{"id":3769,"date":"2023-07-15T15:59:12","date_gmt":"2023-07-15T15:59:12","guid":{"rendered":"https:\/\/statorials.org\/my\/%e1%80%95%e1%80%94%e1%80%ba%e1%80%92%e1%80%ab%e1%80%99%e1%80%bb%e1%80%ac%e1%80%b8%e1%80%9e%e1%80%8a%e1%80%ba-%e1%80%a1%e1%80%94%e1%80%ae%e1%80%b8%e1%80%85%e1%80%95%e1%80%ba%e1%80%86%e1%80%af%e1%80%b6\/"},"modified":"2023-07-15T15:59:12","modified_gmt":"2023-07-15T15:59:12","slug":"%e1%80%95%e1%80%94%e1%80%ba%e1%80%92%e1%80%ab%e1%80%99%e1%80%bb%e1%80%ac%e1%80%b8%e1%80%9e%e1%80%8a%e1%80%ba-%e1%80%a1%e1%80%94%e1%80%ae%e1%80%b8%e1%80%85%e1%80%95%e1%80%ba%e1%80%86%e1%80%af%e1%80%b6","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/%e1%80%95%e1%80%94%e1%80%ba%e1%80%92%e1%80%ab%e1%80%99%e1%80%bb%e1%80%ac%e1%80%b8%e1%80%9e%e1%80%8a%e1%80%ba-%e1%80%a1%e1%80%94%e1%80%ae%e1%80%b8%e1%80%85%e1%80%95%e1%80%ba%e1%80%86%e1%80%af%e1%80%b6\/","title":{"rendered":"Pandas dataframe (\u1025\u1015\u1019\u102c\u1014\u103e\u1004\u1037\u103a\u1021\u1010\u1030) \u1021\u1014\u102e\u1038\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f\u1018\u101a\u103a\u101c\u102d\u102f\u101b\u103e\u102c\u101b\u1019\u101c\u1032\u104b"},"content":{"rendered":"

<\/p>\n

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\u1000\u1031\u102c\u103a\u101c\u1036\u1010\u1005\u103a\u1001\u102f\u1010\u103d\u1004\u103a \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c pandas DataFrame \u101b\u103e\u102d \u1021\u1010\u1014\u103a\u1038\u1000\u102d\u102f\u101b\u103e\u102c\u1016\u103d\u1031\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1001\u103c\u1031\u1001\u1036 syntax \u1000\u102d\u102f \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n

 #find row with closest value to 101 in points column<\/span>\ndf_closest = df. iloc<\/span> [(df[' dots<\/span> ']- 101<\/span> ). abs<\/span> (). argsort<\/span> ()[:1]]\n<\/strong><\/pre>\n
 \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1025\u1015\u1019\u102c\u101e\u100a\u103a \u1024 syntax \u1000\u102d\u102f \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1025\u1015\u1019\u102c- Pandas DataFrame \u1010\u103d\u1004\u103a \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u101b\u103e\u102c\u1015\u102b\u104b<\/span><\/strong><\/h2>\n
 \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1018\u1010\u103a\u1005\u1000\u1010\u103a\u1018\u1031\u102c\u1021\u101e\u1004\u103a\u1038\u1019\u103b\u102c\u1038\u1019\u103e \u101b\u1019\u103e\u1010\u103a\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \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> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Nets', 'Hawks', 'Kings', 'Spurs', 'Cavs'],\n                   ' points<\/span> ': [99, 100, 96, 104, 89, 93]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n    team points\n0 Mavs 99\n1 Nets 100\n2 Hawks 96\n3 Kings 104\n4 Spurs 89\n5 Cavs 93\n<\/strong><\/pre>\n
 \u101a\u1001\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a 101<\/strong> \u1014\u103e\u1004\u1037\u103a \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038 \u1021\u1005\u1000\u103a<\/strong> \u1000\u1031\u102c\u103a\u101c\u1036\u1010\u103d\u1004\u103a \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c DataFrame \u1021\u1010\u1014\u103a\u1038\u1000\u102d\u102f \u101b\u103d\u1031\u1038\u101c\u102d\u102f\u101e\u100a\u103a\u1006\u102d\u102f\u1015\u102b\u1005\u102d\u102f\u1037\u104b<\/span><\/p>\n
 \u1012\u102b\u1000\u102d\u102f\u101c\u102f\u1015\u103a\u1016\u102d\u102f\u1037 \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f \u101e\u102f\u1036\u1038\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u1010\u101a\u103a\u104b<\/span><\/p>\n
 #find row with closest value to 101 in points column\n<\/span>df_closest = df. iloc<\/span> [(df[' dots<\/span> ']- 101<\/span> ). abs<\/span> (). argsort<\/span> ()[:1]]\n\n#view results<\/span>\nprint<\/span> (df_closest)\n\n   team points\n1 Nets 100\n<\/strong><\/pre>\n
 \u101b\u101c\u1012\u103a\u1019\u103e\u104a Nets \u101e\u100a\u103a 101<\/strong> \u1014\u103e\u1004\u1037\u103a \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038 \u1021\u1019\u103e\u1010\u103a \u1000\u1031\u102c\u103a\u101c\u1036<\/strong> \u1019\u103b\u102c\u1038\u1010\u103d\u1004\u103a \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u101b\u103e\u102d\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a \u104b<\/span><\/p>\n
 pandas DataFrame \u101b\u103e\u102d \u1021\u1010\u1014\u103a\u1038\u1010\u1005\u103a\u1001\u102f\u101c\u102f\u1036\u1038\u1021\u1005\u102c\u1038 \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f\u101e\u102c \u1015\u103c\u101e\u101b\u1014\u103a tolist() \u1000\u102d\u102f<\/strong> \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n
 #display value closest to 101 in the points column\n<\/span>df_closest[' points<\/span> ']. tolist<\/span> ()\n\n[100]<\/strong><\/pre>\n
 \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1000\u102d\u102f\u101b\u103e\u102c\u1016\u103d\u1031\u101b\u1014\u103a argsort()<\/strong> function \u1015\u103c\u102e\u1038\u1014\u1031\u102c\u1000\u103a \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u1015\u103c\u1031\u102c\u1004\u103a\u1038\u101c\u1032\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u1000\u102d\u102f \u101e\u1010\u102d\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n
 \u1025\u1015\u1019\u102c\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a\u104a \u1021\u1019\u103e\u1010\u103a\u1019\u103b\u102c\u1038<\/strong> \u1000\u1031\u102c\u103a\u101c\u1036\u101b\u103e\u102d 101<\/strong> \u1014\u103e\u1004\u1037\u103a \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038 2 \u1001\u102f\u1016\u103c\u1004\u1037\u103a DataFrame \u1010\u103d\u1004\u103a \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f\u101b\u103e\u102c\u1016\u103d\u1031\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b syntax \u1000\u102d\u102f\u101e\u102f\u1036\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n
 #find rows with two closest values to 101 in points column\n<\/span>df_closest2 = df. iloc<\/span> [(df[' dots<\/span> ']- 101<\/span> ). abs<\/span> (). argsort<\/span> ()[:2]]\n\n#view results<\/span>\nprint<\/span> (df_closest2)\n\n   team points\n1 Nets 100\n0 Mavs 99\n<\/strong><\/pre>\n
 \u101b\u101c\u1012\u103a\u1019\u103e\u104a Nets \u101e\u100a\u103a \u1021\u1019\u103e\u1010\u103a\u1019\u103b\u102c\u1038<\/strong> \u1000\u1031\u102c\u103a\u101c\u1036\u1010\u103d\u1004\u103a 101<\/strong> \u1014\u103e\u1004\u1037\u103a \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u101b\u103e\u102d\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u1015\u103c\u102e\u1038 Mavs \u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1021\u1019\u103e\u1010\u103a\u1019\u103b\u102c\u1038<\/strong> \u1000\u1031\u102c\u103a\u101c\u1036\u1010\u103d\u1004\u103a 101<\/strong> \u1014\u103e\u1004\u1037\u103a \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u101b\u103e\u102d\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\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><\/span><\/h2>\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 \u1021\u1001\u103c\u102c\u1038\u1018\u102f\u1036\u1015\u1014\u103a\u1012\u102b\u1010\u102c\u101d\u1014\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
 Pandas- \u1000\u1031\u102c\u103a\u101c\u1036\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1021\u1001\u103c\u1031\u1001\u1036\u104d \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u101b\u103d\u1031\u1038\u1001\u103b\u101a\u103a\u1014\u100a\u103a\u1038<\/a>
 Pandas- \u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1000\u1031\u102c\u103a\u101c\u1036\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1021\u1010\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u1031\u102b\u1004\u103a\u1038\u1005\u1015\u103a\u1014\u100a\u103a\u1038<\/a>
 Pandas- \u1021\u1001\u103b\u102d\u102f\u1037\u1000\u101c\u103d\u1032\u101b\u1004\u103a \u1021\u1010\u1014\u103a\u1038\u1021\u102c\u1038\u101c\u102f\u1036\u1038\u1000\u102d\u102f \u1016\u103b\u1000\u103a\u1014\u100a\u103a\u1038<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u1000\u1031\u102c\u103a\u101c\u1036\u1010\u1005\u103a\u1001\u102f\u1010\u103d\u1004\u103a \u101e\u1010\u103a\u1019\u103e\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1010\u1005\u103a\u1001\u102f\u1014\u103e\u1004\u1037\u103a \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c pandas DataFrame \u101b\u103e\u102d \u1021\u1010\u1014\u103a\u1038\u1000\u102d\u102f\u101b\u103e\u102c\u1016\u103d\u1031\u101b\u1014\u103a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1021\u1001\u103c\u1031\u1001\u1036 syntax \u1000\u102d\u102f \u101e\u1004\u103a\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b #find row with closest value to 101 in points column df_closest = df. iloc [(df[‘ dots ‘]- 101 ). abs (). argsort ()[:1]] \u1021\u1031\u102c\u1000\u103a\u1016\u1031\u102c\u103a\u1015\u103c\u1015\u102b \u1025\u1015\u1019\u102c\u101e\u100a\u103a \u1024 syntax \u1000\u102d\u102f \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a \u1019\u100a\u103a\u101e\u102d\u102f\u1037\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1019\u100a\u103a\u1000\u102d\u102f \u1015\u103c\u101e\u1011\u102c\u1038\u101e\u100a\u103a\u104b \u1025\u1015\u1019\u102c- Pandas DataFrame \u1010\u103d\u1004\u103a \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u101b\u103e\u102c\u1015\u102b\u104b \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c\u1018\u1010\u103a\u1005\u1000\u1010\u103a\u1018\u1031\u102c\u1021\u101e\u1004\u103a\u1038\u1019\u103b\u102c\u1038\u1019\u103e \u101b\u1019\u103e\u1010\u103a\u1021\u101b\u1031\u1021\u1010\u103d\u1000\u103a\u1019\u103b\u102c\u1038\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c \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 […]<\/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 DataFrame (\u1025\u1015\u1019\u102c\u1014\u103e\u1004\u1037\u103a\u1021\u1010\u1030) - Statorials \u1010\u103d\u1004\u103a \u1021\u1014\u102e\u1038\u1005\u1015\u103a\u1006\u102f\u1036\u1038\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u1018\u101a\u103a\u101c\u102d\u102f\u101b\u103e\u102c\u1019\u101c\u1032\u104b<\/title>\n<meta name=\"description\" content=\"\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a \u1025\u1015\u1019\u102c\u1010\u1005\u103a\u1001\u102f\u1021\u1015\u102b\u1021\u101d\u1004\u103a \u1015\u1014\u103a\u1012\u102b 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