Projeto_análise_de_dados_da_empresa.ipynb

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    "colab": {
      "name": "Projeto_análise de dados da empresa",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "-chXvs9TZQXP",
        "colab": {
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        },
        "outputId": "98fd11a9-31cd-47b4-e666-e871044b8bb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-rIXnpzwevjB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "EahyxITAbp3p"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "planilha = pd.read_excel(\"/content/gdrive/MyDrive/controle_da_empresa.xlsx\", None)\n",
        "df = planilha[\"Vendas\"]\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "904aZP0YcMoC",
        "outputId": "fd4a7c1b-545b-4854-9cc4-57c56cd27537"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Data da Venda                 Item  Quantidade  Vendedor  Preço Unitário  \\\n",
              "0    2020-01-02     Lente 50mm f/1.4           2    Carlos            1649   \n",
              "1    2020-01-02     Filtro ND 5 Stop           2    Raquel            1220   \n",
              "2    2020-01-03     Filtro ND 5 Stop           2    Raquel            1220   \n",
              "3    2020-01-03  Lente 16-35mm f/2.8           2    Carlos            8997   \n",
              "4    2020-01-04  Lente 16-35mm f/2.8           2  Fernanda            8997   \n",
              "\n",
              "   Total de Vendas  \n",
              "0             3298  \n",
              "1             2440  \n",
              "2             2440  \n",
              "3            17994  \n",
              "4            17994  "
            ],
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              "      <th></th>\n",
              "      <th>Data da Venda</th>\n",
              "      <th>Item</th>\n",
              "      <th>Quantidade</th>\n",
              "      <th>Vendedor</th>\n",
              "      <th>Preço Unitário</th>\n",
              "      <th>Total de Vendas</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>Lente 50mm f/1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>Carlos</td>\n",
              "      <td>1649</td>\n",
              "      <td>3298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-02</td>\n",
              "      <td>Filtro ND 5 Stop</td>\n",
              "      <td>2</td>\n",
              "      <td>Raquel</td>\n",
              "      <td>1220</td>\n",
              "      <td>2440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>Filtro ND 5 Stop</td>\n",
              "      <td>2</td>\n",
              "      <td>Raquel</td>\n",
              "      <td>1220</td>\n",
              "      <td>2440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-03</td>\n",
              "      <td>Lente 16-35mm f/2.8</td>\n",
              "      <td>2</td>\n",
              "      <td>Carlos</td>\n",
              "      <td>8997</td>\n",
              "      <td>17994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-04</td>\n",
              "      <td>Lente 16-35mm f/2.8</td>\n",
              "      <td>2</td>\n",
              "      <td>Fernanda</td>\n",
              "      <td>8997</td>\n",
              "      <td>17994</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "plt.hist('Total de Vendas', data=df)\n",
        "plt.title(\"Histograma de Vendas\")\n",
        "plt.ylabel(\"Quantidade de ocorrências\")\n",
        "plt.xlabel(\"Valor da Compra\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "_6pKK4m6gWdu",
        "outputId": "826f55ab-a6be-469f-d88b-e4526e5b1c32"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Data da Venda'] = pd.to_datetime(df['Data da Venda'], infer_datetime_format=True)\n",
        "\n",
        "plt.plot('Data da Venda', 'Total de Vendas', data=df)\n",
        "plt.title(\"Vendas\")\n",
        "plt.ylabel(\"Total de Vendas\")\n",
        "plt.xlabel(\"Data\")\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "AqkhAVF8lp_N",
        "outputId": "0a3157f4-2c8f-46ec-b4a7-735bba3e325f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('Vendedor')['Total de Vendas'].sum().plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "WOcOj6_tmZ93",
        "outputId": "61e0ece7-4f47-41df-cd37-2b7aca4c875f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fda69697390>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pywaffle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CRaFmS0qnoLX",
        "outputId": "89448a27-e06f-4e77-a444-eac9f171d305"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pywaffle\n",
            "  Downloading pywaffle-1.1.0-py2.py3-none-any.whl (30 kB)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from pywaffle) (3.2.2)\n",
            "Collecting fontawesomefree\n",
            "  Downloading fontawesomefree-6.1.1-py3-none-any.whl (26.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 26.1 MB 1.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pywaffle) (1.21.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pywaffle) (0.11.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pywaffle) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pywaffle) (1.4.3)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->pywaffle) (3.0.9)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->pywaffle) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->pywaffle) (1.15.0)\n",
            "Installing collected packages: fontawesomefree, pywaffle\n",
            "Successfully installed fontawesomefree-6.1.1 pywaffle-1.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from pywaffle import Waffle"
      ],
      "metadata": {
        "id": "XiaKxuIOn-IO"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dict(df[\"Item\"].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RerjQoKYszms",
        "outputId": "b537da70-2a70-4fbb-e646-d252adb08e3d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Canon 5D Mark III': 8,\n",
              " 'Canon 6D Mark II': 8,\n",
              " 'DJI Mavic Air 2': 8,\n",
              " 'DJI Ronin-S': 7,\n",
              " 'Filtro ND 2 Stop': 6,\n",
              " 'Filtro ND 3 Stop': 11,\n",
              " 'Filtro ND 5 Stop': 14,\n",
              " 'Lente 16-35mm f/2.8': 12,\n",
              " 'Lente 50mm f/1.4': 10,\n",
              " 'Lente 85mm f/1.4': 6}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = dict(df[\"Item\"].value_counts())\n",
        "\n",
        "fig = plt.figure(\n",
        "                FigureClass=Waffle,\n",
        "                rows=7,\n",
        "                columns=17,\n",
        "                values=a,\n",
        "                colors=(\"#232066\", \"#983D3D\", \"#DCB732\", \"#7fffd4\", \"#ffe4c4\", \"#8a2be2\", \"#6495ed\", \"#228b22\", \"#808080\", \"#daa520\"),\n",
        "                legend={'loc': 'upper left','bbox_to_anchor': (1.1, 1)}\n",
        "                )\n",
        "\n",
        "fig.set_tight_layout(False)\n",
        "plt.show();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189
        },
        "id": "2ifNzv8goGFt",
        "outputId": "7f44d48b-19a8-4e67-d688-09b6ec57aea6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Waffle size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}