Python Multi Tryit Editor v1.0

prog.py
data.csv

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import pandas as pdimport numpy as np​full_health_data = pd.read_csv("data.csv", header=0, sep=",")​Max_Pulse= full_health_data["Max_Pulse"]percentile10 = np.percentile(Max_Pulse, 10)​print(percentile10)​

Duration,Average_Pulse,Max_Pulse,Calorie_Burnage,Hours_Work,Hours_Sleep
60,110,130,409,0,8
60,117,145,479,0,8
60,103,135,340,8,7.5
45,109,175,282,8,8
45,117,148,406,0,6.5
60,102,127,300,0,7.5
60,110,136,374,0,7.5
45,104,134,253,0,9
30,109,133,195,8,8
60,98,124,269,7.5,8
60,103,147,329,8,7
60,100,120,250,0,5
60,106,128,345,8.5,7.5
60,104,132,379,8.5,7.5
60,98,123,275,9,7
60,98,120,215,6.5,7
60,100,120,300,9,8
45,90,112,180,8,8
60,103,123,323,8,8
45,97,125,243,8,7
60,108,131,364,8,7
45,100,119,282,0,7
60,130,101,300,8,7
45,105,132,246,0,8
60,102,126,334,8,8
60,100,120,250,8,7
60,92,118,241,7,6
60,103,132,353,9,8.5
60,100,132,280,3,8
60,102,129,380,3,8
60,92,115,243,7.5,8
60,101,124,299,8.5,7.5
60,93,113,223,8,7
60,107,136,361,2,8
60,114,140,415,9,8.5
60,100,120,300,8,7
60,100,120,300,7,6
45,104,129,266,8,7
60,98,126,286,7.5,7
60,100,122,329,8,7
60,111,138,400,8,8
60,111,131,397,0,8
60,99,119,273,8,7
60,109,153,387,8,7
45,111,136,300,11,7
45,108,129,298,5,7
60,111,139,397,0,8
60,107,136,380,8.5,8
80,123,146,643,8.5,8
60,106,130,263,8,8
60,118,151,486,7,7
30,136,175,238,0,7.5
60,121,146,450,5,8
60,118,121,413,8,7
45,115,144,305,5,7
20,153,172,226,0,8
45,123,152,321,5,8
210,108,160,1376,8,8
160,110,137,1034,8,8
160,109,135,853,8,8
45,118,141,341,8,8
20,110,130,131,0,8
180,90,130,800,0,8
150,105,135,873,8,8
150,107,130,816,3,8
20,106,136,110,3,8
300,108,143,1500,0,8
150,97,129,1115,0,8
90,100,127,700,0,8
150,97,127,953,0,8
45,114,146,304,0,8
90,98,125,563,0,8
45,105,134,251,0,8
45,110,141,300,7,7.5
120,100,130,500,7,7.5
270,100,131,1729,0,7
30,159,182,319,5,7.5
45,149,169,344,0,8
30,103,139,151,4,8
120,100,130,500,0,8
45,100,120,225,8.5,8
30,151,170,300,0,7
45,102,136,234,6.5,8
120,100,157,1000,0,8
45,129,103,242,5,8
20,83,107,50,9.5,8
180,101,127,600,0,7.5
45,107,137,1200,8,8
30,90,107,105,7,8
15,80,100,50,9,8
20,150,171,127,0,7.5
20,151,168,229,0,7
30,95,128,128,8,7.5
25,152,168,244,0,8
30,109,131,188,8,7.5
90,93,124,604,8,8
20,95,112,77,8,8
90,90,110,500,8,8
90,90,100,500,0,8
90,90,100,500,5,8
30,92,108,92,8,8
30,93,128,124,8,8
180,90,120,800,8,8
30,90,120,86,7,8
90,90,120,500,8,8
210,137,184,1860,0,7.5
60,102,124,325,0,8
45,107,124,275,0,8
15,124,139,124,5,6
60,108,131,367,0,8
60,108,151,351,0,8
60,116,141,443,0,8
60,97,122,277,0,8
60,105,125,353,0,8
60,103,124,332,0,8
30,112,137,193,0,8
45,100,120,100,0,7.5
60,119,169,336,0,8
60,107,127,344,0,8
60,111,151,368,0,8
60,98,122,271,0,8
60,97,124,275,0,8
60,109,127,382,0,6
90,99,125,466,0,7.5
60,114,151,384,0,12
60,104,134,342,0,7.5
60,107,138,357,0,7.5
60,103,133,335,0,7.5
60,106,132,327,0,7.5
60,103,136,339,0,7.5
20,136,156,189,8.5,7.5
45,117,143,317,0,8
45,115,137,318,0,7.5
45,113,138,308,0,7
20,141,162,222,0,7.5
60,108,135,390,0,8
60,97,127,288,0,8
45,100,120,250,0,8
45,122,149,335,0,7.5
60,136,170,470,8,8
45,106,126,270,8,8
60,107,136,400,9.5,7
60,112,146,361,9,7.5
30,103,127,185,9,7.5
60,110,150,409,8,7
60,106,134,343,8,8.5
60,109,129,353,8,8
60,109,138,374,8,6
30,150,167,275,0,9
60,105,128,328,9,8
60,97,131,270,9,8
60,100,120,270,8,8
60,114,150,382,0,8
30,80,120,240,10,7
30,85,120,250,10,7
45,90,130,260,8,7
45,95,130,270,8,7
45,100,140,280,0,7
60,105,140,290,7,8
60,110,145,300,7,8
60,115,145,310,8,8
75,120,150,320,0,8
75,125,150,330,8,8

Duration,Average_Pulse,Max_Pulse,Calorie_Burnage,Hours_Work,Hours_Sleep