├── Bar Chart Race - Dica Pandas #7.ipynb ├── Conversao de Colunas Pandas - Dicas Pandas #1 .ipynb ├── Dica_da_Pandas_Selecionando_dados_com_Pandas.ipynb ├── Dica_de_Pandas_Conversao_Strings.ipynb ├── Dica_de_Pandas_Rolling.ipynb ├── Estilos - Dicas Pandas #5.ipynb ├── Filtrando e Exportando para CSV os dados do ENEM por Estado Brasileiro.ipynb ├── Gráficos fofos em Python.ipynb ├── Importando_arquivo_zip_no_Pandas_Plotly_Backend_Dica_de_Pandas_8.ipynb ├── Modificando colunas do DataFrame - Dicas Pandas #2.ipynb ├── Ordenando o describe( ) - Dicas Pandas #3.ipynb ├── Os DataFrames são iguais? - Dicas Pandas #4.ipynb ├── Pandas_Categorical_Dtype_Dica_de_Pandas_11.ipynb ├── Pandas_do_ZERO_à_Análise_de_Dados.ipynb ├── README.md ├── Variáveis categóricas - Dica Pandas #6.ipynb └── dados ├── HIST_PAINEL_COVIDBR_12jun2020.xlsx ├── TA_PRECO_MEDICAMENTO_GOV.csv ├── arquivo_geral.csv └── bcdata-dolar-2023.csv /Dica_de_Pandas_Rolling.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "authorship_tag": "ABX9TyNHD9BPR6KkmU2mKfd5W6Dm", 8 | "include_colab_link": true 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "id": "view-in-github", 23 | "colab_type": "text" 24 | }, 25 | "source": [ 26 | "\"Open" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "source": [ 32 | "import pandas as pd\n", 33 | "import numpy as np\n", 34 | "import random" 35 | ], 36 | "metadata": { 37 | "id": "l0yQRRnl7mZY" 38 | }, 39 | "execution_count": 63, 40 | "outputs": [] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 66, 45 | "metadata": { 46 | "id": "8recwtAe7lMK" 47 | }, 48 | "outputs": [], 49 | "source": [ 50 | "data = []\n", 51 | "date_range = pd.date_range(\"01/01/2022\", \"01/07/2023\")\n", 52 | "\n", 53 | "for i in range(len(date_range)):\n", 54 | "\n", 55 | " sales_cash = random.randint(1, 10)\n", 56 | " sales_card = random.randint(1, 10)\n", 57 | "\n", 58 | " day_type = \"Dia da Semana\"\n", 59 | "\n", 60 | " if date_range[i].weekday()>4:\n", 61 | " day_type = \"Final de Semana\"\n", 62 | "\n", 63 | " data.append([str(date_range[i].date()), day_type, \"Dinheiro\", sales_cash])\n", 64 | " data.append([str(date_range[i].date()), day_type, \"Cartão\", sales_card])\n", 65 | "\n", 66 | "data = pd.DataFrame(data, columns=[\"Data\", \"Tipo_Dia\", \"Tipo_Pagamento\", \"Vendas\"])\n", 67 | "data[\"Data\"] = pd.to_datetime(data[\"Data\"])" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "source": [ 73 | "data" 74 | ], 75 | "metadata": { 76 | "colab": { 77 | "base_uri": "https://localhost:8080/", 78 | "height": 423 79 | }, 80 | "id": "2cEOk0218GUJ", 81 | "outputId": "c244839d-7c96-4ee5-fdeb-d02253348f47" 82 | }, 83 | "execution_count": 67, 84 | "outputs": [ 85 | { 86 | "output_type": "execute_result", 87 | "data": { 88 | "text/plain": [ 89 | " Data Tipo_Dia Tipo_Pagamento Vendas\n", 90 | "0 2022-01-01 Final de Semana Dinheiro 9\n", 91 | "1 2022-01-01 Final de Semana Cartão 8\n", 92 | "2 2022-01-02 Final de Semana Dinheiro 6\n", 93 | "3 2022-01-02 Final de Semana Cartão 7\n", 94 | "4 2022-01-03 Dia da Semana Dinheiro 7\n", 95 | ".. ... ... ... ...\n", 96 | "739 2023-01-05 Dia da Semana Cartão 3\n", 97 | "740 2023-01-06 Dia da Semana Dinheiro 2\n", 98 | "741 2023-01-06 Dia da Semana Cartão 9\n", 99 | "742 2023-01-07 Final de Semana Dinheiro 5\n", 100 | "743 2023-01-07 Final de Semana Cartão 9\n", 101 | "\n", 102 | "[744 rows x 4 columns]" 103 | ], 104 | "text/html": [ 105 | "\n", 106 | "
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DataTipo_DiaTipo_PagamentoVendas
02022-01-01Final de SemanaDinheiro9
12022-01-01Final de SemanaCartão8
22022-01-02Final de SemanaDinheiro6
32022-01-02Final de SemanaCartão7
42022-01-03Dia da SemanaDinheiro7
...............
7392023-01-05Dia da SemanaCartão3
7402023-01-06Dia da SemanaDinheiro2
7412023-01-06Dia da SemanaCartão9
7422023-01-07Final de SemanaDinheiro5
7432023-01-07Final de SemanaCartão9
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744 rows × 4 columns

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DataTipo_DiaTipo_PagamentoVendasroll_soma_win2roll_avg_win3roll_max_win2roll_soma_win2d
02022-01-01Final de SemanaDinheiro9NaNNaNNaN9.0
12022-01-01Final de SemanaCartão817.0NaN9.017.0
22022-01-02Final de SemanaDinheiro614.07.6666678.023.0
32022-01-02Final de SemanaCartão713.07.0000007.030.0
42022-01-03Dia da SemanaDinheiro714.06.6666677.037.0
...........................
7392023-01-05Dia da SemanaCartão39.04.3333336.064.0
7402023-01-06Dia da SemanaDinheiro25.03.6666673.054.0
7412023-01-06Dia da SemanaCartão911.04.6666679.063.0
7422023-01-07Final de SemanaDinheiro514.05.3333339.061.0
7432023-01-07Final de SemanaCartão914.07.6666679.070.0
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744 rows × 8 columns

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6093719000106556620193304557Rio de Janeiro33RJ22F13...AAABBACAAA
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" 215 | ], 216 | "text/plain": [ 217 | " NU_INSCRICAO NU_ANO CO_MUNICIPIO_RESIDENCIA NO_MUNICIPIO_RESIDENCIA \\\n", 218 | "10600 190001015227 2019 3302056 Italva \n", 219 | "21074 190001025702 2019 3305109 São João de Meriti \n", 220 | "22657 190001027285 2019 3304557 Rio de Janeiro \n", 221 | "30365 190001034993 2019 3304557 Rio de Janeiro \n", 222 | "60937 190001065566 2019 3304557 Rio de Janeiro \n", 223 | "\n", 224 | " CO_UF_RESIDENCIA SG_UF_RESIDENCIA NU_IDADE TP_SEXO TP_ESTADO_CIVIL \\\n", 225 | "10600 33 RJ 32 F 1 \n", 226 | "21074 33 RJ 32 M 1 \n", 227 | "22657 33 RJ 26 M 1 \n", 228 | "30365 33 RJ 19 F 1 \n", 229 | "60937 33 RJ 22 F 1 \n", 230 | "\n", 231 | " TP_COR_RACA ... Q016 Q017 Q018 Q019 Q020 Q021 Q022 Q023 Q024 \\\n", 232 | "10600 3 ... A A A B B A B A A \n", 233 | "21074 3 ... A A A B A A B A A \n", 234 | "22657 3 ... B A A B A A B A B \n", 235 | "30365 3 ... A A A A A A B A A \n", 236 | "60937 3 ... A A A B B A C A A \n", 237 | "\n", 238 | " Q025 \n", 239 | "10600 A \n", 240 | "21074 A \n", 241 | "22657 B \n", 242 | "30365 B \n", 243 | "60937 A \n", 244 | "\n", 245 | "[5 rows x 136 columns]" 246 | ] 247 | }, 248 | "execution_count": 23, 249 | "metadata": {}, 250 | "output_type": "execute_result" 251 | } 252 | ], 253 | "source": [ 254 | "microdados_enem_rj.head()" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": 24, 260 | "metadata": {}, 261 | "outputs": [], 262 | "source": [ 263 | "microdados_enem_rj.reset_index(inplace=True,drop=True)" 264 | ] 265 | }, 266 | { 267 | "cell_type": "code", 268 | "execution_count": 25, 269 | "metadata": { 270 | "scrolled": true 271 | }, 272 | "outputs": [ 273 | { 274 | "data": { 275 | "text/html": [ 276 | "
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NU_INSCRICAONU_ANOCO_MUNICIPIO_RESIDENCIANO_MUNICIPIO_RESIDENCIACO_UF_RESIDENCIASG_UF_RESIDENCIANU_IDADETP_SEXOTP_ESTADO_CIVILTP_COR_RACA...Q016Q017Q018Q019Q020Q021Q022Q023Q024Q025
019000101522720193302056Italva33RJ32F13...AAABBABAAA
119000102570220193305109São João de Meriti33RJ32M13...AAABAABAAA
219000102728520193304557Rio de Janeiro33RJ26M13...BAABAABABB
319000103499320193304557Rio de Janeiro33RJ19F13...AAAAAABAAB
419000106556620193304557Rio de Janeiro33RJ22F13...AAABBACAAA
..................................................................
33870519000611889520193304557Rio de Janeiro33RJ18F03...BABDABEBAB
33870619000611889620193305109São João de Meriti33RJ52F23...AAABAABBAA
33870719000611889720193304557Rio de Janeiro33RJ23F11...BAADBBEABB
33870819000611889820193300308Barra do Piraí33RJ32F12...AAABBABAAA
33870919000611889920193304904São Gonçalo33RJ41F03...BAABAACABB
\n", 584 | "

338710 rows × 136 columns

\n", 585 | "
" 586 | ], 587 | "text/plain": [ 588 | " NU_INSCRICAO NU_ANO CO_MUNICIPIO_RESIDENCIA NO_MUNICIPIO_RESIDENCIA \\\n", 589 | "0 190001015227 2019 3302056 Italva \n", 590 | "1 190001025702 2019 3305109 São João de Meriti \n", 591 | "2 190001027285 2019 3304557 Rio de Janeiro \n", 592 | "3 190001034993 2019 3304557 Rio de Janeiro \n", 593 | "4 190001065566 2019 3304557 Rio de Janeiro \n", 594 | "... ... ... ... ... \n", 595 | "338705 190006118895 2019 3304557 Rio de Janeiro \n", 596 | "338706 190006118896 2019 3305109 São João de Meriti \n", 597 | "338707 190006118897 2019 3304557 Rio de Janeiro \n", 598 | "338708 190006118898 2019 3300308 Barra do Piraí \n", 599 | "338709 190006118899 2019 3304904 São Gonçalo \n", 600 | "\n", 601 | " CO_UF_RESIDENCIA SG_UF_RESIDENCIA NU_IDADE TP_SEXO TP_ESTADO_CIVIL \\\n", 602 | "0 33 RJ 32 F 1 \n", 603 | "1 33 RJ 32 M 1 \n", 604 | "2 33 RJ 26 M 1 \n", 605 | "3 33 RJ 19 F 1 \n", 606 | "4 33 RJ 22 F 1 \n", 607 | "... ... ... ... ... ... \n", 608 | "338705 33 RJ 18 F 0 \n", 609 | "338706 33 RJ 52 F 2 \n", 610 | "338707 33 RJ 23 F 1 \n", 611 | "338708 33 RJ 32 F 1 \n", 612 | "338709 33 RJ 41 F 0 \n", 613 | "\n", 614 | " TP_COR_RACA ... Q016 Q017 Q018 Q019 Q020 Q021 Q022 Q023 Q024 \\\n", 615 | "0 3 ... A A A B B A B A A \n", 616 | "1 3 ... A A A B A A B A A \n", 617 | "2 3 ... B A A B A A B A B \n", 618 | "3 3 ... A A A A A A B A A \n", 619 | "4 3 ... A A A B B A C A A \n", 620 | "... ... ... ... ... ... ... ... ... ... ... ... \n", 621 | "338705 3 ... B A B D A B E B A \n", 622 | "338706 3 ... A A A B A A B B A \n", 623 | "338707 1 ... B A A D B B E A B \n", 624 | "338708 2 ... A A A B B A B A A \n", 625 | "338709 3 ... B A A B A A C A B \n", 626 | "\n", 627 | " Q025 \n", 628 | "0 A \n", 629 | "1 A \n", 630 | "2 B \n", 631 | "3 B \n", 632 | "4 A \n", 633 | "... ... \n", 634 | "338705 B \n", 635 | "338706 A \n", 636 | "338707 B \n", 637 | "338708 A \n", 638 | "338709 B \n", 639 | "\n", 640 | "[338710 rows x 136 columns]" 641 | ] 642 | }, 643 | "execution_count": 25, 644 | "metadata": {}, 645 | "output_type": "execute_result" 646 | } 647 | ], 648 | "source": [ 649 | "microdados_enem_rj" 650 | ] 651 | }, 652 | { 653 | "cell_type": "code", 654 | "execution_count": 27, 655 | "metadata": {}, 656 | "outputs": [ 657 | { 658 | "data": { 659 | "text/plain": [ 660 | "array(['RJ'], dtype=object)" 661 | ] 662 | }, 663 | "execution_count": 27, 664 | "metadata": {}, 665 | "output_type": "execute_result" 666 | } 667 | ], 668 | "source": [ 669 | "microdados_enem_rj.SG_UF_RESIDENCIA.unique()" 670 | ] 671 | }, 672 | { 673 | "cell_type": "code", 674 | "execution_count": 28, 675 | "metadata": {}, 676 | "outputs": [ 677 | { 678 | "data": { 679 | "text/plain": [ 680 | "array(['NU_INSCRICAO', 'NU_ANO', 'CO_MUNICIPIO_RESIDENCIA',\n", 681 | " 'NO_MUNICIPIO_RESIDENCIA', 'CO_UF_RESIDENCIA', 'SG_UF_RESIDENCIA',\n", 682 | " 'NU_IDADE', 'TP_SEXO', 'TP_ESTADO_CIVIL', 'TP_COR_RACA',\n", 683 | " 'TP_NACIONALIDADE', 'CO_MUNICIPIO_NASCIMENTO',\n", 684 | " 'NO_MUNICIPIO_NASCIMENTO', 'CO_UF_NASCIMENTO', 'SG_UF_NASCIMENTO',\n", 685 | " 'TP_ST_CONCLUSAO', 'TP_ANO_CONCLUIU', 'TP_ESCOLA', 'TP_ENSINO',\n", 686 | " 'IN_TREINEIRO', 'CO_ESCOLA', 'CO_MUNICIPIO_ESC',\n", 687 | " 'NO_MUNICIPIO_ESC', 'CO_UF_ESC', 'SG_UF_ESC',\n", 688 | " 'TP_DEPENDENCIA_ADM_ESC', 'TP_LOCALIZACAO_ESC', 'TP_SIT_FUNC_ESC',\n", 689 | " 'IN_BAIXA_VISAO', 'IN_CEGUEIRA', 'IN_SURDEZ',\n", 690 | " 'IN_DEFICIENCIA_AUDITIVA', 'IN_SURDO_CEGUEIRA',\n", 691 | " 'IN_DEFICIENCIA_FISICA', 'IN_DEFICIENCIA_MENTAL',\n", 692 | " 'IN_DEFICIT_ATENCAO', 'IN_DISLEXIA', 'IN_DISCALCULIA',\n", 693 | " 'IN_AUTISMO', 'IN_VISAO_MONOCULAR', 'IN_OUTRA_DEF', 'IN_GESTANTE',\n", 694 | " 'IN_LACTANTE', 'IN_IDOSO', 'IN_ESTUDA_CLASSE_HOSPITALAR',\n", 695 | " 'IN_SEM_RECURSO', 'IN_BRAILLE', 'IN_AMPLIADA_24', 'IN_AMPLIADA_18',\n", 696 | " 'IN_LEDOR', 'IN_ACESSO', 'IN_TRANSCRICAO', 'IN_LIBRAS',\n", 697 | " 'IN_TEMPO_ADICIONAL', 'IN_LEITURA_LABIAL', 'IN_MESA_CADEIRA_RODAS',\n", 698 | " 'IN_MESA_CADEIRA_SEPARADA', 'IN_APOIO_PERNA', 'IN_GUIA_INTERPRETE',\n", 699 | " 'IN_COMPUTADOR', 'IN_CADEIRA_ESPECIAL', 'IN_CADEIRA_CANHOTO',\n", 700 | " 'IN_CADEIRA_ACOLCHOADA', 'IN_PROVA_DEITADO', 'IN_MOBILIARIO_OBESO',\n", 701 | " 'IN_LAMINA_OVERLAY', 'IN_PROTETOR_AURICULAR', 'IN_MEDIDOR_GLICOSE',\n", 702 | " 'IN_MAQUINA_BRAILE', 'IN_SOROBAN', 'IN_MARCA_PASSO', 'IN_SONDA',\n", 703 | " 'IN_MEDICAMENTOS', 'IN_SALA_INDIVIDUAL', 'IN_SALA_ESPECIAL',\n", 704 | " 'IN_SALA_ACOMPANHANTE', 'IN_MOBILIARIO_ESPECIFICO',\n", 705 | " 'IN_MATERIAL_ESPECIFICO', 'IN_NOME_SOCIAL', 'CO_MUNICIPIO_PROVA',\n", 706 | " 'NO_MUNICIPIO_PROVA', 'CO_UF_PROVA', 'SG_UF_PROVA',\n", 707 | " 'TP_PRESENCA_CN', 'TP_PRESENCA_CH', 'TP_PRESENCA_LC',\n", 708 | " 'TP_PRESENCA_MT', 'CO_PROVA_CN', 'CO_PROVA_CH', 'CO_PROVA_LC',\n", 709 | " 'CO_PROVA_MT', 'NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n", 710 | " 'NU_NOTA_MT', 'TX_RESPOSTAS_CN', 'TX_RESPOSTAS_CH',\n", 711 | " 'TX_RESPOSTAS_LC', 'TX_RESPOSTAS_MT', 'TP_LINGUA',\n", 712 | " 'TX_GABARITO_CN', 'TX_GABARITO_CH', 'TX_GABARITO_LC',\n", 713 | " 'TX_GABARITO_MT', 'TP_STATUS_REDACAO', 'NU_NOTA_COMP1',\n", 714 | " 'NU_NOTA_COMP2', 'NU_NOTA_COMP3', 'NU_NOTA_COMP4', 'NU_NOTA_COMP5',\n", 715 | " 'NU_NOTA_REDACAO', 'Q001', 'Q002', 'Q003', 'Q004', 'Q005', 'Q006',\n", 716 | " 'Q007', 'Q008', 'Q009', 'Q010', 'Q011', 'Q012', 'Q013', 'Q014',\n", 717 | " 'Q015', 'Q016', 'Q017', 'Q018', 'Q019', 'Q020', 'Q021', 'Q022',\n", 718 | " 'Q023', 'Q024', 'Q025'], dtype=object)" 719 | ] 720 | }, 721 | "execution_count": 28, 722 | "metadata": {}, 723 | "output_type": "execute_result" 724 | } 725 | ], 726 | "source": [ 727 | "microdados_enem_rj.columns.values" 728 | ] 729 | }, 730 | { 731 | "cell_type": "code", 732 | "execution_count": 30, 733 | "metadata": {}, 734 | "outputs": [ 735 | { 736 | "data": { 737 | "text/plain": [ 738 | "NU_NOTA_CN 491.892385\n", 739 | "NU_NOTA_CH 523.361916\n", 740 | "NU_NOTA_LC 535.900530\n", 741 | "NU_NOTA_MT 539.564877\n", 742 | "dtype: float64" 743 | ] 744 | }, 745 | "execution_count": 30, 746 | "metadata": {}, 747 | "output_type": "execute_result" 748 | } 749 | ], 750 | "source": [ 751 | "microdados_enem_rj[['NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n", 752 | " 'NU_NOTA_MT']].mean()" 753 | ] 754 | }, 755 | { 756 | "cell_type": "code", 757 | "execution_count": 63, 758 | "metadata": {}, 759 | "outputs": [ 760 | { 761 | "data": { 762 | "text/html": [ 763 | "\n", 764 | "\n", 771 | "\n", 772 | "
\n", 773 | " \n", 774 | "
\n", 775 | " \n" 781 | ], 782 | "text/plain": [ 783 | "" 784 | ] 785 | }, 786 | "execution_count": 63, 787 | "metadata": {}, 788 | "output_type": "execute_result" 789 | } 790 | ], 791 | "source": [ 792 | "chart = ctc.Bar('Médias Notas RJ',width='500px',height='400px')\n", 793 | "chart.set_options(\n", 794 | " labels=['C. Natureza', 'C. Humanas', 'Linguagens', 'Matemática', 'Redação'],\n", 795 | " x_label=\"Provas\",\n", 796 | " y_label=\"Média por Prova\" ,\n", 797 | " colors=['#D9F1BB','#F7B7A3','#EA5F89','#9B3192', '#FFFF77']\n", 798 | " )\n", 799 | "chart.add_series(\"Média\",list(microdados_enem_rj[['NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n", 800 | " 'NU_NOTA_MT', 'NU_NOTA_REDACAO']].mean()))\n", 801 | "chart.render_notebook()" 802 | ] 803 | }, 804 | { 805 | "cell_type": "code", 806 | "execution_count": 68, 807 | "metadata": {}, 808 | "outputs": [ 809 | { 810 | "data": { 811 | "text/html": [ 812 | "\n", 813 | "\n", 820 | "\n", 821 | "
\n", 822 | " \n", 823 | "
\n", 824 | " \n" 830 | ], 831 | "text/plain": [ 832 | "" 833 | ] 834 | }, 835 | "execution_count": 68, 836 | "metadata": {}, 837 | "output_type": "execute_result" 838 | } 839 | ], 840 | "source": [ 841 | "municipio = \"Angra dos Reis\"\n", 842 | "\n", 843 | "radar_chart = ctc.Radar('Médias Notas RJ',width='600px',height='600px')\n", 844 | "radar_chart.set_options(\n", 845 | " labels=['C. Natureza', 'C. Humanas', 'Linguagens', 'Matemática'],\n", 846 | " is_show_legend=True, #by default, it is true. You can turn it off.\n", 847 | " legend_pos='upRight' #location of the legend\n", 848 | " )\n", 849 | "\n", 850 | "radar_chart.add_series(\"Média dos Municípios RJ\",list(microdados_enem_rj[['NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n", 851 | " 'NU_NOTA_MT','NU_NOTA_REDACAO']].mean()))\n", 852 | "radar_chart.add_series(\"Média do Município {}\".format(municipio),\n", 853 | " list(microdados_enem_rj.groupby(['NO_MUNICIPIO_RESIDENCIA'])['NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n", 854 | " 'NU_NOTA_MT','NU_NOTA_REDACAO'].mean().loc[municipio]))\n", 855 | "\n", 856 | "radar_chart.render_notebook()" 857 | ] 858 | }, 859 | { 860 | "cell_type": "code", 861 | "execution_count": 51, 862 | "metadata": {}, 863 | "outputs": [ 864 | { 865 | "data": { 866 | "text/plain": [ 867 | "NU_NOTA_CN 475.984038\n", 868 | "NU_NOTA_CH 508.694181\n", 869 | "NU_NOTA_LC 527.015926\n", 870 | "NU_NOTA_MT 520.845753\n", 871 | "Name: Angra dos Reis, dtype: float64" 872 | ] 873 | }, 874 | "execution_count": 51, 875 | "metadata": {}, 876 | "output_type": "execute_result" 877 | } 878 | ], 879 | "source": [ 880 | "microdados_enem_rj.groupby(['NO_MUNICIPIO_RESIDENCIA'])['NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n", 881 | " 'NU_NOTA_MT'].mean().loc['Angra dos Reis']" 882 | ] 883 | }, 884 | { 885 | "cell_type": "code", 886 | "execution_count": 52, 887 | "metadata": {}, 888 | "outputs": [ 889 | { 890 | "data": { 891 | "text/html": [ 892 | "
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NU_NOTA_CNNU_NOTA_CHNU_NOTA_LCNU_NOTA_MT
NO_MUNICIPIO_RESIDENCIA
Angra dos Reis475.984038508.694181527.015926520.845753
Aperibé493.393789518.341379528.701724542.992547
Araruama488.075321514.994308529.860574532.785762
Areal474.303145503.188304523.457895515.313208
Armação dos Búzios490.474903525.274910535.007040530.700000
...............
Três Rios482.053123511.984375527.105288522.841703
Valença495.254925522.922297534.024662546.180657
Varre-Sai492.420492503.276692512.384211534.113934
Vassouras485.166593513.554167528.432917525.596239
Volta Redonda507.400156535.504062544.084667569.327552
\n", 1003 | "

92 rows × 4 columns

\n", 1004 | "
" 1005 | ], 1006 | "text/plain": [ 1007 | " NU_NOTA_CN NU_NOTA_CH NU_NOTA_LC NU_NOTA_MT\n", 1008 | "NO_MUNICIPIO_RESIDENCIA \n", 1009 | "Angra dos Reis 475.984038 508.694181 527.015926 520.845753\n", 1010 | "Aperibé 493.393789 518.341379 528.701724 542.992547\n", 1011 | "Araruama 488.075321 514.994308 529.860574 532.785762\n", 1012 | "Areal 474.303145 503.188304 523.457895 515.313208\n", 1013 | "Armação dos Búzios 490.474903 525.274910 535.007040 530.700000\n", 1014 | "... ... ... ... ...\n", 1015 | "Três Rios 482.053123 511.984375 527.105288 522.841703\n", 1016 | "Valença 495.254925 522.922297 534.024662 546.180657\n", 1017 | "Varre-Sai 492.420492 503.276692 512.384211 534.113934\n", 1018 | "Vassouras 485.166593 513.554167 528.432917 525.596239\n", 1019 | "Volta Redonda 507.400156 535.504062 544.084667 569.327552\n", 1020 | "\n", 1021 | "[92 rows x 4 columns]" 1022 | ] 1023 | }, 1024 | "execution_count": 52, 1025 | "metadata": {}, 1026 | "output_type": "execute_result" 1027 | } 1028 | ], 1029 | "source": [ 1030 | "microdados_enem_rj.groupby(['NO_MUNICIPIO_RESIDENCIA'])['NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n", 1031 | " 'NU_NOTA_MT'].mean()" 1032 | ] 1033 | }, 1034 | { 1035 | "cell_type": "code", 1036 | "execution_count": null, 1037 | "metadata": {}, 1038 | "outputs": [], 1039 | "source": [] 1040 | }, 1041 | { 1042 | "cell_type": "code", 1043 | "execution_count": null, 1044 | "metadata": {}, 1045 | "outputs": [], 1046 | "source": [] 1047 | }, 1048 | { 1049 | "cell_type": "code", 1050 | "execution_count": null, 1051 | "metadata": {}, 1052 | "outputs": [], 1053 | "source": [] 1054 | } 1055 | ], 1056 | "metadata": { 1057 | "kernelspec": { 1058 | "display_name": "Python 3", 1059 | "language": "python", 1060 | "name": "python3" 1061 | }, 1062 | "language_info": { 1063 | "codemirror_mode": { 1064 | "name": "ipython", 1065 | "version": 3 1066 | }, 1067 | "file_extension": ".py", 1068 | "mimetype": "text/x-python", 1069 | "name": "python", 1070 | "nbconvert_exporter": "python", 1071 | "pygments_lexer": "ipython3", 1072 | "version": "3.7.4" 1073 | } 1074 | }, 1075 | "nbformat": 4, 1076 | "nbformat_minor": 2 1077 | } 1078 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Dicas de Pandas 2 | 3 | Esse repositório foi criado para a série de vídeos de Dicas do Pandas do canal [Programação Dinâmica](https://www.youtube.com/programacaodinamica). 4 | 5 | - [Assita os vídeos da playlist de Dica de Pandas](https://www.youtube.com/playlist?list=PL5TJqBvpXQv6SSsEgQrNwpOLTupXPuiMQ) 6 | -------------------------------------------------------------------------------- /Variáveis categóricas - Dica Pandas #6.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Variáveis categóricas - Dica Pandas #6" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "from IPython.display import YouTubeVideo, Image" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 2, 29 | "metadata": {}, 30 | "outputs": [ 31 | { 32 | "data": { 33 | "image/jpeg": 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NU_INSCRICAONU_ANOCO_MUNICIPIO_RESIDENCIANO_MUNICIPIO_RESIDENCIACO_UF_RESIDENCIASG_UF_RESIDENCIANU_IDADETP_SEXOTP_ESTADO_CIVILTP_COR_RACA...Q018Q019Q020Q021Q022Q023Q024Q025Q026Q027
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0 rows × 137 columns

\n", 128 | "
" 129 | ], 130 | "text/plain": [ 131 | "Empty DataFrame\n", 132 | "Columns: [NU_INSCRICAO, NU_ANO, CO_MUNICIPIO_RESIDENCIA, NO_MUNICIPIO_RESIDENCIA, CO_UF_RESIDENCIA, SG_UF_RESIDENCIA, NU_IDADE, TP_SEXO, TP_ESTADO_CIVIL, TP_COR_RACA, TP_NACIONALIDADE, CO_MUNICIPIO_NASCIMENTO, NO_MUNICIPIO_NASCIMENTO, CO_UF_NASCIMENTO, SG_UF_NASCIMENTO, TP_ST_CONCLUSAO, TP_ANO_CONCLUIU, TP_ESCOLA, TP_ENSINO, IN_TREINEIRO, CO_ESCOLA, CO_MUNICIPIO_ESC, NO_MUNICIPIO_ESC, CO_UF_ESC, SG_UF_ESC, TP_DEPENDENCIA_ADM_ESC, TP_LOCALIZACAO_ESC, TP_SIT_FUNC_ESC, IN_BAIXA_VISAO, IN_CEGUEIRA, IN_SURDEZ, IN_DEFICIENCIA_AUDITIVA, IN_SURDO_CEGUEIRA, IN_DEFICIENCIA_FISICA, IN_DEFICIENCIA_MENTAL, IN_DEFICIT_ATENCAO, IN_DISLEXIA, IN_DISCALCULIA, IN_AUTISMO, IN_VISAO_MONOCULAR, IN_OUTRA_DEF, IN_GESTANTE, IN_LACTANTE, IN_IDOSO, IN_ESTUDA_CLASSE_HOSPITALAR, IN_SEM_RECURSO, IN_BRAILLE, IN_AMPLIADA_24, IN_AMPLIADA_18, IN_LEDOR, IN_ACESSO, IN_TRANSCRICAO, IN_LIBRAS, IN_LEITURA_LABIAL, IN_MESA_CADEIRA_RODAS, IN_MESA_CADEIRA_SEPARADA, IN_APOIO_PERNA, IN_GUIA_INTERPRETE, IN_COMPUTADOR, IN_CADEIRA_ESPECIAL, IN_CADEIRA_CANHOTO, IN_CADEIRA_ACOLCHOADA, IN_PROVA_DEITADO, IN_MOBILIARIO_OBESO, IN_LAMINA_OVERLAY, IN_PROTETOR_AURICULAR, IN_MEDIDOR_GLICOSE, IN_MAQUINA_BRAILE, IN_SOROBAN, IN_MARCA_PASSO, IN_SONDA, IN_MEDICAMENTOS, IN_SALA_INDIVIDUAL, IN_SALA_ESPECIAL, IN_SALA_ACOMPANHANTE, IN_MOBILIARIO_ESPECIFICO, IN_MATERIAL_ESPECIFICO, IN_NOME_SOCIAL, CO_MUNICIPIO_PROVA, NO_MUNICIPIO_PROVA, CO_UF_PROVA, SG_UF_PROVA, TP_PRESENCA_CN, TP_PRESENCA_CH, TP_PRESENCA_LC, TP_PRESENCA_MT, CO_PROVA_CN, CO_PROVA_CH, CO_PROVA_LC, CO_PROVA_MT, NU_NOTA_CN, NU_NOTA_CH, NU_NOTA_LC, NU_NOTA_MT, TX_RESPOSTAS_CN, TX_RESPOSTAS_CH, TX_RESPOSTAS_LC, TX_RESPOSTAS_MT, TP_LINGUA, TX_GABARITO_CN, ...]\n", 133 | "Index: []\n", 134 | "\n", 135 | "[0 rows x 137 columns]" 136 | ] 137 | }, 138 | "execution_count": 5, 139 | "metadata": {}, 140 | "output_type": "execute_result" 141 | } 142 | ], 143 | "source": [ 144 | "microdados_enem.head()" 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 6, 150 | "metadata": {}, 151 | "outputs": [ 152 | { 153 | "data": { 154 | "text/plain": [ 155 | "array(['NU_INSCRICAO', 'NU_ANO', 'CO_MUNICIPIO_RESIDENCIA',\n", 156 | " 'NO_MUNICIPIO_RESIDENCIA', 'CO_UF_RESIDENCIA', 'SG_UF_RESIDENCIA',\n", 157 | " 'NU_IDADE', 'TP_SEXO', 'TP_ESTADO_CIVIL', 'TP_COR_RACA',\n", 158 | " 'TP_NACIONALIDADE', 'CO_MUNICIPIO_NASCIMENTO',\n", 159 | " 'NO_MUNICIPIO_NASCIMENTO', 'CO_UF_NASCIMENTO', 'SG_UF_NASCIMENTO',\n", 160 | " 'TP_ST_CONCLUSAO', 'TP_ANO_CONCLUIU', 'TP_ESCOLA', 'TP_ENSINO',\n", 161 | " 'IN_TREINEIRO', 'CO_ESCOLA', 'CO_MUNICIPIO_ESC',\n", 162 | " 'NO_MUNICIPIO_ESC', 'CO_UF_ESC', 'SG_UF_ESC',\n", 163 | " 'TP_DEPENDENCIA_ADM_ESC', 'TP_LOCALIZACAO_ESC', 'TP_SIT_FUNC_ESC',\n", 164 | " 'IN_BAIXA_VISAO', 'IN_CEGUEIRA', 'IN_SURDEZ',\n", 165 | " 'IN_DEFICIENCIA_AUDITIVA', 'IN_SURDO_CEGUEIRA',\n", 166 | " 'IN_DEFICIENCIA_FISICA', 'IN_DEFICIENCIA_MENTAL',\n", 167 | " 'IN_DEFICIT_ATENCAO', 'IN_DISLEXIA', 'IN_DISCALCULIA',\n", 168 | " 'IN_AUTISMO', 'IN_VISAO_MONOCULAR', 'IN_OUTRA_DEF', 'IN_GESTANTE',\n", 169 | " 'IN_LACTANTE', 'IN_IDOSO', 'IN_ESTUDA_CLASSE_HOSPITALAR',\n", 170 | " 'IN_SEM_RECURSO', 'IN_BRAILLE', 'IN_AMPLIADA_24', 'IN_AMPLIADA_18',\n", 171 | " 'IN_LEDOR', 'IN_ACESSO', 'IN_TRANSCRICAO', 'IN_LIBRAS',\n", 172 | " 'IN_LEITURA_LABIAL', 'IN_MESA_CADEIRA_RODAS',\n", 173 | " 'IN_MESA_CADEIRA_SEPARADA', 'IN_APOIO_PERNA', 'IN_GUIA_INTERPRETE',\n", 174 | " 'IN_COMPUTADOR', 'IN_CADEIRA_ESPECIAL', 'IN_CADEIRA_CANHOTO',\n", 175 | " 'IN_CADEIRA_ACOLCHOADA', 'IN_PROVA_DEITADO', 'IN_MOBILIARIO_OBESO',\n", 176 | " 'IN_LAMINA_OVERLAY', 'IN_PROTETOR_AURICULAR', 'IN_MEDIDOR_GLICOSE',\n", 177 | " 'IN_MAQUINA_BRAILE', 'IN_SOROBAN', 'IN_MARCA_PASSO', 'IN_SONDA',\n", 178 | " 'IN_MEDICAMENTOS', 'IN_SALA_INDIVIDUAL', 'IN_SALA_ESPECIAL',\n", 179 | " 'IN_SALA_ACOMPANHANTE', 'IN_MOBILIARIO_ESPECIFICO',\n", 180 | " 'IN_MATERIAL_ESPECIFICO', 'IN_NOME_SOCIAL', 'CO_MUNICIPIO_PROVA',\n", 181 | " 'NO_MUNICIPIO_PROVA', 'CO_UF_PROVA', 'SG_UF_PROVA',\n", 182 | " 'TP_PRESENCA_CN', 'TP_PRESENCA_CH', 'TP_PRESENCA_LC',\n", 183 | " 'TP_PRESENCA_MT', 'CO_PROVA_CN', 'CO_PROVA_CH', 'CO_PROVA_LC',\n", 184 | " 'CO_PROVA_MT', 'NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n", 185 | " 'NU_NOTA_MT', 'TX_RESPOSTAS_CN', 'TX_RESPOSTAS_CH',\n", 186 | " 'TX_RESPOSTAS_LC', 'TX_RESPOSTAS_MT', 'TP_LINGUA',\n", 187 | " 'TX_GABARITO_CN', 'TX_GABARITO_CH', 'TX_GABARITO_LC',\n", 188 | " 'TX_GABARITO_MT', 'TP_STATUS_REDACAO', 'NU_NOTA_COMP1',\n", 189 | " 'NU_NOTA_COMP2', 'NU_NOTA_COMP3', 'NU_NOTA_COMP4', 'NU_NOTA_COMP5',\n", 190 | " 'NU_NOTA_REDACAO', 'Q001', 'Q002', 'Q003', 'Q004', 'Q005', 'Q006',\n", 191 | " 'Q007', 'Q008', 'Q009', 'Q010', 'Q011', 'Q012', 'Q013', 'Q014',\n", 192 | " 'Q015', 'Q016', 'Q017', 'Q018', 'Q019', 'Q020', 'Q021', 'Q022',\n", 193 | " 'Q023', 'Q024', 'Q025', 'Q026', 'Q027'], dtype=object)" 194 | ] 195 | }, 196 | "execution_count": 6, 197 | "metadata": {}, 198 | "output_type": "execute_result" 199 | } 200 | ], 201 | "source": [ 202 | "microdados_enem.columns.values" 203 | ] 204 | }, 205 | { 206 | "cell_type": "code", 207 | "execution_count": 7, 208 | "metadata": {}, 209 | "outputs": [], 210 | "source": [ 211 | "cols = ['NU_NOTA_MT', 'Q027']" 212 | ] 213 | }, 214 | { 215 | "cell_type": "code", 216 | "execution_count": 8, 217 | "metadata": {}, 218 | "outputs": [], 219 | "source": [ 220 | "microdados_enem = pd.read_csv('../dados/microdados-enem-2018/DADOS/MICRODADOS_ENEM_2018.csv',usecols=cols,sep=';',encoding='ISO-8859-1',\n", 221 | " dtype={'Q027':'category'})\n" 222 | ] 223 | }, 224 | { 225 | "cell_type": "code", 226 | "execution_count": 9, 227 | "metadata": {}, 228 | "outputs": [ 229 | { 230 | "data": { 231 | "text/html": [ 232 | "
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NU_NOTA_MTQ027Q027_GRUPOS
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NU_NOTA_MTQ027_GRUPOS
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mean5.354056e+026.410552e-01
std1.031512e+021.179987e+00
min0.000000e+000.000000e+00
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50%5.166000e+020.000000e+00
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max9.961000e+025.000000e+00
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" 661 | ], 662 | "text/plain": [ 663 | " NU_NOTA_MT Q027_GRUPOS\n", 664 | "count 3.905099e+06 3.905099e+06\n", 665 | "mean 5.354056e+02 6.410552e-01\n", 666 | "std 1.031512e+02 1.179987e+00\n", 667 | "min 0.000000e+00 0.000000e+00\n", 668 | "25% 4.553000e+02 0.000000e+00\n", 669 | "50% 5.166000e+02 0.000000e+00\n", 670 | "75% 6.007000e+02 1.000000e+00\n", 671 | "max 9.961000e+02 5.000000e+00" 672 | ] 673 | }, 674 | "execution_count": 22, 675 | "metadata": {}, 676 | "output_type": "execute_result" 677 | } 678 | ], 679 | "source": [ 680 | "microdados_enem.describe()" 681 | ] 682 | }, 683 | { 684 | "cell_type": "code", 685 | "execution_count": 23, 686 | "metadata": {}, 687 | "outputs": [ 688 | { 689 | "data": { 690 | "text/plain": [ 691 | "média baixa 1465276\n", 692 | "média alta 1224868\n", 693 | "alta 942984\n", 694 | "baixa 232752\n", 695 | "Name: NU_NOTA_MT, dtype: int64" 696 | ] 697 | }, 698 | "execution_count": 23, 699 | "metadata": {}, 700 | "output_type": "execute_result" 701 | } 702 | ], 703 | "source": [ 704 | "pd.cut(microdados_enem.NU_NOTA_MT, bins=[0,400,500,600,800],labels=['baixa','média baixa','média alta','alta']).value_counts()" 705 | ] 706 | }, 707 | { 708 | "cell_type": "code", 709 | "execution_count": 24, 710 | "metadata": {}, 711 | "outputs": [], 712 | "source": [ 713 | "microdados_enem['NU_NOTA_MT_GRUPOS']=pd.qcut(microdados_enem.NU_NOTA_MT, q=4,labels=['baixa','média baixa','média alta','alta'])\n" 714 | ] 715 | }, 716 | { 717 | "cell_type": "code", 718 | "execution_count": 25, 719 | "metadata": {}, 720 | "outputs": [ 721 | { 722 | "data": { 723 | "text/html": [ 724 | "
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NU_NOTA_MTQ027Q027_GRUPOSNU_NOTA_MT_GRUPOS
0480.3A0média baixa
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105 | "01/06/2023";"5,0344" 106 | "02/06/2023";"4,9552" 107 | "05/06/2023";"4,9230" 108 | "06/06/2023";"4,9286" 109 | "07/06/2023";"4,9113" 110 | "09/06/2023";"4,8916" 111 | "12/06/2023";"4,8826" 112 | "13/06/2023";"4,8527" 113 | "14/06/2023";"4,8456" 114 | "15/06/2023";"4,8216" 115 | "16/06/2023";"4,8280" 116 | "19/06/2023";"4,7797" 117 | "20/06/2023";"4,7924" 118 | "21/06/2023";"4,7789" 119 | "22/06/2023";"4,7744" 120 | "23/06/2023";"4,7793" 121 | "26/06/2023";"4,7692" 122 | "27/06/2023";"4,7897" 123 | "28/06/2023";"4,8557" 124 | "29/06/2023";"4,8578" 125 | "30/06/2023";"4,8186" 126 | "03/07/2023";"4,7870" 127 | "04/07/2023";"4,8050" 128 | "05/07/2023";"4,8571" 129 | "06/07/2023";"4,8971" 130 | "07/07/2023";"4,8793" 131 | "10/07/2023";"4,8729" 132 | "11/07/2023";"4,8943" 133 | "12/07/2023";"4,8052" 134 | "13/07/2023";"4,8032" 135 | "14/07/2023";"4,7951" 136 | "17/07/2023";"4,8296" 137 | "18/07/2023";"4,8034" 138 | "19/07/2023";"4,7994" 139 | "20/07/2023";"4,7882" 140 | "21/07/2023";"4,7726" 141 | "24/07/2023";"4,7451" 142 | "25/07/2023";"4,7490" 143 | "26/07/2023";"4,7362" 144 | "27/07/2023";"4,7196" 145 | "28/07/2023";"4,7247" 146 | "31/07/2023";"4,7409" 147 | "01/08/2023";"4,7746" 148 | "02/08/2023";"4,8077" 149 | "03/08/2023";"4,8791" 150 | "04/08/2023";"4,8603" 151 | "07/08/2023";"4,9004" 152 | "08/08/2023";"4,9211" 153 | "09/08/2023";"4,9009" 154 | "10/08/2023";"4,8512" 155 | "11/08/2023";"4,8912" 156 | "14/08/2023";"4,9475" 157 | "15/08/2023";"4,9806" 158 | "16/08/2023";"4,9765" 159 | "17/08/2023";"4,9810" 160 | "18/08/2023";"4,9718" 161 | "21/08/2023";"4,9841" 162 | "22/08/2023";"4,9425" 163 | "23/08/2023";"4,8971" 164 | "24/08/2023";"4,8742" 165 | "25/08/2023";"4,8766" 166 | "28/08/2023";"4,8933" 167 | "29/08/2023";"4,8700" 168 | "30/08/2023";"4,8647" 169 | "31/08/2023";"4,9213" 170 | "01/09/2023";"4,9312" 171 | "04/09/2023";"4,9170" 172 | "05/09/2023";"4,9699" 173 | "06/09/2023";"4,9756" 174 | "08/09/2023";"4,9829" 175 | "11/09/2023";"4,9360" 176 | "12/09/2023";"4,9499" 177 | "13/09/2023";"4,9165" 178 | "14/09/2023";"4,8745" 179 | "15/09/2023";"4,8683" 180 | "18/09/2023";"4,8529" 181 | "19/09/2023";"4,8569" 182 | "20/09/2023";"4,8481" 183 | "21/09/2023";"4,9223" 184 | "22/09/2023";"4,9125" 185 | "25/09/2023";"4,9600" 186 | "26/09/2023";"4,9711" 187 | "27/09/2023";"5,0283" 188 | "28/09/2023";"5,0469" 189 | "29/09/2023";"5,0070" 190 | "02/10/2023";"5,0673" 191 | "03/10/2023";"5,1094" 192 | "04/10/2023";"5,1520" 193 | "05/10/2023";"5,1707" 194 | "06/10/2023";"5,1912" 195 | "09/10/2023";"5,1660" 196 | "10/10/2023";"5,0856" 197 | "11/10/2023";"5,0490" 198 | "13/10/2023";"5,0619" 199 | "16/10/2023";"5,0612" 200 | "17/10/2023";"5,0378" 201 | "18/10/2023";"5,0562" 202 | "19/10/2023";"5,0534" 203 | "20/10/2023";"5,0522" 204 | "23/10/2023";"5,0158" 205 | "24/10/2023";"5,0059" 206 | "25/10/2023";"4,9975" 207 | "26/10/2023";"5,0049" 208 | "27/10/2023";"4,9474" 209 | "30/10/2023";"5,0068" 210 | "31/10/2023";"5,0569" 211 | "01/11/2023";"5,0188" 212 | "03/11/2023";"4,8904" 213 | "06/11/2023";"4,8993" 214 | "07/11/2023";"4,8664" 215 | "08/11/2023";"4,8849" 216 | "09/11/2023";"4,9001" 217 | "10/11/2023";"4,9213" 218 | "13/11/2023";"4,9240" 219 | "14/11/2023";"4,8676" 220 | "16/11/2023";"4,8569" 221 | "17/11/2023";"4,8843" 222 | "20/11/2023";"4,8717" 223 | "21/11/2023";"4,8800" 224 | "22/11/2023";"4,8962" 225 | "23/11/2023";"4,8925" 226 | "24/11/2023";"4,8921" 227 | "27/11/2023";"4,8945" 228 | "28/11/2023";"4,8861" 229 | "29/11/2023";"4,8927" 230 | "30/11/2023";"4,9349" 231 | "01/12/2023";"4,9185" 232 | "04/12/2023";"4,9085" 233 | "05/12/2023";"4,9516" 234 | "06/12/2023";"4,9025" 235 | "07/12/2023";"4,8943" 236 | "08/12/2023";"4,9152" 237 | "11/12/2023";"4,9434" 238 | "12/12/2023";"4,9470" 239 | "13/12/2023";"4,9573" 240 | "14/12/2023";"4,8906" 241 | "15/12/2023";"4,9391" 242 | "18/12/2023";"4,9393" 243 | "19/12/2023";"4,8657" 244 | "20/12/2023";"4,8760" 245 | "21/12/2023";"4,8749" 246 | "22/12/2023";"4,8613" 247 | "26/12/2023";"4,8362" 248 | "27/12/2023";"4,8300" 249 | "28/12/2023";"4,8407" 250 | "29/12/2023";"4,8407" 251 | --------------------------------------------------------------------------------