{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quick Intro to using the Stata Kernel\n", "\n", "You use code cells as if you are writing in the command mode / do file editor." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1978 Automobile Data)\n" ] } ], "source": [ "sysuse auto" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " +-------+\n", " | rep78 |\n", " |-------|\n", " 46. | 2 |\n", " 47. | 4 |\n", " 48. | 1 |\n", " 49. | 3 |\n", " 50. | 3 |\n", " |-------|\n", " 51. | . |\n", " 52. | 2 |\n", " 53. | 5 |\n", " 54. | 3 |\n", " 55. | 4 |\n", " |-------|\n", " 56. | 4 |\n", " +-------+\n" ] } ], "source": [ "list rep78 in 46/56" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are some **limitations** with this interface in that you don't have the `data editor` or other `gui` features available to you. \n", "\n", "Some of these limitations are addressed through [magics](https://kylebarron.dev/stata_kernel/using_stata_kernel/magics/)\n", "\n", "For example `%browse` provides `pandas` style view of the data in the `stata dataset`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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makepricempgrep78headroomtrunkweightlengthturndisplacementgear_ratioforeign
1AMC Concord40992232.5112930186401213.5799999Domestic
2AMC Pacer47491733113350173402582.53Domestic
3AMC Spirit379922.3122640168351213.0799999Domestic
4Buick Century48162034.5163250196401962.9300001Domestic
5Buick Electra78271544204080222433502.4100001Domestic
6Buick LeSabre57881834213670218432312.73Domestic
7Buick Opel445326.3102230170343042.8699999Domestic
8Buick Regal51892032163280200421962.9300001Domestic
9Buick Riviera103721633.5173880207432312.9300001Domestic
10Buick Skylark40821933.5133400200422313.0799999Domestic
11Cad. Deville113851434204330221444252.28Domestic
12Cad. Eldorado145001423.5163900204433502.1900001Domestic
13Cad. Seville159062133134290204453502.24Domestic
14Chev. Chevette32992932.592110163342312.9300001Domestic
15Chev. Impala57051644203690212432502.5599999Domestic
16Chev. Malibu45042233.5173180193312002.73Domestic
17Chev. Monte Carlo51042222163220200412002.73Domestic
18Chev. Monza3667242272750179401512.73Domestic
19Chev. Nova39551933.5133430197432502.5599999Domestic
20Dodge Colt398430528212016335983.54Domestic
21Dodge Diplomat40101824173600206463182.47Domestic
22Dodge Magnum58861624173600206463182.47Domestic
23Dodge St. Regis63421724.5213740220462252.9400001Domestic
24Ford Fiesta43892841.59180014733983.1500001Domestic
25Ford Mustang41872132102650179431403.0799999Domestic
26Linc. Continental114971233.5224840233514002.47Domestic
27Linc. Mark V135941232.5184720230484002.47Domestic
28Linc. Versailles134661433.5153830201413022.47Domestic
29Merc. Bobcat3829224392580169391402.73Domestic
30Merc. Cougar53791443.5164060221483022.75Domestic
31Merc. Marquis61651533.5233720212443022.26Domestic
32Merc. Monarch45161833153370198412502.4300001Domestic
33Merc. XR-763031443164130217453022.75Domestic
34Merc. Zephyr32912033.5172830195431403.0799999Domestic
35Olds 9888142144204060220433502.4100001Domestic
36Olds Cutl Supr51721932163310198422312.9300001Domestic
37Olds Cutlass47331934.5163300198422312.9300001Domestic
38Olds Delta 8848901844203690218422312.73Domestic
39Olds Omega41811934.5143370200432313.0799999Domestic
40Olds Starfire41952412102730180401512.73Domestic
41Olds Toronado103711633.5174030206433502.4100001Domestic
42Plym. Arrow46472832113260170371563.05Domestic
43Plym. Champ44253452.511180015737862.97Domestic
44Plym. Horizon44822534172200165361053.3699999Domestic
45Plym. Sapporo648626.1.582520182381193.54Domestic
46Plym. Volare40601825163330201442253.23Domestic
47Pont. Catalina57981844203700214422312.73Domestic
48Pont. Firebird49341811.573470198422313.0799999Domestic
49Pont. Grand Prix52221932163210201452312.9300001Domestic
50Pont. Le Mans47231933.5173200199402312.9300001Domestic
51Pont. Phoenix442419.3.5133420203432313.0799999Domestic
52Pont. Sunbird4172242272690179411512.73Domestic
53Audi 500096901753152830189371313.2Foreign
54Audi Fox62952332.511207017436973.7Foreign
55BMW 320i97352542.5122650177341213.6400001Foreign
56Datsun 20062292341.562370170351193.8900001Foreign
57Datsun 210458935528202016532853.7Foreign
58Datsun 51050792442.582280170341193.54Foreign
59Datsun 81081292142.582750184381463.55Foreign
60Fiat Strada42962132.5162130161361053.3699999Foreign
61Honda Accord57992553102240172361073.05Foreign
62Honda Civic44992842.55176014934913.3Foreign
63Mazda GLC39953043.511198015433863.73Foreign
64Peugeot 6041299014.3.5143420192381633.5799999Foreign
65Renault Le Car3895263310183014234793.72Foreign
66Subaru37983552.511205016436973.8099999Foreign
67Toyota Celica58991852.5142410174361343.0599999Foreign
68Toyota Corolla374831539220016535973.21Foreign
69Toyota Corona57191852112670175361343.05Foreign
70VW Dasher71402342.512216017236973.74Foreign
71VW Diesel5397415315204015535903.78Foreign
72VW Rabbit4697254315193015535893.78Foreign
73VW Scirocco6850254216199015636973.78Foreign
74Volvo 260119951752.5143170193371632.98Foreign
\n", "
" ], "text/plain": [ "\n", " +----------------------------------------------------------------------+\n", " 1. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | AMC Concord | 4,099 | 22 | 3 | 2.5 | 11 | 2,930 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 186 | 40 | 121 | 3.58 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 2. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | AMC Pacer | 4,749 | 17 | 3 | 3.0 | 11 | 3,350 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 173 | 40 | 258 | 2.53 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 3. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | AMC Spirit | 3,799 | 22 | . | 3.0 | 12 | 2,640 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 168 | 35 | 121 | 3.08 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 4. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Century | 4,816 | 20 | 3 | 4.5 | 16 | 3,250 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 196 | 40 | 196 | 2.93 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 5. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Electra | 7,827 | 15 | 4 | 4.0 | 20 | 4,080 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 222 | 43 | 350 | 2.41 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 6. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick LeSabre | 5,788 | 18 | 3 | 4.0 | 21 | 3,670 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 218 | 43 | 231 | 2.73 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 7. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Opel | 4,453 | 26 | . | 3.0 | 10 | 2,230 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 170 | 34 | 304 | 2.87 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 8. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Regal | 5,189 | 20 | 3 | 2.0 | 16 | 3,280 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 200 | 42 | 196 | 2.93 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 9. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Riviera | 10,372 | 16 | 3 | 3.5 | 17 | 3,880 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 207 | 43 | 231 | 2.93 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 10. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Skylark | 4,082 | 19 | 3 | 3.5 | 13 | 3,400 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 200 | 42 | 231 | 3.08 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 11. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Cad. Deville | 11,385 | 14 | 3 | 4.0 | 20 | 4,330 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 221 | 44 | 425 | 2.28 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 12. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Cad. Eldorado | 14,500 | 14 | 2 | 3.5 | 16 | 3,900 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 204 | 43 | 350 | 2.19 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 13. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Cad. Seville | 15,906 | 21 | 3 | 3.0 | 13 | 4,290 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 204 | 45 | 350 | 2.24 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 14. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Chev. Chevette | 3,299 | 29 | 3 | 2.5 | 9 | 2,110 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 163 | 34 | 231 | 2.93 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 15. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Chev. Impala | 5,705 | 16 | 4 | 4.0 | 20 | 3,690 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 212 | 43 | 250 | 2.56 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 16. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Chev. Malibu | 4,504 | 22 | 3 | 3.5 | 17 | 3,180 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 193 | 31 | 200 | 2.73 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 17. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Chev. Monte Carlo | 5,104 | 22 | 2 | 2.0 | 16 | 3,220 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 200 | 41 | 200 | 2.73 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 18. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Chev. Monza | 3,667 | 24 | 2 | 2.0 | 7 | 2,750 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 179 | 40 | 151 | 2.73 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 19. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Chev. Nova | 3,955 | 19 | 3 | 3.5 | 13 | 3,430 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 197 | 43 | 250 | 2.56 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 20. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Dodge Colt | 3,984 | 30 | 5 | 2.0 | 8 | 2,120 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 163 | 35 | 98 | 3.54 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 21. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Dodge Diplomat | 4,010 | 18 | 2 | 4.0 | 17 | 3,600 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 206 | 46 | 318 | 2.47 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 22. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Dodge Magnum | 5,886 | 16 | 2 | 4.0 | 17 | 3,600 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 206 | 46 | 318 | 2.47 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 23. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Dodge St. Regis | 6,342 | 17 | 2 | 4.5 | 21 | 3,740 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 220 | 46 | 225 | 2.94 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 24. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Ford Fiesta | 4,389 | 28 | 4 | 1.5 | 9 | 1,800 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 147 | 33 | 98 | 3.15 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 25. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Ford Mustang | 4,187 | 21 | 3 | 2.0 | 10 | 2,650 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 179 | 43 | 140 | 3.08 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 26. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Linc. Continental | 11,497 | 12 | 3 | 3.5 | 22 | 4,840 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 233 | 51 | 400 | 2.47 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 27. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Linc. Mark V | 13,594 | 12 | 3 | 2.5 | 18 | 4,720 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 230 | 48 | 400 | 2.47 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 28. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Linc. Versailles | 13,466 | 14 | 3 | 3.5 | 15 | 3,830 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 201 | 41 | 302 | 2.47 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 29. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Merc. Bobcat | 3,829 | 22 | 4 | 3.0 | 9 | 2,580 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 169 | 39 | 140 | 2.73 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 30. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Merc. Cougar | 5,379 | 14 | 4 | 3.5 | 16 | 4,060 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 221 | 48 | 302 | 2.75 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 31. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Merc. Marquis | 6,165 | 15 | 3 | 3.5 | 23 | 3,720 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 212 | 44 | 302 | 2.26 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 32. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Merc. Monarch | 4,516 | 18 | 3 | 3.0 | 15 | 3,370 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 198 | 41 | 250 | 2.43 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 33. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Merc. XR-7 | 6,303 | 14 | 4 | 3.0 | 16 | 4,130 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 217 | 45 | 302 | 2.75 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 34. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Merc. Zephyr | 3,291 | 20 | 3 | 3.5 | 17 | 2,830 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 195 | 43 | 140 | 3.08 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 35. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Olds 98 | 8,814 | 21 | 4 | 4.0 | 20 | 4,060 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 220 | 43 | 350 | 2.41 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 36. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Olds Cutl Supr | 5,172 | 19 | 3 | 2.0 | 16 | 3,310 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 198 | 42 | 231 | 2.93 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 37. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Olds Cutlass | 4,733 | 19 | 3 | 4.5 | 16 | 3,300 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 198 | 42 | 231 | 2.93 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 38. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Olds Delta 88 | 4,890 | 18 | 4 | 4.0 | 20 | 3,690 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 218 | 42 | 231 | 2.73 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 39. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Olds Omega | 4,181 | 19 | 3 | 4.5 | 14 | 3,370 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 200 | 43 | 231 | 3.08 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 40. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Olds Starfire | 4,195 | 24 | 1 | 2.0 | 10 | 2,730 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 180 | 40 | 151 | 2.73 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 41. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Olds Toronado | 10,371 | 16 | 3 | 3.5 | 17 | 4,030 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 206 | 43 | 350 | 2.41 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 42. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Plym. Arrow | 4,647 | 28 | 3 | 2.0 | 11 | 3,260 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 170 | 37 | 156 | 3.05 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 43. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Plym. Champ | 4,425 | 34 | 5 | 2.5 | 11 | 1,800 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 157 | 37 | 86 | 2.97 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 44. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Plym. Horizon | 4,482 | 25 | 3 | 4.0 | 17 | 2,200 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 165 | 36 | 105 | 3.37 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 45. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Plym. Sapporo | 6,486 | 26 | . | 1.5 | 8 | 2,520 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 182 | 38 | 119 | 3.54 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 46. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Plym. Volare | 4,060 | 18 | 2 | 5.0 | 16 | 3,330 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 201 | 44 | 225 | 3.23 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 47. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Pont. Catalina | 5,798 | 18 | 4 | 4.0 | 20 | 3,700 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 214 | 42 | 231 | 2.73 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 48. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Pont. Firebird | 4,934 | 18 | 1 | 1.5 | 7 | 3,470 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 198 | 42 | 231 | 3.08 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 49. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Pont. Grand Prix | 5,222 | 19 | 3 | 2.0 | 16 | 3,210 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 201 | 45 | 231 | 2.93 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 50. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Pont. Le Mans | 4,723 | 19 | 3 | 3.5 | 17 | 3,200 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 199 | 40 | 231 | 2.93 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 51. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Pont. Phoenix | 4,424 | 19 | . | 3.5 | 13 | 3,420 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 203 | 43 | 231 | 3.08 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 52. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Pont. Sunbird | 4,172 | 24 | 2 | 2.0 | 7 | 2,690 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 179 | 41 | 151 | 2.73 | Domestic |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 53. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Audi 5000 | 9,690 | 17 | 5 | 3.0 | 15 | 2,830 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 189 | 37 | 131 | 3.20 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 54. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Audi Fox | 6,295 | 23 | 3 | 2.5 | 11 | 2,070 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 174 | 36 | 97 | 3.70 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 55. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | BMW 320i | 9,735 | 25 | 4 | 2.5 | 12 | 2,650 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 177 | 34 | 121 | 3.64 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 56. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Datsun 200 | 6,229 | 23 | 4 | 1.5 | 6 | 2,370 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 170 | 35 | 119 | 3.89 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 57. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Datsun 210 | 4,589 | 35 | 5 | 2.0 | 8 | 2,020 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 165 | 32 | 85 | 3.70 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 58. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Datsun 510 | 5,079 | 24 | 4 | 2.5 | 8 | 2,280 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 170 | 34 | 119 | 3.54 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 59. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Datsun 810 | 8,129 | 21 | 4 | 2.5 | 8 | 2,750 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 184 | 38 | 146 | 3.55 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 60. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Fiat Strada | 4,296 | 21 | 3 | 2.5 | 16 | 2,130 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 161 | 36 | 105 | 3.37 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 61. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Honda Accord | 5,799 | 25 | 5 | 3.0 | 10 | 2,240 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 172 | 36 | 107 | 3.05 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 62. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Honda Civic | 4,499 | 28 | 4 | 2.5 | 5 | 1,760 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 149 | 34 | 91 | 3.30 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 63. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Mazda GLC | 3,995 | 30 | 4 | 3.5 | 11 | 1,980 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 154 | 33 | 86 | 3.73 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 64. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Peugeot 604 | 12,990 | 14 | . | 3.5 | 14 | 3,420 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 192 | 38 | 163 | 3.58 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 65. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Renault Le Car | 3,895 | 26 | 3 | 3.0 | 10 | 1,830 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 142 | 34 | 79 | 3.72 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 66. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Subaru | 3,798 | 35 | 5 | 2.5 | 11 | 2,050 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 164 | 36 | 97 | 3.81 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 67. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Toyota Celica | 5,899 | 18 | 5 | 2.5 | 14 | 2,410 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 174 | 36 | 134 | 3.06 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 68. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Toyota Corolla | 3,748 | 31 | 5 | 3.0 | 9 | 2,200 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 165 | 35 | 97 | 3.21 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 69. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Toyota Corona | 5,719 | 18 | 5 | 2.0 | 11 | 2,670 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 175 | 36 | 134 | 3.05 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 70. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | VW Dasher | 7,140 | 23 | 4 | 2.5 | 12 | 2,160 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 172 | 36 | 97 | 3.74 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 71. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | VW Diesel | 5,397 | 41 | 5 | 3.0 | 15 | 2,040 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 155 | 35 | 90 | 3.78 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 72. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | VW Rabbit | 4,697 | 25 | 4 | 3.0 | 15 | 1,930 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 155 | 35 | 89 | 3.78 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 73. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | VW Scirocco | 6,850 | 25 | 4 | 2.0 | 16 | 1,990 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 156 | 36 | 97 | 3.78 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n", " +----------------------------------------------------------------------+\n", " 74. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Volvo 260 | 11,995 | 17 | 5 | 2.5 | 14 | 3,170 |\n", " |----------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 193 | 37 | 163 | 2.98 | Foreign |\n", " +----------------------------------------------------------------------+\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%browse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can select just the first `10 observations` using `%head`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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makepricempgrep78headroomtrunkweightlengthturndisplacementgear_ratioforeign
1AMC Concord40992232.5112930186401213.5799999Domestic
2AMC Pacer47491733113350173402582.53Domestic
3AMC Spirit379922.3122640168351213.0799999Domestic
4Buick Century48162034.5163250196401962.9300001Domestic
5Buick Electra78271544204080222433502.4100001Domestic
6Buick LeSabre57881834213670218432312.73Domestic
7Buick Opel445326.3102230170343042.8699999Domestic
8Buick Regal51892032163280200421962.9300001Domestic
9Buick Riviera103721633.5173880207432312.9300001Domestic
10Buick Skylark40821933.5133400200422313.0799999Domestic
\n", "
" ], "text/plain": [ "\n", " +------------------------------------------------------------------+\n", " 1. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | AMC Concord | 4,099 | 22 | 3 | 2.5 | 11 | 2,930 |\n", " |------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 186 | 40 | 121 | 3.58 | Domestic |\n", " +------------------------------------------------------------------+\n", "\n", " +------------------------------------------------------------------+\n", " 2. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | AMC Pacer | 4,749 | 17 | 3 | 3.0 | 11 | 3,350 |\n", " |------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 173 | 40 | 258 | 2.53 | Domestic |\n", " +------------------------------------------------------------------+\n", "\n", " +------------------------------------------------------------------+\n", " 3. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | AMC Spirit | 3,799 | 22 | . | 3.0 | 12 | 2,640 |\n", " |------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 168 | 35 | 121 | 3.08 | Domestic |\n", " +------------------------------------------------------------------+\n", "\n", " +------------------------------------------------------------------+\n", " 4. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Century | 4,816 | 20 | 3 | 4.5 | 16 | 3,250 |\n", " |------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 196 | 40 | 196 | 2.93 | Domestic |\n", " +------------------------------------------------------------------+\n", "\n", " +------------------------------------------------------------------+\n", " 5. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Electra | 7,827 | 15 | 4 | 4.0 | 20 | 4,080 |\n", " |------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 222 | 43 | 350 | 2.41 | Domestic |\n", " +------------------------------------------------------------------+\n", "\n", " +------------------------------------------------------------------+\n", " 6. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick LeSabre | 5,788 | 18 | 3 | 4.0 | 21 | 3,670 |\n", " |------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 218 | 43 | 231 | 2.73 | Domestic |\n", " +------------------------------------------------------------------+\n", "\n", " +------------------------------------------------------------------+\n", " 7. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Opel | 4,453 | 26 | . | 3.0 | 10 | 2,230 |\n", " |------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 170 | 34 | 304 | 2.87 | Domestic |\n", " +------------------------------------------------------------------+\n", "\n", " +------------------------------------------------------------------+\n", " 8. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Regal | 5,189 | 20 | 3 | 2.0 | 16 | 3,280 |\n", " |------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 200 | 42 | 196 | 2.93 | Domestic |\n", " +------------------------------------------------------------------+\n", "\n", " +------------------------------------------------------------------+\n", " 9. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Riviera | 10,372 | 16 | 3 | 3.5 | 17 | 3,880 |\n", " |------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 207 | 43 | 231 | 2.93 | Domestic |\n", " +------------------------------------------------------------------+\n", "\n", " +------------------------------------------------------------------+\n", " 10. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Buick Skylark | 4,082 | 19 | 3 | 3.5 | 13 | 3,400 |\n", " |------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 200 | 42 | 231 | 3.08 | Domestic |\n", " +------------------------------------------------------------------+\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%head" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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makepricempgrep78headroomtrunkweightlengthturndisplacementgear_ratioforeign
65Renault Le Car3895263310183014234793.72Foreign
66Subaru37983552.511205016436973.8099999Foreign
67Toyota Celica58991852.5142410174361343.0599999Foreign
68Toyota Corolla374831539220016535973.21Foreign
69Toyota Corona57191852112670175361343.05Foreign
70VW Dasher71402342.512216017236973.74Foreign
71VW Diesel5397415315204015535903.78Foreign
72VW Rabbit4697254315193015535893.78Foreign
73VW Scirocco6850254216199015636973.78Foreign
74Volvo 260119951752.5143170193371632.98Foreign
\n", "
" ], "text/plain": [ "\n", " +-------------------------------------------------------------------+\n", " 65. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Renault Le Car | 3,895 | 26 | 3 | 3.0 | 10 | 1,830 |\n", " |-------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 142 | 34 | 79 | 3.72 | Foreign |\n", " +-------------------------------------------------------------------+\n", "\n", " +-------------------------------------------------------------------+\n", " 66. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Subaru | 3,798 | 35 | 5 | 2.5 | 11 | 2,050 |\n", " |-------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 164 | 36 | 97 | 3.81 | Foreign |\n", " +-------------------------------------------------------------------+\n", "\n", " +-------------------------------------------------------------------+\n", " 67. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Toyota Celica | 5,899 | 18 | 5 | 2.5 | 14 | 2,410 |\n", " |-------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 174 | 36 | 134 | 3.06 | Foreign |\n", " +-------------------------------------------------------------------+\n", "\n", " +-------------------------------------------------------------------+\n", " 68. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Toyota Corolla | 3,748 | 31 | 5 | 3.0 | 9 | 2,200 |\n", " |-------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 165 | 35 | 97 | 3.21 | Foreign |\n", " +-------------------------------------------------------------------+\n", "\n", " +-------------------------------------------------------------------+\n", " 69. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Toyota Corona | 5,719 | 18 | 5 | 2.0 | 11 | 2,670 |\n", " |-------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 175 | 36 | 134 | 3.05 | Foreign |\n", " +-------------------------------------------------------------------+\n", "\n", " +-------------------------------------------------------------------+\n", " 70. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | VW Dasher | 7,140 | 23 | 4 | 2.5 | 12 | 2,160 |\n", " |-------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 172 | 36 | 97 | 3.74 | Foreign |\n", " +-------------------------------------------------------------------+\n", "\n", " +-------------------------------------------------------------------+\n", " 71. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | VW Diesel | 5,397 | 41 | 5 | 3.0 | 15 | 2,040 |\n", " |-------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 155 | 35 | 90 | 3.78 | Foreign |\n", " +-------------------------------------------------------------------+\n", "\n", " +-------------------------------------------------------------------+\n", " 72. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | VW Rabbit | 4,697 | 25 | 4 | 3.0 | 15 | 1,930 |\n", " |-------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 155 | 35 | 89 | 3.78 | Foreign |\n", " +-------------------------------------------------------------------+\n", "\n", " +-------------------------------------------------------------------+\n", " 73. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | VW Scirocco | 6,850 | 25 | 4 | 2.0 | 16 | 1,990 |\n", " |-------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 156 | 36 | 97 | 3.78 | Foreign |\n", " +-------------------------------------------------------------------+\n", "\n", " +-------------------------------------------------------------------+\n", " 74. | make | price | mpg | rep78 | headroom | trunk | weight |\n", " | Volvo 260 | 11,995 | 17 | 5 | 2.5 | 14 | 3,170 |\n", " |-------------------------------------------------------------------|\n", " | length | turn | displa~t | gear_r~o | foreign |\n", " | 193 | 37 | 163 | 2.98 | Foreign |\n", " +-------------------------------------------------------------------+" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%tail" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On **windows** and **macos** (in automation mode) you can also use `browse` to pull up the `data editor`" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "browse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Otherwise the commands you add into a `code-cell` are `stata` commands" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " | Headroom (in.)\n", " Car type | 1.5 2.0 2.5 3.0 3.5 | Total\n", "-----------+-------------------------------------------------------+----------\n", " Domestic | 3 10 4 7 13 | 52 \n", " Foreign | 1 3 10 6 2 | 22 \n", "-----------+-------------------------------------------------------+----------\n", " Total | 4 13 14 13 15 | 74 \n", "\n", "\n", " | Headroom (in.)\n", " Car type | 4.0 4.5 5.0 | Total\n", "-----------+---------------------------------+----------\n", " Domestic | 10 4 1 | 52 \n", " Foreign | 0 0 0 | 22 \n", "-----------+---------------------------------+----------\n", " Total | 10 4 1 | 74 \n" ] } ], "source": [ "tabulate foreign headroom" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/pdf": 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weU3ykEgsQMrOSTUtrFSPRRrZHUtRGy++JAnSqwIDsoFOA/jJk9THPoi/JprkfRWl1OQ78Qnp43c0SavisQYbpO/JSKC4Rp+QH2cGqwSByjPSgbTf6Nc09QhyBXs0C3nAKrd/QaSmE2KKeYOV9O5DbjFhhLDb0dTygmtOU6VNheby4XFEBsTvpW5/xPOvVG4luRvtAejDOwQDbTCZ4TrxJC8cJVPuDFAuE9K5nGgf1qNIdsyfrb4nP0arWRW8iSyzGFfykHT5gtxwGLW28BT58mOMEsZKUVANDndSu+cXGYuq7BCq7klWxAwgC4+PnZgzx7xRlzrokMZBTs7Aa3zmOC7qUZJVC54cDMjis8+vCYPUIP8oa8mhV1yM+F8cqjuHEownn+O9QZxATIlUOl4irZdpmveFErkJZSJFz/utJaU4TPcBSHyTpLyGhh3LEqV5K5vYaU/IGL/jqVc0Lms0OiDEqzmrgqc+/AJ8mxusD+0DJjmtNu4mFzKyTfEbW/9RUt9EpUoRBUlZXegE0vE0OLAPLdv04CWkd0X2hsFiV2SpyGvJ2oDEbywXseFFiiwKtUAJaUwIdTmiCTkNDgbe4DN4gzo3nGZAQDGicdAhKA0+F15UA57lHiv5SDidUHFESnBKDiUMDj7/fEESZN3vhw2Ng6W4j/347HKPtdXJiz+ONGlyjzuE799yjwPtkBBBFSfy2IgfN4jlLwYmWmPSYIN0tl7Zv4U2GZ7EDMj3uX0iDClLaRni38ThcRY4SEm86EdSY1FhSCqQ0pIWmhC1XOLxmHjZwLudTKObAInUsgs5UbJ6MthEImWJCXH2/Tbex8JyqppDMkgPQB9dJDKwBo+JVwZt7hMvLFFNRo/aHP/dZhZIm1mPH9IHVF0mSRSeyCClkHCmdAJjyQJsAJ69RQCYWPQHjqgmlWoJKk6VA38XV8oXXJG8EvUNQlTEH5WTTlgaNBGaKmlJQYirtE/IHH+gdR6D1mlX7py/u8bfII3LEoya26dywN91bNME6hh1OQAzj1h6B6nkYEDs38muRylMvDv70eRAlO7Ax98SdZckcKd4jdEJDXBQokmmKYZ9oQlGhBCBDeITMhmYeJ3NWIhB/Pa0z1EJc/wF+T67biDmrpq8nUgEPWVBSfWt93YDSJA4vwBDFoRBehyrHDFkIR0IaWMZ28WsDkDf0h1p7DuR7aQnZI7e0QaHuyTETX6LgKYkJpnSIYPHjkfwPjQPc0cVTp3f1rbQJIFHkG6FKtxNyMs5iwVYeNLwo29hSIIEmmwlmR+btQBDEpK9j1nKjg5JUOkN8chLl43JwJSEweYmCdaSntklYUGaJNiWPrsuCcRZUozw2VzZyp5w5/CmMc3m3tS9KhXcIay48rr8dqHxXS0Vy5PzHX+TSoXG1+IbCfT5DByJHZIcS/VKLjw+mlJ1iredeAds4ztFHa1zabhFnIvwEshtDDSInPb32X0iDbEPRFnwkaYyDhC/5pETcrraqfj5DCRf1IlYEK4VLnY8DnJaZ3F6uiQ/E1qsPnOnhD58iw7xm5Otq4zmxCNpTEQCfSevqG+QxgFKE6/xWeNN6jzZuK8bBwvybX5NHrJgLCwRSp3nJW3w52PKENKO1FjCfV7DBpDzf1JdG5qklnoA/UQcxLpJmsZax7/Mjqlxx+0YkORTinJDw30CAp38ftNoHxagjU4WOk7nEM4knEBMlW4bfEL6zNiIifdzzRBZ4D0xdLadz8ThzxHRfSgv/477IPqOGR8Qv9Inn91eaA5Da2C5peX1Fc/glhyhmyfpeH2le6kBDjoPRdHOkIaWjsTjitBb/kF5g/TRF1pn8dfDRjp98cRoG31Avs+sCYHIlkWrGjPudKAlfNZGJnTI3nH3s6sLIOaVMHBcJ5oMQHxAqqkSppFhYYdoTFMopcK8mGKH+I24qLrfLOXEiwNQ1yYm9QnpDHQ0G9q4rOEmcakDSZedgQmZbE68nwtCZZgog9psO6H+5bRaFynL5DusxzMMmdeCaOMsF2/hvYVT2gItjDNawsTJ0CgarSIO4gRbqgk5pdu1slcNLzO00q2EIccU6qY/IYODhdf5zHiTurbPIro4aBDx6/f5NXlI77UEAoWew5SeA4UpCyATER1IPcqH7hBzZEsOC48Ep01RqSiiqAs+lWiJR4UpgbneTdp7QXSpJCQQuE4swvd4XKm7Rhpag+zDD7TBZEYbxJMEehl3jt8BG48L7efSUCbC2dWYc3yk0KkbLxZCA6FLPYoP5NmqsAcTckvNVUyhBNnQ+IT0au5GuGNgS5w36RWFdn1MEsXiUlI1IH6T6ycE+rHjqWpHITH7g/iAdAZCqeN1Nmu8QT0Ztrjpi4MF+XF2vQohaNR1krbhtJXmGhVREO/e3RPijtGTipQ7xKF78H8WnoXR7ZI6hC4wa6UAGRcp6YqrWbwo0h2SsltKoh0tIhRnQMIErBE/bJDBgKPR8AabwRvU5dbk/sjeYGAB2uxUNvrsPvYXXmllodkV1yjw1uLzSl/T/UoVJ0RL9a9DqGJLpXkCpOPJ1IX3asDizFQaUasGK5N6v9iOdJ8PO4QE3xiZDS19K2KLjXiHbAxIdkEbXNqFQft41Z0c4w/Sx98g32fXvcckOKruRM0/7XzOgPyElGeCQvuUMHGDMGnJQV0WHrnP1uODaXymN0pkwalIpknN1b8tiN84aZbCbzY8mqSFqoM609ohjQOUJl7js8ab1A0cphYDHbC4HFgfJh7pSULqXN1MLsbRKbHw/AJq7Xbi1b1cL1oQt24jOwstDMvWhc0TVZhcxE3HkO4NvzolyyxVtUGSY6YeldcWXrKM0oa3QVyg3wB9/OjXQltcZrhGnMxa4twSnQwsyG9mN9wGzm00WtI8shIVQYkFXCnOXSq2LX5AByRVwJ1IDUjsVmh0kYodJzpuQ9KGCprCIDYw/igfjMtRTdQFEcno6jnRAzueNdI9XumoTn1AJgMDbXLpLsggHp/keU+iZjCwQb7P7hPdUG6Di2My2Cz8/ZQccq2LTmALEBhnohKPCxJJTBp64ekCJ05xG3Lzh2MOT3o14mQz4iHzOxZEz6tlC6TwsqCsP/FBVMIy1G3IhEwOJl7nM3iTum6syjIsDhok/te3+XXtkBTiKz6cEtpT8TmnQKLxLnUIZqU1FO4QeRSNa66tTDzRi/ayHIOE3y6B8WPkOgRGIDHpcZUXhDaqYliiHnjRbjQoOxweGvUdMjmYeJ3PjDeoOzSu5eiPmhx0iN9MPifeR67DQReLjKOjwOwLhi2LtEACYrA4VpKtG0TTYcrYA0nTp5OQNeE2KSQosSogxbkFsSLqv8gsCIWfor0bvRMvlaiUuBnOTrpBEs734aUJOl5nMuNN6hee2xgemfaf+5wsfWta4OMQwwzF5xSKmmvyBBxosPiCabFaEP6a5mzexcQzn9M9op+AX/uNrXQ2OZzWiOBjM9OdkFSyKgfR8LJMCawl0vyqUWfdJ2RwkNJUw+t8Bm9SZ0+4uDsHDYLS5HPifSQC1GqYjgLgNUXpVIIhYWUlGERK5D2/GhB9LWlv3fAemDZ5nDqIgm3rYNfYFqkULQ+shjXuEGmqyjcFMvHAKIUoZXajqG+QxgFKE6/xabyNuqigGvUWBxPyfX5dIhJBnZMTiiOSUCCmMfYsGVA++C1tJwvC4ddzS9omHpYFz5dIBM/WlS47SzU9rSMIK0PFbZD4RyU1Ha/WS4NX2m5Z5qJecUWDTA4m3uAz4w3qBtZx6sBNDjokPYHf5veJmUg8IUMCN9LsdklyHIm6dRmnQAJ2ZdoTzy7ITe9bjsPC43zrhbFBxIC+DCkXz1ObeCb6S7ayARS/E1qmRVxmoSMpANCRAQ3KCzJG72jyHo3HGmzSTlN77M4afkK+zQ1e6QcjMoTcg/j3uW1PGnIcqnaQuEAuaIfwiwU0IA2vdIhCfp0COfdHctk55yy605OulOrNmYDwl2WgegaaPItCflNRjfiCNAbo/onX2SQMk7h/y3XPPjzaAzB5nEh/PHxyzZp3S/ekeuotEuantCaNzsyXvpA9kxumKAdEWFDJx4V3w3HTDSnml0lQ1Asf8a/5jxS4U1+Q1GEYkjQN0bETL28u5JzqIOnUJ0RmptKfC6/xmTO5qOcNiyQfFwcT8n1+TRo0UvCO4vWlWubU5wQIAjTp8eiiaKqaOgDp57mnIW6iOQ83JiJCaWb8xupv1I5+kybR26P6qYrI/QnEb0Rsuq7S8bjwHDdSlI7HQX1AJgMNLR0SxWUN14kz6OmrGKOP//w2Lxg/txLlOla2Pql4bUuMVrRlq0VUDVkN2Fx2SJR1ZHnhxRCcEm5H8bd+BQtDX3JA1K6dmXTCDEBKz1E37rgHLVOjV5NZAWvEecsTMhmYeJ3N4HXi3l2xONvwE5BKxFVGcMyt64TpVkl636sLenmNuqhUIRM3Dz9S97JuhviRzRvLuxK2ULYqZ3R4jVI0L53TIMOvW5Dh/W143WtEa1CffuTiYOB1NjPcIK43WljMPkwGOmRzbifaz0Wh0k3lM74ri6HI/OfcU24+o2A7Ni4nbUByh8am8tHilFH1UkdVq55Om/cmJJhiwrpX54WKBWm+3w943WeUzxqkB2QOT+jiMx4mk5Zk+IwSWvHROG3daz1ukGJyzKxLAXMtMkx7DolMVzzLwFyLHQisCT85vzvEPxX5juc3BDiv+WCCe3ASNEhw+iNJllyW8cYIDbsgZPGd7OuOZ9lcSASb1BsEpcnBwBt8ZrxBXdBRT00NDpyCCfk+v090QvkJ+piVy9mZujYXjVDdzxUHyKA71+2/Y4lMj6MM0FFyf6nfoaz+EedSLoiZM0kz5SMkVGgQ0p9jXdHwhlf9KjGhk/iEyKrV8BOtcZjHFibtZJLi6lHqfpzxJ2Qw2bGah2DvBKGUHCdRx1y9tVB7HHtlVtyXJCmymgVJZMOFzop3PPKjcFdywCDxn/h9jvWJD5nrtXI0CwIvOxyfmLM38RiDvMIVn7VTn5DGAUoTr/FZ4w3qWp2cwXjvg4MFmXz2+X0uB9UTyl07yb4xWRGEam61WPKCiZYs/IRoAarNGVjxoSq2tjm2OVtxS1o9tR0J5bL5GyR9q9oCOX4Lr7pbicJt0J6ANjxCDW0xyTB04vw0aTuRFchgYEEGl31uXSlwi1o3O7l2lSOZlN4D70SmDY1kzaZ4AiN1zFYsLCacycrWiJ14bDnaqUslxxrjWD3wHSK4Y39SuydlE42Cq473RXxC5vgDrfP4K0qTduL0BM9r/An5PrdPBKGchNEDX1ndJgiz5Z1rWkHCAOT+SPWoDCT9OxSzqCGhnj8TUVSaudqzRw/8BkGwCpYDTzg4Ot47cZQGRMzYxx898INJgrA67JN6jr87OajU8zuBrA6I9PcvtIobbEmLIivya3HDjCIdMm5IorMRV1LI0oHqRwtPT6XkQ0RhxnDSlm+NSVucNyEzGnTMgxdP1wnrUWSjvkMGBwsvESo+M96IIiktycYzrmYc2yB+831+nwgD6yDSm5FW7HmrUa6YUdqk0gILEl3f4kpjVvT5dr+j1ShXHEd8KjGx4sgBWYFkwzNDHhgdUZFko04NTcjgYOF1PuEt6vFYUvhcHDTIPzG/Hjsk38Gtr8jftfvq/k/GqIrAJOL1zj8uiBCI9qfPJl6mz/fFB4XBl0NK3KiyBcD3rhL3gqTmVenXgRbVyBolsUfcQhzehEwGJp4IL2xmuEndKdA6jNJgoAH8ZHI50T5yG6nFF9c12WTXV8VErWadCzG1P2kerKzqhMiVlDwsPOI+ata6L9MrqUItcVg1a9tYPdAbBMm0K0vuBC8T1A8tBo1ZatSjrAdkcsCxa3idz+BN6i67tZr15KBB/on5jSiCha5Glkiu9EVMhVwKU2U/BZMef4t/PiHMgDwm/3ziEWFtjviP65pFypWWuAYcR/aWuloAOvGelGbkviMl1S8GosIaaWgNsg3Ppna8zqTBBvHkhvltc/T+3zuHE+kjWYgzTiNyl3Tz6yvQ5GtNjGcxPHUQi/uQmV+QZ8xWDOjE874Mr6KKlNVZVk8tsI56cvVEKeRoL/YaQ0GgaVhhQfP6wkKLs8ioen1hEB+QzgBKA6+zmS2Y1G/wpV1B0uyGgQX4NrtxcS5K/JXW7nARg55IAosOFRh96L7tDrEM9DrIwIvQa48snZ91q15qyp/Cs5dKtOksiMfTIZoa5VFAGl5OgBqVVFh4aNRJxYR0Dpi6jjf4DN6gHlMWr3aMP/678egY9Ll9aCfo6DS41ZlM7q3dltLPkTMYTcauJqHcIU6uF3jLfZxo1/QqZG4P8pH7MO43pOoOEF+9/MkGof4dNhFbyAwskX184UV5AebgE6uzmEBi0vbOV3Mex/AL8G1mTSOsHjV7QADS9Lp1splMcvwbhAec7OHCsybastL2uvW2yZuLFw4My+hta5Ajld371CYatS7qiax02gvQh+9I9rPxaKxFWfQhmXbbRp8Qrn1r21t4P9cJcR5Xl1qOIRW2mtuyow4wG7U1t93Ttf5Dc5udLX9oay9LIPdjc1sDJIHUmtsKyeT2VrZGeUAIYR99Nbd1FqGtvjnxgCOphq2s2VrrBmQ2tzU0xzeuI20sGKXwEsy58xuDyBy4oZtLmI6sWzaVbRwQPkRewJpo9s5l/FSksmCUFSzm+iYaiT1QgMwGD4gAh5ngU3c0c+brauJNDTcx2ul22AB9+OSWG9pgEtokngDlyv7M8SegM0l8+tw+VQdu+LCEqQnqKiClkYN6d4X1shN56YIcDIhfuX8dhbTh3fkZZS7daJHBj3fAGaIRjq6xy5aoe3fAgY+avtSMtrDSUFh+yiA9AWP0hdV5/BWlSdqjRyk+bKMPyG/m1l3G3GP2MJvgQv7MPfekmzP53GdJ46KeTJu7IOI5PgLIxEvIVz2+PM9Ye1qwmphzsUSBO81fE1K5n3ddxFHYmHgC7ncSECmANOoT0jhAiYQ3vMYnGVrUrdr9GoYnBwsy+ezz+1QatEao6GfupyrdEwYgtii5bQ0h0hrpO5yQKOzo+4kWV6etS7p7etlCyOt6YE5unApZ5Aaop1oV2E1pYFVdsp6340YX6Q3Shnf9zK8V5heTDmQnTv49LZUGHF0fffwF+Ta37i+SZzY5h5JZimzyEqJMpTvSK2GoVKI6xK9Y7VMqUQuPdnYXyua0PGXcQeZasjz5xszCy10bhJl4lKrY8JhNYe1OfUIGB9gLHkqdz4zXqMOrFqidgQH4PrtPRKGSC06DADyHOW9ncP6TZkq0UKB4y4R+AhIVUhTivY7lXNmI9oJ60gspSrpKwOX0h+QUQ+jnG4QvkYir40XUeWNataIsGnVOwITk5a9iIOmFitQ6l8Eb1F2scoKIs+pg46BDEpd+m12XhsRbrrMSLporWSHSkNixXD4dRLqzk2maELlmCyTRM/EIe5xOu0PgFcpMN3WQOI/Vf1BdRgvCAnFo/abhxddM8JhEpJmF+AaY40+szmWwKKLRwcQ31kY3x+fgB+I3k8uJ93NvobILHCJZNTxpYBNWZVlcJYu1B0oFJjn5AdCmkyYGaqIhKeqW/bc377Su5EFlKX6Kz+Y6e+Vr7JAIXNTzhpeiQ0q6HlfoxBuE+ujjo9TxBpMZb1B3A825sbGdgyzvgPw4t+4vSBspShL2VEFrwlG0okcQG0sNJdAZEIsSvbWQINvzbCd78Kg7gKy1XuaEVZz5a92mHxA+9vuUhD1/KXgRH7UHDzGGgUZcumhCBgMLr5jMaJM2K+LS4D7+hNTMKMuO9JEMiHrrUfiYurSPOAc0glOXbbSfVSTEbYf4lXxZZRwnnluIVEomp8GVSxgZKT80KUeVhug8TkfOXkHSphhFv+PVI/Bgk7oWzraxkwMuSPDCU+Mz4zXq8LzWWinHycGETD4XXhxH6UrstAvjuj9aLZYQvlJQSpWKgiesE5JdasmGhlcS5D3FmneCedcMrb28oAdj4TlXhLKkqkPEqJ6a+hGP61c3aBb1BiH/k4OBx28oPiP/gzoplHohvJ0DnE/I9/l9YiOqIuWprVhKq5pPPlTqTQGZLNvG6klI/aZD/EgrYFWlFhr57mWpe1R0Gni8S2ABMao6XOFlB6RcHF1vyXjxE60sQmCT+ISk2aAYmHiNS9HkpE20qiq1hl+QH+fWfcdaPjJQ4ahKXHzHEpD01Di9acLeISrDZdkXHmXMz8ghiJjylJlvrtIj/kaaHdJUMiB+Y5sfyrUJDCceJz8VjEm7//ccveEQspwodcyMNSgrl2jCi+Idoy9IhHObG7yfq4ZyFnjzoqM6qra/lSIk6qxtnP8UTZNUWJDcAM4WTzQ+lWNAXYLZ5QhCPZ0SB9cDSUpkOyAXpNHz3kuwMr07nz1RiAxGJ90AUSdj9IHlelFYDFan7M01Qht/ZAy+QX6c2bAOzqEmyhxbKi2XvuM2crf5wrV7STJtAMqu7k9OtLhu/b3CqGH/Cs3WOJbwEiek/rwgNouNjaNXr4fGPqRxtIqajXgVOTrE8ajxO9bgMViDNOlJizfIGH5B2tzIT5/bJ3JQCqGSiWnNETkyMnGmPVgggHilgpiCb5zEDvErSfaSjIWn9NyiSU8fpDoZ988t0Wy3BQirA6AkUEXGADasKkVG6ibtCZnjT7zGZcRuEU8jY9yvNf6EJFO5z67rBBlNCWCc2h3liqgEAsoXqA28PyvbNQDVRFTSPJDSZ8TMYYLnJ2UWNLlSKUtrZgnyqOOAUHR0qKx2ApGBlpA1T/8mXNHYhPiCaJep8Rua6KFYrMEmacbcy9L78BPy48ygfagQHEg7m82TOOOYRhDk8OxtbahsW1UfJsQTdiUaCy/3EPqNuahtJMQGCiyJH2j7uDA7JN8FKmXT8DJBRymLjodGnTxPiEe+GwcTr/MZvEGd/9SqUUSwcdAhKH2fXxeF0ZKm05XB5t+a9+hkq9dApJB2CC6k4+md1gBX3zGRcg77o5eM3yW9nnTT6GTbILXhNE586Jaq1gVLMJJlsvDVGrEAIrI2/kTrXGa43snm/RnrnqaZxUCD+M1kc+L9XB4qfhh9aUIjS2pTrEtVIEi7q19EJJ7uhGjSFXLa8omXq4rubeWojn42syw2QFrL2QbofWn6gAot6zm61zTmFHGeUxKEQtyNgYE32AxeJ57XOb09uo2/IEl86mibs2vSwKh4yzW+CamtGCJb6XJ1YPSwNwHzZOKA2B3fssgbKbA4UCw21xLrKkPJh8TLYdkTlRJHbf0x1ROSvrgKFzY85t8KUCOSU412h6zxmc2B17nMe/uTOjeXHEwOUBqQb3P7RDPERGgMiZUkXs4iCxbNUNcYy2XOZ0Gi8zeIrokkDwae5fdOSutxpY5ZluoV0xxI9R09NmMWFSdOSMLoyNZC40mzOg6UULsRX5DGwMHJ72idS0+wLNqsRcqoY3yEJqTNDmTg1SOO0drtZjW98Ez+KDaCMamb1eJngWWs5oQ4JO+6qTHxEv4Kaggk5c7JdDjITjogI1/tZvUEEPq6WE0tBCviTwTaxWpqIbRLKDpkjE8oO17nMniTuJUtM7HGbxCUJpcT7yO9EB2dk5N3QgWL6uKxnZyaRDJeHeIAlF6YEI3A9IIpTzyfjKIXaobc3fI5nAv4+QBd7jrFcgwIC5n2d4dK8Dbxxj3qfFylUZ+QzgGzOfA6n1nRST1dUCQAZHCwIN/n1+1EPKuK8Sw51VBNXPG/KhK0Vbl9H3oDwg5rssmYA4+r0J4sTCKyQkNlWHkGiyPqde6jHCaEmMsWgxRauI9rqboK1GhXYrFDxvjxNhta5zJ4k7iwMY9SJ1bpDDRI8xt/mN2n2kF9hHuTk5myY12OkTBq3iyYUojN3yBqN8kjLjQL0XSDyBSBeDXMBW/qfZC5I6/Zpg4hFjnkldteeGlQ8LCxXw3iC9IYQGniNTat50Y915ryatXiYEJ+nF0vVEeZeCeDjmIH2YRWg5EoSeNyZMC7APkkwYBE6SUNsfDUk+Q8kmwlwZIsFVMmL2r+eX9Sfcze27kGSb2l9E3Hw75+Zy8FJobt1A8LMjmYeINP403qzhyHXWVoctAhPrLyfX6fioOmLCsSc6kw7WymUlOvN8ZFAjOWPzaIhxhTndrwbryYRIhu2POL0gPihQLSfD5IllrIihkb5FgPMzrVfjPRHnSyOhTQoL0gffyBxltvXGa4RTwHDIGNgQH5Pjt4lXBKsV/joXNE3vPGP6WYpgTiRx6SxYtXsSAxeVVRaHh+Qw3njf9URhxSGWa0uM5V0ORM6Ku1fhMiOasvLFp14Vk1F/jIQxJVoR6jGwhKnQOB8cDrfBpvUpeW8uHDFH4aBw5Uh6QW8uP8PpUHWjEaIEoru1ApSI8Y0Q/5OJDGZCGN9+MawI+k7ZJw2tDyqGdl4vLwAAUTlZhtEMA7QaaaJGyHePRaktJDTqE08LwmQT/EWR3UN0hjYEPrXKpXLuKOXlJOGwMD8pvZdXmgAPNap4YLScd6tEsoJFWQdLZCPamm1BaEiRCdkNiOZqJ0SQswFS7cC/JrPkRVeMU1eeOzA3I6mMAswo7FP8CbwSbpCRnDdzyVlcYk2RP7deJ5IsEtL5Ax/oL8ODdoP/ccKuU038B0oZLdqfrUfNHzlNJYpHdBoqkTNy+8aHPfY4wlHy9s6ghWgc/ZGG98Lsh4q1MJPniZIPEXYcYENurOxoQMDuYbn4PP4E3qVeGIXZ8cNEjSuomg/dPCi3LgPya3kjGtpcYjyoHSc3Mvl6tLNonKBkktWCZhw7N3qkX4T93GWwA2lnKtD37wDfXM5SXTCZFkxAnAQDvwMn1boDJPjXi9+94hORxhYKJ1Lo22aNvzPFI+xo971SG/md0n8lBxRTwHX/ikhHOxsTIOVZ3M9gjbaXFhRXyJArg1WqajY/lJtrequO4D0wzvJKwYen95fDGeQ1KRCxIPIDm7Da/8hATePq/QqHcISoOB8hwKr3OZpx0n9dy/dEwGByhNyPfZNdVQmJ7rItbxdqypppZEaKI2MPXCNwnZILhOBmDgETN1x3d9JCEZaj3upIhQK4nVFwhOQtr4UAMSx1rbMsjCE5OqfFPAWZOi3iEbBwuv88lUJCYt6qlP5pnQwQFVPiBZ9x/m96k8sL85FcyVaqA4MwlapBI5hvv0KprYBlGsTEFq4NHv6SsT73Ny0qsqaYmvxO3pYqznOOt++4DUo505KBuerG4dYUXLRnwC2vgHjSZWIViDy0Tf4Te0VSj9K9evj79D2uwmHi0W7SBBqpmCOhE43HVmaOpy/nwtk+w6AlKS3iaZEBqkvmY6sGgPrwYnx0L1UBGR5YQAL3lunPCRowcGgMpIqQ6koVmoVK5duKJlO2kvkwzIHH7iDSbhDeIcS47IdRt/QmpmFGyf2adywHBSUk6SbaG2rAg/TphbT8Dm3QvB54DkvNHtHMiJlVc3uIg6z1JmzPPJzrJzpcstyUEOwgAcFLz4FnnLIe9Zd6yElNUaPikvyBh94TUerf9GPC8gcR+38Sfkx5k1CeC2q4MnfsndB1UELjMn3XdE4sfQ6nmMbYek+O4bSwuNpuRZm3Iu6aiD1OuuXtXwdogyPl9DuwIPqyCUVx5uyM1TTt3Ac8Xm5POkYJ34AvTxBxrN37jMcIN4QlMCRO8OBhZksTnxPvMXFA/ZB0k4Opx1rH4WVUDmoBpWfZSTNT0uiD4Uc+S2Tzy9Kklz2B1aklObzIvjkpcEjo5xiqgbICUGHjZIQzNBeUSxc/I8nfhhQbRnNAYm3mAT3iCuE6cqamv8Celcxma12TVpaK94O771PYFykmZRSniZtpStTKXnKT3N2hh6mUpLdnqTmZBUjlJ8ISppu/Uf2mBGLWmDtILTD2itTJW3TwbxBsm11cFAR5OveGNyK1PlUSjXuXOnZwzfIdUOUSW4MbdPVYL6k5ElAvK5GzaonjkcNSmng+2JBzQhsl7iRNs28TTsyWFWXXVUiTR+Sw9L245CUgfA6tWm247Va1KUwaA9IGv8hncYXFqWRdzGO7f08xx/Qb7NDl5Mg/y+hEY+yCpnogCclJMrQDoVEwI7qT7socV4QJwMz/yxGwMvoR2/kyqKJDOtFOGBZ+ETRRSKheRI0qULEhOeZdvweF8+SiXLMKkPCK4GBwNv8mm8QT0KzIdLcNU5KJXWIL+Z36fyoNBMNdTpdYejqQZf+Iy4Wi1vN0WzsVENkhPNeYghXXhqBKlssWSVkk4eRdoi3bDpoaYako1cECeoHseRdxh4yUA75xv1BRkcxMGCF54an8br1Km1d44T0zk5WJDv82sSEQ2VJpZae+c+EpFSg1vDsfPchTSNTYh9lNUQRw687DV04VKclgSdUvEmm5etcj+D4yCIia/ZIfbMaoJsePJ3OhHqYxWdeofganAw8CIjxafxBvVIiftfsmGdA5wPyG/m96lE+Ep9MnHZxeTkzEbeKXeGqBqrX5U1NrND/CpvJTEfG17eUyqJ0GLAT0i+hfWh57zk6I1hCoWuGBD5qrwJVFIz8OSLyF0yPoP6BukcDLzw1PhkRjt1eDrK5NQVLiYHA/Kb+TWJSEpIF31qAwpjeYgu6fhMjd2MzlTVWAC2wX4yeh0r7qndZG0S4tKG7tlyv9k1KSgQB9zJ8asJkRBKP+WOJ/Zi3vx8Uu+QCgo6AwNvcJnxBvVkuCv53TnA+IR8n92n8uDpGTJYrDsFFUt4xYC3l2dYOdwWglR3SFinnZ3lhfamwSqU8M4d65dV1Lui4Zt6TI0i/cMDcvAGeBw2JohYdTzpE7IfCRnEN0hnYMPL5+fqrc1F3ZdCuC+cucnBhHyfXROG7eaD9c5bftuFCQqcWyJ1LmpuFyYc6peeeBNKvT8XLaL3dFPFeRg3FhwT0fVFOoJ5qRsTAyJd3q8+bGjzgsQkviCdgcPEi74Im4ab1Muq+CjkZGABBpcL7cNHHtfVBw/a6SSqyDKvY+jVymHlJWoWXFcm7nkDlXLY8JRZmnJYlxZEa4ycSuu82NAh26UJnzEfeCkVxqGfxCegjY+OgKuuWgwu90sTidKiGrbxB2Remph4LSmtiCLoj1DWfbfcoopeUR3OuaDR8xrTBrHTXFGQiZdnhZwdhxXzuvn9XJ7k5V1CZ9sRqhJahzgNMkl1UV/uYeClFulljyQfGvEJGON3rET9uZVXvWGLtt2qMGiNPyGTy4n3WVxB0bGuiQWoOxmUSAOFyFeIx+jSXDJaG4SbF7UJMvC8fZrnTcQVYJRdevP4Do6TPyQGK1jZIfRn4tCOV8o2vkLajTp1YfKALA6id4M3+Cyl2alTXXo8osoHBx2C0uBz4VUe0jpJg5uIfMKrfVbEsbv7rmJC4pSa2K4Ngn6+/WH2Ay8Gsh1Xu+clotAS1fG1JCsVhGMFBoDhVASjNtOzXVj5kKTHh1tEXrTzmZoBGeMvvM4l5bCIJ4o7P3MPa4zfIFFGg8uJ93N5SKeLBrwUQ7LBUmHyBAm/HTY3arK6TgxNsACce44GyMRKLSqPGB1cK+QuV8lHC222TvpSHoqIsckdUhWKSAzF0/CSPXR0AurEOyBbWcN7l1mirpAai5yGRbmuyCrjrdEHZPFYaAQ2glB+l2Y1gmXzDJEZx/NK4Yq9FtHFO1sQgQtds+Px8yTi0KsaZ2uxt2LSqKkmyufmDof0fYPwD6UcQNjP4DkAqUJqHohwN+q2YUIGBwuv8wlvUrd4Lp3mphstVxx0yOG38/vUa0jNJDWwHGd0lah4kbX1SetrpUgFfoek+EBZbHg+ga3qZoaPbEcWUhomxvB2IAHsa3oCOkSFMfua6gWL0/F8PT56NHid+gZpHBw2vMan8TbqChn78PWf32cGgyz48ilP95RIikvoAhBRSF8OzZ4NFOpYiB1ik0xMt89EY9X0upF+BSKp2N5eQInJBjE2ogebTEk2SNobXAxGe+DhNBtpDTiEjXgDRPr7+AurMWl1J2nOLj+cbz+Hn5DFZKER1480gpvFCSZSjEqxpTVH2x/BRCo2Ps/LfLwOGyTfdBOJL7R8Lq5q2OXItwvWQqGEweXsR/B9qaMg6sXx07nwvgjR0OQ0VQszVqO8/ruNfRw4i0MCsCh710BO8SCIo89q9AbZWewz6ypBICpIL67kXJ1eKiE1gXcMX5p0XJnaALI+bj5QORPNXwmGo5ikIaoAwK3yrIPTIULV6xC5b5CWlaTawv7ES15S8Q+sU6+UY4MUAy3f2NCUtsIlqZvEHU5pVM0Fk4EB2dgceB9IQmqWGgVIQo4CqU/aMKAIAsUDFldB78KC6C9nLRaW4q/onODFyjJdoZR3dhwYdRv6oXqQBiStjE6B3zS8nOp8qziv8apThrYE5ASM4RdaZxLaIu4p5lTJNgYaRDfFt7k1Ucj9Bd8MdvdN/u6V4hD3SMyQ12rAmDMv7+yQ6nIRVS08p41ux30e2fAGoGRmjnKaH+Pna4LeAIII32UFGFg+pZQFCqiRhjUAY3RL0bE6j8ZapDkAaeQdo7drWA3yfW6fqgRtFKmwXA/yzRQO42lV2negHfw8rlh9CBMSxV69Ch0v6lmozWPCqasPMizXg3f/GNnUH2xJ8pN04YSkzdm27WjphaZBFu0O6MPLKXSkwWPG6pRlxWUFUvzpoydPPiBtbiB9bu07dCYsfqEG/+/xVP/7+z8cP3kUtdzc9tZbVLhvtyvhxGUOTJkz5ZXX8U/li/xV9HjWyTnWF574L7l6YZMbrNNrjP7l1zB6ZEfEK+K69/wTNNlo2PKDhEECzIfy/nz8t78oib9qln/T/vibNIRzYC/HX/58/NtffrFLx1/+ePzd6ffHX/7n8d/9wpH6GfMhPCcuYFIwaC+rD947aC7FZPOf4bhqL1qBOsfml6/N/8JD/oz3+7+B8C/k3y1BxomMujYzFn/CPp8BI8KB/xfP4Hz6V0yBExrXKlp8TmHCPp5C2oR18vB+S2z+6k04/3YX/pl9T+6ABDqzdIfl962uv0JS/9Pxd//+H//H749/w+k+/u7Xf/j98b8cf/m7LrmHcTaayI//itxZG/nATXIVmOsYWzYlRCGb3OKE/am6pX92EkLP+w3x+TbcH2DeZhHrH+Y29LP7G/7EzS4O+nxLjrHH7kLa6f1nt8G7H3Ssfpofj/Hlr5OjjMJZSe3Te85rQQbMgrQJ/FyvpSernkQdM/iZII0ZfFNE179uBhlFN0FuA6zd20Gf8u9zIfw6zte/kv/bX8+/arswMd7C2oEN9ukMogbqosa/cga/Pcj/nNDmIpErFEmERWpPf/VJ/o+/Vzh0X/d3jnE70PNk/7ffHxrkv49/+j+/dxyc+WP////8uz+Nf/qv44+/fI1//P2mGA7/4f8B0cDFtgplbmRzdHJlYW0KZW5kb2JqCjEgMCBvYmoKPDwgL1R5cGUgL1BhZ2UgL1BhcmVudCAyIDAgUiAvUmVzb3VyY2VzIDQgMCBSIC9Db250ZW50cyAzIDAgUiAvTWVkaWFCb3ggWzAgMCAzOTYgMjg4XQo+PgplbmRvYmoKNCAwIG9iago8PCAvUHJvY1NldCBbIC9QREYgL1RleHQgXSAvQ29sb3JTcGFjZSA8PCAvQ3MxIDUgMCBSID4+IC9Gb250IDw8IC9UVDEgNiAwIFIKPj4gPj4KZW5kb2JqCjcgMCBvYmoKPDwgL04gMyAvQWx0ZXJuYXRlIC9EZXZpY2VSR0IgL0xlbmd0aCAyNjEyIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4AZ2Wd1RT2RaHz703vdASIiAl9Bp6CSDSO0gVBFGJSYBQAoaEJnZEBUYUESlWZFTAAUeHImNFFAuDgmLXCfIQUMbBUURF5d2MawnvrTXz3pr9x1nf2ee319ln733XugBQ/IIEwnRYAYA0oVgU7uvBXBITy8T3AhgQAQ5YAcDhZmYER/hEAtT8vT2ZmahIxrP27i6AZLvbLL9QJnPW/3+RIjdDJAYACkXVNjx+JhflApRTs8UZMv8EyvSVKTKGMTIWoQmirCLjxK9s9qfmK7vJmJcm5KEaWc4ZvDSejLtQ3pol4aOMBKFcmCXgZ6N8B2W9VEmaAOX3KNPT+JxMADAUmV/M5yahbIkyRRQZ7onyAgAIlMQ5vHIOi/k5aJ4AeKZn5IoEiUliphHXmGnl6Mhm+vGzU/liMSuUw03hiHhMz/S0DI4wF4Cvb5ZFASVZbZloke2tHO3tWdbmaPm/2d8eflP9Pch6+1XxJuzPnkGMnlnfbOysL70WAPYkWpsds76VVQC0bQZA5eGsT+8gAPIFALTenPMehmxeksTiDCcLi+zsbHMBn2suK+g3+5+Cb8q/hjn3mcvu+1Y7phc/gSNJFTNlReWmp6ZLRMzMDA6Xz2T99xD/48A5ac3Jwyycn8AX8YXoVVHolAmEiWi7hTyBWJAuZAqEf9Xhfxg2JwcZfp1rFGh1XwB9hTlQuEkHyG89AEMjAyRuP3oCfetbEDEKyL68aK2Rr3OPMnr+5/ofC1yKbuFMQSJT5vYMj2RyJaIsGaPfhGzBAhKQB3SgCjSBLjACLGANHIAzcAPeIACEgEgQA5YDLkgCaUAEskE+2AAKQTHYAXaDanAA1IF60AROgjZwBlwEV8ANcAsMgEdACobBSzAB3oFpCILwEBWiQaqQFqQPmULWEBtaCHlDQVA4FAPFQ4mQEJJA+dAmqBgqg6qhQ1A99CN0GroIXYP6oAfQIDQG/QF9hBGYAtNhDdgAtoDZsDscCEfCy+BEeBWcBxfA2+FKuBY+DrfCF+Eb8AAshV/CkwhAyAgD0UZYCBvxREKQWCQBESFrkSKkAqlFmpAOpBu5jUiRceQDBoehYZgYFsYZ44dZjOFiVmHWYkow1ZhjmFZMF+Y2ZhAzgfmCpWLVsaZYJ6w/dgk2EZuNLcRWYI9gW7CXsQPYYew7HA7HwBniHHB+uBhcMm41rgS3D9eMu4Drww3hJvF4vCreFO+CD8Fz8GJ8Ib4Kfxx/Ht+PH8a/J5AJWgRrgg8hliAkbCRUEBoI5wj9hBHCNFGBqE90IoYQecRcYimxjthBvEkcJk6TFEmGJBdSJCmZtIFUSWoiXSY9Jr0hk8k6ZEdyGFlAXk+uJJ8gXyUPkj9QlCgmFE9KHEVC2U45SrlAeUB5Q6VSDahu1FiqmLqdWk+9RH1KfS9HkzOX85fjya2Tq5FrleuXeyVPlNeXd5dfLp8nXyF/Sv6m/LgCUcFAwVOBo7BWoUbhtMI9hUlFmqKVYohimmKJYoPiNcVRJbySgZK3Ek+pQOmw0iWlIRpC06V50ri0TbQ62mXaMB1HN6T705PpxfQf6L30CWUlZVvlKOUc5Rrls8pSBsIwYPgzUhmljJOMu4yP8zTmuc/jz9s2r2le/7wplfkqbip8lSKVZpUBlY+qTFVv1RTVnaptqk/UMGomamFq2Wr71S6rjc+nz3eez51fNP/k/IfqsLqJerj6avXD6j3qkxqaGr4aGRpVGpc0xjUZmm6ayZrlmuc0x7RoWgu1BFrlWue1XjCVme7MVGYls4s5oa2u7act0T6k3as9rWOos1hno06zzhNdki5bN0G3XLdTd0JPSy9YL1+vUe+hPlGfrZ+kv0e/W3/KwNAg2mCLQZvBqKGKob9hnmGj4WMjqpGr0SqjWqM7xjhjtnGK8T7jWyawiZ1JkkmNyU1T2NTeVGC6z7TPDGvmaCY0qzW7x6Kw3FlZrEbWoDnDPMh8o3mb+SsLPYtYi50W3RZfLO0sUy3rLB9ZKVkFWG206rD6w9rEmmtdY33HhmrjY7POpt3mta2pLd92v+19O5pdsN0Wu067z/YO9iL7JvsxBz2HeIe9DvfYdHYou4R91RHr6OG4zvGM4wcneyex00mn351ZzinODc6jCwwX8BfULRhy0XHhuBxykS5kLoxfeHCh1FXbleNa6/rMTdeN53bEbcTd2D3Z/bj7Kw9LD5FHi8eUp5PnGs8LXoiXr1eRV6+3kvdi72rvpz46Pok+jT4Tvna+q30v+GH9Av12+t3z1/Dn+tf7TwQ4BKwJ6AqkBEYEVgc+CzIJEgV1BMPBAcG7gh8v0l8kXNQWAkL8Q3aFPAk1DF0V+nMYLiw0rCbsebhVeH54dwQtYkVEQ8S7SI/I0shHi40WSxZ3RslHxUXVR01Fe0WXRUuXWCxZs+RGjFqMIKY9Fh8bFXskdnKp99LdS4fj7OIK4+4uM1yWs+zacrXlqcvPrpBfwVlxKh4bHx3fEP+JE8Kp5Uyu9F+5d+UE15O7h/uS58Yr543xXfhl/JEEl4SyhNFEl8RdiWNJrkkVSeMCT0G14HWyX/KB5KmUkJSjKTOp0anNaYS0+LTTQiVhirArXTM9J70vwzSjMEO6ymnV7lUTokDRkUwoc1lmu5iO/kz1SIwkmyWDWQuzarLeZ0dln8pRzBHm9OSa5G7LHcnzyft+NWY1d3Vnvnb+hvzBNe5rDq2F1q5c27lOd13BuuH1vuuPbSBtSNnwy0bLjWUb326K3tRRoFGwvmBos+/mxkK5QlHhvS3OWw5sxWwVbO3dZrOtatuXIl7R9WLL4oriTyXckuvfWX1X+d3M9oTtvaX2pft34HYId9zd6brzWJliWV7Z0K7gXa3lzPKi8re7V+y+VmFbcWAPaY9kj7QyqLK9Sq9qR9Wn6qTqgRqPmua96nu37Z3ax9vXv99tf9MBjQPFBz4eFBy8f8j3UGutQW3FYdzhrMPP66Lqur9nf19/RO1I8ZHPR4VHpcfCj3XVO9TXN6g3lDbCjZLGseNxx2/94PVDexOr6VAzo7n4BDghOfHix/gf754MPNl5in2q6Sf9n/a20FqKWqHW3NaJtqQ2aXtMe9/pgNOdHc4dLT+b/3z0jPaZmrPKZ0vPkc4VnJs5n3d+8kLGhfGLiReHOld0Prq05NKdrrCu3suBl69e8blyqdu9+/xVl6tnrjldO32dfb3thv2N1h67npZf7H5p6bXvbb3pcLP9luOtjr4Ffef6Xfsv3va6feWO/50bA4sG+u4uvnv/Xtw96X3e/dEHqQ9eP8x6OP1o/WPs46InCk8qnqo/rf3V+Ndmqb307KDXYM+ziGePhrhDL/+V+a9PwwXPqc8rRrRG6ketR8+M+YzderH0xfDLjJfT44W/Kf6295XRq59+d/u9Z2LJxPBr0euZP0reqL45+tb2bedk6OTTd2nvpqeK3qu+P/aB/aH7Y/THkensT/hPlZ+NP3d8CfzyeCZtZubf94Tz+wplbmRzdHJlYW0KZW5kb2JqCjUgMCBvYmoKWyAvSUNDQmFzZWQgNyAwIFIgXQplbmRvYmoKMiAwIG9iago8PCAvVHlwZSAvUGFnZXMgL01lZGlhQm94IFswIDAgMzk2IDI4OF0gL0NvdW50IDEgL0tpZHMgWyAxIDAgUiBdID4+CmVuZG9iago4IDAgb2JqCjw8IC9UeXBlIC9DYXRhbG9nIC9QYWdlcyAyIDAgUiA+PgplbmRvYmoKNiAwIG9iago8PCAvVHlwZSAvRm9udCAvU3VidHlwZSAvVHJ1ZVR5cGUgL0Jhc2VGb250IC9BQUFBQUIrSGVsdmV0aWNhIC9Gb250RGVzY3JpcHRvcgo5IDAgUiAvRW5jb2RpbmcgL01hY1JvbWFuRW5jb2RpbmcgL0ZpcnN0Q2hhciAzMiAvTGFzdENoYXIgMTE2IC9XaWR0aHMgWyAyNzgKMCAwIDAgMCAwIDAgMCAzMzMgMzMzIDAgMCAyNzggMCAyNzggMCA1NTYgNTU2IDU1NiA1NTYgNTU2IDU1NiAwIDAgMCAwIDAgMAowIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgMCAwIDAgNjY3IDAgMCAwIDAgMCAwIDk0NCAwIDAgMCAwIDAgMCAwCjAgMCAwIDU1NiA1MDAgMCA1NTYgMCA1NTYgNTU2IDIyMiAwIDAgMjIyIDAgMCAwIDAgMCAzMzMgNTAwIDI3OCBdID4+CmVuZG9iago5IDAgb2JqCjw8IC9UeXBlIC9Gb250RGVzY3JpcHRvciAvRm9udE5hbWUgL0FBQUFBQitIZWx2ZXRpY2EgL0ZsYWdzIDMyIC9Gb250QkJveCBbLTk1MSAtNDgxIDE0NDUgMTEyMl0KL0l0YWxpY0FuZ2xlIDAgL0FzY2VudCA3NzAgL0Rlc2NlbnQgLTIzMCAvQ2FwSGVpZ2h0IDY4NCAvU3RlbVYgMCAvWEhlaWdodAo1MTMgL01heFdpZHRoIDE1MDAgL0ZvbnRGaWxlMiAxMCAwIFIgPj4KZW5kb2JqCjEwIDAgb2JqCjw8IC9MZW5ndGgxIDExMDc2IC9MZW5ndGggNzAxOCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeAHFWgl8VNW5/85d5t7Z9zWZzEwms2TfSEhIIJeQhS3sQkINJkAgQZAtRKCCwYKYiBRFENFWrRZZxAwhwgQKUhoKbW0LraDi0lrR+vqap68vWBUy8757J6Qkv9Yfv/fz13fvfOec76zf+Z/vfGe507xqTQOooRVomDa3fsUikJ7kOgBybcGy+hUx3lyG/tkFLc3uGM9eAKC3LVqxeFmMl88CUO5YvHTdQHlLMYDhlcaG+oWxdLiJfn4jRsR4MgL9pMZlzWtjvOka+nVLly8YSLfwyOcsq1870D68h7z7vvplDbH8yVg/JK1Yvro5xgc/RX/WilUNA/lJNQBzHxCMtUEO9k18KNDhuxiA+1S5Q4oR0/GZ+tLPv75HW3wd9GKzAPdUfV/y382aWfRlw82A8nH+K4yQS/WJKVhOlhxJBlARTO9VPj6YIpVDxxaG2lRyAljSDfekktfJSRgDIyAZnGDA5IbU18lPoHRYzCkYNSQGXifHoQpGw0gIDhYLQx1MlWLiBirqhkrMc3vVJ4ZVDSfIaWBgYWqYHHOXb2iylYVhZmoYJiCVIOUhpaaOtUEr2Qc7kJ5HoqGJPArrkNqQnkZiBkMHkOsmj3YyvHCCrAMHmSgoGdcsk91lUyhdvwsTWdcPXe/YPjpJ7KhcHxJ7pxrkYxXkefIcLAQX+TH4yHoYD0Gy92jyUlcdJh2AFUitSLTkEnKgMyHHdZqkgY8hWMYPCQw55vpzdrrr4+wwRTpdZwNhBr2fJiAnaF1nnD90ve5c7DqNdCiWdDAZcxxzHXAude1MCJO9na4nnGGCZR6PeWucWPSYa1nybtfCbCl98u4wdajTVYjpswWlK7/A48pzXnNlBsI8QT7dOdmVkv1rVxIWxGxurNQn6F3xzp2uUZiU4CwPjEI6SQ6SZyCFPNPpm+g6gUHs7tEJyQW7w+S7R8cHs31hsl7IHx/cnTw+4Eue7PIlVwQCGJ59gdvMfYcby+VwqVyQ83MeLo4z8QZex2t4Fa/geZ4Lk1c6S1yyk+QQlCAsh47yMp4Nk1cxkjlJDkuRh4/zDE/xwJvC0T92iQprCpNDXToxhIFjMikkC5PDR2NRhwUXI4YYKUFHiWF00AWK8BRMhBB5LCyDLZaWEluJYYy+sKLsXzl1UsotN/VfPzbiDO2eNLM6dNBZE8oRA1Fnza3stluBf+k3r8GkhtLU1Ekz1h1tWbFkUXmDt7zOW96AVBd6tKXRFmqd73YfWbJCTHCHaH/d/AWNol/fEFrhbSgLLfGWuY+0SOWGJS8Sk1u8ZUdgUfms6iOLhIayzhahpdxbX1ZzdH7pqtohbbUNtrWq9J+0VSpWtkpsa75UblhbtWLyfLGtWrGtWrGt+cJ8qS2x8+VNM0tXN6N2usubJrlDwZmhCdPnVofc9TVlYbIPI8vWALBnQMeegiDbCg4mE1wA0XeQrop+5K7oJ+x50EWWRf+bLsJR7RaJipQUwxl4DJ6BDpDBfgwHYR7sgV+QJTi574YuuEISIANaceKHYTK8QaLRS7AIXsL8zXAWdsERUGGZZWDG1O3EF12PvIDh+bA5+iNIggJ4GE5BIda6HXqjB6JHMXUG3AUH4RCW/xXxUkcYY/TV6DXgYTrWuRlTLkUnRzvQ2qWhDZuGsZvhNPHRV6ONaMmLULpn4Tl4AX4KfyUPka5oY7QlejH6IeqqDeJhJr4bSBf5kO5gHo4+G/1LNIJIBCEFW62DnfAi1t+B7xkCpJzcS5rJTrKLEqiHqC5mC2uN9CMOyWhNK9E0LYdHEIFu6IG/wVfkM8pG6+hm+lw0L/o/oIRJ2EuxJw3Qgu9WfLdjn04SGcki48g0soE8SXaR31Mp1F1UNXU/tZb6hJ5C302vo3/PrGY62W3sHpkycj16Mno+ehmsuCx8B1bBRuzdWbgIffA1obGueOIjRaSUzMO3lTxDdZMXSDc1jZwhF6mD5A/kI/IZuUGxlIoyU6lUM7WTOkSdpX5DN9G76KfpP9DXmTEsxb7Afizzce9G5kfaIr+JFkU/jH6JNpYHD45MKUyBe6Aee7sCl54HsReH8e3AUeuBc/AL6f2IxEMvfIkoADEQB8khVfhOIVPJItJEfkhO4HtakuULCgeCklN6ykrFUzOp+dQyqpW6TLXScXQKPZGeS3fge4G+Qt+gbzAsY2TMTCUzAbYxy5i9+O5j9jOdzG/ZQnYMO4Wdzbaybew2egF7ib0i2yjbLuuUfSb7HO3iZG45tw1H5xeosz9FXf7Hw5AklD4H7oMFpIzMh904Gi+QemhH7VpIHkG8VkAwWktvpCupLNSG0/Bd1Na9sAHa6Lvhhejb9EF4CzVlKVbZCi8zpeBkn8LReQiyUIv+8e7EUX8eXsF5cQhxAkiN3CXNOw97GgJCckpyMOD3JXkTPW5cE+LjHHab1WI2GQ16nVqlVMh5TsYyNEUgrdxbUecO+etCjN87fny6yHvrMaL+tog6nOvuUMXQPCG3WK4ek4bkFDDnomE5hVhOYTAn0bmLoTg9zV3udYd+XeZ1h8nc6dUYfqzMW+MO9UrhKim8QwqrMezxYAF3ua2xzB0ide7yUEVLY3t5XVl6GukWEAJFeppoWARQihWHYFz9BrTAME7MUR5yeMvKQ3YvhjGN9pXXLwxNm15dXhbn8dRgHEbNqMY20tOaQignPKpa6F34aFiA+XViqP7u6hBdXxOi6sS69Kkhq7csZF3/se0f7K1Q+bbbEkOUr6K+ob0iJNQ9iuCKbJ3I1W9DbtJMN1ZLbampDpEtA0KIMi5BSUVxY4uGr26JOyT3lnob25fUIbgwo7rTITgk6xyCadWddsEuMelp3baNRR7sfXf62PSxol/ksW2M+X/+Xiz+d2dE37ax54/oT5oxCAAREfBOQDlD7gVSI14UtkB0GgqgfUEB4oRPDcFuNqE840IU6gztC7G+CfWh1pm3xGgsiwlXt6SsU253SKtUaQ3mr2vXjcKRwvw6r7v9Oi7ndd7evw6NqR+Ikfl010FMFAd6UFdCpP5WuEVcTX3Y60abt1Ec3xZpTJH32spvi0BehEaUOWTCFX5atSfkrsEI3G6mTQqDfFr1EUK214RJdEsYypzduMem75mHyWmiqjWVYfvIpKdhRIoHQxlp7gpsuULUFXe7u33CwnZ3hbsRlYnxST4mNLTXZCKCM6sRJ5iFLQo1cYPBhpqaUVhPplgPFsHs7TVYw5KBGtCXojL7MVNWGq62tH9a9fTqUGtZXEgoq8FRQPU9M606dAY1t6YGc2UPSooSi1vrmMw5KHN2CqbnxmrBzU0rVlHT3i7WObPa6wmdaW+PaxfnW4wPExgeIQxEhEHMIkIeJq3TsCx6Xk+cNAYerwfFqhExHYEqfUujcFP/zQjnD8qNJUeitPkSwgXfEsKFd4LwqDtCuGhQ0iEIF6PMRSLCo/99CI8ZgnDJNyMsDMqNQo5FaQUJ4dJvCeFxd4Jw2R0hXD4o6RCEK1DmchHhyn8fwuOHIDzhmxGeOCg3CjkJpZ0oITz5W0K46k4QnnJHCE8dlHQIwtNQ5qkiwtP/fQjPGILwzG9GeNag3CjkXSjtLAnh2d8SwnPuBOHqO0K4ZlDSIQjPRZlrRIS/M4iwEBeC2+1w6zCzC9+6Yb57COS13wz5vMGOoNT3oPjzJMjrviXI6+8E8vl3BPmCQUmHQL4QZV4gQt7w/wj5otsgZw1QShXiTcZB2I5UhuGDSB3MahCQegb8bPRHIJWys6GDewyvnVZDC96KFKE/Hmk00mZyXqI2rKcN08R6rbj5xaMRunghiCdIvG8BN54NYzFStORQeOoSHywGLJIMODyFyUGB58n/y6MaUkg9hLvFaECLQbzUAT2eqY1StOlWIvpmsEjyi1H5+G7EE9Xf8JSHF4FUDvUBnUafYSzMn9jJ7G9keziB28Gd5v7I++Uy+WwsQeH5EZiLeO9AY1/GxS4V+cwwMEi8LgxwEUnkMUy/h2H0OfRp9OXvwQksBTA79QTWxKKflZ2r9+gDSKXM9vDNP7Gnvh4XZqpu4CUVork9Mo+qZy+DCcYIcpNebrRYrQ75SfIsomgizwoaAVqZyTq72fJ3z9IZtjCXsyU1dUpfVa/jfUfvm71TyhvKPoGSkuwsQnEyvc5qMXozSMAf8OfpRuYbqXk/yKycnrNz3RMVyQUWZW3RSfZy5Lc73o18GPng8ycjf7m2cemT++dMJcE/7yQ+SZ4ylMeK8hghX1DxejCaUR5mstYoioTXxSiSnLebzH/3lHwXb7NESd7sff82OYyGkfl6XcBP5yYQawIx6zgZXflcRoUoxd6x/qzkeUUnIvNI/va3iId4Pn+SWL5Y3bChb2Xk7U93RT6QZDgYuUha4SpoIF2wgFejWMgrdCgGN0KxEHi7dkGDLXWKrq+quP9Wu6IQ2VnW/JH5eSP8AW9ertkk4w6Wx2sJtexKXcsl1V3pKZySu/rL+7vM2ATi3oGO2AYNfsFIUmgFiw2QhWBn2IUesQGxa/0D8FZh5SNzzd6OS5eu4uWTqCNC9B0mnt2DmhgPKwXrVpZU8OY8LRufx6kNBfRyW4EyodKpa+mxvdnb3wslvSVYx7h1wgiIU/uJz+G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= 74\n", "-------------+---------------------------------- F(1, 72) = 29.42\n", " Model | 12791822.9 1 12791822.9 Prob > F = 0.0000\n", " Residual | 31302355.5 72 434754.937 R-squared = 0.2901\n", "-------------+---------------------------------- Adj R-squared = 0.2802\n", " Total | 44094178.4 73 604029.841 Root MSE = 659.36\n", "\n", "------------------------------------------------------------------------------\n", " weight | Coef. Std. Err. t P>|t| [95% Conf. Interval]\n", "-------------+----------------------------------------------------------------\n", " price | .1419244 .0261645 5.42 0.000 .0897663 .1940824\n", " _cons | 2144.459 178.5954 12.01 0.000 1788.436 2500.483\n", "------------------------------------------------------------------------------\n" ] } ], "source": [ "reg weight price" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/pdf": 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help using `%help`" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "Stata 16 help for scatter\n", "\n", "\n", "\n", "\n", "\n", "

\n", "\n", "
\n", "\n", "\n", "
\n",
       "
\n",
       "[G-2] graph twoway scatter -- Twoway scatterplots\n",
       "
\n",
       "
\n",
       "Syntax\n",
       "
\n",
       "        [twoway] scatter varlist [if] [in] [weight] [, options]\n",
       "
\n",
       "
\n",
       "    where varlist is\n",
       "                y_1 [y_2 [...]] x\n",
       "
\n",
       "    options                         Description\n",
       "    -------------------------------------------------------------------------\n",
       "    marker_options                  change look of markers (color, size,\n",
       "                                      etc.)\n",
       "    marker_label_options            add marker labels; change look or\n",
       "                                      position\n",
       "    connect_options                 change look of lines or connecting method\n",
       "
\n",
       "    composite_style_option          overall style of the plot\n",
       "
\n",
       "    jitter_options                  jitter marker positions using random\n",
       "                                      noise\n",
       "
\n",
       "    axis_choice_options             associate plot with alternate axis\n",
       "
\n",
       "    twoway_options                  titles, legends, axes, added lines and\n",
       "                                      text, by, regions, name, aspect ratio,\n",
       "                                      etc.\n",
       "    -------------------------------------------------------------------------\n",
       "
\n",
       "
\n",
       "    marker_options                  Description\n",
       "    -------------------------------------------------------------------------\n",
       "    msymbol(symbolstylelist)        shape of marker\n",
       "    mcolor(colorstylelist)          color and opacity of marker, inside and\n",
       "                                      out\n",
       "    msize(markersizestylelist)      size of marker\n",
       "    msangle(anglestyle)             angle of marker symbol\n",
       "    mfcolor(colorstylelist)         inside or \"fill\" color and opacity\n",
       "    mlcolor(colorstylelist)         color and opacity of outline\n",
       "    mlwidth(linewidthstylelist)     thickness of outline\n",
       "    mlalign(linealignmentstyle)     outline alignment (inside, outside,\n",
       "                                      center)\n",
       "    mlstyle(linestylelist)          overall style of outline\n",
       "    mstyle(markerstylelist)         overall style of marker\n",
       "    -------------------------------------------------------------------------\n",
       "
\n",
       "
\n",
       "    marker_label_options            Description\n",
       "    -------------------------------------------------------------------------\n",
       "    mlabel(varlist)                 specify marker variables\n",
       "    mlabposition(clockposlist)      where to locate label\n",
       "    mlabvposition(varname)          where to locate label 2\n",
       "    mlabgap(sizelist)               gap between marker and label\n",
       "    mlabangle(anglestylelist)       angle of label\n",
       "    mlabsize(textsizestylelist)     size of label\n",
       "    mlabcolor(colorstylelist)       color and opacity of label\n",
       "    mlabformat(%fmtlist)            format of label\n",
       "    mlabtextstyle(textstylelist)    overall style of text\n",
       "    mlabstyle(markerlabelstylelist) overall style of label\n",
       "    -------------------------------------------------------------------------\n",
       "
\n",
       "
\n",
       "    connect_options                 Description\n",
       "    -------------------------------------------------------------------------\n",
       "    connect(connectstylelist)       how to connect points\n",
       "    sort[(varlist)]                 how to order data before connecting\n",
       "    cmissing({y|n} ...)             missing values are ignored\n",
       "
\n",
       "    lpattern(linepatternstylelist)  line pattern (solid, dashed, etc.)\n",
       "    lwidth(linewidthstylelist)      thickness of line\n",
       "    lcolor(colorstylelist)          color and opacity of line\n",
       "    lalign(linealignmentstyle)      line alignment (inside, outside, center)\n",
       "    lstyle(linestylelist)           overall style of line\n",
       "    -------------------------------------------------------------------------\n",
       "
\n",
       "
\n",
       "    composite_style_option          Description\n",
       "    -------------------------------------------------------------------------\n",
       "    pstyle(pstylelist)              all the ...style() options above\n",
       "    -------------------------------------------------------------------------\n",
       "    See Appendix: Styles and composite styles under Remarks below.\n",
       "
\n",
       "
\n",
       "    jitter_options                  Description\n",
       "    -------------------------------------------------------------------------\n",
       "    jitter(#)                       perturb location of point\n",
       "    jitterseed(#)                   random-number seed for jitter()\n",
       "    -------------------------------------------------------------------------\n",
       "    See Jittered markers under Remarks below.\n",
       "
\n",
       "
\n",
       "    axis_choice_options             Description\n",
       "    -------------------------------------------------------------------------\n",
       "    yaxis(# [# ...])                which y axis to use\n",
       "    xaxis(# [# ...])                which x axis to use\n",
       "    -------------------------------------------------------------------------\n",
       "
\n",
       "
\n",
       "    twoway_options                  Description\n",
       "    -------------------------------------------------------------------------\n",
       "    added_line_options              draw lines at specified y or x values\n",
       "    added_text_options              display text at specified (y,x) value\n",
       "
\n",
       "    axis_options                    labels, ticks, grids, log scales\n",
       "    title_options                   titles, subtitles, notes, captions\n",
       "    legend_options                  legend explaining what means what\n",
       "
\n",
       "    scale(#)                        resize text and markers\n",
       "    region_options                  outlining, shading, aspect ratio\n",
       "    aspect_option                   constrain aspect ratio of plot region\n",
       "    scheme(schemename)              overall look\n",
       "    play(recordingname)             play edits from recordingname\n",
       "
\n",
       "    by(varlist, ...)                repeat for subgroups\n",
       "    nodraw                          suppress display of graph\n",
       "    name(name, ...)                 specify name for graph\n",
       "    saving(filename, ...)           save graph in file\n",
       "
\n",
       "    advanced_options                difficult to explain\n",
       "    -------------------------------------------------------------------------\n",
       "
\n",
       "    aweights, fweights, and pweights are allowed; see weight.\n",
       "
\n",
       "
\n",
       "Menu\n",
       "
\n",
       "    Graphics > Twoway graph (scatter, line, etc.)\n",
       "
\n",
       "
\n",
       "Description\n",
       "
\n",
       "    scatter draws scatterplots and is the mother of all the twoway plottypes,\n",
       "    such as twoway line and twoway lfit.\n",
       "
\n",
       "    scatter is both a command and a plottype as defined in [G-2] graph\n",
       "    twoway.  Thus the syntax for scatter is\n",
       "
\n",
       "        . graph twoway scatter ...\n",
       "
\n",
       "        . twoway scatter ...\n",
       "
\n",
       "        . scatter ...\n",
       "
\n",
       "    Being a plottype, scatter may be combined with other plottypes in the \n",
       "    twoway family, as in,\n",
       "
\n",
       "        . twoway (scatter ...) (line ...) (lfit ...) ...\n",
       "
\n",
       "    which can equivalently be written as\n",
       "
\n",
       "        . scatter ... || line ... || lfit ... || ...\n",
       "
\n",
       "
\n",
       "Options\n",
       "
\n",
       "    marker_options specify how the points on the graph are to be designated.\n",
       "        Markers are the ink used to mark where points are on a plot.  Markers\n",
       "        have shape, color, and size, and other characteristics.  See [G-3]\n",
       "        marker_options for a description of markers and the options that\n",
       "        specify them.\n",
       "
\n",
       "        msymbol(O D S T + X o d s t smplus x) is the default.  msymbol(i)\n",
       "        will suppress the appearance of the marker altogether.\n",
       "
\n",
       "    marker_label_options specify labels to appear next to or in place of the\n",
       "        markers.  For instance, if you were plotting country data, marker\n",
       "        labels would allow you to have \"Argentina\", \"Bolivia\", ..., appear\n",
       "        next to each point and, with a few data, that might be desirable.\n",
       "        See [G-3] marker_label_options for a description of marker labels and\n",
       "        the options that control them.\n",
       "
\n",
       "        By default, no marker labels are displayed.  If you wish to display\n",
       "        marker labels in place of the markers, specify mlabposition(0) and\n",
       "        msymbol(i).\n",
       "
\n",
       "    connect_options specify how the points are to be connected.  The default\n",
       "        is not to connect the points.\n",
       "
\n",
       "        connect() specifies whether points are to be connected and, if so,\n",
       "        how the line connecting them is to be shaped.  The line between each\n",
       "        pair of points can connect them directly or in stairstep fashion.\n",
       "
\n",
       "        sort specifies that the data be sorted by the x variable before the\n",
       "        points are connected.  Unless you are after a special effect or your\n",
       "        data are already sorted, do not forget to specify this option.  If\n",
       "        you are after a special effect, and if the data are not already\n",
       "        sorted, you can specify sort(varlist) to specify exactly how the data\n",
       "        should be sorted.  Understand that specifying sort or sort(varlist)\n",
       "        when it is not necessary will slow Stata down a little.  You must\n",
       "        specify sort if you wish to connect points, and you specify the\n",
       "        twoway_option by() with total.\n",
       "
\n",
       "        cmissing(y) and cmissing(n) specify whether missing values are\n",
       "        ignored when points are connected; whether the line should have a\n",
       "        break in it.  The default is cmissing(y), meaning that there will be\n",
       "        no breaks.\n",
       "
\n",
       "        lpattern() specifies how the style of the line is to be drawn:\n",
       "        solid, dashed, etc.\n",
       "
\n",
       "        lwidth() specifies the width of the line.\n",
       "
\n",
       "        lcolor() specifies the color and opacity of the line.\n",
       "
\n",
       "        lalign() specifies the alignment of the line.\n",
       "
\n",
       "        lstyle() specifies the overall style of the line.\n",
       "
\n",
       "        See [G-3] connect_options for more information on these and related\n",
       "        options.  See lines for an overview of lines.\n",
       "
\n",
       "    pstyle(pstyle) specifies the overall style of the plot and is a composite\n",
       "        of mstyle(), mlabstyle(), lstyle(), connect(), and cmissing().  The\n",
       "        default is pstyle(p1) for the first plot, pstyle(p2) for the second,\n",
       "        and so on.  See Appendix: Styles and composite styles under Remarks\n",
       "        below.\n",
       "
\n",
       "    jitter(#) adds spherical random noise to the data before plotting.  #\n",
       "        represents the size of the noise as a percentage of the graphical\n",
       "        area.  This option is useful when plotting data which otherwise would\n",
       "        result in points plotted on top of each other.  See Jittered markers\n",
       "        under Remarks below.\n",
       "
\n",
       "        Commonly specified are jitter(5) or jitter(6); jitter(0) is the\n",
       "        default.\n",
       "
\n",
       "    jitterseed(#) specifies the seed for the random noise added by the\n",
       "        jitter() option.  # should be specified as a positive integer.  Use\n",
       "        this option to reproduce the same plotted points when the jitter()\n",
       "        option is specified.\n",
       "
\n",
       "    axis_choice_options are for use when you have multiple x or y axes.  See\n",
       "        [G-3] axis_choice_options for more information.\n",
       "
\n",
       "    twoway_options include\n",
       "
\n",
       "        added_line_options, which specify that horizontal or vertical lines\n",
       "        be drawn on the graph; see [G-3] added_line_options.  If your\n",
       "        interest is in drawing grid lines through the plot region, see\n",
       "        axis_options below.\n",
       "
\n",
       "        added_text_options, which specify text to be displayed on the graph\n",
       "        (inside the plot region); see [G-3] added_text_options.\n",
       "
\n",
       "        axis_options, which allow you to specify labels, ticks, and grids.\n",
       "        These options also allow you to obtain logarithmic scales; see [G-3]\n",
       "        axis_options.\n",
       "
\n",
       "        title_options, allow you to specify titles, subtitles, notes, and\n",
       "        captions to be placed on the graph; see [G-3] title_options.\n",
       "
\n",
       "        legend_options, which allows specifying the legend explaining the\n",
       "        symbols and line styles used; see [G-3] legend_options.\n",
       "
\n",
       "        scale(#), which makes all the text and markers on a graph larger or\n",
       "        smaller (scale(1) means no change); see [G-3] scale_option.\n",
       "
\n",
       "        region_options, which allow you to control the aspect ratio and to\n",
       "        specify that the graph be outlined, or given a background shading;\n",
       "        see [G-3] region_options.\n",
       "
\n",
       "        scheme(schemename), which specifies the overall look of the graph;\n",
       "        see [G-3] scheme_option.\n",
       "
\n",
       "        play(recordingname) applies the edits from recordingname to the\n",
       "        graph, where recordingname is the name under which edits previously\n",
       "        made in the Graph Editor have been recorded and stored.  See Graph\n",
       "        Recorder in [G-1] Graph Editor.\n",
       "
\n",
       "        by(varlist, ...), which allows drawing multiple graphs for each\n",
       "        subgroup of the data; see [G-3] by_option.\n",
       "
\n",
       "        nodraw, which prevents the graph from being displayed; see [G-3]\n",
       "        nodraw_option.\n",
       "
\n",
       "        name(name), which allows you to save the graph in memory under a name\n",
       "        different from Graph; see [G-3] name_option.\n",
       "
\n",
       "        saving(filename[, asis replace]), which allows you to save the graph\n",
       "        to disk; see [G-3] saving_option.\n",
       "
\n",
       "        other options that allow you to suppress the display of the graph, to\n",
       "        name the graph, etc.\n",
       "
\n",
       "        See [G-3] twoway_options.\n",
       "
\n",
       "
\n",
       "Remarks\n",
       "
\n",
       "    Remarks are presented under the following headings:\n",
       "
\n",
       "        Typical use\n",
       "        Scatter syntax\n",
       "        The overall look for the graph\n",
       "        The size and aspect ratio of the graph\n",
       "        Titles\n",
       "        Axis titles\n",
       "        Axis labels and ticking\n",
       "        Grid lines\n",
       "        Added lines\n",
       "        Axis range\n",
       "        Log scales\n",
       "        Multiple axes\n",
       "        Markers\n",
       "        Weighted markers\n",
       "        Jittered markers\n",
       "        Connected lines\n",
       "        Graphs by groups\n",
       "        Saving graphs\n",
       "        Video example\n",
       "        Appendix:  Styles and composite styles\n",
       "
\n",
       "
\n",
       "Typical use\n",
       "
\n",
       "    The scatter plottype by default individually marks the location of each\n",
       "    point:\n",
       "
\n",
       "        . sysuse uslifeexp2\n",
       "
\n",
       "        . scatter le year\n",
       "          (click to run)\n",
       "
\n",
       "    With the specification of options, you can produce the same effect as \n",
       "    twoway connected,\n",
       "
\n",
       "        . scatter le year, connect(l)\n",
       "          (click to run)\n",
       "
\n",
       "    or twoway line:\n",
       "
\n",
       "        . scatter le year, connect(l) msymbol(i)\n",
       "          (click to run)\n",
       "
\n",
       "    In fact, all the other twoway plottypes eventually work their way back to\n",
       "    executing scatter.  scatter literally is the mother of all twoway graphs\n",
       "    in Stata.\n",
       "
\n",
       "
\n",
       "Scatter syntax\n",
       "
\n",
       "    See [G-2] graph twoway for an overview of graph twoway syntax.\n",
       "    Especially for graph twoway scatter, the only thing to know is that if\n",
       "    more than two variables are specified, all but the last are given the\n",
       "    interpretation of being y variables.  For example,\n",
       "
\n",
       "        . scatter y1var y2var xvar\n",
       "
\n",
       "    would plot y1var versus xvar and overlay that with a plot of y2var versus\n",
       "    xvar, so it is the same as typing\n",
       "
\n",
       "        . scatter y1var xvar || scatter y2var xvar\n",
       "
\n",
       "    If, using the multiple-variable syntax, you specify scatter-level options\n",
       "    (that is, all options except twoway_options as defined in the syntax\n",
       "    diagram), you specify arguments for y1var, y2var, ..., separated by\n",
       "    spaces.  That is, you might type\n",
       "
\n",
       "        . scatter y1var y2var xvar, ms(O i) c(. l)\n",
       "
\n",
       "    ms() and c() are abbreviations for the msymbol() and connect() options;\n",
       "    see [G-3] marker_options and [G-3] connect_options.  In any case, the\n",
       "    results from the above are the same as if you typed\n",
       "
\n",
       "        . scatter y1var xvar, ms(O) c(.) || scatter y2var xvar, ms(i) c(l)\n",
       "
\n",
       "    There need not be a one-to-one correspondence between options and y\n",
       "    variables when you use the multiple-variable syntax.  If you typed\n",
       "
\n",
       "        . scatter y1var y2var xvar, ms(O) c(l)\n",
       "
\n",
       "    then options ms() and c() will have default values for the second\n",
       "    scatter, and if you typed\n",
       "
\n",
       "        . scatter y1var y2var xvar, ms(O S i) c(l l l)\n",
       "
\n",
       "    the extra options for the nonexistent third variable would be ignored.\n",
       "
\n",
       "    If you wish to specify the default for one of the y variables, you may\n",
       "    specify period (.):\n",
       "
\n",
       "        . scatter y1var y2var xvar, ms(. O) c(. l)\n",
       "
\n",
       "    There are other shorthands available to make specifying multiple\n",
       "    arguments easier; see [G-4] stylelists.\n",
       "
\n",
       "    Because multiple variables are interpreted as multiple y variables, to\n",
       "    produce graphs containing multiple x variables, you must chain together\n",
       "    separate scatter commands:\n",
       "
\n",
       "        . scatter yvar x1var, ... || . scatter yvar x2var, ...\n",
       "
\n",
       "
\n",
       "The overall look for the graph\n",
       "
\n",
       "    The overall look of the graph is mightily affected by the scheme, and\n",
       "    there is a scheme() option that will allow you to specify which scheme to\n",
       "    use.  We showed earlier the results of scatter le year.  Here is the same\n",
       "    graph repeated using the economist scheme:\n",
       "
\n",
       "        . sysuse uslifeexp2, clear\n",
       "
\n",
       "        . scatter le year,\n",
       "                title(\"Scatterplot\")\n",
       "                subtitle(\"Life expectancy at birth, U.S.\")\n",
       "                note(\"1\")\n",
       "                caption(\"Source:  National Vital Statistics Report,\n",
       "                         Vol. 50 No. 6\")\n",
       "                scheme(economist)\n",
       "          (click to run)\n",
       "
\n",
       "    See [G-4] Schemes intro.\n",
       "
\n",
       "
\n",
       "The size and aspect ratio of the graph\n",
       "
\n",
       "    The size and aspect ratio of the graph are controlled by the\n",
       "    region_options ysize(#) and xsize(#), which specify the height and width\n",
       "    in inches of the graph.  For instance,\n",
       "
\n",
       "        . scatter yvar xvar, xsize(4) ysize(4)\n",
       "
\n",
       "    would produce a 4x4 inch square graph.  See [G-3] region_options.\n",
       "
\n",
       "
\n",
       "Titles\n",
       "
\n",
       "    By default, no titles appear on the graph, but the title_options title(),\n",
       "    subtitle(), note(), caption(), and legend() allow you to specify the\n",
       "    titles that you wish to appear, as well as to control their position and\n",
       "    size.  For instance,\n",
       "
\n",
       "        . scatter yvar xvar, title(\"My title\")\n",
       "
\n",
       "    would draw the graph and include the title \"My title\" (without the\n",
       "    quotes) at the top.  Multiple-line titles are allowed.  Typing\n",
       "
\n",
       "        . scatter yvar xvar, title(\"My title\" \"Second line\")\n",
       "
\n",
       "    would create a two-line title.  The above, however, would probably look\n",
       "    better as a title followed by a subtitle:\n",
       "
\n",
       "        . scatter yvar xvar, title(\"My title\") subtitle(\"Second line\")\n",
       "
\n",
       "    In any case, see [G-3] title_options.\n",
       "
\n",
       "
\n",
       "Axis titles\n",
       "
\n",
       "    Titles do, by default, appear on the y and x axes.  The axes are titled\n",
       "    with the variable names being plotted or, if the variables have variable\n",
       "    labels, with their variable labels.  The axis_title_options ytitle() and\n",
       "    xtitle() allow you to override that.  If you specify\n",
       "
\n",
       "        . scatter yvar xvar, ytitle(\"\")\n",
       "
\n",
       "    the title on the y axis would disappear.  If you specify\n",
       "
\n",
       "        . scatter yvar xvar, ytitle(\"Rate of change\")\n",
       "
\n",
       "    the y-axis title would become \"Rate of change\".  As with all titles,\n",
       "    multiple-line titles are allowed:\n",
       "
\n",
       "        . scatter yvar xvar, ytitle(\"Time to event\" \"Rate of change\")\n",
       "
\n",
       "    See [G-3] axis_title_options.\n",
       "
\n",
       "
\n",
       "Axis labels and ticking\n",
       "
\n",
       "    By default, approximately five major ticks and labels are placed on each\n",
       "    axis.  The axis_label_options ylabel() and xlabel() allow you to control\n",
       "    that.  Typing\n",
       "
\n",
       "        . scatter yvar xvar, ylabel(#10)\n",
       "
\n",
       "    would put approximately 10 labels and ticks on the y axis.  Typing\n",
       "
\n",
       "        . scatter yvar xvar, ylabel(0(1)9)\n",
       "
\n",
       "    would put exactly 10 labels at the values 0, 1, ..., 9.\n",
       "
\n",
       "    ylabel() and xlabel() have other features, and options are also provided\n",
       "    for minor labels and minor ticks; see [G-3] axis_label_options.\n",
       "
\n",
       "
\n",
       "Grid lines\n",
       "
\n",
       "    If you use a member of the s2 family of schemes -- see [G-4] Scheme s2 --\n",
       "    grid lines are included in y but not x, by default.  You can specify\n",
       "    option xlabel(,grid) to add x grid lines, and you can specify\n",
       "    ylabel(,nogrid) to suppress y grid lines.\n",
       "
\n",
       "    Grid lines are considered an extension of ticks and are specified as\n",
       "    suboptions inside the axis_label_options ylabel() and xlabel().  For\n",
       "    instance,\n",
       "
\n",
       "        . sysuse auto, clear\n",
       "
\n",
       "        . scatter mpg weight, xlabel(,grid)\n",
       "          (click to run)\n",
       "
\n",
       "    In the above example, the grid lines are placed at the same values as the\n",
       "    default ticks and labels, but you can control that, too.  See [G-3]\n",
       "    axis_label_options.\n",
       "
\n",
       "
\n",
       "Added lines\n",
       "
\n",
       "    Lines may be added to the graph for emphasis by using the\n",
       "    added_line_options yline() and xline(); see [G-3] added_line_options.\n",
       "
\n",
       "
\n",
       "Axis range\n",
       "
\n",
       "    The extent or range of an axis is set according to all the things that\n",
       "    appear on it -- the data being plotted and the values on the axis being\n",
       "    labeled or ticked.  In the graph that just appeared above,\n",
       "
\n",
       "        . sysuse auto, clear\n",
       "
\n",
       "        . scatter mpg weight\n",
       "          (click to run)\n",
       "
\n",
       "    variable mpg varies between 12 and 41 and yet the y axis extends from 10\n",
       "    to 41.  The axis was extended to include 10<12 because the value 10 was\n",
       "    labeled.  Variable weight varies between 1,760 and 4,840; the x axis\n",
       "    extends from 1,760 to 5,000.  This axis was extended to include\n",
       "    5,000>4,840 because the value 5,000 was labeled.\n",
       "
\n",
       "    You can prevent axes from being extended by specifying the ylabel(minmax)\n",
       "    and xlabel(minmax) options.  minmax specifies that only the minimum and\n",
       "    maximum are to be labeled:\n",
       "
\n",
       "        . scatter mpg weight, ylabel(minmax) xlabel(minmax)\n",
       "          (click to run)\n",
       "
\n",
       "    In other cases, you may wish to widen the range of an axis.  This you can\n",
       "    do by specifying the range() descriptor of the axis_scale_options\n",
       "    yscale() or xscale().  For instance,\n",
       "
\n",
       "        . scatter mpg weight, xscale(range(1000 5000))\n",
       "
\n",
       "    would widen the x axis to include 1,000--5,000.  We typed out the name of\n",
       "    the option, but most people would type\n",
       "
\n",
       "        . scatter mpg weight, xscale(r(1000 5000))\n",
       "
\n",
       "    range() can widen, but never narrow, the extent of an axis.  Typing\n",
       "
\n",
       "        . scatter mpg weight, xscale(r(1000 4000))\n",
       "
\n",
       "    would not omit cars with weight>4000 from the plot.  If that is your\n",
       "    desire, type\n",
       "
\n",
       "        . scatter mpg weight if weight<=4000\n",
       "
\n",
       "    See [G-3] axis_scale_options for more information on range(), yscale(),\n",
       "    and xscale(); see [G-3] axis_label_options for more information on\n",
       "    ylabel(minmax) and xlabel(minmax).\n",
       "
\n",
       "
\n",
       "Log scales\n",
       "
\n",
       "    By default, arithmetic scales for the axes are used.  Log scales can be\n",
       "    obtained by specifying the log suboption of yscale() and xscale().  For\n",
       "    instance,\n",
       "
\n",
       "        . sysuse lifeexp, clear\n",
       "
\n",
       "        . scatter lexp gnppc, xscale(log) xlab(,g)\n",
       "          (click to run)\n",
       "
\n",
       "    The important option above is xscale(log), which caused gnppc to be\n",
       "    presented on a log scale.\n",
       "
\n",
       "    We included xlab(,g) (abbreviated form of xlabel(, grid)) to obtain x\n",
       "    grid lines.  The values 30,000 and 40,000 are overprinted.  We could\n",
       "    improve the graph by typing\n",
       "
\n",
       "        . generate gnp000 = gnppc/1000\n",
       "
\n",
       "        . label var gnp000 \"GNP per capita, thousands of dollars\"\n",
       "
\n",
       "        . scatter lexp gnp000, xsca(log) xlab(.5 2.5 10(10)40, grid)\n",
       "          (click to run)\n",
       "
\n",
       "    See [G-3] axis_options.\n",
       "
\n",
       "
\n",
       "Multiple axes\n",
       "
\n",
       "    Graphs may have more than one y axis and more than one x axis.  There are\n",
       "    two reasons to do this:  you might include an extra axis so that you have\n",
       "    an extra place to label special values or so that you may plot multiple\n",
       "    variables on different scales.  In either case, specify the yaxis() or\n",
       "    xaxis() option.  See [G-3] axis_choice_options.\n",
       "
\n",
       "
\n",
       "Markers\n",
       "
\n",
       "    Markers are the ink used to mark where points are on the plot.  Many\n",
       "    people think of markers in terms of their shape (circles, diamonds,\n",
       "    etc.), but they have other properties, including, most importantly, their\n",
       "    color and size.  The shape of the marker is specified by the msymbol()\n",
       "    option, its color by the mcolor() option, and its size by the msize()\n",
       "    option.\n",
       "
\n",
       "    By default, solid circles are used for the first y variable, solid\n",
       "    diamonds for the second, solid squares for the third, and so on; see\n",
       "    marker_options under Options for the remaining details, if you care.  In\n",
       "    any case, when you type\n",
       "
\n",
       "        . scatter yvar xvar\n",
       "
\n",
       "    results are as if you typed\n",
       "
\n",
       "        . scatter yvar xvar, msymbol(O)\n",
       "
\n",
       "    You can vary the symbol used by specifying other msymbol() arguments.\n",
       "    Similarly, you can vary the color and size of the symbol by specifying\n",
       "    the mcolor() and msize() options.  See [G-3] marker_options.\n",
       "
\n",
       "    In addition to the markers themselves, you can request that the\n",
       "    individual points be labeled.  These marker labels are numbers or text\n",
       "    that appear beside the marker symbol -- or in place of it -- to identify\n",
       "    the points.  See [G-3] marker_label_options.\n",
       "
\n",
       "
\n",
       "Weighted markers\n",
       "
\n",
       "    If weights are specified -- see weight -- the size of the marker is\n",
       "    scaled according to the size of the weights.  aweights, fweights, and\n",
       "    pweights are allowed and all are treated the same; iweights are not\n",
       "    allowed because scatter would not know what to do with negative values.\n",
       "    Weights affect the size of the marker and nothing else about the plot.\n",
       "
\n",
       "    Below we use U.S. state-averaged data to graph the divorce rate in a\n",
       "    state versus the state's median age.  We scale the symbols to be\n",
       "    proportional to the population size:\n",
       "
\n",
       "        . sysuse census, clear\n",
       "
\n",
       "        . generate drate = divorce / pop18p\n",
       "
\n",
       "        . label var drate \"Divorce rate\"\n",
       "
\n",
       "        . scatter drate medage [w=pop18p] if state!=\"Nevada\", msymbol(Oh)\n",
       "             note(\"Stata data excluding Nevada\"\n",
       "             \"Area of symbol proportional to state's population aged 18+\")\n",
       "          (click to run)\n",
       "
\n",
       "    Note the use of the msymbol(Oh) option.  Hollow scaled markers look much\n",
       "    better than solid ones.\n",
       "
\n",
       "    scatter scales the symbols so that the sizes are a fair representation\n",
       "    when the weights represent population weights.  If all the weights except\n",
       "    one are 1,000 and the exception is 999, the symbols will all be of almost\n",
       "    equal size.  The weight 999 observation will not be a dot and the weight\n",
       "    1,000 observation giant circles as would be the result if the exception\n",
       "    had weight 1.\n",
       "
\n",
       "    Weights are ignored when the mlabel() option is specified.  See [G-3]\n",
       "    marker_label_options.\n",
       "
\n",
       "
\n",
       "Jittered markers\n",
       "
\n",
       "    scatter will add spherical random noise to your data before plotting if\n",
       "    you specify jitter(#), where # represents the size of the noise as a\n",
       "    percentage of the graphical area.  This can be useful for creating graphs\n",
       "    of categorical data when, were the data not jittered, many of the points\n",
       "    would be on top of each other, making it impossible to tell whether the\n",
       "    plotted point represented one or 1,000 observations.\n",
       "
\n",
       "    For instance, in a variation on auto.dta used below, mpg is recorded in\n",
       "    units of 5 mpg, and weight is recorded in units of 500 pounds.  A\n",
       "    standard scatter has considerable overprinting:\n",
       "
\n",
       "        . sysuse autornd, clear\n",
       "
\n",
       "        . scatter mpg weight\n",
       "          (click to run)\n",
       "
\n",
       "    There are 74 points in the graph, even though it appears because of\n",
       "    overprinting as if there are only 19.  Jittering solves that problem:\n",
       "
\n",
       "        . scatter mpg weight, jitter(7)\n",
       "          (click to run)\n",
       "
\n",
       "
\n",
       "Connected lines\n",
       "
\n",
       "    The connect() option allows you to connect the points of a graph.  The\n",
       "    default is not to connect the points.\n",
       "
\n",
       "    If you want connected points, you probably want to specify connect(l),\n",
       "    which is usually abbreviated c(l).  The l means that the points are to be\n",
       "    connected with straight lines.  Points can be connected in other ways\n",
       "    (such as a stairstep fashion), but usually c(l) is the right choice.  The\n",
       "    command\n",
       "
\n",
       "        . scatter yvar xvar, c(l)\n",
       "
\n",
       "    will plot yvar versus xvar, marking the points in the usual way, and\n",
       "    drawing straight lines between the points.  It is common also to specify\n",
       "    the sort option,\n",
       "
\n",
       "        . scatter yvar xvar, c(l) sort\n",
       "
\n",
       "    because otherwise points are connected in the order of the data.  If the\n",
       "    data are already in the order of xvar, the sort is unnecessary.  You can\n",
       "    also omit the sort when creating special effects.\n",
       "
\n",
       "    connect() is often specified with the msymbol(i) option to suppress the\n",
       "    display of the individual points:\n",
       "
\n",
       "        . scatter yvar xvar, c(l) sort m(i)\n",
       "
\n",
       "    See [G-3] connect_options.\n",
       "
\n",
       "
\n",
       "Graphs by groups\n",
       "
\n",
       "    Option by() specifies that graphs are to be drawn separately for each of\n",
       "    the different groups and the results arrayed into one display.  Below we\n",
       "    use country data and group the results by region of the world:\n",
       "
\n",
       "        . sysuse lifeexp, clear\n",
       "
\n",
       "        . scatter lexp gnppc, by(region)\n",
       "          (click to run)\n",
       "
\n",
       "    Variable region is a numeric variable taking on values 1, 2, and 3.\n",
       "    Separate graphs were drawn for each value of region.  The graphs were\n",
       "    titled \"Eur & C. Asia\", \"N.A.\", and \"S.A.\" because numeric variable\n",
       "    region had been assigned a value label, but results would have been the\n",
       "    same had variable region been a string directly containing \"Eur & C.\n",
       "    Asia\", \"N.A.\", and \"S.A.\".\n",
       "
\n",
       "    See [G-3] by_option for more information on this useful option.\n",
       "
\n",
       "
\n",
       "Saving graphs\n",
       "
\n",
       "    To save a graph to disk for later printing or reviewing, include the\n",
       "    saving() option,\n",
       "
\n",
       "        . scatter ..., ... saving(filename)\n",
       "
\n",
       "    or use the graph save command afterward:\n",
       "
\n",
       "        . scatter ...\n",
       "        . graph save filename\n",
       "
\n",
       "    See [G-3] saving_option and [G-2] graph save.  Also see gph files for\n",
       "    information on how files such as filename.gph can be put to subsequent\n",
       "    use.\n",
       "
\n",
       "
\n",
       "Video example\n",
       "
\n",
       "    Basic scatterplots in Stata\n",
       "
\n",
       "
\n",
       "Appendix:  Styles and composite styles\n",
       "
\n",
       "    Many options end in the word style, including mstyle(), mlabstyle(), and\n",
       "    lstyle().  Option mstyle(), for instance, is described as setting the\n",
       "    \"overall look\" of a marker.  What does that mean?\n",
       "
\n",
       "    How something looks -- a marker, a marker label, a line -- is specified\n",
       "    by many detail options.  For markers, option msymbol() specifies its\n",
       "    shape, mcolor() specifies its color and opacity, msize() specifies its\n",
       "    size, and so on.\n",
       "
\n",
       "    A style specifies a composite of related option settings.  If you typed\n",
       "    option mstyle(p1), you would be specifying a whole set of values for\n",
       "    msymbol(), mcolor(), msize(), and all the other m*() options.  p1 is\n",
       "    called the name of a style, and p1 contains the settings.\n",
       "
\n",
       "    Concerning mstyle() and all the other options ending in the word style,\n",
       "    throughout this manual you will read statements such as\n",
       "
\n",
       "        Option whateverstyle() specifies the overall look of whatever, such\n",
       "        as its (insert list here).  The other options allow you to change the\n",
       "        attributes of a whatever, but whateverstyle() is the starting point.\n",
       "
\n",
       "        You need not specify whateverstyle() just because there is something\n",
       "        you want to change about the look of a whatever, and in fact, most\n",
       "        people seldom specify the whateverstyle() option.  You specify\n",
       "        whateverstyle() when another style exists that is exactly what you\n",
       "        desire or when another style would allow you to specify fewer changes\n",
       "        to obtain what you want.\n",
       "
\n",
       "    Styles actually come in two forms called composite styles and detail\n",
       "    styles, and the above statement applies only to composite styles and\n",
       "    appears only in manual entries concerning composite styles.  Composite\n",
       "    styles are specified in options that end in the word style.  The\n",
       "    following are examples of composite styles:\n",
       "
\n",
       "        mstyle(symbolstyle)\n",
       "        mlstyle(linestyle)\n",
       "        mlabstyle(markerlabelstyle)\n",
       "        lstyle(linestyle)\n",
       "        pstyle(pstyle)\n",
       "
\n",
       "    The following are examples of detail styles:\n",
       "
\n",
       "        mcolor(colorstyle)\n",
       "        mlwidth(linewidthstyle)\n",
       "        mlabsize(textsizestyle)\n",
       "        lpattern(linepatternstyle)\n",
       "
\n",
       "    In the above examples, distinguish carefully between option names such as\n",
       "    mcolor() and option arguments such as colorstyle.  colorstyle is an\n",
       "    example of a detail style because it appears in the option mcolor(), and\n",
       "    the option name does not end in the word style.\n",
       "
\n",
       "    Detail styles specify precisely how an attribute of something looks, and\n",
       "    composite styles specify an \"overall look\" in terms of detail-style\n",
       "    values.\n",
       "
\n",
       "    Composite styles sometimes contain other composite styles as members.\n",
       "    For instance, when you specify the mstyle() option -- which specifies the\n",
       "    overall look of markers -- you are also specifying an mlstyle() -- which\n",
       "    specifies the overall look of the lines that outline the shape of the\n",
       "    markers.  That does not mean you cannot specify the mlstyle() option,\n",
       "    too.  It just means that specifying mstyle() implies an mlstyle().  The\n",
       "    order in which you specify the options does not matter.  You can type\n",
       "
\n",
       "        . scatter ..., ... mstyle(...) ... mlstyle(...) ...\n",
       "
\n",
       "    or\n",
       "
\n",
       "        . scatter ..., ... mlstyle(...) ... mstyle(...) ...\n",
       "
\n",
       "    and, either way, mstyle() will be set as you specify, and then mlstyle()\n",
       "    will be reset as you wish.  The same applies for mixing composite-style\n",
       "    and detail-style options.  Option mstyle() implies an mcolor() value.\n",
       "    Even so, you may type\n",
       "
\n",
       "        . scatter ..., ... mstyle(...) ... mcolor(...) ...\n",
       "
\n",
       "    or\n",
       "
\n",
       "        . scatter ..., ... mcolor(...) ... mstyle(...) ...\n",
       "
\n",
       "    and the outcome will be the same.\n",
       "
\n",
       "    The grandest composite style of them all is pstyle(pstyle).  It contains\n",
       "    all the other composite styles and scatter (twoway, in fact) makes great\n",
       "    use of this grand style.  When you type\n",
       "
\n",
       "        . scatter y1var y2var xvar, ...\n",
       "
\n",
       "    results are as if you typed\n",
       "
\n",
       "        . scatter y1var y2var xvar, pstyle(p1 p2) ...\n",
       "
\n",
       "    That is, y1var versus xvar is plotted using pstyle(p1), and y2var versus\n",
       "    xvar is plotted using pstyle(p2).  It is the pstyle(p1) that sets all the\n",
       "    defaults -- which marker symbols are used, what color they are, etc.\n",
       "
\n",
       "    The same applies if you type\n",
       "
\n",
       "        . scatter y1var xvar, ... || scatter y2var xvar, ...\n",
       "
\n",
       "    y1var versus xvar is plotted using pstyle(p1), and y2var versus xvar is\n",
       "    plotted using pstyle(p2), just as if you had typed\n",
       "
\n",
       "        . scatter y1var xvar, pstyle(p1) ... || scatter y2var xvar,\n",
       "            pstyle(p2) ...\n",
       "
\n",
       "    The same applies if you mix scatter with other plottypes:\n",
       "
\n",
       "        . scatter y1var xvar, ... || line y2var xvar, ...\n",
       "
\n",
       "    is equivalent to\n",
       "
\n",
       "        . scatter y1var xvar, pstyle(p1) ... || line y2var xvar, pstyle(p2)\n",
       "            ...\n",
       "
\n",
       "    and,\n",
       "
\n",
       "        . twoway (..., ...) (..., ...), ...\n",
       "
\n",
       "    is equivalent to\n",
       "
\n",
       "        . twoway (..., pstyle(p1) ...) (..., pstyle(p2) ...), ...\n",
       "
\n",
       "    which is why we said that it is twoway, and not just scatter, that\n",
       "    exploits scheme().\n",
       "
\n",
       "    You can put this to use.  Pretend that you have a dataset on husbands and\n",
       "    wives and it contains the variables\n",
       "
\n",
       "        hinc        husband's income\n",
       "        winc        wife's income\n",
       "        hed         husband's education\n",
       "        wed         wife's education\n",
       "
\n",
       "    You wish to draw a graph of income versus education, drawing no\n",
       "    distinctions between husbands and wives.  You type\n",
       "
\n",
       "        . scatter hinc hed || scatter winc wed\n",
       "
\n",
       "    You intend to treat husbands and wives the same in the graph, but in the\n",
       "    above example, they are treated differently because msymbol(O) will be\n",
       "    used to mark the points of hinc versus hed and msymbol(D) will be used to\n",
       "    designate winc versus wed.  The color of the symbols will be different,\n",
       "    too.\n",
       "
\n",
       "    You could address that problem in many different ways.  You could specify\n",
       "    the msymbol() and mcolor() options (see [G-3] marker_options), along with\n",
       "    whatever other detail options are necessary to make the two scatters\n",
       "    appear the same.  Being knowledgeable, you realize you do not have to do\n",
       "    that.  There is, you know, a composite style that specifies this.  So you\n",
       "    get out your manuals, flip through, and discover that the relevant\n",
       "    composite style for the marker symbols is mstyle().\n",
       "
\n",
       "    Easiest of all, however, would be to remember that pstyle() contains all\n",
       "    the other styles.  Rather than resetting mstyle(), just reset pstyle(),\n",
       "    and whatever needs to be set to make the two plots the same will be set.\n",
       "    Type\n",
       "
\n",
       "        . scatter hinc hed || scatter winc wed, pstyle(p1)\n",
       "
\n",
       "    or, if you prefer,\n",
       "
\n",
       "        . scatter hinc hed, pstyle(p1) || scatter winc wed, pstyle(p1)\n",
       "
\n",
       "    You do not need to specify pstyle(p1) for the first plot, however,\n",
       "    because that is the default.\n",
       "
\n",
       "    As another example, you have a dataset containing\n",
       "
\n",
       "        mpg         Mileage ratings of cars\n",
       "        weight      Each car's weight\n",
       "        prediction  A predicted mileage rating based on weight\n",
       "
\n",
       "    You wish to draw the graph\n",
       "
\n",
       "        . scatter mpg weight || line prediction weight\n",
       "
\n",
       "    but you wish the appearance of the line to \"match\" that of the markers\n",
       "    used to plot mpg versus weight.  You could go digging to find out which\n",
       "    option controlled the line style and color and then dig some more to\n",
       "    figure out which line style and color goes with the markers used in the\n",
       "    first plot, but much easier is simply to type\n",
       "
\n",
       "        . scatter mpg weight || line prediction weight, pstyle(p1)\n",
       "
\n",
       "
\n", "\n", "
\n", "\n", "\n", "
\n", "

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"cell_type": "markdown", "metadata": {}, "source": [ "**NOTE:** But this is `stata` -- Python code won't work" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "import: unknown subcommand \"pandas\"\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "r(198);\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can however call python using `stata16` python integration" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "----------------------------------------------- python (type end to exit) ------\n", "\n", ">>> s = pd.Series([1,2,3])\n", ">>> s\n", "0 1\n", "1 2\n", "2 3\n", "dtype: int64\n", ">>> end\n", "--------------------------------------------------------------------------------\n" ] } ], "source": [ "set python_exec \"/Users/rsitworkshop/opt/anaconda3/bin/python\"\n", "python:\n", "import pandas as pd\n", "s = pd.Series([1,2,3])\n", "s\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But the graphics don't carry through as you are constrained in similar fashion to the `stata` environment when using `python`" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "----------------------------------------------- python (type end to exit) ------\n", "\n", ">>> s = pd.Series([1,2,3])\n", ">>> s.plot()\n", "\n", ">>> end\n", "--------------------------------------------------------------------------------\n" ] } ], "source": [ "python:\n", "import pandas as pd\n", "s = pd.Series([1,2,3])\n", "s.plot()\n", "end" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "----------------------------------------------- python (type end to exit) ------\n", "\n", ">>> import matplotlib.pyplot as plt\n", ">>> s = pd.Series([1,2,3])\n", ">>> ax = s.plot()\n", ">>> plt.savefig(\"test.png\")\n", ">>> end\n", "--------------------------------------------------------------------------------\n" ] } ], "source": [ "python:\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "s = pd.Series([1,2,3])\n", "ax = s.plot()\n", "plt.savefig(\"test.png\")\n", "end" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "total 632\n", "-rw-r--r-- 1 rsitworkshop staff 17862 30 Mar 16:20 code.log\n", "drwxr-xr-x 7 rsitworkshop staff 224 30 Mar 16:20 data/\n", "-rw-r--r-- 1 rsitworkshop staff 162395 30 Mar 16:20 gravity-model-example.ipy\n", "> nb\n", "-rw-r--r-- 1 rsitworkshop staff 1096 30 Mar 16:20 gravity-model-example.py\n", "-rw-r--r-- 1 rsitworkshop staff 12219 30 Mar 16:20 jupyter-ipystata.ipynb\n", "-rw-r--r-- 1 rsitworkshop staff 100533 30 Mar 17:08 stata-jupyter-kernel.ipyn\n", "> b\n", "-rw-r--r-- 1 rsitworkshop staff 20214 30 Mar 17:08 test.png\n" ] } ], "source": [ "ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "kernelspec": { "display_name": "Stata", "language": "stata", "name": "stata" }, "language_info": { "codemirror_mode": "stata", "file_extension": ".do", "mimetype": "text/x-stata", "name": "stata", "version": "15.1" } }, "nbformat": 4, "nbformat_minor": 4 }