Alexa Group 3 Lesson (Data Analysis) Hacks
• 23 min read
week 31 student lesson group 3
import numpy as np
#Create a NumPy array of the age
ages = np.array([16, 17, 22, 12, 17, 18, 16, 12, 15, 14, 25, 19, 13, 11, 16, 14, 13, 19, 17, 14, 22, 13, 20])
# Calculate the percentile rank of age
agepercentile = np.percentile(ages, [25, 50, 75])
# Print the results
print("The youngest quarter percentile of students in CSP are", agepercentile[0], "years")
print("The 50th percentile of students in CSP are", agepercentile[1], "years")
print("The 75th percentile of students in CSP are", agepercentile[2], "years")
# Determine the number of students who are in the yougnest 15%
not_low_15 = np.percentile(ages, 15)
number_low15 = ages[ages <= not_low_15]
print("There are", len(number_low15), "students in the youngest 15 percent of the class.")
The youngest quarter percentile of students in CSP are 13.5 years The 50th percentile of students in CSP are 16.0 years The 75th percentile of students in CSP are 18.5 years There are 6 students in the youngest 15 percent of the class.
import pandas as pd
# read the CSV file
df = pd.read_csv("/home/alexac54767/vscode/Alexa-Fastpage/_notebooks/files/Starbucks World Stats.csv")
# display the data in a table
print(df)
Country Population Numer of Starbucks \
0 United States 316128839 11851
1 China 1357380000 1462
2 Canada 35158304 1425
3 Japan 127338621 1052
4 United Kingdom 64097085 770
.. ... ... ...
58 Aruba 102911 3
59 Finland 5439407 3
60 Colombia 48321405 2
61 Netherland Antilles 227049 2
62 Monaco 37831 1
Starbucks per million inhabitants
0 37.49
1 1.08
2 40.53
3 8.26
4 12.01
.. ...
58 29.15
59 0.55
60 0.04
61 8.81
62 26.43
[63 rows x 4 columns]
import pandas as pd
# read the CSV file into a Pandas DataFrame
df = pd.read_csv('/home/alexac54767/vscode/Alexa-Fastpage/_notebooks/files/Starbucks World Stats.csv')
# print the 2 specific columns (country and #Starbucks it has) for the first 10 countries
df_2rows = df[['Country', 'Numer of Starbucks']].head(10)
print(df_2rows)
Country Numer of Starbucks 0 United States 11851 1 China 1462 2 Canada 1425 3 Japan 1052 4 United Kingdom 770 5 Korea, Rep. 684 6 Mexico 423 7 Taiwan 316 8 Philippines 234 9 Turkey 213
Question Hacks
-
What is NumPy and how is it used in data analysis?
- NumPy helps with math and data analysis in Python. It is used to control large data sets that would normally be much more complicated and messy to analyze.
- NumPy helps with math and data analysis in Python. It is used to control large data sets that would normally be much more complicated and messy to analyze.
-
What is Pandas and how is it used in data analysis?
- Pandas is a software library that is used in Python. It allows for easy analyzing and manipulation of data.
- Pandas is a software library that is used in Python. It allows for easy analyzing and manipulation of data.
-
How is NunPy different than Pandas for data analysis?
- NunPy uses the data to perform mathematical expressions/other analytical operations. The data analytics that Pandas perfoms is more searching and manipulation of the data.
- NunPy uses the data to perform mathematical expressions/other analytical operations. The data analytics that Pandas perfoms is more searching and manipulation of the data.
-
What is a DataFrame?
- A DataFrame is a two-dimensional labeled data structure. It holds data and can be indexed by labels.
- A DataFrame is a two-dimensional labeled data structure. It holds data and can be indexed by labels.
-
What are some common operations you can perform with NunPy?
- Some common operations you can perform with NunPy are data analytical operations like mean, median, standard deviation, quartiles, etc.
- Some common operations you can perform with NunPy are data analytical operations like mean, median, standard deviation, quartiles, etc.
-
How Can You Incorporate Either of these Data Analysis Tools (NunPy, Pandas) into your project?
- I could find a CSV related to my project topic (fitness) and analyze it using NunPy and Pandas. For example, I could find a CSV file about nutrition in foods and provide organized information to my users. That way they could incorporate a healthy/aware diet into their fitness lives.
import pandas as pd
pd.set_option('display.max_rows', 332)
# Load the data
df = pd.read_csv('/home/alexac54767/vscode/Alexa-Fastpage/_notebooks/files/nutrients_csvfile.csv')
# Keep only required columns
df = df.loc[:, ['Food', 'Grams', 'Calories', 'Protein', 'Fat', 'Sat.Fat', 'Fiber', 'Carbs']]
# Drop rows with missing values
df.dropna(inplace=True)
# Convert binary categorical variable to numeric
df['Calories'] = df['Calories'].apply(lambda x: 1 if x == 'Low' else 0)
print(df)
Food Grams Calories Protein Fat \
0 Cows' milk 976 0 32 40
1 Milk skim 984 0 36 t
2 Buttermilk 246 0 9 5
3 Evaporated, undiluted 252 0 16 20
4 Fortified milk 1,419 0 89 42
5 Powdered milk 103 0 27 28
6 skim, instant 85 0 30 t
7 skim, non-instant 85 0 30 t
8 Goats' milk 244 0 8 10
9 (1/2 cup ice cream) 540 0 24 24
10 Cocoa 252 0 8 11
11 skim. milk 250 0 18 4
12 (cornstarch) 248 0 9 10
13 Custard 248 0 13 14
14 Ice cream 188 0 6 18
15 Ice milk 190 0 9 10
16 Cream or half-and-half 120 0 4 15
17 or whipping 119 0 2 44
18 Cheese 225 0 30 11
19 uncreamed 225 0 38 t
20 Cheddar 17 0 4 6
21 Cheddar, grated cup 56 0 14 19
22 Cream cheese 28 0 2 11
23 Processed cheese 28 0 7 9
24 Roquefort type 28 0 6 9
25 Swiss 28 0 7 8
26 Eggs raw 100 0 12 12
27 Eggs Scrambled or fried 128 0 13 16
28 Yolks 34 0 6 10
29 Butter 14 0 t 11
30 Butter 112 0 114 115
31 Butter 112 0 114 115
32 Hydrogenated cooking fat 100 0 0 100
33 Lard 110 0 0 110
34 Margarine 112 0 t 91
35 Margarine, 2 pat or 14 0 t 11
36 Mayonnaise 15 0 t 12
37 Corn oil 14 0 0 14
38 Olive oil 14 0 0 14
39 Safflower seed oil 14 0 0 14
40 French dressing 15 0 t 6
41 Thousand Island sauce 15 0 t 8
43 Bacon 16 0 4 8
44 Beef 85 0 23 16
45 Hamburger 85 0 21 17
46 Ground lean 85 0 24 10
47 Roast beef 85 0 16 36
48 Steak 85 0 20 27
49 Steak, lean, as round 85 0 24 12
50 Corned beef 85 0 22 10
51 Corned beef hash canned 85 0 12 8
52 Corned beef hash Dried 56 0 19 4
53 Pot-pie 227 0 18 28
54 Corned beef hash Stew 235 0 15 10
55 chicken 85 0 23 9
56 Fried, breast or leg and thigh chicken 85 0 25 15
57 Roasted chicken 100 0 25 20
58 Chicken livers, fried 100 0 22 14
59 Duck, domestic 100 0 16 28
60 Lamb, chop, broiled 115 0 24 35
61 Leg roasted 86 0 20 14
62 Shoulder, braised 85 0 18 23
63 Pork, chop, 1 thick 100 0 16 21
64 Ham pan-broiled 85 0 16 22
65 Ham, as 57 0 13 13
66 Ham, canned, spiced 57 0 8 14
67 Pork roast 85 0 21 24
68 Pork sausage 100 0 18 44
69 Turkey 100 0 27 15
70 Veal 85 0 23 9
71 Roast 85 0 13 14
72 Clams 85 0 12 1
73 Cod 100 0 28 5
74 Crab meat 85 0 14 2
75 Fish sticks fried 112 0 19 10
76 Flounder 100 0 30 8
77 Haddock 85 0 16 5
78 Halibut 100 0 26 8
79 Herring 100 0 22 13
80 Lobster 100 0 18 1
81 Mackerel 85 0 18 9
82 Oysters 230 0 232 233
83 Oyster stew 85 0 19 6
84 Salmon 85 0 17 5
85 Sardines 85 0 22 9
86 Scallops 100 0 18 8
87 Shad 85 0 20 10
88 Shrimp 85 0 23 1
89 Swordfish 100 0 27 6
90 Tuna 85 0 25 7
91 Artichoke 100 0 2 t
92 Asparagus 96 0 1 t
93 Beans 125 0 1 t
94 Lima 160 0 8 t
95 Lima, dry, cooked 192 0 16 t
96 Navy, baked with pork 200 0 11 6
97 Red kidney 260 0 15 1
98 Bean sprouts 50 0 1 t
99 Beet greens 100 0 2 t
101 Broccoli 150 0 5 t
102 Brussels sprouts 130 0 6 t
103 Sauerkraut 150 0 1 t
104 Steamed cabbage 170 0 2 t
105 Carrots 150 0 1 t
106 Raw, grated 110 0 1 t
107 Strips, from raw 50 0 t t
108 Cauliflower 120 0 3 t
109 Celery 100 0 1 t
110 Stalk raw 40 0 1 t
111 Chard steamed 150 0 2 t
112 Collards 150 0 5 t
113 Corn 100 0 3 1
114 cooked or canned 200 0 5 t
115 Cucumbers 50 0 t 0
116 Dandelion greens 180 0 5 1
117 Eggplant 180 0 2 t
118 Endive 57 0 1 t
119 Kale 110 0 4 1
120 Kohlrabi 140 0 2 t
121 Lambs quarters, steamed 150 0 5 t
122 Lentils 200 0 15 t
123 Lettuce 100 0 1 t
124 Iceberg 100 0 t t
125 Mushrooms canned 120 0 2 t
126 Mustard greens 140 0 3 t
127 Okra 100 0 1 t
128 Onions 210 0 2 t
129 Raw, green 50 0 t t
130 Parsley 50 0 t t
131 Parsnips 155 0 2 1
132 Peas 100 0 3 t
133 Fresh, steamed peas 100 0 5 t
135 Split cooked peas 100 0 8 t
136 heated peas 100 0 3 t
137 Peppers canned 38 0 t t
138 Peppers Raw, green, sweet 100 0 1 t
139 Peppers with beef and crumbs 150 0 19 9
140 Potatoes, baked 100 0 2 t
141 French-fried 60 0 -1 7
142 Potatoes Mashed with milk and butter 200 0 4 12
143 Potatoes, pan-tried 100 0 4 14
144 Scalloped with cheese potatoes 100 0 6 8
145 Steamed potatoes before peeling 100 0 2 t
146 Potato chips 20 0 1 7
147 Radishes 50 0 t 0
148 Rutabagas 100 0 t 0
149 Soybeans 200 0 22 11
150 Spinach 100 0 3 t
151 Squash 210 0 1 t
152 Winter, mashed 200 0 4 t
153 Sweet potatoes 110 0 2 1
154 Candied 175 0 2 6
155 Tomatoes 240 0 2 t
156 Raw, 2 by 2 1/2 150 0 1 t
157 Tomato juice 240 0 2 t
158 Tomato catsup 17 0 t t
159 Turnip greens 145 0 4 1
160 Turnips, steamed 155 0 1 t
161 Watercress stems, raw 50 0 1 t
162 Apple juice canned 250 0 t 0
163 Apple vinegar 100 0 t 0
164 Apples, raw 130 0 t t
165 Stewed or canned 240 0 t t
166 Apricots 250 0 2 t
167 Dried, uncooked 75 0 4 t
168 Fresh 114 0 1 t
169 Nectar, or juice 250 0 1 t
170 Avocado 108 0 2 18
171 Banana 150 0 1 t
172 Blackberries 144 0 2 1
173 Blueberries 250 0 1 t
174 Cantaloupe 380 0 1 t
175 Cherries 257 0 2 1
176 Fresh, raw 114 0 1 t
177 Cranberry sauce sweetened 277 0 t t
178 Dates 178 0 4 t
179 Figs 42 0 2 t
180 Fresh, raw figs 114 0 2 t
181 figs Canned with syrup 115 0 1 t
182 Fruit cocktail, canned 256 0 1 t
183 Grapefruit sections 250 0 1 t
184 Grapefruit, fresh, 5" diameter 285 0 1 t
185 Grapefruit juice 250 0 1 t
186 Grapes 153 0 1 t
187 European, as Muscat, Tokay 160 0 1 t
188 Grape juice 250 0 1 t
189 Lemon juice 125 0 t t
190 Lemonade concentratefrozen 220 0 t t
191 Limeade concentrate frozen 218 0 t t
192 Olives large 65 0 1 10
193 OlivesRipe 65 0 1 13
194 Oranges 3" diameter 180 0 2 t
195 Orange juice 250 0 2 t
196 Frozen 210 0 2 t
197 Papaya 200 0 1 t
198 Peaches 257 0 1 t
199 Fresh, raw 114 0 1 t
200 Pears 255 0 1 t
201 Raw, 3 by 2V 182 0 1 1
202 Persimmons 125 0 1 t
203 Pineapple 122 0 t t
204 Pineapple Crushed 260 0 1 t
205 Raw, diced 140 0 1 t'
206 Pineapple juice 250 0 1 t
207 Plums 256 0 1 t
208 Raw, 2" diameter 60 0 t t
209 Prunes 270 0 3 1
210 Prune juice 240 0 1 t
211 Raisins 88 0 2 t
212 Raspberries 100 0 t t
213 Raw, red 100 0 t t
214 Rhubarb sweetened 270 0 1 t
215 Strawberries 227 0 1 t
216 Raw 149 0 t t
217 Tangerines 114 0 1 t
218 Watermelon 925 0 2 1
219 Biscuits 38 0 3 4
220 Bran flakes 25 0 3 t
221 Bread, cracked wheat 23 0 2 1
222 Rye 23 0 2 1
223 White, 20 slices, or 454 0 39 15
224 Whole-wheat 454 0 48 14
225 Whole-wheat 23 0 2 1
226 Corn bread ground meal 50 0 3 4
227 Cornflakes 25 0 2 t
228 Corn grits cooked 242 0 8 t
229 Corn meal 118 0 9 4
230 Crackers 14 0 1 1
231 Soda, 2 1/2 square 11 0 1 1
232 Farina 238 0 3 t
233 Flour 110 0 39 22
234 Wheat (all purpose) 110 0 12 1
235 Wheat (whole) 120 0 13 2
236 Macaroni 140 0 5 1
237 Baked with cheese 220 0 18 25
238 Muffins 48 0 4 5
239 Noodles 160 0 7 2
240 Oatmeal 236 0 5 3
241 Pancakes 4" diam. 108 0 7 9
242 Wheat, pancakes 4" diam. 108 0 7 9
243 Pizza 14" diam. 75 0 8 6
244 Popcorn salted 28 0 3 7
245 Puffed rice 14 0 t t
246 Puffed wheat presweetened 28 0 1 t
247 Rice 208 0 15 3
248 Converted 187 0 14 t
249 White 191 0 14 t
250 Rice flakes 30 0 2 t
251 Rice polish 50 0 6 6
252 Rolls 50 0 3 12
253 of refined flour 38 0 3 2
254 whole-wheat 40 0 4 1
255 Spaghetti with meat sauce 250 0 13 10
256 with tomatoes and cheese 250 0 6 5
257 Spanish rice 250 0 4 4
258 Shredded wheat biscuit 28 0 3 1
259 Waffles 75 0 8 9
260 Wheat germ 68 0 17 7
261 Wheat-germ cereal toasted 65 0 20 7
262 Wheat meal cereal unrefined 30 0 4 1
263 Wheat, cooked 200 0 12 1
264 Bean soups 250 0 8 5
265 Beef soup 250 0 6 4
266 Bouillon 240 0 5 0
267 chicken soup 250 0 4 2
268 Clam chowder 255 0 5 2
269 Cream soups 255 0 7 12
270 Noodle 250 0 6 4
271 Split-pea soup 250 0 8 3
272 Tomato soup 245 0 6 7
273 Vegetable 250 0 4 2
274 Apple betty 100 0 1 4
275 Bread pudding 200 0 11 12
276 Cakes 40 0 3 t
277 Chocolate fudge 120 0 5 14
278 Cupcake 50 0 3 3
279 Fruit cake 30 0 2 4
280 Gingerbread 55 0 2 7
281 Plain, with no icing 55 0 4 5
282 Sponge cake 40 0 3 2
283 Candy 25 0 t 3
284 Chocolate creams 30 0 t 4
285 Fudge 90 0 t 12
286 Hard candies 28 0 t 0
287 Marshmallows 30 0 1 0
288 Milk chocolate 56 0 2 6
289 Chocolate syrup 40 0 t t
290 Doughnuts 33 0 2 7
291 Gelatin, made with water 239 0 4 t
292 Honey 42 0 t 0
293 Ice cream 300 0 0 0
294 Ices 150 0 0 0
295 preserves 20 0 0 0
296 Jellies 20 0 0 0
297 Molasses 20 0 0 0
298 Cane Syrup 20 0 0 0
299 9" diam. pie 135 0 3 13
300 Cherry Pie 135 0 3 13
301 Custard 130 0 7 11
302 Lemon meringue 120 0 4 12
303 Mince 135 0 3 9
304 Pumpkin Pie 130 0 5 12
305 Puddings Sugar 200 0 0 0
306 3 teaspoons sugar 12 0 0 0
307 Brown, firm-packed, dark sugar 220 0 0 t
308 Syrup 40 0 0 0
309 table blends sugar 40 0 0 0
310 Tapioca cream pudding 250 0 10 10
311 Almonds 70 0 13 38
312 roasted and salted 70 0 13 40
313 Brazil nuts 70 0 10 47
314 Cashews 70 0 12 32
315 coconut sweetened 50 0 1 20
316 Peanut butter 50 0 12 25
317 Peanut butter, natural 50 0 13 24
318 Peanuts 50 0 13 25
319 Pecans 52 0 5 35
320 Sesame seeds 50 0 9 24
321 Sunflower seeds 50 0 12 26
322 Walnuts 50 0 7 32
323 Beer 480 0 t 0
324 Gin 28 0 0 0
325 Wines 120 0 t 0
326 Table (12.2% alcohol) 120 0 t 0
327 Carbonated drinks Artificially sweetened 346 0 0 0
328 Club soda 346 0 0 0
329 Cola drinks 346 0 0 0
330 Fruit-flavored soda 346 0 0 0
331 Ginger ale 346 0 0 0
332 Root beer 346 0 0 0
333 Coffee 230 0 t 0
334 Tea 230 0 0 t
Sat.Fat Fiber Carbs
0 36 0 48
1 t 0 52
2 4 0 13
3 18 0 24
4 23 1.4 119
5 24 0 39
6 t 0 42
7 t 1 42
8 8 0 11
9 22 0 70
10 10 0 26
11 3 1 13
12 9 0 40
13 11 0 28
14 16 0 29
15 9 0 32
16 13 0 5
17 27 1 3
18 10 0 6
19 t 0 6
20 5 0 t
21 17 0 1
22 10 0 1
23 8 0 t
24 8 0 t
25 7 0 t
26 10 0 t
27 14 0 1
28 8 0 t
29 10 0 t
30 116 117 118
31 116 117 118
32 88 0 0
33 92 0 0
34 76 0 t
35 9 0 t
36 5 0 t
37 5 0 0
38 3 0 0
39 3 0 0
40 2 0 2
41 3 0 1
43 7 0 1
44 15 0 0
45 15 0 0
46 9 0 0
47 35 0 0
48 25 0 0
49 11 0 0
50 9 0 0
51 7 t 6
52 4 0 0
53 25 t 32
54 9 t 15
55 7 0 0
56 11 0 0
57 16 0 0
58 12 0 2.30
59 0 0 0
60 33 0 0
61 14 0 0
62 21 0 0
63 18 0 0
64 19 0 0
65 11 0 0
66 12 0 1
67 21 0 0
68 40 0 0
69 0 0 0
70 8 0 0
71 13 0 0
72 0 0 2
73 0 0 0
74 0 0 1
75 5 0 8
76 0 0 0
77 4 0 6
78 0 0 0
79 0 0 0
80 0 0 t
81 0 a 0
82 234 235 236
83 1 0 0
84 1 0 0
85 4 0 0
86 0 0 10
87 0 0 0
88 0 0 0
89 0 0 0
90 3 0 0
91 t 2 10
92 t 0.5 3
93 t 0.8 6
94 t 3.0 24
95 t 2 48
96 6 2 37
97 0 2.5 42
98 0 0.3 3
99 0 1.4 6
101 0 1.9 8
102 0 1.7 12
103 0 1.2 7
104 0 1.3 9
105 0 0.9 10
106 0 1.2 10
107 0 0.5 5
108 0 1 6
109 0 1 4
110 0 0.3 1
111 0 1.4 7
112 0 2 8
113 t 0.8 21
114 0 1.6 41
115 0 0.2 1
116 0 3.2 16
117 0 1.0 9
118 0 0.6 2
119 0 0.9 8
120 0 1.5 9
121 0 3.2 7
122 0 2.4 38
123 0 0.5 2
124 0 0.5 3
125 0 t 4
126 0 1.2 6
127 0 1 7
128 0 1.6 18
129 0 1 5
130 0 t t
131 0 3 22
132 0 0.1 13
133 0 2.2 12
135 0 0.4 21
136 0 1 10
137 0 t 2
138 0 1.4 6
139 8 1 24
140 0 0.5 22
141 3 0.4 20
142 11 0.7 28
143 6 0.40 33
144 7 0.40 14
145 0 0.40 19
146 4 t 10
147 0 0.3 2
148 0 1.4 8
149 0 3.2 20
150 0 1 3
151 0 0.6 8
152 0 2.6 23
153 0 1 36
154 5 1.5 80
155 0 1 9
156 0 0.6 6
157 0 0.6 10
158 0 t 4
159 0 1.8 8
160 0 1.8 9
161 0 0.3 1
162 0 0 34
163 0 0 3
164 0 1 18
165 0 2 26
166 0 1 57
167 0 1 50
168 0 0.70 14
169 0 2 36
170 12 1.80 6
171 0 0.9 23
172 0 6.60 19
173 0 2 65
174 0 2.20 9
175 0 2 26
176 0 0.8 15
177 0 1.2 142
178 0 3.6 134
179 0 1.9 30
180 0 1 22
181 0 1 32
182 0 0.5 50
183 0 0.5 44
184 t 1 14
185 0 1 24
186 0 0.8 16
187 0 0.7 26
188 0 t 42
189 0 t 10
190 0 t 112
191 0 t 108
192 9 0.8 3
193 12 1 1
194 t 1 16
195 0 0.2 25
196 t 0.4 78
197 0 1.8 18
198 0 1 52
199 0 0.6 10
200 0 2 50
201 0 2 25
202 0 2 20
203 0 0.4 26
204 0 0.7 55
205 0 0.6 19
206 0 0.2 32
207 0 0.7 50
208 0 0.2 7
209 0 0.8 81
210 0 0.7 45
211 0 0.7 82
212 0 2 25
213 0 5 14
214 0 1.9 98
215 0 1.3 60
216 0 1.9 12
217 0 1 10
218 0 3.6 29
219 3 t 18
220 0 0.10 32
221 1 0.10 12
222 1 0.10 12
223 12 9.00 229
224 10 67.50 216
225 0 0.31 11
226 2 0.30 15
227 0 0.1 25
228 0 0.2 27
229 2 1.6 74
230 0 t 10
231 0 t 8
232 0 8 22
233 0 2.9 33
234 0 0.3 84
235 0 2.8 79
236 0 0.1 32
237 24 t 44
238 4 t 19
239 2 0.1 37
240 2 4.6 26
241 0 0.1 28
242 0 0.1 28
243 5 t 23
244 2 0.5 20
245 0 t 12
246 0 0.6 26
247 0 1.2 154
248 0 0.4 142
249 0 0.3 150
250 0 0.1 26
251 0 1.2 28
252 11 0.1 23
253 2 t 20
254 0 0.1 20
255 6 0.50 35
256 3 0.50 36
257 0 1.20 40
258 0 0.70 23
259 1 0.10 30
260 3 2.50 34
261 3 2.50 36
262 0 0.70 25
263 0 4.40 35
264 4 0.60 30
265 4 0.50 11
266 0 0 0
267 2 0 10
268 8 0.50 12
269 11 1.20 18
270 3 0.20 13
271 3 0.50 25
272 6 0.50 22
273 2 0 14
274 0 0.5 29
275 11 0.20 56
276 0 0 23
277 12 0.3 70
278 2 t 31
279 3 0.2 17
280 6 t 28
281 4 t 31
282 2 0 22
283 3 0 19
284 4 0 24
285 11 0.1 80
286 0 0 28
287 0 0 23
288 6 0.2 44
289 t 0 22
290 4 t 17
291 t 0 36
292 0 0 30
293 12 10 0
294 0 0 48
295 0 t 14
296 0 0 13
297 0 8 11
298 0 0 13
299 11 0.1 53
300 11 0.1 55
301 10 0 34
302 10 0.1 45
303 8 0.70 62
304 11 8 34
305 0 0 199
306 0 0 12
307 0 0 210
308 0 0 25
309 0 0 29
310 9 0 42
311 28 1.8 13
312 31 1.8 13
313 31 2 7
314 28 0.9 20
315 19 2 26
316 17 0.9 9
317 10 0.9 8
318 16 1.2 9
319 25 1.1 7
320 13 3.1 10
321 7 1.9 10
322 7 1 8
323 0 0 8
324 0 0 t
325 0 0 9
326 0 0 5
327 0 0 0
328 0 0 0
329 0 0 38
330 0 0 42
331 0 0 28
332 0 0 35
333 0 0 1
334 0 0 1