Data Analysis (Other (Not Listed) Sample)

In [1]: import numpy as np import math import pandas as pd from datascience import * In [2]: #Question 1.1.1 pi = np.pi e = np.e interesting_numbers = np.array([0, 1, -1, pi, e]) print('interesting_numbers:', interesting_numbers) interesting_numbers: [ 0. 1. -1. 3.14159265 2.71828183] In [3]: #Question 1.1.2 hello_world_components = np.array(["Hello", ",", " ", "world", "!"]) print("hello_world_components:", hello_world_components) hello_world_components: ['Hello' ',' ' ' 'world' '!'] In [4]: #Question 1.1.3 #Create an array with multiples of 99 from 0 to 9999 multiples_of_99 = np.arange(0, 10000, 99) multiples_of_99 Out[4]: array([ 0, 99, 198, 297, 396, 495, 594, 693, 792, 891, 990, 1089, 1188, 1287, 1386, 1485, 1584, 1683, 1782, 1881, 1980, 2079, 2178, 2277, 2376, 2475, 2574, 2673, 2772, 2871, 2970, 3069, 3168, 3267, 3366, 3465, 3564, 3663, 3762, 3861, 3960, 4059, 4158, 4257, 4356, 4455, 4554, 4653, 4752, 4851, 4950, 5049, 5148, 5247, 5346, 5445, 5544, 5643, 5742, 5841, 5940, 6039, 6138, 6237, 6336, 6435, 6534, 6633, 6732, 6831, 6930, 7029, 7128, 7227, 7326, 7425, 7524, 7623, 7722, 7821, 7920, 8019, 8118, 8217, 8316, 8415, 8514, 8613, 8712, 8811, 8910, 9009, 9108, 9207, 9306, 9405, 9504, 9603, 9702, 9801, 9900, 9999]) In [5]: #Question 1.1.4 #Calculate the number of hours in December (31 days * 24 hours/day) hrs_in_dec = 31 * 24 #Create an array of the time in seconds since the start of the month collection_times = np.arange(0, hrs_in_dec * 3600, 3600) print("collection_times:", collection_times, len(collection_times)) collection_times: [ 0 3600 7200 10800 14400 18000 21600 25200 28800 32400 36000 39600 43200 46800 50400 54000 57600 61200 64800 68400 72000 75600 79200 82800 86400 90000 93600 97200 100800 104400 108000 111600 115200 118800 122400 126000 129600 133200 136800 140400 144000 147600 151200 154800 158400 162000 165600 169200 172800 176400 180000 183600 187200 190800 194400 198000 201600 205200 208800 212400 216000 219600 223200 226800 230400 234000 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2131200 2134800 2138400 2142000 2145600 2149200 2152800 2156400 2160000 2163600 2167200 2170800 2174400 2178000 2181600 2185200 2188800 2192400 2196000 2199600 2203200 2206800 2210400 2214000 2217600 2221200 2224800 2228400 2232000 2235600 2239200 2242800 2246400 2250000 2253600 2257200 2260800 2264400 2268000 2271600 2275200 2278800 2282400 2286000 2289600 2293200 2296800 2300400 2304000 2307600 2311200 2314800 2318400 2322000 2325600 2329200 2332800 2336400 2340000 2343600 2347200 2350800 2354400 2358000 2361600 2365200 2368800 2372400 2376000 2379600 2383200 2386800 2390400 2394000 2397600 2401200 2404800 2408400 2412000 2415600 2419200 2422800 2426400 2430000 2433600 2437200 2440800 2444400 2448000 2451600 2455200 2458800 2462400 2466000 2469600 2473200 2476800 2480400 2484000 2487600 2491200 2494800 2498400 2502000 2505600 2509200 2512800 2516400 2520000 2523600 2527200 2530800 2534400 2538000 2541600 2545200 2548800 2552400 2556000 2559600 2563200 2566800 2570400 2574000 2577600 2581200 2584800 2588400 2592000 2595600 2599200 2602800 2606400 2610000 2613600 2617200 2620800 2624400 2628000 2631600 2635200 2638800 2642400 2646000 2649600 2653200 2656800 2660400 2664000 2667600 2671200 2674800] 744 In [5]: #Question1.2.1 population_amounts = np.array([3070043704, 3967226955, 3838395618, 4967981410, 5477939000, 6068051718, 1418205317, 4168513437, 4259766092, 5332187700, 7405299858]) population_1973 = population_amounts.item(3) print("population_1973:", population_1973) population_1973: 4967981410 In [6]: #Queswtion 1.3.1 population_magnitudes = np.log10(population_amounts) #Print the result print("population_magnitudes:", population_magnitudes) population_magnitudes: [ 9.48714456 9.59848705 9.58414973 9.69617996 9.73861719 9.78304927 9.15173911 9.61998121 9.62938575 9.72690543 9.86954265] In [7]: #if there's an array named restaurant_bills containing the original bill amounts #Compute the total charge for each bill, including a 20% tip restaurant_bills = make_array(20.12, 39.90, 31.01) print("Restaurant bills:\t", restaurant_bills) #Array multiplication tips = 0.2 * restaurant_bills print("Tips:\t\t\t", tips) total_charges = restaurant_bills * 1.2 print("total_charges:", total_charges) Restaurant bills: [ 20.12 39.9 31.01] Tips: [ 4.024 7.98 6.202] total_charges: [ 24.144 47.88 37.212] In [ ]: #Question 1.3.3 more_restaurant_bills = Table.read_table("more_restaurant_bills.csv").column("Bill Amount") #Calculate the total charge for each bill more_total_charges = more_restaurant_bills * 1.2 sum_of_bills = np.sum(more_total_charges) sum_of_bills ...