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import pandas as pd data = { "age": [50, 40, 30], "qualified": [True, False, False] } df = pd.DataFrame(data) newdf = df.set_axis(["John", "Peter", "Alex"]) print(newdf)
Duration,Pulse,Maxpulse,Calories 60,110,130,409.1 60,117,145,479.0 60,103,135,340.0 45,109,175,282.4 45,117,148,406.0 60,102,127,300.5 60,110,136,374.0 45,104,134,253.3 30,109,133,195.1 60,98,124,269.0 60,103,147,329.3 60,100,120,250.7 60,106,128,345.3 60,104,132,379.3 60,98,123,275.0 60,98,120,215.2 60,100,120,300.0 45,90,112, 60,103,123,323.0 45,97,125,243.0 60,108,131,364.2 45,100,119,282.0 60,130,101,300.0 45,105,132,246.0 60,102,126,334.5 60,100,120,250.0 60,92,118,241.0 60,103,132 60,100,132,280.0 60,102,129,380.3 60,92,115,243.0 45,90,112,180.1 60,101,124,299.0 60,93,113,223.0 60,107,136,361.0 60,114,140,415.0 60,102,127,300.5 60,100,120,300.1 60,100,120,300.0 45,104,129,266.0 45,90,112,180.1 60,98,126,286.0 60,100,122,329.4 60,111,138,400.0 60,111,131,397.0 60,99,119,273.0 60,109,153,387.6 45,111,136,300.0 45,108,129,298.0 60,111,139,397.6 60,107,136,380.2 80,123,146,643.1 60,106,130,263.0 60,118,151,486.0 30,136,175,238.0 60,121,146,450.7 60,118,121,413.0 45,115,144,305.0 20,153,172,226.4 45,123,152,321.0 210,108,160,1376.0 160,110,137,1034.4 160,109,135,853.0 45,118,141,341.0 20,110,130,131.4 180,90,130,800.4 150,105,135,873.4 150,107,130,816.0 20,106,136,110.4 300,108,143,1500.2 150,97,129,1115.0 60,109,153,387.6 90,100,127,700.0 150,97,127,953.2 45,114,146,304.0 90,98,125,563.2 45,105,134,251.0 45,110,141,300.0 120,100,130,500.4 270,100,131,1729.0 30,159,182,319.2 45,149,169,344.0 30,103,139,151.1 120,100,130,500.0 45,100,120,225.3 30,151,170,300.1 45,102,136,234.0 120,100,157,1000.1 45,129,103,242.0 20,83,107,50.3 180,101,127,600.1 45,107,137, 30,90,107,105.3 15,80,100,50.5 20,150,171,127.4 20,151,168,229.4 30,95,128,128.2 25,152,168,244.2 30,109,131,188.2 90,93,124,604.1 20,95,112,77.7 90,90,110,500.0 90,90,100,500.0 90,90,100,500.4 30,92,108,92.7 30,93,128,124.0 180,90,120,800.3 30,90,120,86.2 90,90,120,500.3 210,137,184,1860.4 60,102,124,325.2 45,107,124,275.0 15,124,139,124.2 45,100,120,225.3 60,108,131,367.6 60,108,151,351.7 60,116,141,443.0 60,97,122,277.4 60,105,125, 60,103,124,332.7 30,112,137,193.9 45,100,120,100.7 60,119,169,336.7 60,107,127,344.9 60,111,151,368.5 60,98,122,271.0 60,97,124,275.3 60,109,127,382.0 90,99,125,466.4 60,114,151,384.0 60,104,134,342.5 60,107,138,357.5 60,103,133,335.0 60,106,132,327.5 60,103,136,339.0 20,136,156,189.0 45,117,143,317.7 45,115,137,318.0 45,113,138,308.0 20,141,162,222.4 60,108,135,390.0 60,97,127, 45,100,120,250.4 45,122,149,335.4 60,136,170,470.2 45,106,126,270.8 60,107,136,400.0 60,112,146,361.9 30,103,127,185.0 60,110,150,409.4 60,106,134,343.0 60,109,129,353.2 60,109,138,374.0 30,150,167,275.8 60,105,128,328.0 60,111,151,368.5 60,97,131,270.4 60,100,120,270.4 60,114,150,382.8 30,80,120,240.9 30,85,120,250.4 45,90,130,260.4 45,95,130,270.0 45,100,140,280.9 60,105,140,290.8 60,110,145,300.4 60,115,145,310.2 75,120,150,320.4 75,125,150,330.4
age qualified John 50 True Peter 40 False Alex 30 False