche, mobilier, acces, gratuit, protection, couverture, surveillance, lumiere, url_info, d_service, source, proprietaire, gestionnaire, date_maj, commentaires]\n",
              "Index: []\n",
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              "      <th>geo_point_2d</th>\n",
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              "      <th>code_com</th>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for rows with missing or malformed data in 'geo_point_2d'\n",
        "df['geo_point_2d'].apply(lambda x: len(x.split(',')) != 2).sum()\n"
      ],
      "metadata": {
        "colab": {
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        "id": "YYdgAIUCWM88",
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            ]
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          "metadata": {},
          "execution_count": 35
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      "source": [
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        "id": "W1oHEtroWelq",
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      "execution_count": 37,
      "outputs": [
        {
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              "Series([], Name: geo_point_2d, dtype: object)"
            ],
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          "metadata": {},
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        "link = (\"/content/244400644_stationnements_cyclables.csv\")\n",
        "df2 = pd.read_csv(link,sep=';')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
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      "execution_count": 42,
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [geo_point_2d, Geo Shape, id_local, id_osm, code_com, coordonneesxy, capacite, capacite_cargo, type_accroche, mobilier, acces, gratuit, protection, couverture, surveillance, lumiere, url_info, d_service, source, proprietaire, gestionnaire, date_maj, commentaires]\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-60826e76-c0b6-4875-8603-2fc3b163653f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-60826e76-c0b6-4875-8603-2fc3b163653f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    }
  ]
}