Para calcular a m\u00e9dia m\u00f3vel de uma ou mais colunas em um DataFrame do pandas, podemos usar a seguinte sintaxe:<\/span><\/p>\n Este tutorial fornece v\u00e1rios exemplos de uso pr\u00e1tico desta fun\u00e7\u00e3o.<\/span><\/p>\n Suponha que temos o seguinte DataFrame do pandas:<\/span><\/p>\n Podemos usar a seguinte sintaxe para criar uma nova coluna contendo a m\u00e9dia m\u00f3vel de \u201cvendas\u201d dos 5 per\u00edodos anteriores:<\/span><\/p>\n Podemos verificar manualmente que a m\u00e9dia m\u00f3vel de vendas exibida para o per\u00edodo 5 \u00e9 a m\u00e9dia dos 5 per\u00edodos anteriores:<\/span><\/p>\n M\u00e9dia m\u00f3vel no per\u00edodo 5: (61,417+64,900+66,698+64,927+73,720)\/5 = 66,33<\/strong><\/span><\/p>\n Podemos usar sintaxe semelhante para calcular a m\u00e9dia m\u00f3vel de m\u00faltiplas colunas:<\/span><\/p>\n Tamb\u00e9m podemos criar um gr\u00e1fico de linha r\u00e1pido usando Matplotlib para visualizar as vendas brutas versus a m\u00e9dia m\u00f3vel de vendas:<\/span> <\/p>\n A linha azul mostra a m\u00e9dia m\u00f3vel de vendas de 5 per\u00edodos e a linha laranja mostra os dados brutos de vendas.<\/span><\/p>\n Os tutoriais a seguir explicam como realizar outras tarefas comuns em pandas:<\/span><\/p>\n Como calcular a correla\u00e7\u00e3o deslizante em pandas<\/a> Uma m\u00e9dia m\u00f3vel \u00e9 simplesmente a m\u00e9dia de v\u00e1rios per\u00edodos anteriores em uma s\u00e9rie temporal. Para calcular a m\u00e9dia m\u00f3vel de uma ou mais colunas em um DataFrame do pandas, podemos usar a seguinte sintaxe: df[‘ column_name ‘]. rolling ( rolling_window ). mean () Este tutorial fornece v\u00e1rios exemplos de uso pr\u00e1tico desta fun\u00e7\u00e3o. Exemplo: […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1326","post","type-post","status-publish","format-standard","hentry","category-guia"],"yoast_head":"\n df[' column_name<\/span> ']. rolling<\/span> ( rolling_window<\/span> ). mean<\/span> ()\n<\/strong><\/pre>\n Exemplo: calculando a m\u00e9dia m\u00f3vel em pandas<\/strong><\/span><\/h3>\n
import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\n\n#make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (0)\n\n#create dataset<\/span>\nperiod = np. arange<\/span> (1, 101, 1)\nleads = np. random<\/span> . uniform<\/span> (1, 20, 100)\nsales = 60 + 2*period + np. random<\/span> . normal<\/span> (loc=0, scale=.5*period, size=100)\ndf = pd. DataFrame<\/span> ({' period<\/span> ': period, ' leads<\/span> ': leads, ' sales<\/span> ': sales})\n\n#view first 10 rows\n<\/span>df. head<\/span> (10)\n\n period leads sales\n0 1 11.427457 61.417425\n1 2 14.588598 64.900826\n2 3 12.452504 66.698494\n3 4 11.352780 64.927513\n4 5 9.049441 73.720630\n5 6 13.271988 77.687668\n6 7 9.314157 78.125728\n7 8 17.943687 75.280301\n8 9 19.309592 73.181613\n9 10 8.285389 85.272259\n<\/strong><\/pre>\n #find rolling mean of previous 5 sales periods\n<\/span>df[' rolling_sales_5<\/span> '] = df[' sales<\/span> ']. rolling<\/span> (5). mean<\/span> ()\n\n#view first 10 rows\n<\/span>df. head<\/span> (10)\n\n\tperiod leads sales rolling_sales_5\n0 1 11.427457 61.417425 NaN\n1 2 14.588598 64.900826 NaN\n2 3 12.452504 66.698494 NaN\n3 4 11.352780 64.927513 NaN\n4 5 9.049441 73.720630 66.332978\n5 6 13.271988 77.687668 69.587026\n6 7 9.314157 78.125728 72.232007\n7 8 17.943687 75.280301 73.948368\n8 9 19.309592 73.181613 75.599188\n9 10 8.285389 85.272259 77.909514\n<\/strong><\/pre>\n #find rolling mean of previous 5 leads periods \ndf[' rolling_leads_5<\/span> '] = df[' leads<\/span> ']. rolling<\/span> (5). mean<\/span> ()<\/span>\n\n#find rolling mean of previous 5 leads periods\n<\/span>df[' rolling_sales_5<\/span> '] = df[' sales<\/span> ']. rolling<\/span> (5). mean<\/span> ()\n\n#view first 10 rows\n<\/span>df. head<\/span> (10)\n\n\tperiod leads sales rolling_sales_5 rolling_leads_5\n0 1 11.427457 61.417425 NaN NaN\n1 2 14.588598 64.900826 NaN NaN\n2 3 12.452504 66.698494 NaN NaN\n3 4 11.352780 64.927513 NaN NaN\n4 5 9.049441 73.720630 66.332978 11.774156\n5 6 13.271988 77.687668 69.587026 12.143062\n6 7 9.314157 78.125728 72.232007 11.088174\n7 8 17.943687 75.280301 73.948368 12.186411\n8 9 19.309592 73.181613 75.599188 13.777773\n9 10 8.285389 85.272259 77.909514 13.624963\n<\/strong><\/pre>\n import<\/span> matplotlib. pyplot<\/span> as<\/span> plt<\/span>\nplt. plot<\/span> (df[' rolling_sales_5<\/span> '], label=' Rolling Mean<\/span> ')<\/span>\nplt. plot<\/span> (df[' sales<\/span> '], label=' Raw Data<\/span> ')<\/span>\nplt. legend<\/span> ()<\/span>\nplt. ylabel<\/span> (' Sales<\/span> ')<\/span>\nplt. xlabel<\/span> (' Period<\/span> ')<\/span>\nplt. show<\/span> ()<\/span>\n<\/span><\/strong><\/pre>\n![\"Tra\u00e7ando](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/roulantmeanpandas1.png\")
<\/p>\n Recursos adicionais<\/strong><\/h3>\n
Como calcular a m\u00e9dia das colunas no Pandas<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"