diff --git a/EVI - 2018/EVI 04/Automobile_data.csv b/EVI - 2018/EVI 04/Automobile_data.csv
new file mode 100755
index 0000000..37649b0
--- /dev/null
+++ b/EVI - 2018/EVI 04/Automobile_data.csv
@@ -0,0 +1 @@
+symboling,normalized-losses,make,fuel-type,aspiration,num-of-doors,body-style,drive-wheels,engine-location,wheel-base,length,width,height,curb-weight,engine-type,num-of-cylinders,engine-size,fuel-system,bore,stroke,compression-ratio,horsepower,peak-rpm,city-mpg,highway-mpg,price
3,122,alfa-romero,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,13495
3,122,alfa-romero,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,16500
1,122,alfa-romero,gas,std,two,hatchback,rwd,front,94.5,171.2,65.5,52.4,2823,ohcv,six,152,mpfi,2.68,3.47,9,154,5000,19,26,16500
2,164,audi,gas,std,four,sedan,fwd,front,99.8,176.6,66.2,54.3,2337,ohc,four,109,mpfi,3.19,3.4,10,102,5500,24,30,13950
2,164,audi,gas,std,four,sedan,4wd,front,99.4,176.6,66.4,54.3,2824,ohc,five,136,mpfi,3.19,3.4,8,115,5500,18,22,17450
2,122,audi,gas,std,two,sedan,fwd,front,99.8,177.3,66.3,53.1,2507,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,15250
1,158,audi,gas,std,four,sedan,fwd,front,105.8,192.7,71.4,55.7,2844,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,17710
1,122,audi,gas,std,four,wagon,fwd,front,105.8,192.7,71.4,55.7,2954,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,18920
1,158,audi,gas,turbo,four,sedan,fwd,front,105.8,192.7,71.4,55.9,3086,ohc,five,131,mpfi,3.13,3.4,8.3,140,5500,17,20,23875
0,122,audi,gas,turbo,two,hatchback,4wd,front,99.5,178.2,67.9,52,3053,ohc,five,131,mpfi,3.13,3.4,7,160,5500,16,22,13207.12935
2,192,bmw,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16430
0,192,bmw,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16925
0,188,bmw,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2710,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,20970
0,188,bmw,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2765,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,21105
1,122,bmw,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3055,ohc,six,164,mpfi,3.31,3.19,9,121,4250,20,25,24565
0,122,bmw,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3230,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,30760
0,122,bmw,gas,std,two,sedan,rwd,front,103.5,193.8,67.9,53.7,3380,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,41315
0,122,bmw,gas,std,four,sedan,rwd,front,110,197,70.9,56.3,3505,ohc,six,209,mpfi,3.62,3.39,8,182,5400,15,20,36880
2,121,chevrolet,gas,std,two,hatchback,fwd,front,88.4,141.1,60.3,53.2,1488,l,three,61,2bbl,2.91,3.03,9.5,48,5100,47,53,5151
1,98,chevrolet,gas,std,two,hatchback,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6295
0,81,chevrolet,gas,std,four,sedan,fwd,front,94.5,158.8,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6575
1,118,dodge,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.41,68,5500,37,41,5572
1,118,dodge,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6377
1,118,dodge,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,7957
1,148,dodge,gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229
1,148,dodge,gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692
1,148,dodge,gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,7609
1,148,dodge,gas,turbo,?,sedan,fwd,front,93.7,157.3,63.8,50.6,2191,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,8558
-1,110,dodge,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.34,3.46,8.5,88,5000,24,30,8921
3,145,dodge,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2811,ohc,four,156,mfi,3.6,3.9,7,145,5000,19,24,12964
2,137,honda,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1713,ohc,four,92,1bbl,2.91,3.41,9.6,58,4800,49,54,6479
2,137,honda,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1819,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,31,38,6855
1,101,honda,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1837,ohc,four,79,1bbl,2.91,3.07,10.1,60,5500,38,42,5399
1,101,honda,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1940,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,6529
1,101,honda,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1956,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7129
0,110,honda,gas,std,four,sedan,fwd,front,96.5,163.4,64,54.5,2010,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7295
0,78,honda,gas,std,four,wagon,fwd,front,96.5,157.1,63.9,58.3,2024,ohc,four,92,1bbl,2.92,3.41,9.2,76,6000,30,34,7295
0,106,honda,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2236,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,7895
0,106,honda,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2289,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,9095
0,85,honda,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2304,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,8845
0,85,honda,gas,std,four,sedan,fwd,front,96.5,175.4,62.5,54.1,2372,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,10295
0,85,honda,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2465,ohc,four,110,mpfi,3.15,3.58,9,101,5800,24,28,12945
1,107,honda,gas,std,two,sedan,fwd,front,96.5,169.1,66,51,2293,ohc,four,110,2bbl,3.15,3.58,9.1,100,5500,25,31,10345
0,122,isuzu,gas,std,four,sedan,rwd,front,94.3,170.7,61.8,53.5,2337,ohc,four,111,2bbl,3.31,3.23,8.5,78,4800,24,29,6785
1,122,isuzu,gas,std,two,sedan,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,13207.12935
0,122,isuzu,gas,std,four,sedan,fwd,front,94.5,155.9,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,13207.12935
2,122,isuzu,gas,std,two,hatchback,rwd,front,96,172.6,65.2,51.4,2734,ohc,four,119,spfi,3.43,3.23,9.2,90,5000,24,29,11048
0,145,jaguar,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,32250
0,122,jaguar,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,35550
0,122,jaguar,gas,std,two,sedan,rwd,front,102,191.7,70.6,47.8,3950,ohcv,twelve,326,mpfi,3.54,2.76,11.5,262,5000,13,17,36000
1,104,mazda,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1890,ohc,four,91,2bbl,3.03,3.15,9,68,5000,30,31,5195
1,104,mazda,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1900,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6095
1,104,mazda,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1905,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6795
1,113,mazda,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1945,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6695
1,113,mazda,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1950,ohc,four,91,2bbl,3.08,3.15,9,68,5000,31,38,7395
3,150,mazda,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,3.329751244,3.255422886,9.4,101,6000,17,23,10945
3,150,mazda,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,3.329751244,3.255422886,9.4,101,6000,17,23,11845
3,150,mazda,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2385,rotor,two,70,4bbl,3.329751244,3.255422886,9.4,101,6000,17,23,13645
3,150,mazda,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2500,rotor,two,80,mpfi,3.329751244,3.255422886,9.4,135,6000,16,23,15645
1,129,mazda,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8845
0,115,mazda,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8495
1,129,mazda,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10595
0,115,mazda,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10245
0,122,mazda,diesel,std,?,sedan,fwd,front,98.8,177.8,66.5,55.5,2443,ohc,four,122,idi,3.39,3.39,22.7,64,4650,36,42,10795
0,115,mazda,gas,std,four,hatchback,fwd,front,98.8,177.8,66.5,55.5,2425,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,11245
0,118,mazda,gas,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2670,ohc,four,140,mpfi,3.76,3.16,8,120,5000,19,27,18280
0,122,mazda,diesel,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2700,ohc,four,134,idi,3.43,3.64,22,72,4200,31,39,18344
-1,93,mercedes-benz,diesel,turbo,four,sedan,rwd,front,110,190.9,70.3,56.5,3515,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,25552
-1,93,mercedes-benz,diesel,turbo,four,wagon,rwd,front,110,190.9,70.3,58.7,3750,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28248
0,93,mercedes-benz,diesel,turbo,two,hardtop,rwd,front,106.7,187.5,70.3,54.9,3495,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28176
-1,93,mercedes-benz,diesel,turbo,four,sedan,rwd,front,115.6,202.6,71.7,56.3,3770,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,31600
-1,122,mercedes-benz,gas,std,four,sedan,rwd,front,115.6,202.6,71.7,56.5,3740,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,34184
3,142,mercedes-benz,gas,std,two,convertible,rwd,front,96.6,180.3,70.5,50.8,3685,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,35056
0,122,mercedes-benz,gas,std,four,sedan,rwd,front,120.9,208.1,71.7,56.7,3900,ohcv,eight,308,mpfi,3.8,3.35,8,184,4500,14,16,40960
1,122,mercedes-benz,gas,std,two,hardtop,rwd,front,112,199.2,72,55.4,3715,ohcv,eight,304,mpfi,3.8,3.35,8,184,4500,14,16,45400
1,122,mercury,gas,turbo,two,hatchback,rwd,front,102.7,178.4,68,54.8,2910,ohc,four,140,mpfi,3.78,3.12,8,175,5000,19,24,16503
2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1918,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,37,41,5389
2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1944,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6189
2,161,mitsubishi,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,2004,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6669
1,161,mitsubishi,gas,turbo,two,hatchback,fwd,front,93,157.3,63.8,50.8,2145,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7689
3,153,mitsubishi,gas,turbo,two,hatchback,fwd,front,96.3,173,65.4,49.4,2370,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9959
3,153,mitsubishi,gas,std,two,hatchback,fwd,front,96.3,173,65.4,49.4,2328,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8499
3,122,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2833,ohc,four,156,spdi,3.58,3.86,7,145,5000,19,24,12629
3,122,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2921,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14869
3,122,mitsubishi,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2926,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14489
1,125,mitsubishi,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2365,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,6989
1,125,mitsubishi,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2405,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8189
1,125,mitsubishi,gas,turbo,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279
-1,137,mitsubishi,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279
1,128,nissan,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1889,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,5499
1,128,nissan,diesel,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,2017,ohc,four,103,idi,2.99,3.47,21.9,55,4800,45,50,7099
1,128,nissan,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1918,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6649
1,122,nissan,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1938,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6849
1,103,nissan,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2024,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7349
1,128,nissan,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1951,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7299
1,128,nissan,gas,std,two,hatchback,fwd,front,94.5,165.6,63.8,53.3,2028,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7799
1,122,nissan,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1971,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7499
1,103,nissan,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2037,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7999
2,168,nissan,gas,std,two,hardtop,fwd,front,95.1,162.4,63.8,53.3,2008,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,8249
0,106,nissan,gas,std,four,hatchback,fwd,front,97.2,173.4,65.2,54.7,2324,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,8949
0,106,nissan,gas,std,four,sedan,fwd,front,97.2,173.4,65.2,54.7,2302,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,9549
0,128,nissan,gas,std,four,sedan,fwd,front,100.4,181.7,66.5,55.1,3095,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,13499
0,108,nissan,gas,std,four,wagon,fwd,front,100.4,184.6,66.5,56.1,3296,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,14399
0,108,nissan,gas,std,four,sedan,fwd,front,100.4,184.6,66.5,55.1,3060,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,19,25,13499
3,194,nissan,gas,std,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3071,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,17199
3,194,nissan,gas,turbo,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,7.8,200,5200,17,23,19699
1,231,nissan,gas,std,two,hatchback,rwd,front,99.2,178.5,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,18399
0,161,peugot,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3020,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,11900
0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3197,l,four,152,idi,3.7,3.52,21,95,4150,28,33,13200
0,122,peugot,gas,std,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3230,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,12440
0,122,peugot,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3430,l,four,152,idi,3.7,3.52,21,95,4150,25,25,13860
0,161,peugot,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,15580
0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,16900
0,122,peugot,gas,std,four,wagon,rwd,front,114.2,198.9,68.4,56.7,3285,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,16695
0,122,peugot,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3485,l,four,152,idi,3.7,3.52,21,95,4150,25,25,17075
0,161,peugot,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,16630
0,161,peugot,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,17950
0,161,peugot,gas,turbo,four,sedan,rwd,front,108,186.7,68.3,56,3130,l,four,134,mpfi,3.61,3.21,7,142,5600,18,24,18150
1,119,plymouth,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1918,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,37,41,5572
1,119,plymouth,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7957
1,154,plymouth,gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229
1,154,plymouth,gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692
1,154,plymouth,gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,2191,ohc,four,98,2bbl,2.97,3.23,9.4,68,5500,31,38,7609
-1,74,plymouth,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,24,30,8921
3,122,plymouth,gas,turbo,two,hatchback,rwd,front,95.9,173.2,66.3,50.2,2818,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,12764
3,186,porsche,gas,std,two,hatchback,rwd,front,94.5,168.9,68.3,50.2,2778,ohc,four,151,mpfi,3.94,3.11,9.5,143,5500,19,27,22018
3,122,porsche,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,32528
3,122,porsche,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,34028
3,122,porsche,gas,std,two,convertible,rwd,rear,89.5,168.9,65,51.6,2800,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,37028
1,122,porsche,gas,std,two,hatchback,rwd,front,98.4,175.7,72.3,50.5,3366,dohcv,eight,203,mpfi,3.94,3.11,10,288,5750,17,28,13207.12935
0,122,renault,gas,std,four,wagon,fwd,front,96.1,181.5,66.5,55.2,2579,ohc,four,132,mpfi,3.46,3.9,8.7,104.2561576,5125.369458,23,31,9295
2,122,renault,gas,std,two,hatchback,fwd,front,96.1,176.8,66.6,50.5,2460,ohc,four,132,mpfi,3.46,3.9,8.7,104.2561576,5125.369458,23,31,9895
3,150,saab,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2658,ohc,four,121,mpfi,3.54,3.07,9.31,110,5250,21,28,11850
2,104,saab,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2695,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,12170
3,150,saab,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2707,ohc,four,121,mpfi,2.54,2.07,9.3,110,5250,21,28,15040
2,104,saab,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2758,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,15510
3,150,saab,gas,turbo,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2808,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18150
2,104,saab,gas,turbo,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2847,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18620
2,83,subaru,gas,std,two,hatchback,fwd,front,93.7,156.9,63.4,53.7,2050,ohcf,four,97,2bbl,3.62,2.36,9,69,4900,31,36,5118
2,83,subaru,gas,std,two,hatchback,fwd,front,93.7,157.9,63.6,53.7,2120,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7053
2,83,subaru,gas,std,two,hatchback,4wd,front,93.3,157.3,63.8,55.7,2240,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7603
0,102,subaru,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2145,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4800,32,37,7126
0,102,subaru,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2190,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4400,28,33,7775
0,102,subaru,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2340,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,26,32,9960
0,102,subaru,gas,std,four,sedan,4wd,front,97,172,65.4,54.3,2385,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,24,25,9233
0,102,subaru,gas,turbo,four,sedan,4wd,front,97,172,65.4,54.3,2510,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,24,29,11259
0,89,subaru,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2290,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,28,32,7463
0,89,subaru,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2455,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,25,31,10198
0,85,subaru,gas,std,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2420,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,23,29,8013
0,85,subaru,gas,turbo,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2650,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,23,23,11694
1,87,toyota,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,1985,ohc,four,92,2bbl,3.05,3.03,9,62,4800,35,39,5348
1,87,toyota,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,2040,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6338
1,74,toyota,gas,std,four,hatchback,fwd,front,95.7,158.7,63.6,54.5,2015,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6488
0,77,toyota,gas,std,four,wagon,fwd,front,95.7,169.7,63.6,59.1,2280,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,37,6918
0,81,toyota,gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,2290,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,7898
0,91,toyota,gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,3110,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,8778
0,91,toyota,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2081,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,6938
0,91,toyota,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2109,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,7198
0,91,toyota,diesel,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,34,36,7898
0,91,toyota,diesel,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,38,47,7788
0,91,toyota,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2094,ohc,four,98,2bbl,3.19,3.03,9,70,4800,38,47,7738
0,91,toyota,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2122,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,8358
0,91,toyota,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,52.8,2140,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,9258
1,168,toyota,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2169,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8058
1,168,toyota,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2204,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8238
1,168,toyota,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2265,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9298
1,168,toyota,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2300,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9538
2,134,toyota,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2540,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,8449
2,134,toyota,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2536,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9639
2,134,toyota,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2551,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9989
2,134,toyota,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2679,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11199
2,134,toyota,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2714,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11549
2,134,toyota,gas,std,two,convertible,rwd,front,98.4,176.2,65.6,53,2975,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,17669
-1,65,toyota,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2326,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,29,34,8948
-1,65,toyota,diesel,turbo,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2480,ohc,four,110,idi,3.27,3.35,22.5,73,4500,30,33,10698
-1,65,toyota,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,9988
-1,65,toyota,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,10898
-1,65,toyota,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2458,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,11248
3,197,toyota,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,2976,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,20,24,16558
3,197,toyota,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,3016,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,19,24,15998
-1,90,toyota,gas,std,four,sedan,rwd,front,104.5,187.8,66.5,54.1,3131,dohc,six,171,mpfi,3.27,3.35,9.2,156,5200,20,24,15690
-1,122,toyota,gas,std,four,wagon,rwd,front,104.5,187.8,66.5,54.1,3151,dohc,six,161,mpfi,3.27,3.35,9.2,156,5200,19,24,15750
2,122,volkswagen,diesel,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2261,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7775
2,122,volkswagen,gas,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2209,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,7975
2,94,volkswagen,diesel,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2264,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7995
2,94,volkswagen,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2212,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8195
2,94,volkswagen,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2275,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8495
2,94,volkswagen,diesel,turbo,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2319,ohc,four,97,idi,3.01,3.4,23,68,4500,37,42,9495
2,94,volkswagen,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2300,ohc,four,109,mpfi,3.19,3.4,10,100,5500,26,32,9995
3,122,volkswagen,gas,std,two,convertible,fwd,front,94.5,159.3,64.2,55.6,2254,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,11595
3,256,volkswagen,gas,std,two,hatchback,fwd,front,94.5,165.7,64,51.4,2221,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,9980
0,122,volkswagen,gas,std,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2661,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,24,13295
0,122,volkswagen,diesel,turbo,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2579,ohc,four,97,idi,3.01,3.4,23,68,4500,33,38,13845
0,122,volkswagen,gas,std,four,wagon,fwd,front,100.4,183.1,66.9,55.1,2563,ohc,four,109,mpfi,3.19,3.4,9,88,5500,25,31,12290
-2,103,volvo,gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2912,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,12940
-1,74,volvo,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3034,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,13415
-2,103,volvo,gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2935,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,15985
-1,74,volvo,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3042,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,16515
-2,103,volvo,gas,turbo,four,sedan,rwd,front,104.3,188.8,67.2,56.2,3045,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18420
-1,74,volvo,gas,turbo,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3157,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18950
-1,95,volvo,gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,2952,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,16845
-1,95,volvo,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.8,55.5,3049,ohc,four,141,mpfi,3.78,3.15,8.7,160,5300,19,25,19045
-1,95,volvo,gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3012,ohcv,six,173,mpfi,3.58,2.87,8.8,134,5500,18,23,21485
-1,95,volvo,diesel,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3217,ohc,six,145,idi,3.01,3.4,23,106,4800,26,27,22470
-1,95,volvo,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3062,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,19,25,22625
\ No newline at end of file
diff --git a/EVI - 2018/EVI 04/Ejemplos/basics.py b/EVI - 2018/EVI 04/Ejemplos/basics.py
new file mode 100644
index 0000000..1f82b31
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/basics.py
@@ -0,0 +1,132 @@
+#####################
+# Atomic data types #
+#####################
+
+a = 89 # Integer
+b = 0.5 # Float
+c = 9 + 0.4j # Complex
+d = True # Boolean
+e = None # Nonetype
+
+########################
+# Composite data types #
+########################
+
+f = [1, '2', 3] # List
+g = (1, '2', 3) # Tuple
+h = {1: 'one', # Dict
+ 'two': 2,
+ 3: 'Three'}
+i = set([1, 2, 3]) # Set
+j = frozenset(f) # Frozen set
+
+######################
+# Control structures #
+######################
+
+# If
+if 1 < 2:
+ print("It's true!")
+elif 2 < 1:
+ print("It's false!")
+else:
+ print("It's neither!")
+
+# For
+for i in f:
+ print(i)
+
+# While
+while a > 80:
+ print(a)
+ a -= 1
+
+########################
+# Function definitions #
+########################
+
+# No arguments
+
+def fun():
+ return 89
+
+fun()
+
+# Functions without a return statement return None
+
+def non():
+ pass
+
+print(non())
+
+# Positional arguments
+
+def sum(a, b):
+ return a + b
+
+print(sum(1, 2))
+
+# Keyword arguments
+
+def divide(dividend = 1, divisor = 1):
+ return dividend / divisor # Unsafe!!
+
+print(divide())
+print(divide(divisor = 2))
+print(divide(dividend = 4, divisor = 2))
+print(divide(divisor = 4, dividend = 2))
+
+# Both argument types
+
+def xnp(x, n, p = 1):
+ # Keyword args MUST appear AFTER positional args!
+ return (x * n) + p
+
+# Variable length arguments
+
+def varargs(*args):
+ for a in args:
+ print("Argument " + str(args.index(a)) + " is " + str(a))
+
+varargs(1)
+varargs(2, 3)
+varargs(4, 5, 6)
+
+# Variable keyword arguments
+
+def varkwargs(**kwargs):
+ for k in kwargs.keys():
+ print('Argument "' + str(k) + '" is "' + str(kwargs[k]))
+
+varkwargs(a = 1, b = 2, c = 3)
+
+# Everything!
+
+def allargs(a, b, c = None, *args, **kwargs):
+ print("a is " + str(a))
+
+ print("b is " + str(b))
+
+ if c is None:
+ print("c is None")
+ else:
+ print("c is Some")
+
+ for a in args:
+ print("Argument " + str(args.index(a)) + " is " + str(a))
+
+ for k in kwargs.keys():
+ print('Argument "' + str(k) + '" is "' + str(kwargs[k]))
+
+allargs('a', 1, None, 2, 3, 4, q = "Hail", w = "Caesar!")
+allargs(1, 2, c = 89)
+
+# Nested functions
+
+def outer(x):
+ def inner(y):
+ return x + y
+
+ return inner(9)
+
+print(outer(1))
diff --git a/EVI - 2018/EVI 04/Ejemplos/classes.py b/EVI - 2018/EVI 04/Ejemplos/classes.py
new file mode 100644
index 0000000..2699eaa
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/classes.py
@@ -0,0 +1,33 @@
+################
+# Simple class #
+################
+
+class Class(object):
+ def __init__(self, a):
+ self.a = a
+
+ def method(self):
+ return self.a
+
+o = Class(1)
+print(o.method())
+print(o.a) # !
+
+####################
+# With inheritance #
+####################
+
+class Subclass(Class):
+ def __init__(self, a, b):
+ super(Subclass, self).__init__(a)
+ self.b = b
+
+ def method(self):
+ return self.b
+
+ def sub_method(self):
+ return self.a
+
+s = Subclass(1, 2)
+print(s.method())
+print(s.sub_method())
diff --git a/EVI - 2018/EVI 04/Ejemplos/data.json b/EVI - 2018/EVI 04/Ejemplos/data.json
new file mode 100644
index 0000000..eed0a1b
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/data.json
@@ -0,0 +1 @@
+{"info": {"Weight": 80, "Height": 1.72}, "Age": 27, "Name": "Miguel", "ID": 18810993}
\ No newline at end of file
diff --git a/EVI - 2018/EVI 04/Ejemplos/db.py b/EVI - 2018/EVI 04/Ejemplos/db.py
new file mode 100644
index 0000000..26e24c9
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/db.py
@@ -0,0 +1,36 @@
+import sqlite3
+
+# "Connect" to a database
+conn = sqlite3.connect('example.db')
+
+# Get a cursor to operate on the database
+c = conn.cursor()
+
+# Create table
+c.execute('''CREATE TABLE stocks
+ (date text, trans text, symbol text, qty real, price real)''')
+
+# Insert a row of data
+c.execute("INSERT INTO stocks VALUES ('2006-01-05','BUY','RHAT',100,35.14)")
+
+# Save (commit) the changes
+conn.commit()
+
+# Select
+t = ('RHAT',)
+c.execute('SELECT * FROM stocks WHERE symbol=?', t)
+print(c.fetchone())
+
+# Larger example that inserts many records at a time
+purchases = [('2006-03-28', 'BUY', 'IBM', 1000, 45.00),
+ ('2006-04-05', 'BUY', 'MSFT', 1000, 72.00),
+ ('2006-04-06', 'SELL', 'IBM', 500, 53.00),
+ ]
+c.executemany('INSERT INTO stocks VALUES (?,?,?,?,?)', purchases)
+
+for row in c.execute('SELECT * FROM stocks ORDER BY price'):
+ print(row)
+
+# We can also close the connection if we are done with it.
+# Just be sure any changes have been committed or they will be lost.
+conn.close()
diff --git a/EVI - 2018/EVI 04/Ejemplos/example.db b/EVI - 2018/EVI 04/Ejemplos/example.db
new file mode 100644
index 0000000..151009e
Binary files /dev/null and b/EVI - 2018/EVI 04/Ejemplos/example.db differ
diff --git a/EVI - 2018/EVI 04/Ejemplos/example.json b/EVI - 2018/EVI 04/Ejemplos/example.json
new file mode 100644
index 0000000..04288b3
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/example.json
@@ -0,0 +1,28 @@
+{
+ "firstName": "John",
+ "lastName": "Smith",
+ "isAlive": true,
+ "age": 25,
+ "address": {
+ "streetAddress": "21 2nd Street",
+ "city": "New York",
+ "state": "NY",
+ "postalCode": "10021-3100"
+ },
+ "phoneNumbers": [
+ {
+ "type": "home",
+ "number": "212 555-1234"
+ },
+ {
+ "type": "office",
+ "number": "646 555-4567"
+ },
+ {
+ "type": "mobile",
+ "number": "123 456-7890"
+ }
+ ],
+ "children": [],
+ "spouse": null
+}
diff --git a/EVI - 2018/EVI 04/Ejemplos/functional.py b/EVI - 2018/EVI 04/Ejemplos/functional.py
new file mode 100644
index 0000000..f1d3ec5
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/functional.py
@@ -0,0 +1,31 @@
+################################
+# Lambda (anonymous) functions #
+################################
+
+f = lambda x, y: x + y
+print(f(1, 2))
+print()
+
+##########################
+# Higher order functions #
+##########################
+
+# Map: Applies a function to many lists. Returns a generator.
+
+for i in map(lambda x: x*x, [1, 2, 3, 4, 5, 6]):
+ print(i)
+print()
+
+for i in map(f, [1, 2, 3, 4, 5, 6], [2, 3, 4, 5, 6, 7]):
+ print(i)
+print()
+
+# Custom higher order function
+
+def hof(function = lambda x: x, *args):
+ for a in args:
+ print(function(a))
+
+hof(lambda s: s.upper(), "a", "b", "c")
+print()
+hof(lambda x: x is None, 1, ["a", "b"], None, {})
diff --git a/EVI - 2018/EVI 04/Ejemplos/generators.py b/EVI - 2018/EVI 04/Ejemplos/generators.py
new file mode 100644
index 0000000..2c435d8
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/generators.py
@@ -0,0 +1,46 @@
+####################
+# Generator object #
+####################
+
+def simple_generator():
+ i = 0
+ while(i < 10):
+ yield i
+ i += 1
+ raise StopIteration
+
+g = simple_generator()
+
+for i in g:
+ print(i)
+
+try:
+ print(next(g))
+except StopIteration:
+ print("Generator exhausted!")
+else:
+ print("Generator success!")
+finally:
+ print("At the end!")
+
+####################
+# Collection class #
+####################
+
+class Collection(object):
+ def __init__(self):
+ self.c = []
+
+ def add(self, x):
+ self.c.append(x)
+
+ def __iter__(self):
+ for i in self.c:
+ yield i
+
+c = Collection()
+c.add(1)
+c.add(2)
+c.add(3)
+for i in c:
+ print(i)
diff --git a/EVI - 2018/EVI 04/Ejemplos/http_client.py b/EVI - 2018/EVI 04/Ejemplos/http_client.py
new file mode 100644
index 0000000..ca94165
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/http_client.py
@@ -0,0 +1,9 @@
+import urllib2
+
+# Create an HTTP request.
+request = urllib2.Request("http://www.concisa.net.ve/2016/evi-2016-tutoriales/")
+# Perform the request.
+response = urllib2.urlopen(request)
+# Get the response data.
+document = response.read()
+print(document)
diff --git a/EVI - 2018/EVI 04/Ejemplos/http_server.py b/EVI - 2018/EVI 04/Ejemplos/http_server.py
new file mode 100644
index 0000000..2ba8df8
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/http_server.py
@@ -0,0 +1,8 @@
+from http.server import HTTPServer, SimpleHTTPRequestHandler
+
+def run(server_class = HTTPServer, handler_class = SimpleHTTPRequestHandler):
+ server_address = ('127.0.0.1', 8000)
+ httpd = server_class(server_address, handler_class)
+ httpd.serve_forever()
+
+run()
diff --git a/EVI - 2018/EVI 04/Ejemplos/jsoning.py b/EVI - 2018/EVI 04/Ejemplos/jsoning.py
new file mode 100644
index 0000000..f4cb7d9
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/jsoning.py
@@ -0,0 +1,16 @@
+import json
+
+data = {"Name": "Miguel",
+ "Age": 27,
+ "ID": 18810993,
+ "info" : {"Height": 1.72,
+ "Weight": 80
+ }
+ }
+
+with open("data.json", "w") as f:
+ f.write(json.dumps(data))
+
+with open("example.json") as f:
+ j = json.loads(f.read())
+ print(j["isAlive"])
diff --git a/EVI - 2018/EVI 04/Ejemplos/lists.py b/EVI - 2018/EVI 04/Ejemplos/lists.py
new file mode 100644
index 0000000..b607be4
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/lists.py
@@ -0,0 +1,60 @@
+a = [1, 2, 3, 4, 5, 6, 7, 8, 9]
+
+#################
+# List indexing #
+#################
+
+# Classical position indexing
+
+i = 0
+while i < len(a):
+ print(a[i])
+ i += 1
+
+# Negative indices
+
+print(a[-1])
+print(a[-2])
+print(a[-3])
+
+################
+# List slicing #
+################
+
+# Elements between indices 3 and 7
+
+print(a[3:7])
+
+# Elements from index 5 onwards
+
+print(a[5:])
+
+# Elements from the start up to index 8
+
+print(a[:8])
+
+#######################
+# List comprehensions #
+#######################
+
+# The first 10 square natural numbers
+
+l = [x * x for x in range(1, 10)]
+print(l)
+
+# The first even square natural numbers
+
+l = [x * x for x in range(1, 10) if (x * x) % 2 == 0]
+print(l)
+
+# Some numbers from the first list
+
+l = [x for x in a if x > 2 and x < 7]
+print(l)
+
+#######
+# zip #
+#######
+
+l = zip([1, 2, 3, 4], ["a", "b", "c"], ["Hello", ",", "World", "!"])
+print(l)
diff --git a/EVI - 2018/EVI 04/Ejemplos/qsort.py b/EVI - 2018/EVI 04/Ejemplos/qsort.py
new file mode 100644
index 0000000..00afd4a
--- /dev/null
+++ b/EVI - 2018/EVI 04/Ejemplos/qsort.py
@@ -0,0 +1,16 @@
+import random as r
+
+def qsort(l):
+ if len(l) == 0:
+ return []
+ elif len(l) == 1:
+ return l
+ else:
+ less = qsort([x for x in l[1:] if x <= l[0]])
+ more = qsort([x for x in l[1:] if x > l[0]])
+ return less + [l[0]] + more
+
+a = [r.randint(0, 100) for x in xrange(100)]
+b = qsort(a)
+print a
+print b
diff --git a/EVI - 2018/EVI 04/Modulo1.ipynb b/EVI - 2018/EVI 04/Modulo1.ipynb
new file mode 100755
index 0000000..8ca3242
--- /dev/null
+++ b/EVI - 2018/EVI 04/Modulo1.ipynb
@@ -0,0 +1,1533 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "logpyjn.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# *Fundamentos de Python 3 en Jupyter*\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "### El lenguaje de programación Python: Funciones"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "25\n"
+ ]
+ }
+ ],
+ "source": [
+ "x = 20 \n",
+ "y = 5\n",
+ "print(x+y) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "`add_numbers` es una función que toma dos números y los suma."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "3"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def add_numbers(x, y):\n",
+ " return x + y\n",
+ "\n",
+ "add_numbers(1, 2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "`add_numbers` puede actualizarse agregando un tercer parámetro. Usando `print` se pueden imprimir múltiples opciones en una sola celda de `Jupyter`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "3\n",
+ "6\n"
+ ]
+ }
+ ],
+ "source": [
+ "def add_numbers(x,y,z=None):\n",
+ " if (z==None):\n",
+ " return x+y\n",
+ " else:\n",
+ " return x+y+z\n",
+ "\n",
+ "print(add_numbers(1, 2))\n",
+ "print(add_numbers(1, 2, 3))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "A `add_numbers` también pordemos agregarle un parámetro opcional llamado `flag`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "¡Flag es verdad, y es muy útil!\n",
+ "23\n"
+ ]
+ }
+ ],
+ "source": [
+ "def add_numbers(x, y, z=None, flag=False):\n",
+ " if (flag):\n",
+ " print('¡Flag es verdad, y es muy útil!')\n",
+ " if (z==None):\n",
+ " return x + y\n",
+ " else:\n",
+ " return x + y + z\n",
+ " \n",
+ "print(add_numbers(1, 2, 20, flag=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Se puede también asignar la función `add_numbers` a una variable `a`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "-2"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def add_numbers(x,y,z):\n",
+ " return x+y+z\n",
+ "\n",
+ "a = add_numbers\n",
+ "a(1, 2, -5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "La función `min` devuelve el elemento más pequeño en un iterable o el más pequeño de dos o más argumentos."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-6\n"
+ ]
+ }
+ ],
+ "source": [
+ "x = (1,3,5, -6)\n",
+ "minimum = min(x)\n",
+ "print(minimum)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "La función `pow(x,y)` devuelve `x` a la potencia de `y`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "4\n"
+ ]
+ }
+ ],
+ "source": [
+ "p = pow(2,2)\n",
+ "print(p)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "La función `x//y` devuelve la parte entera del cociente entre `x` y `y`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2.5\n",
+ "2\n"
+ ]
+ }
+ ],
+ "source": [
+ "div = 5/2\n",
+ "print(div)\n",
+ "divint = 5//2\n",
+ "print(divint)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "La función `x%y` devuelve el resto de la división entre `x` y `y`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0\n",
+ "1\n"
+ ]
+ }
+ ],
+ "source": [
+ "mod1 = 4%2\n",
+ "print(mod1)\n",
+ "mod2 = 5%2\n",
+ "print(mod2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "### El lenguaje de programación Python: Tipos y Secuencias"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Use `type` para devolver el tipo del objeto."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "str"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type('Esto es una cadena de caracteres')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NoneType"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(None)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "int"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(10000)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "float"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(100000.0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "function"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(add_numbers)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Una `tupla` es una estructura de dato inalterable. Las `tuplas` son secuencias, al igual que las `listas`. La diferencia entre las `tuplas` y las `listas` es que las `tuplas` no se pueden modificar a diferencia de las `listas`. Las `tuplas` usan paréntesis, mientras que las `listas` usan corchetes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "x = (1, 'b', 25, 'c')\n",
+ "print(type(x))\n",
+ "y = [1, 'b', 25, 'c']\n",
+ "print(type(y))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Veamos algunas modificaciones en la estructura de datos `lista`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Usemos `append` para gregar un elemento a la `lista`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1, 'b', 25, 'c', 2.5]\n"
+ ]
+ }
+ ],
+ "source": [
+ "y.append(2.5)\n",
+ "print(y)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Este es un ejemplo de cómo se puede hacer un ciclo para recorrer los elementos de la `lista`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1\n",
+ "b\n",
+ "25\n",
+ "c\n",
+ "2.5\n"
+ ]
+ }
+ ],
+ "source": [
+ "for elemento in y:\n",
+ " print(elemento)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "O podemos utilizar el operador de índices:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1\n",
+ "b\n",
+ "25\n",
+ "c\n",
+ "2.5\n"
+ ]
+ }
+ ],
+ "source": [
+ "i=0\n",
+ "while( i != len(y) ):\n",
+ " print(y[i])\n",
+ " i = i + 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Use `+` para concatenar `listas`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[1, 25, 3, 'z']"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[1,25] + [3,'z']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Use `*` para repetir `listas`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[3, 'z', 3, 'z', 3, 'z', 3, 'z']"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "[3, 'z']*4"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Use el operador `in` para chequear si algo está dentro de la `lista`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "'z' in [3,'z', 25]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Use la notación `bracket []` para picar o rebanar `listas` o `cadenas de caracteres`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "c\n",
+ "[2]\n",
+ "[2, 'c']\n",
+ "E\n",
+ "E\n",
+ "Es\n"
+ ]
+ }
+ ],
+ "source": [
+ "y =[2, 'c', 34, -2, 'f']\n",
+ "print(y[1]) #primer elemento de la lista\n",
+ "print(y[0:1]) #primer elemento devuelto como lista\n",
+ "print(y[0:2]) #dos primeros elementos devueltos como lista\n",
+ "\n",
+ "x = 'Esto es una cadena de caracteres'\n",
+ "print(x[0]) #primer caracter\n",
+ "print(x[0:1]) #primer caracter, especificando último caracter\n",
+ "print(x[0:2]) #dos primeros caracteres"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Esta instrucción devuelve el último elemento de la lista o de la cadena."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "f\n",
+ "s\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(y[-1])\n",
+ "print(x[-1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Esta instrucción devuelve un pedazo de la `lista` o `cadena` empezando desde el cuarto elemento desde el final y deteniéndose antes del segundo elemento del final."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2, 'c', 34, -2, 'f']\n",
+ "['c', 34]\n",
+ "Esto es una cadena de caracteres\n",
+ "er\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(y)\n",
+ "print(y[-4:-2])\n",
+ "print(x)\n",
+ "print(x[-4:-2])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Este es un pedazo de la `lista` o `cadena` desde el inicio y se detiene antes del tercer elemento."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2, 'c', 34, -2, 'f']\n",
+ "[2, 'c', 34]\n",
+ "Esto es una cadena de caracteres\n",
+ "Est\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(y)\n",
+ "print(y[:3])\n",
+ "print(x)\n",
+ "print(x[:3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Y este es un pedazo de la `lista` o `cadena` empezando desde el tercer elemento y siguiendo hasta el final."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[2, 'c', 34, -2, 'f']\n",
+ "[-2, 'f']\n",
+ "Esto es una cadena de caracteres\n",
+ "o es una cadena de caracteres\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(y)\n",
+ "print(y[3:])\n",
+ "print(x)\n",
+ "print(x[3:])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "### Leer y escribir archivos CSV"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Vamos a importar un archivo `txt` como \n",
+ "uno `CSV` que contiene como columnas las etiquetas:\n",
+ "\n",
+ "* Nombre,Departamento,Mes de Cumpleaños\n",
+ "\n",
+ "Y como filas:\n",
+ "\n",
+ "* Adelis Nieves, Matemáticas, Junio\n",
+ "* Miguel Astor, Redes, Septiembre\n",
+ "* Francisco Sans, Computación Gráfica, Diciembre\n",
+ "* Antonio Escalante, Letras, Diciembre"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Los nombres de las columnas son: Nombre, Departamento, Mes de Cumpleaños\n",
+ "\tAdelis Nieves trabaja en el departamento de Matemáticas, y nació en el mes de Junio.\n",
+ "\tMiguel Astor trabaja en el departamento de Redes, y nació en el mes de Septiembre.\n",
+ "\tFrancisco Sans trabaja en el departamento de Computación Gráfica, y nació en el mes de Diciembre.\n",
+ "\tAntonio Escalante trabaja en el departamento de Letras, y nació en el mes de Diciembre.\n",
+ "Líneas procesadas: 5.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import csv\n",
+ "\n",
+ "with open('empleados_cumple.txt') as csv_file:\n",
+ " csv_reader = csv.reader(csv_file, delimiter=',')\n",
+ " line_count = 0\n",
+ " for row in csv_reader:\n",
+ " if line_count == 0:\n",
+ " print(f'Los nombres de las columnas son: {\", \".join(row)}')\n",
+ " line_count += 1\n",
+ " else:\n",
+ " print(f'\\t{row[0]} trabaja en el departamento de {row[1]}, y nació en el mes de {row[2]}.')\n",
+ " line_count += 1\n",
+ " print(f'Líneas procesadas: {line_count}.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "
\n",
+ "### Lenguaje de programación Python: Python Numérico (NumPy)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Creando arreglos"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Podemos generar una `lista` y convertirla en un `arreglo NumPy`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1, 25, 31, 5])"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "milista = [1, 25, 31, 5]\n",
+ "x = np.array(milista)\n",
+ "x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "O simplemente podemos pasarle una lista directamente."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1, 25, 31, 5])"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y = np.array([1, 25, 31, 5])\n",
+ "y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Podemos pasarle una `lista` de `listas` para crear un `arreglo multidimensional`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1, 25, 31, 5],\n",
+ " [10, 2, 11, 12],\n",
+ " [ 5, 7, 0, 1]])"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m = np.array([[1, 25, 31, 5], [10, 2, 11, 12], [5, 7, 0, 1]])\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Usa el método `shape` para encontrar las dimensiones del arreglo. (filas, columnas)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(3, 4)"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "`arange` devuelve valores espaciados uniformemente dentro de un intervalo dado."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28])"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "n = np.arange(0, 30, 2) # empieza en 0 cuenta de 2 en 2, se detiene antes de 30\n",
+ "n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "`reshape` devuelve una matriz con los mismos datos con una nueva forma."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0, 2, 4, 6, 8],\n",
+ " [10, 12, 14, 16, 18],\n",
+ " [20, 22, 24, 26, 28]])"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "n = n.reshape(3, 5) #cambia la forma de manera que se obtenga una matriz 3x5\n",
+ "n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Combinando arreglos"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1, 1, 1],\n",
+ " [1, 1, 1]])"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "p = np.ones([2, 3], int)\n",
+ "p"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Use `vstack` para apilar matrices en secuencia verticalmente (por fila)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1, 1, 1],\n",
+ " [1, 1, 1],\n",
+ " [2, 2, 2],\n",
+ " [2, 2, 2]])"
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.vstack([p, 2*p])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Use `hstack` para apilar arreglos en secuencia horizontalmente (por columna)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[1, 1, 1, 2, 2, 2],\n",
+ " [1, 1, 1, 2, 2, 2]])"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.hstack([p, 2*p])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Operaciones"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Use `+`, `-`, `*`, `/` and `**` para realizar operaciones de suma , resta, multiplicación, división y potencia elemento a elemento."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 1 25 31 5]\n",
+ "[ 1 25 31 5]\n",
+ "[ 2 50 62 10]\n",
+ "[0 0 0 0]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(x)\n",
+ "print(y)\n",
+ "print(x + y) # suma elemento a elemento [1 2 3] + [4 5 6] = [5 7 9]\n",
+ "print(x - y) # resta elemento a elemento [1 2 3] - [4 5 6] = [-3 -3 -3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 1 25 31 5]\n",
+ "[ 1 25 31 5]\n",
+ "[ 1 625 961 25]\n",
+ "[1. 1. 1. 1.]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(x)\n",
+ "print(y)\n",
+ "print(x * y) # multiplicación elemento a elemento [1 2 3] * [4 5 6] = [4 10 18]\n",
+ "print(x / y) # división elemento a elemento [1 2 3] / [4 5 6] = [0.25 0.4 0.5]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 1 25 31 5]\n",
+ "[ 1 625 961 25]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(x)\n",
+ "print(x**2) # potencia elemento a elemento [1 2 3] ^2 = [1 4 9]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Producto punto o escalar:** \n",
+ "\n",
+ "$ \\begin{bmatrix}x_1 \\ x_2 \\ x_3\\end{bmatrix}\n",
+ "\\cdot\n",
+ "\\begin{bmatrix}y_1 \\\\ y_2 \\\\ y_3\\end{bmatrix}\n",
+ "= x_1 y_1 + x_2 y_2 + x_3 y_3$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 1 25 31 5]\n",
+ "[ 1 25 31 5]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "1612"
+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "print(x)\n",
+ "print(y)\n",
+ "x.dot(y) #producto escalar "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 1 25 31 5]\n",
+ "[[ 1 25 31 5]\n",
+ " [ 1 625 961 25]]\n",
+ "2\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(y)\n",
+ "z = np.array([y, y**2])\n",
+ "print(z)\n",
+ "print(len(z)) #número de filas del arreglo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Vamos a mirar la transposición de matrices. La transposición permuta las dimensiones de la matriz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1, 1],\n",
+ " [ 25, 625],\n",
+ " [ 31, 961],\n",
+ " [ 5, 25]])"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "zt = np.transpose(z, axes=None)\n",
+ "zt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Las dimensiones del arreglo `zt` son `(4,2)` después de la transposición."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(4, 2)"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "zt.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Se puede usar también `.T` para obtener la transpuesta de un arreglo."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 1 1]\n",
+ " [ 25 625]\n",
+ " [ 31 961]\n",
+ " [ 5 25]]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1, 25, 31, 5],\n",
+ " [ 1, 625, 961, 25]])"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "print(zt)\n",
+ "zt.T"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Indexando/Rebanando"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144])"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s = np.arange(13)**2\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Use la notación de `bracket []` para obtener valores en el índice indicado. Recuerde que la indexación comienza en 0."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0, 16, 144)"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s[0], s[4], s[-1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Use `:` para indicar un rango. `array[empieza:termina]`\n",
+ "\n",
+ "\n",
+ "Dejando `empieza` or `termina` vacío quedará predeterminado al principio / final de la matriz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1, 4, 9, 16])"
+ ]
+ },
+ "execution_count": 69,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s[1:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Use contadores negativos desde el final."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 81, 100, 121, 144])"
+ ]
+ },
+ "execution_count": 70,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s[-4:]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Se puede usar un segundo `:` para indicar el tamaño del paso. `array [empieza: termina: paso]`\n",
+ "\n",
+ "Aquí estamos comenzando el quinto elemento desde el final, y contando hacia atrás cada 2 hasta que se alcanza el comienzo de la matriz."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([64, 36, 16, 4, 0])"
+ ]
+ },
+ "execution_count": 71,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s[-5::-2]"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/EVI - 2018/EVI 04/Modulo2.ipynb b/EVI - 2018/EVI 04/Modulo2.ipynb
new file mode 100755
index 0000000..3374a6e
--- /dev/null
+++ b/EVI - 2018/EVI 04/Modulo2.ipynb
@@ -0,0 +1,3170 @@
+{
+ "cells": [
+ {
+ "attachments": {
+ "logpypan.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# *Introducción a Pandas en los cuadernos de Jupyter*\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### La estructura de Datos `Serie`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "`Arreglo` unidimensional con etiquetas en los ejes (incluidas series de tiempo). Los parámetros de una `Serie` son: `data` (matriz, diccionario o escalar), `index` (arreglo de índices), `dtype` (`numpy.dtype` o `None`) y `copy` (booleano o por defecto `False`)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Importamos la biblioteca `Pandas`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "pd.Series?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Podemos convertir una `lista` en una `serie` y pandas asigna de manera inmediata una lista de índices que empieza en 0."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Tigre\n",
+ "1 Oso\n",
+ "2 Camello\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "animales = ['Tigre', 'Oso', 'Camello']\n",
+ "pd.Series(animales)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 1\n",
+ "1 2\n",
+ "2 3\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "numeros = [1, 2, 3]\n",
+ "pd.Series(numeros)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Tigre\n",
+ "1 Oso\n",
+ "2 None\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "animales = ['Tigre', 'Oso', None]\n",
+ "pd.Series(animales)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Es importante saber como `NumPy` y `Pandas` manejan los datos faltantes. En Python tenemos el tipo `None` para indicar un dato faltante. Si tenemos una lista de números, `Pandas` automáticamente convierte este valor `None` en un valor designado como `NaN`, que significa `Not a Number`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 1.0\n",
+ "1 2.0\n",
+ "2 NaN\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "numeros = [1, 2, None]\n",
+ "pd.Series(numeros)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Importamos la biblioteca `NumPy`. También es importante saber que `NaN` no es `None`. Cuando hacemos un test para saber si `NaN` es `NaN` tambien obtendremos `False`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "np.nan == None"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.nan == np.nan"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Se necesita la función especial `isnan` de `NumPy` para chequear la presencia de un `no número` en nuestros datos."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "True\n",
+ "True\n",
+ "True\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(np.isnan(np.nan))\n",
+ "print(None is None)\n",
+ "print(np.nan is np.nan)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "¿Cómo creamos una `serie` en `Pandas`? Podemos utilizar una estructura de datos `diccionario` con sus `claves` y convertirlo en una `serie`, donde los índices de la `serie` son las claves del diccionario."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Béisbol Cuba\n",
+ "Capoeira Brasil\n",
+ "Corrida de Toros España\n",
+ "Golf Escocia\n",
+ "Pelota Vasca País Vasco\n",
+ "Rayuela Chile\n",
+ "Rugby Gales\n",
+ "Sumo Japón\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "deportes = {'Capoeira': 'Brasil',\n",
+ " 'Rayuela': 'Chile',\n",
+ " 'Pelota Vasca': 'País Vasco',\n",
+ " 'Béisbol': 'Cuba',\n",
+ " 'Rugby': 'Gales',\n",
+ " 'Golf': 'Escocia',\n",
+ " 'Corrida de Toros': 'España',\n",
+ " 'Sumo': 'Japón'}\n",
+ "s = pd.Series(deportes)\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Luego, podemos chequear la lista de índices con el atributo `.index`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['Béisbol', 'Capoeira', 'Corrida de Toros', 'Golf', 'Pelota Vasca',\n",
+ " 'Rayuela', 'Rugby', 'Sumo'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.index"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "En este otro ejemplo, pasamos directamente una lista con su conjunto de índices para crear la `Serie`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "India Tigre\n",
+ "America Oso\n",
+ "Africa Camello\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s = pd.Series(['Tigre', 'Oso', 'Camello'], index=['India', 'America', 'Africa'])\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Aquí tenemos un ejemplo de un elemento nuevo en la lista de índices que no tiene un valor asignado, no existe un país asociado al índice `Natación` y `Pandas` representa este valor faltante con `NaN`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Capoeira Brasil\n",
+ "Sumo Japón\n",
+ "Pelota Vasca País Vasco\n",
+ "Natación NaN\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "deportes = {'Capoeira': 'Brasil',\n",
+ " 'Rayuela': 'Chile',\n",
+ " 'Pelota Vasca': 'País Vasco',\n",
+ " 'Béisbol': 'Cuba',\n",
+ " 'Rugby': 'Gales',\n",
+ " 'Golf': 'Escocia',\n",
+ " 'Corrida de Toros': 'España',\n",
+ " 'Sumo': 'Japón'}\n",
+ "s = pd.Series(deportes, index=['Capoeira', 'Sumo', 'Pelota Vasca', 'Natación'])\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Búsqueda en una `Serie`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Béisbol Cuba\n",
+ "Capoeira Brasil\n",
+ "Corrida de Toros España\n",
+ "Golf Escocia\n",
+ "Pelota Vasca País Vasco\n",
+ "Rayuela Chile\n",
+ "Rugby Gales\n",
+ "Sumo Japón\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "deportes = {'Capoeira': 'Brasil',\n",
+ " 'Rayuela': 'Chile',\n",
+ " 'Pelota Vasca': 'País Vasco',\n",
+ " 'Béisbol': 'Cuba',\n",
+ " 'Rugby': 'Gales',\n",
+ " 'Golf': 'Escocia',\n",
+ " 'Corrida de Toros': 'España',\n",
+ " 'Sumo': 'Japón'}\n",
+ "s = pd.Series(deportes)\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Podemos hacer búsquedas en las `series` por posición de índices o por etiqueta de índices. Si queremos hacer búsqueda por ubicación numérica (empezando desde 0) utilizamos el atributo `iloc`. Si por otra parte, hacemos búqueda por etiqueta de índice entonces usamos el atributo `loc`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'País Vasco'"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.iloc[4]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'País Vasco'"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s.loc['Pelota Vasca']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "`Pandas` trata de que el código sea más legible. Si le pasamos por parámetro un valor numérico a la `Serie` esta se comportará como si la búsqueda se hace con el atributo `iloc`, si en cambio le pasamos un objeto, hará la búsqueda por etiqueta como con el atributo `loc`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'País Vasco'"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s[4]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'País Vasco'"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s['Pelota Vasca']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "¿Qué pasa cuando tenemos una lista de índices que son enteros?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "99 Brasil\n",
+ "100 Chile\n",
+ "101 País Vasco\n",
+ "102 Cuba\n",
+ "103 Gales\n",
+ "104 Escocia\n",
+ "105 España\n",
+ "106 Japón\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "deportes = {99: 'Brasil',\n",
+ " 100: 'Chile',\n",
+ " 101: 'País Vasco',\n",
+ " 102: 'Cuba',\n",
+ " 103: 'Gales',\n",
+ " 104: 'Escocia',\n",
+ " 105: 'España',\n",
+ " 106: 'Japón'}\n",
+ "s = pd.Series(deportes)\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Cuando tenemos un caso como este es más seguro utilizar los atributos `iloc` o `loc` según sea el caso. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "0",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#Esta instrucción no llamará s.iloc[0] como esperaríamos y va a generar un error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 621\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 622\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 623\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 625\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 2558\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2559\u001b[0m return self._engine.get_value(s, k,\n\u001b[0;32m-> 2560\u001b[0;31m tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m 2561\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2562\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
+ "\u001b[0;31mKeyError\u001b[0m: 0"
+ ]
+ }
+ ],
+ "source": [
+ "s[0] #Esta instrucción no llamará s.iloc[0] como esperaríamos y va a generar un error"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s.iloc[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "s.loc[99]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Ya que sabemos hacer búsquedas en las `Series`, ahora vamos a trabajar con los datos (encontrar valores, resumir los datos o transformarlos)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 105.0\n",
+ "1 223.0\n",
+ "2 5.0\n",
+ "3 102.0\n",
+ "4 27.0\n",
+ "5 -126.0\n",
+ "dtype: float64"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s = pd.Series([105.00, 223.00, 5, 102.00, 27, -126])\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Una forma de trabajar es iterar sobre un conjunto de datos e invocar una operación de interés"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "336.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "total = 0\n",
+ "for elemento in s:\n",
+ " total+=elemento\n",
+ "print(total)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Con `NumPy` podemos tener acceso a las funciones universales binarias o unarias (vectorizadas, cálculos más rápidos). En este ejemplo, `np.sum` hará la suma de todos los elementos en la `serie`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "336.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "total = np.sum(s)\n",
+ "print(total)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "También podemos generar una `serie` grande de números aleatorios y con el método `.head()` podemos desplegar un encabezado con los 5 primeros elementos de la `serie` y con `len` chequear el tamaño de la misma."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 42\n",
+ "1 872\n",
+ "2 956\n",
+ "3 330\n",
+ "4 9\n",
+ "dtype: int64\n",
+ "10000\n"
+ ]
+ }
+ ],
+ "source": [
+ "s = pd.Series(np.random.randint(0,1000,10000))\n",
+ "print(s.head())\n",
+ "print(len(s))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Los cuadernos de `Jupyter` tienen **funciones mágicas** que pueden ser útiles. Una de ellas es `%%timeit` que nos servirá para ver cuál de los dos métodos para sumar elementos de una `serie` es más rápido.\n",
+ "Basta con tipear el símbolo `%` y la tecla `Tab` para obtener una lista de las **funciones mágicas** de `Jupyter`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "803 µs ± 52.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit -n 100\n",
+ "sumar = 0\n",
+ "for elemento in s:\n",
+ " sumar+=elemento"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "128 µs ± 21.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%timeit -n 100\n",
+ "sumar = np.sum(s)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "`NumPy` y `Pandas` tienen el `broadcasting`, se puede aplicar una operación a cada valor de la `serie` y modificarla."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 44\n",
+ "1 874\n",
+ "2 958\n",
+ "3 332\n",
+ "4 11\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "s+=2 #Suma 2 a cada elemento de la serie usando broadcasting\n",
+ "s.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Una manera poco eficiente de hacer esto es iterar sobre cada elemento de la `serie` para hacer la suma. El método `.iteritems()` devuelve un iterador sobre los pares `(key, value)` (clave, valor) de un diccionario, en este caso de nuestra `serie` s."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
+ " \n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "0 46\n",
+ "1 876\n",
+ "2 960\n",
+ "3 334\n",
+ "4 13\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "for etiqueta, valor in s.iteritems():\n",
+ " s.set_value(etiqueta, valor+2)\n",
+ "s.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%timeit -n 10\n",
+ "s = pd.Series(np.random.randint(0,1000,10000))\n",
+ "for etiqueta, valor in s.iteritems():\n",
+ " s.loc[etiqueta]= valor+2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%timeit -n 10\n",
+ "s = pd.Series(np.random.randint(0,1000,10000))\n",
+ "s+=2\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Podemos agregar elementos a una `serie` de la siguiente forma:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 1\n",
+ "1 2\n",
+ "2 3\n",
+ "Animal Oso\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "s = pd.Series([1, 2, 3])\n",
+ "s.loc['Animal'] = 'Oso'\n",
+ "s"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Este es un ejemplo de una `serie` donde los valores del conjunto de índices no son únicos. Esto hace que las tablas de datos funcionen diferente y es por ello que agregar nuevos elementos debe hacerse con el método `append`, que en primera instancia, no modificará la `serie` sino que devuelve una nueva `serie` con los elementos que se agregaron."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "deportes_originales = pd.Series({'Capoeira': 'Brasil',\n",
+ " 'Rayuela': 'Chile',\n",
+ " 'Pelota Vasca': 'País Vasco',\n",
+ " 'Béisbol': 'Cuba',\n",
+ " 'Rugby': 'Gales',\n",
+ " 'Golf': 'Escocia',\n",
+ " 'Corrida de Toros': 'España',\n",
+ " 'Sumo': 'Japón'})\n",
+ "paises_que_aman_el_beisbol = pd.Series(['Venezuela',\n",
+ " 'USA',\n",
+ " 'Cuba',\n",
+ " 'Puerto Rico',\n",
+ " 'Dominicana'], \n",
+ " index=['Béisbol',\n",
+ " 'Béisbol',\n",
+ " 'Béisbol',\n",
+ " 'Béisbol',\n",
+ " 'Béisbol'])\n",
+ "todos_los_paises = deportes_originales.append(paises_que_aman_el_beisbol)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Béisbol Cuba\n",
+ "Capoeira Brasil\n",
+ "Corrida de Toros España\n",
+ "Golf Escocia\n",
+ "Pelota Vasca País Vasco\n",
+ "Rayuela Chile\n",
+ "Rugby Gales\n",
+ "Sumo Japón\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "deportes_originales"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Béisbol Venezuela\n",
+ "Béisbol USA\n",
+ "Béisbol Cuba\n",
+ "Béisbol Puerto Rico\n",
+ "Béisbol Dominicana\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "paises_que_aman_el_beisbol"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Béisbol Cuba\n",
+ "Capoeira Brasil\n",
+ "Corrida de Toros España\n",
+ "Golf Escocia\n",
+ "Pelota Vasca País Vasco\n",
+ "Rayuela Chile\n",
+ "Rugby Gales\n",
+ "Sumo Japón\n",
+ "Béisbol Venezuela\n",
+ "Béisbol USA\n",
+ "Béisbol Cuba\n",
+ "Béisbol Puerto Rico\n",
+ "Béisbol Dominicana\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "todos_los_paises"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Béisbol Cuba\n",
+ "Béisbol Venezuela\n",
+ "Béisbol USA\n",
+ "Béisbol Cuba\n",
+ "Béisbol Puerto Rico\n",
+ "Béisbol Dominicana\n",
+ "dtype: object"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "todos_los_paises.loc['Béisbol']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "### La estructura de datos `DataFrame`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "El `DataFrame` o Tabla de Datos es el corazón de la biblioteca `Pandas`. Es el objeto primario para el análisis de datos. Es una especie de **arreglo bidimensional** con etiquetas en los ejes. En este ejemplo, crearemos tres diccionarios que serán luego las filas de nuestro `DataFrame`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Artículo comprado | \n",
+ " Costo | \n",
+ " Nombre | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Libro | \n",
+ " 1200 | \n",
+ " Adelis | \n",
+ "
\n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Raspberry pi 3 | \n",
+ " 15000 | \n",
+ " Miguel | \n",
+ "
\n",
+ " \n",
+ " | Tienda 2 | \n",
+ " Balón | \n",
+ " 5000 | \n",
+ " Jaime | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "En un `DataFrame` también se puede extraer información usando los atributos `loc` y `iloc`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Artículo comprado Balón\n",
+ "Costo 5000\n",
+ "Nombre Jaime\n",
+ "Name: Tienda 2, dtype: object"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.loc['Tienda 2']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "También podemos chequear el tipo de dato usando la función `type` de Python."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "pandas.core.series.Series"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(df.loc['Tienda 2'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "En los `DataFrame` también se pueden tener listas de índices no únicos. En el ejemplo, hay dos índices con el mismo nombre `Tienda 1`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Artículo comprado | \n",
+ " Costo | \n",
+ " Nombre | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Libro | \n",
+ " 1200 | \n",
+ " Adelis | \n",
+ "
\n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Raspberry pi 3 | \n",
+ " 15000 | \n",
+ " Miguel | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "También podemos seleccionar columnas agregando un parámetro extra al atributo `loc`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Tienda 1 1200\n",
+ "Tienda 1 15000\n",
+ "Name: Costo, dtype: int64"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.loc['Tienda 1', 'Costo']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Usar el atributo `.T` para obtener la transpuesta del `DataFrame` o Tabla de Datos."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Tienda 1 | \n",
+ " Tienda 1 | \n",
+ " Tienda 2 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Artículo comprado | \n",
+ " Libro | \n",
+ " Raspberry pi 3 | \n",
+ " Balón | \n",
+ "
\n",
+ " \n",
+ " | Costo | \n",
+ " 1200 | \n",
+ " 15000 | \n",
+ " 5000 | \n",
+ "
\n",
+ " \n",
+ " | Nombre | \n",
+ " Adelis | \n",
+ " Miguel | \n",
+ " Jaime | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Usando `.T.loc[]` se puede seleccionar una columna usando como parámetro la etiqueta de su nombre. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Tienda 1 1200\n",
+ "Tienda 1 15000\n",
+ "Tienda 2 5000\n",
+ "Name: Costo, dtype: object"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.T.loc['Costo']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Tienda 1 1200\n",
+ "Tienda 1 15000\n",
+ "Tienda 2 5000\n",
+ "Name: Costo, dtype: int64"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['Costo']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Tienda 1 1200\n",
+ "Tienda 1 15000\n",
+ "Name: Costo, dtype: int64"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.loc['Tienda 1']['Costo']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "`loc` también tiene soporte para rebanar o seleccionar del `DataFrame` con la notación `[]`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Nombre | \n",
+ " Costo | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Adelis | \n",
+ " 1200 | \n",
+ "
\n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Miguel | \n",
+ " 15000 | \n",
+ "
\n",
+ " \n",
+ " | Tienda 2 | \n",
+ " Jaime | \n",
+ " 5000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "También podemos eliminar datos del `DataFrame` con la función `drop()`. Esta función toma un solo parámetro que es el índice del conjunto de datos que deseamos eliminar. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Artículo comprado | \n",
+ " Costo | \n",
+ " Nombre | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 2 | \n",
+ " Balón | \n",
+ " 5000 | \n",
+ " Jaime | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Podemos ver que nuestro `DataFrame` original sigue intacto. Solo hicimos una **extracción** de información."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Artículo comprado | \n",
+ " Costo | \n",
+ " Nombre | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Libro | \n",
+ " 1200 | \n",
+ " Adelis | \n",
+ "
\n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Raspberry pi 3 | \n",
+ " 15000 | \n",
+ " Miguel | \n",
+ "
\n",
+ " \n",
+ " | Tienda 2 | \n",
+ " Balón | \n",
+ " 5000 | \n",
+ " Jaime | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "También podemos hacer una copia del `DataFrame` con la función `copy()` para guardar la extracción de información. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Artículo comprado | \n",
+ " Costo | \n",
+ " Nombre | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 2 | \n",
+ " Balón | \n",
+ " 5000 | \n",
+ " Jaime | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Podemos eliminar una columna de manera sencilla, usando simplemente la palabra clave `del` y el índice o nombre de la comuna. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Artículo comprado | \n",
+ " Nombre | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 2 | \n",
+ " Balón | \n",
+ " Jaime | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Finalmente, es muy sencillo agregar una columna al `DataFrame`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Artículo comprado | \n",
+ " Costo | \n",
+ " Nombre | \n",
+ " Ubicación | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Libro | \n",
+ " 1200 | \n",
+ " Adelis | \n",
+ " Venezuela | \n",
+ "
\n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Raspberry pi 3 | \n",
+ " 15000 | \n",
+ " Miguel | \n",
+ " Chile | \n",
+ "
\n",
+ " \n",
+ " | Tienda 2 | \n",
+ " Balón | \n",
+ " 5000 | \n",
+ " Jaime | \n",
+ " Argentina | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Usemos `!cat` para leer un archivo de formato `CSV`. Nota: `!cat` funciona para **Linux** y **Mac** pero puede no funcionar para **Windows** :("
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15\r\n",
+ ",№ Summer,01 !,02 !,03 !,Total,№ Winter,01 !,02 !,03 !,Total,№ Games,01 !,02 !,03 !,Combined total\r\n",
+ "Afghanistan (AFG),13,0,0,2,2,0,0,0,0,0,13,0,0,2,2\r\n",
+ "Algeria (ALG),12,5,2,8,15,3,0,0,0,0,15,5,2,8,15\r\n",
+ "Argentina (ARG),23,18,24,28,70,18,0,0,0,0,41,18,24,28,70\r\n",
+ "Armenia (ARM),5,1,2,9,12,6,0,0,0,0,11,1,2,9,12\r\n",
+ "Australasia (ANZ) [ANZ],2,3,4,5,12,0,0,0,0,0,2,3,4,5,12\r\n",
+ "Australia (AUS) [AUS] [Z],25,139,152,177,468,18,5,3,4,12,43,144,155,181,480\r\n",
+ "Austria (AUT),26,18,33,35,86,22,59,78,81,218,48,77,111,116,304\r\n",
+ "Azerbaijan (AZE),5,6,5,15,26,5,0,0,0,0,10,6,5,15,26\r\n",
+ "Bahamas (BAH),15,5,2,5,12,0,0,0,0,0,15,5,2,5,12\r\n",
+ "Bahrain (BRN),8,0,0,1,1,0,0,0,0,0,8,0,0,1,1\r\n",
+ "Barbados (BAR) [BAR],11,0,0,1,1,0,0,0,0,0,11,0,0,1,1\r\n",
+ "Belarus (BLR),5,12,24,39,75,6,6,4,5,15,11,18,28,44,90\r\n",
+ "Belgium (BEL),25,37,52,53,142,20,1,1,3,5,45,38,53,56,147\r\n",
+ "Bermuda (BER),17,0,0,1,1,7,0,0,0,0,24,0,0,1,1\r\n",
+ "Bohemia (BOH) [BOH] [Z],3,0,1,3,4,0,0,0,0,0,3,0,1,3,4\r\n",
+ "Botswana (BOT),9,0,1,0,1,0,0,0,0,0,9,0,1,0,1\r\n",
+ "Brazil (BRA),21,23,30,55,108,7,0,0,0,0,28,23,30,55,108\r\n",
+ "British West Indies (BWI) [BWI],1,0,0,2,2,0,0,0,0,0,1,0,0,2,2\r\n",
+ "Bulgaria (BUL) [H],19,51,85,78,214,19,1,2,3,6,38,52,87,81,220\r\n",
+ "Burundi (BDI),5,1,0,0,1,0,0,0,0,0,5,1,0,0,1\r\n",
+ "Cameroon (CMR),13,3,1,1,5,1,0,0,0,0,14,3,1,1,5\r\n",
+ "Canada (CAN),25,59,99,121,279,22,62,56,52,170,47,121,155,173,449\r\n",
+ "Chile (CHI) [I],22,2,7,4,13,16,0,0,0,0,38,2,7,4,13\r\n",
+ "China (CHN) [CHN],9,201,146,126,473,10,12,22,19,53,19,213,168,145,526\r\n",
+ "Colombia (COL),18,2,6,11,19,1,0,0,0,0,19,2,6,11,19\r\n",
+ "Costa Rica (CRC),14,1,1,2,4,6,0,0,0,0,20,1,1,2,4\r\n",
+ "Ivory Coast (CIV) [CIV],12,0,1,0,1,0,0,0,0,0,12,0,1,0,1\r\n",
+ "Croatia (CRO),6,6,7,10,23,7,4,6,1,11,13,10,13,11,34\r\n",
+ "Cuba (CUB) [Z],19,72,67,70,209,0,0,0,0,0,19,72,67,70,209\r\n",
+ "Cyprus (CYP),9,0,1,0,1,10,0,0,0,0,19,0,1,0,1\r\n",
+ "Czech Republic (CZE) [CZE],5,14,15,15,44,6,7,9,8,24,11,21,24,23,68\r\n",
+ "Czechoslovakia (TCH) [TCH],16,49,49,45,143,16,2,8,15,25,32,51,57,60,168\r\n",
+ "Denmark (DEN) [Z],26,43,68,68,179,13,0,1,0,1,39,43,69,68,180\r\n",
+ "Djibouti (DJI) [B],7,0,0,1,1,0,0,0,0,0,7,0,0,1,1\r\n",
+ "Dominican Republic (DOM),13,3,2,1,6,0,0,0,0,0,13,3,2,1,6\r\n",
+ "Ecuador (ECU),13,1,1,0,2,0,0,0,0,0,13,1,1,0,2\r\n",
+ "Egypt (EGY) [EGY] [Z],21,7,9,10,26,1,0,0,0,0,22,7,9,10,26\r\n",
+ "Eritrea (ERI),4,0,0,1,1,0,0,0,0,0,4,0,0,1,1\r\n",
+ "Estonia (EST),11,9,9,15,33,9,4,2,1,7,20,13,11,16,40\r\n",
+ "Ethiopia (ETH),12,21,7,17,45,2,0,0,0,0,14,21,7,17,45\r\n",
+ "Finland (FIN),24,101,84,117,302,22,42,62,57,161,46,143,146,174,463\r\n",
+ "France (FRA) [O] [P] [Z],27,202,223,246,671,22,31,31,47,109,49,233,254,293,780\r\n",
+ "Gabon (GAB),9,0,1,0,1,0,0,0,0,0,9,0,1,0,1\r\n",
+ "Georgia (GEO),5,6,5,14,25,6,0,0,0,0,11,6,5,14,25\r\n",
+ "Germany (GER) [GER] [Z],15,174,182,217,573,11,78,78,53,209,26,252,260,270,782\r\n",
+ "United Team of Germany (EUA) [EUA],3,28,54,36,118,3,8,6,5,19,6,36,60,41,137\r\n",
+ "East Germany (GDR) [GDR],5,153,129,127,409,6,39,36,35,110,11,192,165,162,519\r\n",
+ "West Germany (FRG) [FRG],5,56,67,81,204,6,11,15,13,39,11,67,82,94,243\r\n",
+ "Ghana (GHA) [GHA],13,0,1,3,4,1,0,0,0,0,14,0,1,3,4\r\n",
+ "Great Britain (GBR) [GBR] [Z],27,236,272,272,780,22,10,4,12,26,49,246,276,284,806\r\n",
+ "Greece (GRE) [Z],27,30,42,39,111,18,0,0,0,0,45,30,42,39,111\r\n",
+ "Grenada (GRN),8,1,0,0,1,0,0,0,0,0,8,1,0,0,1\r\n",
+ "Guatemala (GUA),13,0,1,0,1,1,0,0,0,0,14,0,1,0,1\r\n",
+ "Guyana (GUY) [GUY],16,0,0,1,1,0,0,0,0,0,16,0,0,1,1\r\n",
+ "Haiti (HAI) [J],14,0,1,1,2,0,0,0,0,0,14,0,1,1,2\r\n",
+ "Hong Kong (HKG) [HKG],15,1,1,1,3,4,0,0,0,0,19,1,1,1,3\r\n",
+ "Hungary (HUN),25,167,144,165,476,22,0,2,4,6,47,167,146,169,482\r\n",
+ "Iceland (ISL),19,0,2,2,4,17,0,0,0,0,36,0,2,2,4\r\n",
+ "India (IND) [F],23,9,6,11,26,9,0,0,0,0,32,9,6,11,26\r\n",
+ "Indonesia (INA),14,6,10,11,27,0,0,0,0,0,14,6,10,11,27\r\n",
+ "Iran (IRI) [K],15,15,20,25,60,10,0,0,0,0,25,15,20,25,60\r\n",
+ "Iraq (IRQ),13,0,0,1,1,0,0,0,0,0,13,0,0,1,1\r\n",
+ "Ireland (IRL),20,9,8,12,29,6,0,0,0,0,26,9,8,12,29\r\n",
+ "Israel (ISR),15,1,1,5,7,6,0,0,0,0,21,1,1,5,7\r\n",
+ "Italy (ITA) [M] [S],26,198,166,185,549,22,37,34,43,114,48,235,200,228,663\r\n",
+ "Jamaica (JAM) [JAM],16,17,30,20,67,7,0,0,0,0,23,17,30,20,67\r\n",
+ "Japan (JPN),21,130,126,142,398,20,10,17,18,45,41,140,143,160,443\r\n",
+ "Kazakhstan (KAZ),5,16,17,19,52,6,1,3,3,7,11,17,20,22,59\r\n",
+ "Kenya (KEN),13,25,32,29,86,3,0,0,0,0,16,25,32,29,86\r\n",
+ "North Korea (PRK),9,14,12,21,47,8,0,1,1,2,17,14,13,22,49\r\n",
+ "South Korea (KOR),16,81,82,80,243,17,26,17,10,53,33,107,99,90,296\r\n",
+ "Kuwait (KUW),12,0,0,2,2,0,0,0,0,0,12,0,0,2,2\r\n",
+ "Kyrgyzstan (KGZ),5,0,1,2,3,6,0,0,0,0,11,0,1,2,3\r\n",
+ "Latvia (LAT),10,3,11,5,19,10,0,4,3,7,20,3,15,8,26\r\n",
+ "Lebanon (LIB),16,0,2,2,4,16,0,0,0,0,32,0,2,2,4\r\n",
+ "Liechtenstein (LIE),16,0,0,0,0,18,2,2,5,9,34,2,2,5,9\r\n",
+ "Lithuania (LTU),8,6,5,10,21,8,0,0,0,0,16,6,5,10,21\r\n",
+ "Luxembourg (LUX) [O],22,1,1,0,2,8,0,2,0,2,30,1,3,0,4\r\n",
+ "Macedonia (MKD),5,0,0,1,1,5,0,0,0,0,10,0,0,1,1\r\n",
+ "Malaysia (MAS) [MAS],12,0,3,3,6,0,0,0,0,0,12,0,3,3,6\r\n",
+ "Mauritius (MRI),8,0,0,1,1,0,0,0,0,0,8,0,0,1,1\r\n",
+ "Mexico (MEX),22,13,21,28,62,8,0,0,0,0,30,13,21,28,62\r\n",
+ "Moldova (MDA),5,0,2,5,7,6,0,0,0,0,11,0,2,5,7\r\n",
+ "Mongolia (MGL),12,2,9,13,24,13,0,0,0,0,25,2,9,13,24\r\n",
+ "Montenegro (MNE),2,0,1,0,1,2,0,0,0,0,4,0,1,0,1\r\n",
+ "Morocco (MAR),13,6,5,11,22,6,0,0,0,0,19,6,5,11,22\r\n",
+ "Mozambique (MOZ),9,1,0,1,2,0,0,0,0,0,9,1,0,1,2\r\n",
+ "Namibia (NAM),6,0,4,0,4,0,0,0,0,0,6,0,4,0,4\r\n",
+ "Netherlands (NED) [Z],25,77,85,104,266,20,37,38,35,110,45,114,123,139,376\r\n",
+ "Netherlands Antilles (AHO) [AHO] [I],13,0,1,0,1,2,0,0,0,0,15,0,1,0,1\r\n",
+ "New Zealand (NZL) [NZL],22,42,18,39,99,15,0,1,0,1,37,42,19,39,100\r\n",
+ "Niger (NIG),11,0,0,1,1,0,0,0,0,0,11,0,0,1,1\r\n",
+ "Nigeria (NGR),15,3,8,12,23,0,0,0,0,0,15,3,8,12,23\r\n",
+ "Norway (NOR) [Q],24,56,49,43,148,22,118,111,100,329,46,174,160,143,477\r\n",
+ "Pakistan (PAK),16,3,3,4,10,2,0,0,0,0,18,3,3,4,10\r\n",
+ "Panama (PAN),16,1,0,2,3,0,0,0,0,0,16,1,0,2,3\r\n",
+ "Paraguay (PAR),11,0,1,0,1,1,0,0,0,0,12,0,1,0,1\r\n",
+ "Peru (PER) [L],17,1,3,0,4,2,0,0,0,0,19,1,3,0,4\r\n",
+ "Philippines (PHI),20,0,2,7,9,4,0,0,0,0,24,0,2,7,9\r\n",
+ "Poland (POL),20,64,82,125,271,22,6,7,7,20,42,70,89,132,291\r\n",
+ "Portugal (POR),23,4,8,11,23,7,0,0,0,0,30,4,8,11,23\r\n",
+ "Puerto Rico (PUR),17,0,2,6,8,6,0,0,0,0,23,0,2,6,8\r\n",
+ "Qatar (QAT),8,0,0,4,4,0,0,0,0,0,8,0,0,4,4\r\n",
+ "Romania (ROU),20,88,94,119,301,20,0,0,1,1,40,88,94,120,302\r\n",
+ "Russia (RUS) [RUS],5,132,121,142,395,6,49,40,35,124,11,181,161,177,519\r\n",
+ "Russian Empire (RU1) [RU1],3,1,4,3,8,0,0,0,0,0,3,1,4,3,8\r\n",
+ "Soviet Union (URS) [URS],9,395,319,296,1010,9,78,57,59,194,18,473,376,355,1204\r\n",
+ "Unified Team (EUN) [EUN],1,45,38,29,112,1,9,6,8,23,2,54,44,37,135\r\n",
+ "Saudi Arabia (KSA),10,0,1,2,3,0,0,0,0,0,10,0,1,2,3\r\n",
+ "Senegal (SEN),13,0,1,0,1,5,0,0,0,0,18,0,1,0,1\r\n",
+ "Serbia (SRB) [SRB],3,1,2,4,7,2,0,0,0,0,5,1,2,4,7\r\n",
+ "Serbia and Montenegro (SCG) [SCG],3,2,4,3,9,3,0,0,0,0,6,2,4,3,9\r\n",
+ "Singapore (SIN),15,0,2,2,4,0,0,0,0,0,15,0,2,2,4\r\n",
+ "Slovakia (SVK) [SVK],5,7,9,8,24,6,2,2,1,5,11,9,11,9,29\r\n",
+ "Slovenia (SLO),6,4,6,9,19,7,2,4,9,15,13,6,10,18,34\r\n",
+ "South Africa (RSA),18,23,26,27,76,6,0,0,0,0,24,23,26,27,76\r\n",
+ "Spain (ESP) [Z],22,37,59,35,131,19,1,0,1,2,41,38,59,36,133\r\n",
+ "Sri Lanka (SRI) [SRI],16,0,2,0,2,0,0,0,0,0,16,0,2,0,2\r\n",
+ "Sudan (SUD),11,0,1,0,1,0,0,0,0,0,11,0,1,0,1\r\n",
+ "Suriname (SUR) [E],11,1,0,1,2,0,0,0,0,0,11,1,0,1,2\r\n",
+ "Sweden (SWE) [Z],26,143,164,176,483,22,50,40,54,144,48,193,204,230,627\r\n",
+ "Switzerland (SUI),27,47,73,65,185,22,50,40,48,138,49,97,113,113,323\r\n",
+ "Syria (SYR),12,1,1,1,3,0,0,0,0,0,12,1,1,1,3\r\n",
+ "Chinese Taipei (TPE) [TPE] [TPE2],13,2,7,12,21,11,0,0,0,0,24,2,7,12,21\r\n",
+ "Tajikistan (TJK),5,0,1,2,3,4,0,0,0,0,9,0,1,2,3\r\n",
+ "Tanzania (TAN) [TAN],12,0,2,0,2,0,0,0,0,0,12,0,2,0,2\r\n",
+ "Thailand (THA),15,7,6,11,24,3,0,0,0,0,18,7,6,11,24\r\n",
+ "Togo (TOG),9,0,0,1,1,1,0,0,0,0,10,0,0,1,1\r\n",
+ "Tonga (TGA),8,0,1,0,1,1,0,0,0,0,9,0,1,0,1\r\n",
+ "Trinidad and Tobago (TRI) [TRI],16,2,5,11,18,3,0,0,0,0,19,2,5,11,18\r\n",
+ "Tunisia (TUN),13,3,3,4,10,0,0,0,0,0,13,3,3,4,10\r\n",
+ "Turkey (TUR),21,39,25,24,88,16,0,0,0,0,37,39,25,24,88\r\n",
+ "Uganda (UGA),14,2,3,2,7,0,0,0,0,0,14,2,3,2,7\r\n",
+ "Ukraine (UKR),5,33,27,55,115,6,2,1,4,7,11,35,28,59,122\r\n",
+ "United Arab Emirates (UAE),8,1,0,0,1,0,0,0,0,0,8,1,0,0,1\r\n",
+ "United States (USA) [P] [Q] [R] [Z],26,976,757,666,2399,22,96,102,84,282,48,1072,859,750,2681\r\n",
+ "Uruguay (URU),20,2,2,6,10,1,0,0,0,0,21,2,2,6,10\r\n",
+ "Uzbekistan (UZB),5,5,5,10,20,6,1,0,0,1,11,6,5,10,21\r\n",
+ "Venezuela (VEN),17,2,2,8,12,4,0,0,0,0,21,2,2,8,12\r\n",
+ "Vietnam (VIE),14,0,2,0,2,0,0,0,0,0,14,0,2,0,2\r\n",
+ "Virgin Islands (ISV),11,0,1,0,1,7,0,0,0,0,18,0,1,0,1\r\n",
+ "Yugoslavia (YUG) [YUG],16,26,29,28,83,14,0,3,1,4,30,26,32,29,87\r\n",
+ "Independent Olympic Participants (IOP) [IOP],1,0,1,2,3,0,0,0,0,0,1,0,1,2,3\r\n",
+ "Zambia (ZAM) [ZAM],12,0,1,1,2,0,0,0,0,0,12,0,1,1,2\r\n",
+ "Zimbabwe (ZIM) [ZIM],12,3,4,1,8,1,0,0,0,0,13,3,4,1,8\r\n",
+ "Mixed team (ZZX) [ZZX],3,8,5,4,17,0,0,0,0,0,3,8,5,4,17\r\n",
+ "Totals,27,4809,4775,5130,14714,22,959,958,948,2865,49,5768,5733,6078,17579\r\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "hola\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!cat olympics.csv"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Pero ... no hay que preocuparse mucho por eso! Podemos leer este archivo en formato `CSV` en un `DataFrame` usando la función `read_csv`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 6 | \n",
+ " 7 | \n",
+ " 8 | \n",
+ " 9 | \n",
+ " 10 | \n",
+ " 11 | \n",
+ " 12 | \n",
+ " 13 | \n",
+ " 14 | \n",
+ " 15 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " NaN | \n",
+ " № Summer | \n",
+ " 01 ! | \n",
+ " 02 ! | \n",
+ " 03 ! | \n",
+ " Total | \n",
+ " № Winter | \n",
+ " 01 ! | \n",
+ " 02 ! | \n",
+ " 03 ! | \n",
+ " Total | \n",
+ " № Games | \n",
+ " 01 ! | \n",
+ " 02 ! | \n",
+ " 03 ! | \n",
+ " Combined total | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " Afghanistan (AFG) | \n",
+ " 13 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 13 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " Algeria (ALG) | \n",
+ " 12 | \n",
+ " 5 | \n",
+ " 2 | \n",
+ " 8 | \n",
+ " 15 | \n",
+ " 3 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 15 | \n",
+ " 5 | \n",
+ " 2 | \n",
+ " 8 | \n",
+ " 15 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " Argentina (ARG) | \n",
+ " 23 | \n",
+ " 18 | \n",
+ " 24 | \n",
+ " 28 | \n",
+ " 70 | \n",
+ " 18 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 41 | \n",
+ " 18 | \n",
+ " 24 | \n",
+ " 28 | \n",
+ " 70 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " Armenia (ARM) | \n",
+ " 5 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 9 | \n",
+ " 12 | \n",
+ " 6 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 11 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 9 | \n",
+ " 12 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Aquí podemos ignorar la primera fila del `DataFrame` para dejar más limpia la tabla de información no relevante."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " № Summer | \n",
+ " 01 ! | \n",
+ " 02 ! | \n",
+ " 03 ! | \n",
+ " Total | \n",
+ " № Winter | \n",
+ " 01 !.1 | \n",
+ " 02 !.1 | \n",
+ " 03 !.1 | \n",
+ " Total.1 | \n",
+ " № Games | \n",
+ " 01 !.2 | \n",
+ " 02 !.2 | \n",
+ " 03 !.2 | \n",
+ " Combined total | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Afghanistan (AFG) | \n",
+ " 13 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 13 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " | Algeria (ALG) | \n",
+ " 12 | \n",
+ " 5 | \n",
+ " 2 | \n",
+ " 8 | \n",
+ " 15 | \n",
+ " 3 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 15 | \n",
+ " 5 | \n",
+ " 2 | \n",
+ " 8 | \n",
+ " 15 | \n",
+ "
\n",
+ " \n",
+ " | Argentina (ARG) | \n",
+ " 23 | \n",
+ " 18 | \n",
+ " 24 | \n",
+ " 28 | \n",
+ " 70 | \n",
+ " 18 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 41 | \n",
+ " 18 | \n",
+ " 24 | \n",
+ " 28 | \n",
+ " 70 | \n",
+ "
\n",
+ " \n",
+ " | Armenia (ARM) | \n",
+ " 5 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 9 | \n",
+ " 12 | \n",
+ " 6 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 11 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 9 | \n",
+ " 12 | \n",
+ "
\n",
+ " \n",
+ " | Australasia (ANZ) [ANZ] | \n",
+ " 2 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 12 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 12 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "El atributo `.columns` nos permite ver el nombre de las comlumnas del `DataFrame` y el atributo `.rename` modificar el nombre."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Index(['№ Summer', '01 !', '02 !', '03 !', 'Total', '№ Winter', '01 !.1',\n",
+ " '02 !.1', '03 !.1', 'Total.1', '№ Games', '01 !.2', '02 !.2', '03 !.2',\n",
+ " 'Combined total'],\n",
+ " dtype='object')"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " # Summer | \n",
+ " Gold | \n",
+ " Silver | \n",
+ " Bronze | \n",
+ " Total | \n",
+ " # Winter | \n",
+ " Gold.1 | \n",
+ " Silver.1 | \n",
+ " Bronze.1 | \n",
+ " Total.1 | \n",
+ " # Games | \n",
+ " Gold.2 | \n",
+ " Silver.2 | \n",
+ " Bronze.2 | \n",
+ " Combined total | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Afghanistan (AFG) | \n",
+ " 13 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 13 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " | Algeria (ALG) | \n",
+ " 12 | \n",
+ " 5 | \n",
+ " 2 | \n",
+ " 8 | \n",
+ " 15 | \n",
+ " 3 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 15 | \n",
+ " 5 | \n",
+ " 2 | \n",
+ " 8 | \n",
+ " 15 | \n",
+ "
\n",
+ " \n",
+ " | Argentina (ARG) | \n",
+ " 23 | \n",
+ " 18 | \n",
+ " 24 | \n",
+ " 28 | \n",
+ " 70 | \n",
+ " 18 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 41 | \n",
+ " 18 | \n",
+ " 24 | \n",
+ " 28 | \n",
+ " 70 | \n",
+ "
\n",
+ " \n",
+ " | Armenia (ARM) | \n",
+ " 5 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 9 | \n",
+ " 12 | \n",
+ " 6 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 11 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 9 | \n",
+ " 12 | \n",
+ "
\n",
+ " \n",
+ " | Australasia (ANZ) [ANZ] | \n",
+ " 2 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 12 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 12 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Podemos buscar en el `DataFrame` con una **máscara Booleana** qué países tienen (`True`) o no (`False`) una medalla de oro."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Afghanistan (AFG) False\n",
+ "Algeria (ALG) True\n",
+ "Argentina (ARG) True\n",
+ "Armenia (ARM) True\n",
+ "Australasia (ANZ) [ANZ] True\n",
+ "Australia (AUS) [AUS] [Z] True\n",
+ "Austria (AUT) True\n",
+ "Azerbaijan (AZE) True\n",
+ "Bahamas (BAH) True\n",
+ "Bahrain (BRN) False\n",
+ "Barbados (BAR) [BAR] False\n",
+ "Belarus (BLR) True\n",
+ "Belgium (BEL) True\n",
+ "Bermuda (BER) False\n",
+ "Bohemia (BOH) [BOH] [Z] False\n",
+ "Botswana (BOT) False\n",
+ "Brazil (BRA) True\n",
+ "British West Indies (BWI) [BWI] False\n",
+ "Bulgaria (BUL) [H] True\n",
+ "Burundi (BDI) True\n",
+ "Cameroon (CMR) True\n",
+ "Canada (CAN) True\n",
+ "Chile (CHI) [I] True\n",
+ "China (CHN) [CHN] True\n",
+ "Colombia (COL) True\n",
+ "Costa Rica (CRC) True\n",
+ "Ivory Coast (CIV) [CIV] False\n",
+ "Croatia (CRO) True\n",
+ "Cuba (CUB) [Z] True\n",
+ "Cyprus (CYP) False\n",
+ " ... \n",
+ "Sri Lanka (SRI) [SRI] False\n",
+ "Sudan (SUD) False\n",
+ "Suriname (SUR) [E] True\n",
+ "Sweden (SWE) [Z] True\n",
+ "Switzerland (SUI) True\n",
+ "Syria (SYR) True\n",
+ "Chinese Taipei (TPE) [TPE] [TPE2] True\n",
+ "Tajikistan (TJK) False\n",
+ "Tanzania (TAN) [TAN] False\n",
+ "Thailand (THA) True\n",
+ "Togo (TOG) False\n",
+ "Tonga (TGA) False\n",
+ "Trinidad and Tobago (TRI) [TRI] True\n",
+ "Tunisia (TUN) True\n",
+ "Turkey (TUR) True\n",
+ "Uganda (UGA) True\n",
+ "Ukraine (UKR) True\n",
+ "United Arab Emirates (UAE) True\n",
+ "United States (USA) [P] [Q] [R] [Z] True\n",
+ "Uruguay (URU) True\n",
+ "Uzbekistan (UZB) True\n",
+ "Venezuela (VEN) True\n",
+ "Vietnam (VIE) False\n",
+ "Virgin Islands (ISV) False\n",
+ "Yugoslavia (YUG) [YUG] True\n",
+ "Independent Olympic Participants (IOP) [IOP] False\n",
+ "Zambia (ZAM) [ZAM] False\n",
+ "Zimbabwe (ZIM) [ZIM] True\n",
+ "Mixed team (ZZX) [ZZX] True\n",
+ "Totals True\n",
+ "Name: Gold, Length: 147, dtype: bool"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['Gold'] > 0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "La función `.where()` toma una máscara booleana como condición en el argumento, la aplica al `DataFrame`, y devuelve un `DataFrame` de la misma forma. En nuestro ejemplo, reemplaza con `NaN` los casos `False` y con su valor original, los casos `True`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " # Summer | \n",
+ " Gold | \n",
+ " Silver | \n",
+ " Bronze | \n",
+ " Total | \n",
+ " # Winter | \n",
+ " Gold.1 | \n",
+ " Silver.1 | \n",
+ " Bronze.1 | \n",
+ " Total.1 | \n",
+ " # Games | \n",
+ " Gold.2 | \n",
+ " Silver.2 | \n",
+ " Bronze.2 | \n",
+ " Combined total | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Afghanistan (AFG) | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | Algeria (ALG) | \n",
+ " 12.0 | \n",
+ " 5.0 | \n",
+ " 2.0 | \n",
+ " 8.0 | \n",
+ " 15.0 | \n",
+ " 3.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 15.0 | \n",
+ " 5.0 | \n",
+ " 2.0 | \n",
+ " 8.0 | \n",
+ " 15.0 | \n",
+ "
\n",
+ " \n",
+ " | Argentina (ARG) | \n",
+ " 23.0 | \n",
+ " 18.0 | \n",
+ " 24.0 | \n",
+ " 28.0 | \n",
+ " 70.0 | \n",
+ " 18.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 41.0 | \n",
+ " 18.0 | \n",
+ " 24.0 | \n",
+ " 28.0 | \n",
+ " 70.0 | \n",
+ "
\n",
+ " \n",
+ " | Armenia (ARM) | \n",
+ " 5.0 | \n",
+ " 1.0 | \n",
+ " 2.0 | \n",
+ " 9.0 | \n",
+ " 12.0 | \n",
+ " 6.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 11.0 | \n",
+ " 1.0 | \n",
+ " 2.0 | \n",
+ " 9.0 | \n",
+ " 12.0 | \n",
+ "
\n",
+ " \n",
+ " | Australasia (ANZ) [ANZ] | \n",
+ " 2.0 | \n",
+ " 3.0 | \n",
+ " 4.0 | \n",
+ " 5.0 | \n",
+ " 12.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 2.0 | \n",
+ " 3.0 | \n",
+ " 4.0 | \n",
+ " 5.0 | \n",
+ " 12.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Podemos contar cuántas países hay medallas de oro hay en total con `count()`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "100"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "only_gold['Gold'].count()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Si contamos sobre los datos originales, veremos que hay 147 países. Cuenta los países para los cuales la máscara Booleana dio `False` >.<"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "147"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['Gold'].count()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Podemos establecer otro tipo de condiciones para hacer búsquedas más complejas. Por ejemplo, buscar la cantidad de países que han ganado medalla de oro alguna vez."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "101"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(df[(df['Gold'] > 0) | (df['Gold.1'] > 0)])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Buscar qué países han ganado sólo medallas de oro en Invierno y nunca en Verano."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " # Summer | \n",
+ " Gold | \n",
+ " Silver | \n",
+ " Bronze | \n",
+ " Total | \n",
+ " # Winter | \n",
+ " Gold.1 | \n",
+ " Silver.1 | \n",
+ " Bronze.1 | \n",
+ " Total.1 | \n",
+ " # Games | \n",
+ " Gold.2 | \n",
+ " Silver.2 | \n",
+ " Bronze.2 | \n",
+ " Combined total | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Liechtenstein (LIE) | \n",
+ " 16 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 18 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ " 5 | \n",
+ " 9 | \n",
+ " 34 | \n",
+ " 2 | \n",
+ " 2 | \n",
+ " 5 | \n",
+ " 9 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "## Pre-procesamiento de datos"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "### Manejo de datos faltantes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " A | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 24.0 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 23.0 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 12.0 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 17.0 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 2.0 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 5.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "En `dropna()` el argumento `subset` considera la etiqueta para seleccionar el conjunto a descartar, `axis=0` descarta filas (`axis=1` columnas) y `inplace= True` hace que los cambios se ejecuten directamente en `DataFrame`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " A | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 24.0 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 23.0 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 12.0 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 17.0 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 2.0 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 5.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "La función `replace()` permite reemplazar valores faltantes en el `DataFrame` por valores nuevos. En nuestro ejemplo, reemplazaremos con el promedio, que se calcula con la función `mean()`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "13.833333333333334"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "datos1 = pd.DataFrame([24, np.nan, np.nan, 23,np.nan, 12, np.nan, 17, np.nan, 2 ,5], columns = list('A'))\n",
+ "media = datos1['A'].mean()\n",
+ "media"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Ahora usamos la función `replace()`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " A | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 24.000000 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 13.833333 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 13.833333 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 23.000000 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 13.833333 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 12.000000 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 13.833333 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 17.000000 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 13.833333 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 2.000000 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 5.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "## Transformando los datos"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "### Mezclando y combinando `DataFrames`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Artículo comprado | \n",
+ " Costo | \n",
+ " Nombre | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Libro | \n",
+ " 1200 | \n",
+ " Adelis | \n",
+ "
\n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Raspberry pi 3 | \n",
+ " 15000 | \n",
+ " Miguel | \n",
+ "
\n",
+ " \n",
+ " | Tienda 2 | \n",
+ " Balón | \n",
+ " 5000 | \n",
+ " Jaime | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Podemos agregar elementos al `DataFrame` de la siguiente manera:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Artículo comprado | \n",
+ " Costo | \n",
+ " Nombre | \n",
+ " Fecha | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Libro | \n",
+ " 1200 | \n",
+ " Adelis | \n",
+ " Diciembre 1 | \n",
+ "
\n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Raspberry pi 3 | \n",
+ " 15000 | \n",
+ " Miguel | \n",
+ " Febrero 4 | \n",
+ "
\n",
+ " \n",
+ " | Tienda 2 | \n",
+ " Balón | \n",
+ " 5000 | \n",
+ " Jaime | \n",
+ " Mediados de Julio | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Artículo comprado | \n",
+ " Costo | \n",
+ " Nombre | \n",
+ " Fecha | \n",
+ " Entregado | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Libro | \n",
+ " 1200 | \n",
+ " Adelis | \n",
+ " Diciembre 1 | \n",
+ " Sí | \n",
+ "
\n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Raspberry pi 3 | \n",
+ " 15000 | \n",
+ " Miguel | \n",
+ " Febrero 4 | \n",
+ " Sí | \n",
+ "
\n",
+ " \n",
+ " | Tienda 2 | \n",
+ " Balón | \n",
+ " 5000 | \n",
+ " Jaime | \n",
+ " Mediados de Julio | \n",
+ " Sí | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Artículo comprado | \n",
+ " Costo | \n",
+ " Nombre | \n",
+ " Fecha | \n",
+ " Entregado | \n",
+ " Retroalimentación | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Libro | \n",
+ " 1200 | \n",
+ " Adelis | \n",
+ " Diciembre 1 | \n",
+ " Sí | \n",
+ " Positiva | \n",
+ "
\n",
+ " \n",
+ " | Tienda 1 | \n",
+ " Raspberry pi 3 | \n",
+ " 15000 | \n",
+ " Miguel | \n",
+ " Febrero 4 | \n",
+ " Sí | \n",
+ " None | \n",
+ "
\n",
+ " \n",
+ " | Tienda 2 | \n",
+ " Balón | \n",
+ " 5000 | \n",
+ " Jaime | \n",
+ " Mediados de Julio | \n",
+ " Sí | \n",
+ " Negativa | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Pandas `reset_index ()` es un método para restablecer el índice de un `DataFrame`. El establece como índices una lista de enteros que van desde 0 hasta la longitud de los datos."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " index | \n",
+ " Artículo comprado | \n",
+ " Costo | \n",
+ " Nombre | \n",
+ " Fecha | \n",
+ " Entregado | \n",
+ " Retroalimentación | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " Tienda 1 | \n",
+ " Libro | \n",
+ " 1200 | \n",
+ " Adelis | \n",
+ " Diciembre 1 | \n",
+ " Sí | \n",
+ " Positiva | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " Tienda 1 | \n",
+ " Raspberry pi 3 | \n",
+ " 15000 | \n",
+ " Miguel | \n",
+ " Febrero 4 | \n",
+ " Sí | \n",
+ " None | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " Tienda 2 | \n",
+ " Balón | \n",
+ " 5000 | \n",
+ " Jaime | \n",
+ " Mediados de Julio | \n",
+ " Sí | \n",
+ " Negativa | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Podemos tener un par de tablas de datos que nos interese unir o combinar en un mismo `DataFrame`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Función\n",
+ "Nombre \n",
+ "Adriana Gerente de ventas\n",
+ "Andrés Vendedor 1\n",
+ "Cristóbal Gerente de departamento\n",
+ "\n",
+ " Grado\n",
+ "Nombre \n",
+ "Andrés Nivel 3\n",
+ "Cristóbal Nivel 1\n",
+ "Adriana Nivel 2\n"
+ ]
+ }
+ ],
+ "source": [
+ "empleados_df = pd.DataFrame([{'Nombre': 'Adriana', 'Función': 'Gerente de ventas'},\n",
+ " {'Nombre': 'Andrés', 'Función': 'Vendedor 1'},\n",
+ " {'Nombre': 'Cristóbal', 'Función': 'Gerente de departamento'}])\n",
+ "empleados_df = empleados_df.set_index('Nombre')\n",
+ "grado_df = pd.DataFrame([{'Nombre': 'Andrés', 'Grado': 'Nivel 3'},\n",
+ " {'Nombre': 'Cristóbal', 'Grado': 'Nivel 1'},\n",
+ " {'Nombre': 'Adriana', 'Grado': 'Nivel 2'}])\n",
+ "grado_df = grado_df.set_index('Nombre')\n",
+ "print(empleados_df.head())\n",
+ "print()\n",
+ "print(grado_df.head())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "`pd.merge()` conecta filas en el `DataFrames` basado en una o más teclas. Para los conocedores de SQL esta función hace unión de bases de datos por columnas o índices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Función | \n",
+ " Grado | \n",
+ "
\n",
+ " \n",
+ " | Nombre | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Adriana | \n",
+ " Gerente de ventas | \n",
+ " Nivel 2 | \n",
+ "
\n",
+ " \n",
+ " | Andrés | \n",
+ " Vendedor 1 | \n",
+ " Nivel 3 | \n",
+ "
\n",
+ " \n",
+ " | Cristóbal | \n",
+ " Gerente de departamento | \n",
+ " Nivel 1 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Otros ejemplos de cómo variar el parámetro `how` se pueden encontrar en el libro *Python for Data Analysis* - McKinney."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Supongamos que tenemos ahora un nuevo `DataFrame` que coincide en número de filas con el anterior. Por ejemplo:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Fecha de Ingreso\n",
+ "Nombre \n",
+ "Adriana 20/06/2013\n",
+ "Andrés 10/01/2018\n",
+ "Cristóbal 20/03/2011\n",
+ " Art.Vendidos/Total Art.\n",
+ "Nombre \n",
+ "Adriana 0.0123\n",
+ "Andrés 0.1450\n",
+ "Cristóbal 0.5000\n"
+ ]
+ }
+ ],
+ "source": [
+ "fecha_ingreso_df = pd.DataFrame([{'Nombre': 'Adriana', 'Fecha de Ingreso': '20/06/2013'},\n",
+ " {'Nombre': 'Andrés', 'Fecha de Ingreso': '10/01/2018'},\n",
+ " {'Nombre': 'Cristóbal', 'Fecha de Ingreso': '20/03/2011'}])\n",
+ "fecha_ingreso_df = fecha_ingreso_df.set_index('Nombre')\n",
+ "art_vendidos_df = pd.DataFrame([{'Nombre': 'Adriana', 'Art.Vendidos/Total Art.': 123/10000},\n",
+ " {'Nombre': 'Andrés', 'Art.Vendidos/Total Art.': 1450/10000},\n",
+ " {'Nombre': 'Cristóbal', 'Art.Vendidos/Total Art.': 5000/10000}])\n",
+ "art_vendidos_df = art_vendidos_df.set_index('Nombre')\n",
+ "\n",
+ "print(fecha_ingreso_df.head())\n",
+ "print(art_vendidos_df.head())\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "`pd.concat()` pega o apila objetos a lo largo de un eje."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Función | \n",
+ " Grado | \n",
+ " Fecha de Ingreso | \n",
+ " Art.Vendidos/Total Art. | \n",
+ "
\n",
+ " \n",
+ " | Nombre | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Adriana | \n",
+ " Gerente de ventas | \n",
+ " Nivel 2 | \n",
+ " 20/06/2013 | \n",
+ " 0.0123 | \n",
+ "
\n",
+ " \n",
+ " | Andrés | \n",
+ " Vendedor 1 | \n",
+ " Nivel 3 | \n",
+ " 10/01/2018 | \n",
+ " 0.1450 | \n",
+ "
\n",
+ " \n",
+ " | Cristóbal | \n",
+ " Gerente de departamento | \n",
+ " Nivel 1 | \n",
+ " 20/03/2011 | \n",
+ " 0.5000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Hay mucho más que aprender! Por ejemplo: ¿Qué sucede si `axis=0`? **R**: pues posiblemente el resultado sea que Pandas pegue todos los valores y sus índices. Como se muestra a continuación:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Art.Vendidos/Total Art. | \n",
+ " Fecha de Ingreso | \n",
+ " Función | \n",
+ " Grado | \n",
+ "
\n",
+ " \n",
+ " | Nombre | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Adriana | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " Gerente de ventas | \n",
+ " Nivel 2 | \n",
+ "
\n",
+ " \n",
+ " | Andrés | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " Vendedor 1 | \n",
+ " Nivel 3 | \n",
+ "
\n",
+ " \n",
+ " | Cristóbal | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " Gerente de departamento | \n",
+ " Nivel 1 | \n",
+ "
\n",
+ " \n",
+ " | Adriana | \n",
+ " NaN | \n",
+ " 20/06/2013 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | Andrés | \n",
+ " NaN | \n",
+ " 10/01/2018 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | Cristóbal | \n",
+ " NaN | \n",
+ " 20/03/2011 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | Adriana | \n",
+ " 0.0123 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | Andrés | \n",
+ " 0.1450 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | Cristóbal | \n",
+ " 0.5000 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Otra transformación de interés podría ser hacer algún cálculo sobre una columna entera. En nuestro ejemplo, supongamos que deseamos colocar **% de artículos vendidos** y cambiar la etiqueta de esa columna.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Función | \n",
+ " Grado | \n",
+ " Fecha de Ingreso | \n",
+ " % Art. Vendidos | \n",
+ "
\n",
+ " \n",
+ " | Nombre | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Adriana | \n",
+ " Gerente de ventas | \n",
+ " Nivel 2 | \n",
+ " 20/06/2013 | \n",
+ " 1.23 | \n",
+ "
\n",
+ " \n",
+ " | Andrés | \n",
+ " Vendedor 1 | \n",
+ " Nivel 3 | \n",
+ " 10/01/2018 | \n",
+ " 14.50 | \n",
+ "
\n",
+ " \n",
+ " | Cristóbal | \n",
+ " Gerente de departamento | \n",
+ " Nivel 1 | \n",
+ " 20/03/2011 | \n",
+ " 50.00 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "### Normalizando datos"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Tomemos un `DataFrame` que representa dimensiones de cajas a ser vendidas en un almacén. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Largo | \n",
+ " Ancho | \n",
+ " Alto | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 168.7 | \n",
+ " 68.3 | \n",
+ " 46.8 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 170.0 | \n",
+ " 60.2 | \n",
+ " 47.0 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 150.3 | \n",
+ " 65.0 | \n",
+ " 45.0 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 168.7 | \n",
+ " 68.3 | \n",
+ " 46.8 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 145.2 | \n",
+ " 45.9 | \n",
+ " 45.3 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 200.0 | \n",
+ " 70.0 | \n",
+ " 40.9 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 175.4 | \n",
+ " 75.1 | \n",
+ " 45.6 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 163.0 | \n",
+ " 63.5 | \n",
+ " 43.8 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 230.0 | \n",
+ " 65.2 | \n",
+ " 46.8 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 129.6 | \n",
+ " 68.7 | \n",
+ " 49.0 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 178.2 | \n",
+ " 78.0 | \n",
+ " 47.2 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Método de **\"Escala de característica simple\"**: se divide cada valor por el\n",
+ "valor máximo para esa característica, $x_{nuevo} = \\frac{x_{viejo}}{x_{máximo}}$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 91,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Largo | \n",
+ " Ancho | \n",
+ " Alto | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 0.733478 | \n",
+ " 0.875641 | \n",
+ " 0.955102 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 0.739130 | \n",
+ " 0.771795 | \n",
+ " 0.959184 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 0.653478 | \n",
+ " 0.833333 | \n",
+ " 0.918367 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 0.733478 | \n",
+ " 0.875641 | \n",
+ " 0.955102 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 0.631304 | \n",
+ " 0.588462 | \n",
+ " 0.924490 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 0.869565 | \n",
+ " 0.897436 | \n",
+ " 0.834694 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 0.762609 | \n",
+ " 0.962821 | \n",
+ " 0.930612 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 0.708696 | \n",
+ " 0.814103 | \n",
+ " 0.893878 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 1.000000 | \n",
+ " 0.835897 | \n",
+ " 0.955102 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 0.563478 | \n",
+ " 0.880769 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 0.774783 | \n",
+ " 1.000000 | \n",
+ " 0.963265 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Método **Mínimo - Máximo**: toma cada valor, $x_{viejo}$ le resta el mínimo\n",
+ "valor de esa característica y luego se divide por el rango de esa característica, es decir, $x_{nuevo} = \\frac{x_{viejo} - x_{mínimo}}{x_{máximo} - x_{mínimo}}$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Largo | \n",
+ " Ancho | \n",
+ " Alto | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 0.389442 | \n",
+ " 0.697819 | \n",
+ " 0.728395 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 0.402390 | \n",
+ " 0.445483 | \n",
+ " 0.753086 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 0.206175 | \n",
+ " 0.595016 | \n",
+ " 0.506173 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 0.389442 | \n",
+ " 0.697819 | \n",
+ " 0.728395 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 0.155378 | \n",
+ " 0.000000 | \n",
+ " 0.543210 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 0.701195 | \n",
+ " 0.750779 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 0.456175 | \n",
+ " 0.909657 | \n",
+ " 0.580247 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " 0.332669 | \n",
+ " 0.548287 | \n",
+ " 0.358025 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 1.000000 | \n",
+ " 0.601246 | \n",
+ " 0.728395 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " 0.000000 | \n",
+ " 0.710280 | \n",
+ " 1.000000 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 0.484064 | \n",
+ " 1.000000 | \n",
+ " 0.777778 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Método **Puntaje estándar**: \n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Largo | \n",
+ " Ancho | \n",
+ " Alto | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " -0.078861 | \n",
+ " 0.249968 | \n",
+ " 0.451435 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " -0.030668 | \n",
+ " -0.714195 | \n",
+ " 0.545129 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " -0.760972 | \n",
+ " -0.142839 | \n",
+ " -0.391812 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " -0.078861 | \n",
+ " 0.249968 | \n",
+ " 0.451435 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " -0.950036 | \n",
+ " -2.416358 | \n",
+ " -0.251270 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " 1.081470 | \n",
+ " 0.452323 | \n",
+ " -2.312540 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " 0.169517 | \n",
+ " 1.059389 | \n",
+ " -0.110729 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " -0.290167 | \n",
+ " -0.321388 | \n",
+ " -0.953976 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " 2.193608 | \n",
+ " -0.119032 | \n",
+ " 0.451435 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " -1.528347 | \n",
+ " 0.297581 | \n",
+ " 1.482070 | \n",
+ "
\n",
+ " \n",
+ " | 10 | \n",
+ " 0.273316 | \n",
+ " 1.404583 | \n",
+ " 0.638823 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "## Estadística descriptiva"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "### Tabla de resumen estadístico"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " symboling | \n",
+ " normalized-losses | \n",
+ " make | \n",
+ " fuel-type | \n",
+ " aspiration | \n",
+ " num-of-doors | \n",
+ " body-style | \n",
+ " drive-wheels | \n",
+ " engine-location | \n",
+ " wheel-base | \n",
+ " ... | \n",
+ " engine-size | \n",
+ " fuel-system | \n",
+ " bore | \n",
+ " stroke | \n",
+ " compression-ratio | \n",
+ " horsepower | \n",
+ " peak-rpm | \n",
+ " city-mpg | \n",
+ " highway-mpg | \n",
+ " price | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 3 | \n",
+ " 122 | \n",
+ " alfa-romero | \n",
+ " gas | \n",
+ " std | \n",
+ " two | \n",
+ " convertible | \n",
+ " rwd | \n",
+ " front | \n",
+ " 88.6 | \n",
+ " ... | \n",
+ " 130 | \n",
+ " mpfi | \n",
+ " 3.47 | \n",
+ " 2.68 | \n",
+ " 9.0 | \n",
+ " 111.0 | \n",
+ " 5000.0 | \n",
+ " 21 | \n",
+ " 27 | \n",
+ " 13495.0 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 3 | \n",
+ " 122 | \n",
+ " alfa-romero | \n",
+ " gas | \n",
+ " std | \n",
+ " two | \n",
+ " convertible | \n",
+ " rwd | \n",
+ " front | \n",
+ " 88.6 | \n",
+ " ... | \n",
+ " 130 | \n",
+ " mpfi | \n",
+ " 3.47 | \n",
+ " 2.68 | \n",
+ " 9.0 | \n",
+ " 111.0 | \n",
+ " 5000.0 | \n",
+ " 21 | \n",
+ " 27 | \n",
+ " 16500.0 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 1 | \n",
+ " 122 | \n",
+ " alfa-romero | \n",
+ " gas | \n",
+ " std | \n",
+ " two | \n",
+ " hatchback | \n",
+ " rwd | \n",
+ " front | \n",
+ " 94.5 | \n",
+ " ... | \n",
+ " 152 | \n",
+ " mpfi | \n",
+ " 2.68 | \n",
+ " 3.47 | \n",
+ " 9.0 | \n",
+ " 154.0 | \n",
+ " 5000.0 | \n",
+ " 19 | \n",
+ " 26 | \n",
+ " 16500.0 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 2 | \n",
+ " 164 | \n",
+ " audi | \n",
+ " gas | \n",
+ " std | \n",
+ " four | \n",
+ " sedan | \n",
+ " fwd | \n",
+ " front | \n",
+ " 99.8 | \n",
+ " ... | \n",
+ " 109 | \n",
+ " mpfi | \n",
+ " 3.19 | \n",
+ " 3.40 | \n",
+ " 10.0 | \n",
+ " 102.0 | \n",
+ " 5500.0 | \n",
+ " 24 | \n",
+ " 30 | \n",
+ " 13950.0 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 2 | \n",
+ " 164 | \n",
+ " audi | \n",
+ " gas | \n",
+ " std | \n",
+ " four | \n",
+ " sedan | \n",
+ " 4wd | \n",
+ " front | \n",
+ " 99.4 | \n",
+ " ... | \n",
+ " 136 | \n",
+ " mpfi | \n",
+ " 3.19 | \n",
+ " 3.40 | \n",
+ " 8.0 | \n",
+ " 115.0 | \n",
+ " 5500.0 | \n",
+ " 18 | \n",
+ " 22 | \n",
+ " 17450.0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 26 columns
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " symboling | \n",
+ " normalized-losses | \n",
+ " wheel-base | \n",
+ " length | \n",
+ " width | \n",
+ " height | \n",
+ " curb-weight | \n",
+ " engine-size | \n",
+ " bore | \n",
+ " stroke | \n",
+ " compression-ratio | \n",
+ " horsepower | \n",
+ " peak-rpm | \n",
+ " city-mpg | \n",
+ " highway-mpg | \n",
+ " price | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | count | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ " 205.000000 | \n",
+ "
\n",
+ " \n",
+ " | mean | \n",
+ " 0.834146 | \n",
+ " 122.000000 | \n",
+ " 98.756585 | \n",
+ " 174.049268 | \n",
+ " 65.907805 | \n",
+ " 53.724878 | \n",
+ " 2555.565854 | \n",
+ " 126.907317 | \n",
+ " 3.329751 | \n",
+ " 3.255423 | \n",
+ " 10.142537 | \n",
+ " 104.256158 | \n",
+ " 5125.369458 | \n",
+ " 25.219512 | \n",
+ " 30.751220 | \n",
+ " 13207.129353 | \n",
+ "
\n",
+ " \n",
+ " | std | \n",
+ " 1.245307 | \n",
+ " 31.681008 | \n",
+ " 6.021776 | \n",
+ " 12.337289 | \n",
+ " 2.145204 | \n",
+ " 2.443522 | \n",
+ " 520.680204 | \n",
+ " 41.642693 | \n",
+ " 0.270844 | \n",
+ " 0.313597 | \n",
+ " 3.972040 | \n",
+ " 39.519211 | \n",
+ " 476.979093 | \n",
+ " 6.542142 | \n",
+ " 6.886443 | \n",
+ " 7868.768212 | \n",
+ "
\n",
+ " \n",
+ " | min | \n",
+ " -2.000000 | \n",
+ " 65.000000 | \n",
+ " 86.600000 | \n",
+ " 141.100000 | \n",
+ " 60.300000 | \n",
+ " 47.800000 | \n",
+ " 1488.000000 | \n",
+ " 61.000000 | \n",
+ " 2.540000 | \n",
+ " 2.070000 | \n",
+ " 7.000000 | \n",
+ " 48.000000 | \n",
+ " 4150.000000 | \n",
+ " 13.000000 | \n",
+ " 16.000000 | \n",
+ " 5118.000000 | \n",
+ "
\n",
+ " \n",
+ " | 25% | \n",
+ " 0.000000 | \n",
+ " 101.000000 | \n",
+ " 94.500000 | \n",
+ " 166.300000 | \n",
+ " 64.100000 | \n",
+ " 52.000000 | \n",
+ " 2145.000000 | \n",
+ " 97.000000 | \n",
+ " 3.150000 | \n",
+ " 3.110000 | \n",
+ " 8.600000 | \n",
+ " 70.000000 | \n",
+ " 4800.000000 | \n",
+ " 19.000000 | \n",
+ " 25.000000 | \n",
+ " 7788.000000 | \n",
+ "
\n",
+ " \n",
+ " | 50% | \n",
+ " 1.000000 | \n",
+ " 122.000000 | \n",
+ " 97.000000 | \n",
+ " 173.200000 | \n",
+ " 65.500000 | \n",
+ " 54.100000 | \n",
+ " 2414.000000 | \n",
+ " 120.000000 | \n",
+ " 3.310000 | \n",
+ " 3.290000 | \n",
+ " 9.000000 | \n",
+ " 95.000000 | \n",
+ " 5200.000000 | \n",
+ " 24.000000 | \n",
+ " 30.000000 | \n",
+ " 10595.000000 | \n",
+ "
\n",
+ " \n",
+ " | 75% | \n",
+ " 2.000000 | \n",
+ " 137.000000 | \n",
+ " 102.400000 | \n",
+ " 183.100000 | \n",
+ " 66.900000 | \n",
+ " 55.500000 | \n",
+ " 2935.000000 | \n",
+ " 141.000000 | \n",
+ " 3.580000 | \n",
+ " 3.410000 | \n",
+ " 9.400000 | \n",
+ " 116.000000 | \n",
+ " 5500.000000 | \n",
+ " 30.000000 | \n",
+ " 34.000000 | \n",
+ " 16500.000000 | \n",
+ "
\n",
+ " \n",
+ " | max | \n",
+ " 3.000000 | \n",
+ " 256.000000 | \n",
+ " 120.900000 | \n",
+ " 208.100000 | \n",
+ " 72.300000 | \n",
+ " 59.800000 | \n",
+ " 4066.000000 | \n",
+ " 326.000000 | \n",
+ " 3.940000 | \n",
+ " 4.170000 | \n",
+ " 23.000000 | \n",
+ " 288.000000 | \n",
+ " 6600.000000 | \n",
+ " 49.000000 | \n",
+ " 54.000000 | \n",
+ " 45400.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "### Gráficos de cajas (o Boxplots)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Vamos a generar datos aleatoriamente y hacer un **gráfico de caja**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 2 | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " -0.736260 | \n",
+ " 0.440260 | \n",
+ " 0.731900 | \n",
+ " 0.254316 | \n",
+ " -1.807715 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 1.053235 | \n",
+ " -2.661686 | \n",
+ " -1.076339 | \n",
+ " 0.484691 | \n",
+ " 0.170791 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 0.129711 | \n",
+ " -0.386408 | \n",
+ " 1.199626 | \n",
+ " -1.070269 | \n",
+ " -1.157811 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " -0.271514 | \n",
+ " -0.071525 | \n",
+ " -0.862646 | \n",
+ " -0.789977 | \n",
+ " 0.300384 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " -0.166081 | \n",
+ " 0.298657 | \n",
+ " 1.737711 | \n",
+ " 0.311688 | \n",
+ " 1.138495 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "Tomemos los datos del archivo `Automobile_data.csv` para crear un gráfico de caja de 3 variables que definen las dimensiones de los automóviles. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 97,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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nnnnmDvPNWklhASBpBHAqFe/+8/nvkHSQpF0kjZZ0EdkpoWVFbdtsZ5o5cybz589n9OjRzJ8/n5kzZza7SWZ1KfIi8AnAKODaqvmTgauBvcg+C7AWeGdE+C2TDVlS7weoixcvZvHixX8xv6dlir7GZlakwo4AIuL6iBgTEeWq+ZdHxPiI2C0i9omId0XEz4vartlgiIgeH92OOOII9j1jEUcccUSfy5gNZYNxG6hZ25o2bRrLly/njjvugDvu4PGK+WatxmMBmQ3AsmXLmDZt2p9O90hi2rRpLFvmS1rWehwAZgO0bNkytm/fzvg5N7F9+3a/+FvL8ikgS9Lki5ezacvLDa9nwvk3173s2FHDWfcZnzqy5nEAWJI2bXmZhy89tqF1NPpp0kbCw6wIDgBL0piJ53PwNec3vqJrGmkDQGMhZNYIB4Al6fn1l/oIwJLnALBkFfICvLSxawBmzeQAsCQ1+u4fsgApYj1mzeLbQM3MEuUAMDNLlAPAzCxRvgZg1oO+RgPdod4X+i73gHDNUWv/9afd+89HAGY96G000MrHypUr+61jzdFfv4yfc1NNfdzuHABmZolyAJiZJcoBYGaWKF8ENrOWMhRGcoX2GM3VAWBmLWUojOQK7TGWkwPAzFrKUBjJNWsHtPporg4AM2spQ2EkV2iPI4BCLgJLWiTpZUnlisdZVXVmSnpQ0ouS7pJ0eBHbNjOz+hR5BHBNRHy4pwJJU4BvAccDPwI+Dtwi6fURsbnANphZApo9lDe0x3DeO+sU0EeAGyJiOYCky4CPkgVCg2fizCwlHsq7OEUGwImSTgB+D/wPcHFElPOyycCi7ooREZLuzufvQNJsYDZAR0cHpVKpwCYOLeVyua33r925/1qb+664APg6MAd4GpgIXA1cBUzPy8cAm6qW2QjsXr2iiFgALADo7OyMRi/UDGVFXIiy5nH/tbClN7vvKOgicESsiYjfRcT2iLgPOBd4v6RX5FWeB8ZWLbYH4PP/ZmZNMljXALbnP7vHZF0HHNZdqGys1kOAGwZp+2aWsFqGg+5vKG/wcNA1kXSSpD3y318PfBn4fkT8Ia9yFXCCpKmSRgDnASOB7xWxfTOzSkUM5d3uL/5Q3GBwZwAPSXoBWA78FDituzAiVgFnkQXBJuCfgWN8C6iZWfMUcgooIrpqqLMYWFzE9szMrHEeDtrMLFEOADOzRDkAzMwS5QAwM0uUA8DMLFEOADOzRGkof9hB0tPAI81uxyDai2zwPGtN7r/W1e59Nz4i9u6v0pAOgHYnaXVEdDa7HVYf91/rct9lfArIzCxRDgAzs0Q5AJprQbMbYA1x/7Uu9x2+BmBmliwfAZiZJcoBMACSSpI+2cTtd0na2qztp0bSOEllSfv2UWeFpIv6WU9ImlJ4AxMi6WFJp9S57MmS1g2g/kWSVtSzrVbjABiiJJ0q6YFmtyNlEfFoRIyOiMdrqS9pQv5iv99gt81qFxHXRsTkotbXTv3sADAzS5QDoE756YHvSnoifyyQNKaiPCSdJennkp6X9FNJb6ooHyNpsaRnJT0iaaakrflpnrcBVwAH5qcgypK6Kpb9gKQHJW2S9J+V27X+STpR0m8qpj+X99eB+fTf5H/bgyrf6SlzgaTH8n67nD9/7zVk330N8Ju8zz5VUfbW3v4XrGbjJP0w/9veK+mI7gJJH8nnbZJ0t6RpFWU7HE339dyr2JYkXSLpqfxxcUVZX/3cUhwAdZA0ErgN+BVwIPBmYD/gq1VVTwVOJPvY+Qbg6xVlX82XfRNwMHAsMAwgIu4k/5rN/BTE6Igo5csNA6YBk4E3AIcCHyt0B9vfbcBBksbl00cDD+Q/u6dLQPX1llOAc4H3Aq8hG0rgyIry7tMMb8z77HMVZafS+/+C1eZDZP/rY4FbgWsAJM0G5gAnA3sCc4EbJB3Uy3p6fe5VOBJ4FNgXOA64UNLf5WV99XNLcQDU5x/JbqH9dERsiYjngE8BJ0uq/Ee6LD+P/BKwCOgEkLQL2T/rpyPiqfy7kS8cwPbPj4hyRPwO+O/u9Vpt8v5aCxwtaXdgEjAPeGde5Wigp4uAM4ErI2JNRPwR+DzwZI2b7fF/wQbkyoi4LyK2Af9KFuJjyULhsxGxLiK2R8QtwErgpOoVDOC5d39EXBERWyPiLuAe2rDPCvlO4AQdQHY4urFqfpC9M/xtPv1ERdkLQPepmr2BEew40F2tg95ti4ine1mv1W4F2Qv9M8CdwC3AlySNBt4GnNXDMvsBD3dPRMR2SbX2W2//C1a76r8hZH/HA4BvSvpaRfmuwGM9rKPW594TVdNt2WcOgPo8QvYOYVKdyz8N/BEYDzyYzxtXVWd7neu22qwArgWeBW6NiKck/RY4B3gmItZLmlC1zG+BP82TJLI+7OY+a45HgM9ExHdqqFvLc68/bdPPPgVUn5uA4ZIuzC8oSdLrJB1fy8IRsR1YAlwkae/8Iu68qmpPAvvkpyiseKuA3YEZZOeTAX4IfIKeT/8A/DswW9JhkoYD55Md8XV7muzF4fWD0mLrzeVkz6VD8ufiKElTerrQXuNzrz9t088OgDpExIvAVLKLv78GNpG9eBwygNV8nOwi0/3AvWQvQgG8lJffls/7P0kbJf19Ma03gPxc/CrgD8Av8tkryEKhtwBYTHbx9kbgd8A+wI8r1rmF7FrQdXmfzR2c1luliLgK+CJwNfAc2fPqU8DwXhbp77nX3/bapp89FtAQIemNZGHyulo/eGRmjUv5uecAaBJJBwCvBe4iuzXwamC3iPA7fbNB5Ofen/kUUPOMIhuSdhPwS+BF4F+a2iKzNPi5l/MRgJlZonwEYGaWKAeAmVmiHABmZolyAJiZJcoBYGaWKAeAmVmi/h9+Zf1xpiVQVgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x = df['length'] #Variable Largo\n",
+ "y = df['width'] #Variable Ancho\n",
+ "z =df['height'] #Variable Alto\n",
+ "dfbp = pd.DataFrame([x,y,z]).T #Creando un DataFrame con las dimensiones de los autosmóviles\n",
+ "dfbp.boxplot(fontsize=13, return_type='axes') #Gráfico de caja de las 3 variables \n",
+ "#Tarea!!!!! Normalice estos datos y haga el nuevo gráfico de caja"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "### Gráficos de barras (o histogramas)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Vamos a generar datos aleatoriamente y hacer un **gráfico de barras**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 98,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 98,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "np.random.seed(14000) #Generación de números aleatorios\n",
+ "pdhist = pd.Series(np.random.randn(1000)) #Serie de números aleatorios\n",
+ "pdhist.hist(normed=True) # Muestra las barras\n",
+ "pdhist.plot(fontsize=13, kind='kde') #Gráfico de barras (kde = Kernel Density Estimation plot. Haga la prueba con 'hist')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Utilicemos los datos de `Automobile_data.csv` para hacer un **gráfico de barras o histograma** de la variable `price` (precio)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0,0.5,'Frecuencia')"
+ ]
+ },
+ "execution_count": 99,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "p = df['price'] #Seleccionamos la variable price\n",
+ "pdf = pd.Series(p) #Convertimos la selección en una serie de Pandas\n",
+ "pdf.hist(normed=True) # Muestra las barras\n",
+ "pdf.plot(fontsize=11, kind = 'hist') #Gráfico de barras\n",
+ "plt.xlabel('Precio',fontsize=13)\n",
+ "plt.ylabel('Frecuencia', fontsize=13)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Este **gráfico de barras** nos indica que hay un número alto de automóviles con precio menor a 10000, entre otras cosas .... ¿Qué cosas? ;)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "### Gráfico de dispersión\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "Este **gráfico de dispersión** muestra la relación entre las variables `tamaño del motor` y `precio`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0,0.5,'Precio')"
+ ]
+ },
+ "execution_count": 102,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "x= df['engine-size'] #Variable predictora\n",
+ "y= df['price'] #Variable objetivo o que deseamos predecir\n",
+ "plt.scatter(x, y) #Gráfico de dispersión en Matplotlib \n",
+ "plt.title('Gráfico de dispersión de Tamaño del motor Vs. Precio', fontsize=13)#Nombre del gráfico\n",
+ "plt.xlabel('Tamaño del motor', fontsize=13)#Etiquetal del eje-x\n",
+ "plt.ylabel('Precio', fontsize=13)#Etiqueta del eje-y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "### Correlación entre variables"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Tomememos las dos variables del ejemplo anterior..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Gráfico de dispersión de Tamaño del motor Vs. Precio')"
+ ]
+ },
+ "execution_count": 119,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from scipy import stats\n",
+ "x=df['engine-size'] #Variable predictora\n",
+ "y= df['price'] #Variable objetivo o que deseamos predecir\n",
+ "slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)\n",
+ "line = slope*x+intercept\n",
+ "plt.plot(x,y,'o', x, line)\n",
+ "ax = plt.gca()\n",
+ "fig = plt.gcf()\n",
+ "plt.xlabel('Tamaño del motor', fontsize=9)#Etiquetal del eje-x\n",
+ "plt.ylabel('Precio', fontsize=9)#Etiqueta del eje-y\n",
+ "plt.title('Gráfico de dispersión de Tamaño del motor Vs. Precio', fontsize=13)#Nombre del gráfico\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "El gráfico de dispersión anterior revela que hay una relación lineal positiva entre el tamaño del motor y el precio del auto. Es decir, a medida que aumenta el tamaño del motor aumenta el precio."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Este gráfico de dispersión revela que hay una relación lineal negativa entre las millas que recorre el auto por combustible que usa y el precio del mismo. Es decir, mientras más millas por galón el auto es más económico. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5,1,'Gráfico de dispersión de Millas por galón en autopista Vs. Precio')"
+ ]
+ },
+ "execution_count": 121,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from scipy import stats\n",
+ "x=df['highway-mpg'] #Variable predictora\n",
+ "y= df['price'] #Variable objetivo o que deseamos predecir\n",
+ "slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)\n",
+ "line = slope*x+intercept\n",
+ "plt.plot(x,y,'o', x, line)\n",
+ "ax = plt.gca()\n",
+ "fig = plt.gcf()\n",
+ "plt.xlabel('Millas por galón en autopista', fontsize=9)#Etiquetal del eje-x\n",
+ "plt.ylabel('Precio', fontsize=9)#Etiqueta del eje-y\n",
+ "plt.title('Gráfico de dispersión de Millas por galón en autopista Vs. Precio', fontsize=13)#Nombre del gráfico\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ahora calculemos el coeficiente de correlación y el p-valor entre las variables 'Caballos de Fuerza' y 'Precio' usando 'stats.pearson()'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 118,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.757916953745141, 1.6076704005409566e-39)"
+ ]
+ },
+ "execution_count": 118,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from scipy import stats\n",
+ "stats.pearsonr(df['horsepower'], df['price'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Existe una fuerte correlación positiva entre las variables ya que el coeficiente de correlación es cercano a 1 y el p-valor es mucho menor que 0.001"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/EVI - 2018/EVI 04/anieves.pdf b/EVI - 2018/EVI 04/anieves.pdf
new file mode 100644
index 0000000..22b26e9
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diff --git a/EVI - 2018/EVI 04/empleados_cumple.txt b/EVI - 2018/EVI 04/empleados_cumple.txt
new file mode 100755
index 0000000..b72ec6b
--- /dev/null
+++ b/EVI - 2018/EVI 04/empleados_cumple.txt
@@ -0,0 +1,5 @@
+Nombre,Departamento,Mes de Cumpleaños
+Adelis Nieves, Matemáticas, Junio
+Miguel Astor, Redes, Septiembre
+Francisco Sans, Computación Gráfica, Diciembre
+Antonio Escalante, Letras, Diciembre
\ No newline at end of file
diff --git a/EVI - 2018/EVI 04/mastor.pdf b/EVI - 2018/EVI 04/mastor.pdf
new file mode 100644
index 0000000..8e5a86c
Binary files /dev/null and b/EVI - 2018/EVI 04/mastor.pdf differ
diff --git a/EVI - 2018/EVI 04/olympics.csv b/EVI - 2018/EVI 04/olympics.csv
new file mode 100755
index 0000000..75bf8bf
--- /dev/null
+++ b/EVI - 2018/EVI 04/olympics.csv
@@ -0,0 +1,149 @@
+0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15
+,№ Summer,01 !,02 !,03 !,Total,№ Winter,01 !,02 !,03 !,Total,№ Games,01 !,02 !,03 !,Combined total
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+Algeria (ALG),12,5,2,8,15,3,0,0,0,0,15,5,2,8,15
+Argentina (ARG),23,18,24,28,70,18,0,0,0,0,41,18,24,28,70
+Armenia (ARM),5,1,2,9,12,6,0,0,0,0,11,1,2,9,12
+Australasia (ANZ) [ANZ],2,3,4,5,12,0,0,0,0,0,2,3,4,5,12
+Australia (AUS) [AUS] [Z],25,139,152,177,468,18,5,3,4,12,43,144,155,181,480
+Austria (AUT),26,18,33,35,86,22,59,78,81,218,48,77,111,116,304
+Azerbaijan (AZE),5,6,5,15,26,5,0,0,0,0,10,6,5,15,26
+Bahamas (BAH),15,5,2,5,12,0,0,0,0,0,15,5,2,5,12
+Bahrain (BRN),8,0,0,1,1,0,0,0,0,0,8,0,0,1,1
+Barbados (BAR) [BAR],11,0,0,1,1,0,0,0,0,0,11,0,0,1,1
+Belarus (BLR),5,12,24,39,75,6,6,4,5,15,11,18,28,44,90
+Belgium (BEL),25,37,52,53,142,20,1,1,3,5,45,38,53,56,147
+Bermuda (BER),17,0,0,1,1,7,0,0,0,0,24,0,0,1,1
+Bohemia (BOH) [BOH] [Z],3,0,1,3,4,0,0,0,0,0,3,0,1,3,4
+Botswana (BOT),9,0,1,0,1,0,0,0,0,0,9,0,1,0,1
+Brazil (BRA),21,23,30,55,108,7,0,0,0,0,28,23,30,55,108
+British West Indies (BWI) [BWI],1,0,0,2,2,0,0,0,0,0,1,0,0,2,2
+Bulgaria (BUL) [H],19,51,85,78,214,19,1,2,3,6,38,52,87,81,220
+Burundi (BDI),5,1,0,0,1,0,0,0,0,0,5,1,0,0,1
+Cameroon (CMR),13,3,1,1,5,1,0,0,0,0,14,3,1,1,5
+Canada (CAN),25,59,99,121,279,22,62,56,52,170,47,121,155,173,449
+Chile (CHI) [I],22,2,7,4,13,16,0,0,0,0,38,2,7,4,13
+China (CHN) [CHN],9,201,146,126,473,10,12,22,19,53,19,213,168,145,526
+Colombia (COL),18,2,6,11,19,1,0,0,0,0,19,2,6,11,19
+Costa Rica (CRC),14,1,1,2,4,6,0,0,0,0,20,1,1,2,4
+Ivory Coast (CIV) [CIV],12,0,1,0,1,0,0,0,0,0,12,0,1,0,1
+Croatia (CRO),6,6,7,10,23,7,4,6,1,11,13,10,13,11,34
+Cuba (CUB) [Z],19,72,67,70,209,0,0,0,0,0,19,72,67,70,209
+Cyprus (CYP),9,0,1,0,1,10,0,0,0,0,19,0,1,0,1
+Czech Republic (CZE) [CZE],5,14,15,15,44,6,7,9,8,24,11,21,24,23,68
+Czechoslovakia (TCH) [TCH],16,49,49,45,143,16,2,8,15,25,32,51,57,60,168
+Denmark (DEN) [Z],26,43,68,68,179,13,0,1,0,1,39,43,69,68,180
+Djibouti (DJI) [B],7,0,0,1,1,0,0,0,0,0,7,0,0,1,1
+Dominican Republic (DOM),13,3,2,1,6,0,0,0,0,0,13,3,2,1,6
+Ecuador (ECU),13,1,1,0,2,0,0,0,0,0,13,1,1,0,2
+Egypt (EGY) [EGY] [Z],21,7,9,10,26,1,0,0,0,0,22,7,9,10,26
+Eritrea (ERI),4,0,0,1,1,0,0,0,0,0,4,0,0,1,1
+Estonia (EST),11,9,9,15,33,9,4,2,1,7,20,13,11,16,40
+Ethiopia (ETH),12,21,7,17,45,2,0,0,0,0,14,21,7,17,45
+Finland (FIN),24,101,84,117,302,22,42,62,57,161,46,143,146,174,463
+France (FRA) [O] [P] [Z],27,202,223,246,671,22,31,31,47,109,49,233,254,293,780
+Gabon (GAB),9,0,1,0,1,0,0,0,0,0,9,0,1,0,1
+Georgia (GEO),5,6,5,14,25,6,0,0,0,0,11,6,5,14,25
+Germany (GER) [GER] [Z],15,174,182,217,573,11,78,78,53,209,26,252,260,270,782
+United Team of Germany (EUA) [EUA],3,28,54,36,118,3,8,6,5,19,6,36,60,41,137
+East Germany (GDR) [GDR],5,153,129,127,409,6,39,36,35,110,11,192,165,162,519
+West Germany (FRG) [FRG],5,56,67,81,204,6,11,15,13,39,11,67,82,94,243
+Ghana (GHA) [GHA],13,0,1,3,4,1,0,0,0,0,14,0,1,3,4
+Great Britain (GBR) [GBR] [Z],27,236,272,272,780,22,10,4,12,26,49,246,276,284,806
+Greece (GRE) [Z],27,30,42,39,111,18,0,0,0,0,45,30,42,39,111
+Grenada (GRN),8,1,0,0,1,0,0,0,0,0,8,1,0,0,1
+Guatemala (GUA),13,0,1,0,1,1,0,0,0,0,14,0,1,0,1
+Guyana (GUY) [GUY],16,0,0,1,1,0,0,0,0,0,16,0,0,1,1
+Haiti (HAI) [J],14,0,1,1,2,0,0,0,0,0,14,0,1,1,2
+Hong Kong (HKG) [HKG],15,1,1,1,3,4,0,0,0,0,19,1,1,1,3
+Hungary (HUN),25,167,144,165,476,22,0,2,4,6,47,167,146,169,482
+Iceland (ISL),19,0,2,2,4,17,0,0,0,0,36,0,2,2,4
+India (IND) [F],23,9,6,11,26,9,0,0,0,0,32,9,6,11,26
+Indonesia (INA),14,6,10,11,27,0,0,0,0,0,14,6,10,11,27
+Iran (IRI) [K],15,15,20,25,60,10,0,0,0,0,25,15,20,25,60
+Iraq (IRQ),13,0,0,1,1,0,0,0,0,0,13,0,0,1,1
+Ireland (IRL),20,9,8,12,29,6,0,0,0,0,26,9,8,12,29
+Israel (ISR),15,1,1,5,7,6,0,0,0,0,21,1,1,5,7
+Italy (ITA) [M] [S],26,198,166,185,549,22,37,34,43,114,48,235,200,228,663
+Jamaica (JAM) [JAM],16,17,30,20,67,7,0,0,0,0,23,17,30,20,67
+Japan (JPN),21,130,126,142,398,20,10,17,18,45,41,140,143,160,443
+Kazakhstan (KAZ),5,16,17,19,52,6,1,3,3,7,11,17,20,22,59
+Kenya (KEN),13,25,32,29,86,3,0,0,0,0,16,25,32,29,86
+North Korea (PRK),9,14,12,21,47,8,0,1,1,2,17,14,13,22,49
+South Korea (KOR),16,81,82,80,243,17,26,17,10,53,33,107,99,90,296
+Kuwait (KUW),12,0,0,2,2,0,0,0,0,0,12,0,0,2,2
+Kyrgyzstan (KGZ),5,0,1,2,3,6,0,0,0,0,11,0,1,2,3
+Latvia (LAT),10,3,11,5,19,10,0,4,3,7,20,3,15,8,26
+Lebanon (LIB),16,0,2,2,4,16,0,0,0,0,32,0,2,2,4
+Liechtenstein (LIE),16,0,0,0,0,18,2,2,5,9,34,2,2,5,9
+Lithuania (LTU),8,6,5,10,21,8,0,0,0,0,16,6,5,10,21
+Luxembourg (LUX) [O],22,1,1,0,2,8,0,2,0,2,30,1,3,0,4
+Macedonia (MKD),5,0,0,1,1,5,0,0,0,0,10,0,0,1,1
+Malaysia (MAS) [MAS],12,0,3,3,6,0,0,0,0,0,12,0,3,3,6
+Mauritius (MRI),8,0,0,1,1,0,0,0,0,0,8,0,0,1,1
+Mexico (MEX),22,13,21,28,62,8,0,0,0,0,30,13,21,28,62
+Moldova (MDA),5,0,2,5,7,6,0,0,0,0,11,0,2,5,7
+Mongolia (MGL),12,2,9,13,24,13,0,0,0,0,25,2,9,13,24
+Montenegro (MNE),2,0,1,0,1,2,0,0,0,0,4,0,1,0,1
+Morocco (MAR),13,6,5,11,22,6,0,0,0,0,19,6,5,11,22
+Mozambique (MOZ),9,1,0,1,2,0,0,0,0,0,9,1,0,1,2
+Namibia (NAM),6,0,4,0,4,0,0,0,0,0,6,0,4,0,4
+Netherlands (NED) [Z],25,77,85,104,266,20,37,38,35,110,45,114,123,139,376
+Netherlands Antilles (AHO) [AHO] [I],13,0,1,0,1,2,0,0,0,0,15,0,1,0,1
+New Zealand (NZL) [NZL],22,42,18,39,99,15,0,1,0,1,37,42,19,39,100
+Niger (NIG),11,0,0,1,1,0,0,0,0,0,11,0,0,1,1
+Nigeria (NGR),15,3,8,12,23,0,0,0,0,0,15,3,8,12,23
+Norway (NOR) [Q],24,56,49,43,148,22,118,111,100,329,46,174,160,143,477
+Pakistan (PAK),16,3,3,4,10,2,0,0,0,0,18,3,3,4,10
+Panama (PAN),16,1,0,2,3,0,0,0,0,0,16,1,0,2,3
+Paraguay (PAR),11,0,1,0,1,1,0,0,0,0,12,0,1,0,1
+Peru (PER) [L],17,1,3,0,4,2,0,0,0,0,19,1,3,0,4
+Philippines (PHI),20,0,2,7,9,4,0,0,0,0,24,0,2,7,9
+Poland (POL),20,64,82,125,271,22,6,7,7,20,42,70,89,132,291
+Portugal (POR),23,4,8,11,23,7,0,0,0,0,30,4,8,11,23
+Puerto Rico (PUR),17,0,2,6,8,6,0,0,0,0,23,0,2,6,8
+Qatar (QAT),8,0,0,4,4,0,0,0,0,0,8,0,0,4,4
+Romania (ROU),20,88,94,119,301,20,0,0,1,1,40,88,94,120,302
+Russia (RUS) [RUS],5,132,121,142,395,6,49,40,35,124,11,181,161,177,519
+Russian Empire (RU1) [RU1],3,1,4,3,8,0,0,0,0,0,3,1,4,3,8
+Soviet Union (URS) [URS],9,395,319,296,1010,9,78,57,59,194,18,473,376,355,1204
+Unified Team (EUN) [EUN],1,45,38,29,112,1,9,6,8,23,2,54,44,37,135
+Saudi Arabia (KSA),10,0,1,2,3,0,0,0,0,0,10,0,1,2,3
+Senegal (SEN),13,0,1,0,1,5,0,0,0,0,18,0,1,0,1
+Serbia (SRB) [SRB],3,1,2,4,7,2,0,0,0,0,5,1,2,4,7
+Serbia and Montenegro (SCG) [SCG],3,2,4,3,9,3,0,0,0,0,6,2,4,3,9
+Singapore (SIN),15,0,2,2,4,0,0,0,0,0,15,0,2,2,4
+Slovakia (SVK) [SVK],5,7,9,8,24,6,2,2,1,5,11,9,11,9,29
+Slovenia (SLO),6,4,6,9,19,7,2,4,9,15,13,6,10,18,34
+South Africa (RSA),18,23,26,27,76,6,0,0,0,0,24,23,26,27,76
+Spain (ESP) [Z],22,37,59,35,131,19,1,0,1,2,41,38,59,36,133
+Sri Lanka (SRI) [SRI],16,0,2,0,2,0,0,0,0,0,16,0,2,0,2
+Sudan (SUD),11,0,1,0,1,0,0,0,0,0,11,0,1,0,1
+Suriname (SUR) [E],11,1,0,1,2,0,0,0,0,0,11,1,0,1,2
+Sweden (SWE) [Z],26,143,164,176,483,22,50,40,54,144,48,193,204,230,627
+Switzerland (SUI),27,47,73,65,185,22,50,40,48,138,49,97,113,113,323
+Syria (SYR),12,1,1,1,3,0,0,0,0,0,12,1,1,1,3
+Chinese Taipei (TPE) [TPE] [TPE2],13,2,7,12,21,11,0,0,0,0,24,2,7,12,21
+Tajikistan (TJK),5,0,1,2,3,4,0,0,0,0,9,0,1,2,3
+Tanzania (TAN) [TAN],12,0,2,0,2,0,0,0,0,0,12,0,2,0,2
+Thailand (THA),15,7,6,11,24,3,0,0,0,0,18,7,6,11,24
+Togo (TOG),9,0,0,1,1,1,0,0,0,0,10,0,0,1,1
+Tonga (TGA),8,0,1,0,1,1,0,0,0,0,9,0,1,0,1
+Trinidad and Tobago (TRI) [TRI],16,2,5,11,18,3,0,0,0,0,19,2,5,11,18
+Tunisia (TUN),13,3,3,4,10,0,0,0,0,0,13,3,3,4,10
+Turkey (TUR),21,39,25,24,88,16,0,0,0,0,37,39,25,24,88
+Uganda (UGA),14,2,3,2,7,0,0,0,0,0,14,2,3,2,7
+Ukraine (UKR),5,33,27,55,115,6,2,1,4,7,11,35,28,59,122
+United Arab Emirates (UAE),8,1,0,0,1,0,0,0,0,0,8,1,0,0,1
+United States (USA) [P] [Q] [R] [Z],26,976,757,666,2399,22,96,102,84,282,48,1072,859,750,2681
+Uruguay (URU),20,2,2,6,10,1,0,0,0,0,21,2,2,6,10
+Uzbekistan (UZB),5,5,5,10,20,6,1,0,0,1,11,6,5,10,21
+Venezuela (VEN),17,2,2,8,12,4,0,0,0,0,21,2,2,8,12
+Vietnam (VIE),14,0,2,0,2,0,0,0,0,0,14,0,2,0,2
+Virgin Islands (ISV),11,0,1,0,1,7,0,0,0,0,18,0,1,0,1
+Yugoslavia (YUG) [YUG],16,26,29,28,83,14,0,3,1,4,30,26,32,29,87
+Independent Olympic Participants (IOP) [IOP],1,0,1,2,3,0,0,0,0,0,1,0,1,2,3
+Zambia (ZAM) [ZAM],12,0,1,1,2,0,0,0,0,0,12,0,1,1,2
+Zimbabwe (ZIM) [ZIM],12,3,4,1,8,1,0,0,0,0,13,3,4,1,8
+Mixed team (ZZX) [ZZX],3,8,5,4,17,0,0,0,0,0,3,8,5,4,17
+Totals,27,4809,4775,5130,14714,22,959,958,948,2865,49,5768,5733,6078,17579