{"id":1326,"date":"2023-07-26T20:57:06","date_gmt":"2023-07-26T20:57:06","guid":{"rendered":"https:\/\/statorials.org\/my\/%e1%80%99%e1%80%80%e1%80%b1%e1%80%ac%e1%80%84%e1%80%ba%e1%80%b8%e1%80%86%e1%80%ad%e1%80%af%e1%80%b8%e1%80%9d%e1%80%ab%e1%80%b8-%e1%80%9c%e1%80%ad%e1%80%95%e1%80%ba%e1%80%95%e1%80%94%e1%80%ba%e1%80%92\/"},"modified":"2023-07-26T20:57:06","modified_gmt":"2023-07-26T20:57:06","slug":"%e1%80%99%e1%80%80%e1%80%b1%e1%80%ac%e1%80%84%e1%80%ba%e1%80%b8%e1%80%86%e1%80%ad%e1%80%af%e1%80%b8%e1%80%9d%e1%80%ab%e1%80%b8-%e1%80%9c%e1%80%ad%e1%80%95%e1%80%ba%e1%80%95%e1%80%94%e1%80%ba%e1%80%92","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/%e1%80%99%e1%80%80%e1%80%b1%e1%80%ac%e1%80%84%e1%80%ba%e1%80%b8%e1%80%86%e1%80%ad%e1%80%af%e1%80%b8%e1%80%9d%e1%80%ab%e1%80%b8-%e1%80%9c%e1%80%ad%e1%80%95%e1%80%ba%e1%80%95%e1%80%94%e1%80%ba%e1%80%92\/","title":{"rendered":"Pandas \u101b\u103e\u102d moving average \u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1014\u100a\u103a\u1038"},"content":{"rendered":"

<\/p>\n

\n

\u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e<\/strong> \u101e\u100a\u103a \u1021\u1001\u103b\u102d\u1014\u103a\u1005\u102e\u1038\u101b\u102e\u1038\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d \u101a\u1001\u1004\u103a\u1000\u102c\u101c\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u104f \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n

pandas DataFrame \u1010\u103d\u1004\u103a \u1010\u1005\u103a\u1001\u102f \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a \u1010\u1005\u103a\u1001\u102f\u1011\u1000\u103a\u1015\u102d\u102f\u101e\u1031\u102c \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u104f \u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u1019\u103e\u102f\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u102c\u1038 \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n

 df[' column_name<\/span> ']. rolling<\/span> ( rolling_window<\/span> ). mean<\/span> ()\n<\/strong><\/pre>\n
 \u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a \u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u104f \u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1000\u102d\u102f \u1015\u1031\u1038\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1025\u1015\u1019\u102c- \u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u1014\u1031\u101e\u1031\u102c \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u102c\u1038 \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1001\u103c\u1004\u103a\u1038\u104b<\/strong><\/span><\/h3>\n
 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1004\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b \u1015\u1014\u103a\u1012\u102b DataFrame \u101b\u103e\u102d\u101e\u100a\u103a \u1006\u102d\u102f\u1015\u102b\u1005\u102d\u102f\u1037\u104b<\/span><\/p>\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
 \u101a\u1001\u1004\u103a\u1000\u102c\u101c 5 \u1001\u102f\u1021\u1010\u103d\u1000\u103a “ \u101b\u1031\u102c\u1004\u103a\u1038\u1021\u102c\u1038”  \u104f\u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1014\u103e\u102f\u1014\u103a\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \u1000\u1031\u102c\u103a\u101c\u1036\u1021\u101e\u1005\u103a\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1011\u102c\u1038\u1021\u101e\u102d\u102f\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\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
 \u1000\u102c\u101c 5 \u1021\u1010\u103d\u1000\u103a \u1015\u103c\u101e\u1011\u102c\u1038\u101e\u1031\u102c \u101c\u103e\u102d\u1019\u1037\u103a\u101b\u1031\u102c\u1004\u103a\u1038\u101b\u1004\u103d\u1031 \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u101e\u100a\u103a \u101a\u1001\u1004\u103a\u1000\u102c\u101c 5 \u1001\u102f\u104f \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1016\u103c\u1005\u103a\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037 \u1000\u102d\u102f\u101a\u103a\u1010\u102d\u102f\u1004\u103a \u1005\u1005\u103a\u1006\u1031\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\n
 \u1000\u102c\u101c 5 \u1010\u103d\u1004\u103a \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u1001\u103c\u1004\u103a\u1038- (61.417+64.900+66.698+64.927+73.720)\/5 = 66.33<\/strong><\/span><\/p>\n
 \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u104f\u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u1014\u1031\u101e\u1031\u102c\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u102c\u1038 \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a \u1021\u101c\u102c\u1038\u1010\u1030 syntax \u1000\u102d\u102f\u101e\u102f\u1036\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span><\/p>\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
 \u1021\u101b\u1031\u102c\u1004\u103a\u1038\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e \u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u1001\u103c\u1004\u103a\u1038\u1014\u103e\u1004\u1037\u103a \u1021\u101b\u1031\u102c\u1004\u103a\u1038\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1000\u102d\u102f \u1019\u103c\u1004\u103a\u101e\u102c\u1005\u1031\u101b\u1014\u103a Matplotlib \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1021\u1019\u103c\u1014\u103a\u101c\u102d\u102f\u1004\u103a\u1038\u1000\u103d\u1000\u103a\u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f\u101c\u100a\u103a\u1038 \u1016\u1014\u103a\u1010\u102e\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a-<\/span> <\/p>\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
\"Python<\/p>\n
 \u1021\u1015\u103c\u102c\u101b\u1031\u102c\u1004\u103a\u1019\u103b\u1009\u103a\u1038\u101e\u100a\u103a 5 \u1000\u102c\u101c\u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u101b\u1031\u102c\u1004\u103a\u1038\u1001\u103b\u1019\u103e\u102f\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u102c\u1038\u1015\u103c\u101e\u1015\u103c\u102e\u1038 \u101c\u102d\u1019\u1039\u1019\u1031\u102c\u103a\u101b\u1031\u102c\u1004\u103a\u1019\u103b\u1009\u103a\u1038\u101e\u100a\u103a \u1000\u102f\u1014\u103a\u1000\u103c\u1019\u103a\u1038\u1021\u101b\u1031\u102c\u1004\u103a\u1038\u1012\u1031\u1010\u102c\u1000\u102d\u102f\u1015\u103c\u101e\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1011\u1015\u103a\u101c\u1031\u102c\u1004\u103a\u1038\u1021\u101b\u1004\u103a\u1038\u1021\u1019\u103c\u1005\u103a\u1019\u103b\u102c\u1038<\/strong><\/h3>\n
 \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u1021\u1001\u103c\u102c\u1038\u1018\u102f\u1036\u1021\u101c\u102f\u1015\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u101b\u103e\u1004\u103a\u1038\u1015\u103c\u101e\u100a\u103a-<\/span><\/p>\n
 \u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u101c\u103b\u103e\u1031\u102c\u1000\u103b\u1006\u1000\u103a\u1005\u1015\u103a\u1019\u103e\u102f\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1014\u100a\u103a\u1038<\/a>
 Pandas \u101b\u103e\u102d \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u104f \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u102c\u1038 \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1014\u100a\u103a\u1038<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e \u101e\u100a\u103a \u1021\u1001\u103b\u102d\u1014\u103a\u1005\u102e\u1038\u101b\u102e\u1038\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d \u101a\u1001\u1004\u103a\u1000\u102c\u101c\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u104f \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b pandas DataFrame \u1010\u103d\u1004\u103a \u1010\u1005\u103a\u1001\u102f \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a \u1010\u1005\u103a\u1001\u102f\u1011\u1000\u103a\u1015\u102d\u102f\u101e\u1031\u102c \u1000\u1031\u102c\u103a\u101c\u1036\u1019\u103b\u102c\u1038\u104f \u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u1019\u103e\u102f\u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u102c\u1038 \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b df[‘ column_name ‘]. rolling ( rolling_window ). mean () \u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a \u1024\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1000\u102d\u102f \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1001\u103c\u1004\u103a\u1038\u104f \u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1000\u102d\u102f \u1015\u1031\u1038\u1015\u102b\u101e\u100a\u103a\u104b \u1025\u1015\u1019\u102c- \u1015\u1014\u103a\u1012\u102b\u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u1014\u1031\u101e\u1031\u102c \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u102c\u1038 \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1001\u103c\u1004\u103a\u1038\u104b \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1004\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b \u1015\u1014\u103a\u1012\u102b DataFrame \u101b\u103e\u102d\u101e\u100a\u103a \u1006\u102d\u102f\u1015\u102b\u1005\u102d\u102f\u1037\u104b import numpy as np import pandas as pd #make this example […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"\nPandas - Statorials \u1010\u103d\u1004\u103a \u101b\u103d\u1031\u1037\u101c\u103b\u102c\u1038\u1014\u1031\u101e\u1031\u102c \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u102c\u1038 \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1014\u100a\u103a\u1038<\/title>\n<meta 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