-
Notifications
You must be signed in to change notification settings - Fork 0
/
somedataanalysismusic.py
149 lines (109 loc) · 9.78 KB
/
somedataanalysismusic.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
import tkinter as tk
from tkinter import ttk
import matplotlib.pyplot as plt
import statistics
def calculate_average(column, instrument, category, dataset):
total = 0
count = 0
for row in dataset[1:]:
if len(row) > column and (category == '' or row[2] == category) and (instrument == '' or row[4] == instrument) and isinstance(row[column], int):
total += row[column]
count += 1
if count > 0:
return total / count
else:
return None
def display_statistics(dataset):
average_age = calculate_average(0, '', 'performer', dataset)
average_income = calculate_average(1, '', '', dataset)
composer_count = sum([1 for row in dataset[1:] if row[2] == 'composer'])
average_educator_experience = calculate_average(3, '', 'educator', dataset)
average_income_with_band, average_income_without_band = compare_band_incomes(dataset)
window = tk.Tk()
window.title("Statistics")
tk.Label(window, text="Average Age of Performers:").grid(row=0, column=0, sticky="W")
tk.Label(window, text=average_age).grid(row=0, column=1)
tk.Label(window, text="Average Income:").grid(row=1, column=0, sticky="W")
tk.Label(window, text=average_income).grid(row=1, column=1)
tk.Label(window, text="Number of Composers:").grid(row=2, column=0, sticky="W")
tk.Label(window, text=composer_count).grid(row=2, column=1)
tk.Label(window, text="Average Experience of Educators:").grid(row=3, column=0, sticky="W")
tk.Label(window, text=average_educator_experience).grid(row=3, column=1)
tk.Label(window, text="Average Income for those with a Band:").grid(row=4, column=0, sticky="W")
tk.Label(window, text=average_income_with_band).grid(row=4, column=1)
tk.Label(window, text="Average Income for those without a Band:").grid(row=5, column=0, sticky="W")
tk.Label(window, text=average_income_without_band).grid(row=5, column=1)
compare_button = ttk.Button(window, text="Compare Band Incomes", command=lambda: compare_band_incomes(dataset))
compare_button.grid(row=6, columnspan=2)
window.mainloop()
def compare_band_incomes(dataset):
incomes_with_band = []
incomes_without_band = []
for row in dataset[1:]:
if row[5] == 1 and isinstance(row[1], int):
incomes_with_band.append(row[1])
elif row[5] == 0 and isinstance(row[1], int):
incomes_without_band.append(row[1])
band_incomes_window = tk.Toplevel()
band_incomes_window.title("Band Incomes")
mean_with_band = statistics.mean(incomes_with_band) if incomes_with_band else None
std_dev_with_band = statistics.stdev(incomes_with_band) if incomes_with_band else None
mean_without_band = statistics.mean(incomes_without_band) if incomes_without_band else None
std_dev_without_band = statistics.stdev(incomes_without_band) if incomes_without_band else None
tk.Label(band_incomes_window, text="With Band:").grid(row=0, column=0, sticky="W")
tk.Label(band_incomes_window, text="Mean:").grid(row=1, column=0, sticky="E")
tk.Label(band_incomes_window, text=mean_with_band).grid(row=1, column=1)
tk.Label(band_incomes_window, text="Standard Deviation:").grid(row=2, column=0, sticky="E")
tk.Label(band_incomes_window, text=std_dev_with_band).grid(row=2, column=1)
tk.Label(band_incomes_window, text="Without Band:").grid(row=0, column=2, sticky="W")
tk.Label(band_incomes_window, text="Mean:").grid(row=1, column=2, sticky="E")
tk.Label(band_incomes_window, text=mean_without_band).grid(row=1, column=3)
tk.Label(band_incomes_window, text="Standard Deviation:").grid(row=2, column=2, sticky="E")
tk.Label(band_incomes_window, text=std_dev_without_band).grid(row=2, column=3)
plt.figure(figsize=(10, 5))
plt.subplot(1, 2, 1)
plt.hist(incomes_with_band, bins=10, color='blue', alpha=0.7)
plt.xlabel("Income")
plt.ylabel("Number of Musicians")
plt.title("Incomes with Band")
plt.subplot(1, 2, 2)
plt.hist(incomes_without_band, bins=10, color='green', alpha=0.7)
plt.xlabel("Income")
plt.ylabel("Number of Musicians")
plt.title("Incomes without Band")
plt.tight_layout()
plt.show()
def find_correlations(category1, instrument1, category2, instrument2, dataset):
data1 = []
data2 = []
for row in dataset[1:]:
if (category1 == '' or row[2] == category1) and (instrument1 == '' or row[4] == instrument1) and isinstance(row[1], int):
data1.append(row[1])
if (category2 == '' or row[2] == category2) and (instrument2 == '' or row[4] == instrument2) and isinstance(row[1], int):
data2.append(row[1])
correlation = calculate_correlation(data1, data2)
return correlation
def calculate_correlation(data1, data2):
if len(data1) != len(data2):
return None
n = len(data1)
sum_x = sum(data1)
sum_y = sum(data2)
sum_xy = sum([data1[i] * data2[i] for i in range(n)])
sum_x_sq = sum([x ** 2 for x in data1])
sum_y_sq = sum([y ** 2 for y in data2])
numerator = (n * sum_xy) - (sum_x * sum_y)
denominator = ((n * sum_x_sq - (sum_x ** 2)) ** 0.5) * ((n * sum_y_sq - (sum_y ** 2)) ** 0.5)
correlation = numerator / denominator
return correlation
dataset = [['age', 'income', 'title', 'experience', 'instrument', 'band'],
[40, 40840, 'performer', 13, 'guitar', 1], [54, 21689, 'composer', 11, 'voice', 1], [37, 43153, 'composer', 28, 'piano', 0], [47, 58113, 'composer', 31, 'violin', 0], [52, 24518, 'producer', 13, 'guitar', 1], [52, 43574, 'composer', 27, 'drums', 1], [58, 27637, 'performer', 9, 'violin', 1], [57, 25707, 'performer', 8, 'piano', 1], [37, 26169, 'composer', 8, 'piano', 0], [28, 30852, 'educator', 9, 'guitar', 1], [65, 48403, 'composer', 22, 'guitar', 1], [51, 40874, 'educator', 15, 'guitar', 0], [49, 29925, 'performer', 9, 'piano', 1], [37, 20350, 'manager', 9, 'voice', 1], [38, 25049, 'performer', 17, 'drums', 1], [41, 33209, 'producer', 29, 'voice', 1], [37, 35711, 'performer', 14, 'piano', 0], [40, 61691, 'performer', 28, 'saxophone', 1], [43, 24854, 'performer', 6, 'voice', 1], [57, 24583, 'performer', 31, 'drums', 0], [39, 45258, 'performer', 24, 'violin', 1], [39, 36205, 'performer', 15, 'guitar', 1], [29, 33473, 'performer', 13, 'piano', 1], [47, 24387, 'performer', 13, 'guitar', 0], [50, 51984, 'performer', 23, 'guitar', 1], [33, 51034, 'producer', 30, 'voice', 0], [44, 25734, 'educator', 15, 'guitar', 1], [37, 33425, 'manager', 10, 'voice', 1], [41, 33206, 'producer', 16, 'drums', 1], [48, 41256, 'performer', 26, 'voice', 0], [38, 36601, 'educator', 25, 'voice', 0], [39, 49320, 'performer', 23, 'piano', 1], [43, 33309, 'performer', 12, 'voice', 1], [37, 18990, 'educator', 4, 'guitar', 1], [53, 24624, 'performer', 1, 'drums', 1], [56, 70057, 'composer', 31, 'piano', 0], [46, 40902, 'performer', 7, 'drums', 1], [33, 33338, 'performer', 8, 'guitar', 1], [35, 20356, 'manager', 6, 'piano', 1], [32, 29609, 'producer', 11, 'voice', 1], [55, 30095, 'educator', 23, 'drums', 1], [44, 35895, 'composer', 11, 'piano', 1], [55, 29730, 'performer', 9, 'drums', 1], [44, 33757, 'composer', 15, 'piano', 1], [32, 40882, 'performer', 15, 'voice', 0], [39, 32086, 'educator', 9, 'drums', 1], [34, 41498, 'composer', 14, 'guitar', 1], [47, 45242, 'educator', 16, 'voice', 1], [41, 20724, 'performer', 5, 'piano', 1], [22, 39822, 'composer', 17, 'piano', 1], [31, 45467, 'performer', 12, 'voice', 1], [55, 29886, 'performer', 10, 'drums', 1], [34, 35558, 'composer', 11, 'voice', 0], [49, 27715, 'educator', 7, 'guitar', 1], [46, 36945, 'performer', 17, 'guitar', 1], [45, 30362, 'educator', 18, 'guitar', 1], [59, 36526, 'educator', 28, 'voice', 0], [59, 42186, 'composer', 17, 'voice', 1], [52, 60303, 'performer', 42, 'piano', 0], [34, 37642, 'producer', 12, 'voice', 1], [32, 36687, 'producer', 27, 'violin', 1], [39, 48350, 'performer', 40, 'guitar', 1], [39, 27173, 'performer', 12, 'voice', 1], [54, 31991, 'performer', 11, 'voice', 1], [34, 22114, 'performer', 9, 'piano', 1], [66, 25016, 'composer', 22, 'guitar', 1], [53, 22514, 'performer', 3, 'voice', 1], [23, 21079, 'educator', 25, 'drums', 0], [28, 21473, 'performer', 17, 'guitar', 1], [43, 33086, 'educator', 29, 'drums', 0], [51, 21535, 'performer', 9, 'voice', 1], [35, 31174, 'producer', 12, 'drums', 1], [66, 53876, 'performer', 26, 'voice', 0], [40, 30367, 'composer', 27, 'voice', 0], [45, 34015, 'performer', 29, 'voice', 0], [50, 16583, 'educator', 5, 'drums', 1], [36, 37421, 'educator', 10, 'drums', 1], [26, 18467, 'performer', 3, 'voice', 1], [41, 33011, 'performer', 28, 'drums', 0], [28, 34084, 'composer', 15, 'saxophone', 1], [43, 39797, 'manager', 19, 'voice', 1], [48, 63969, 'educator', 33, 'piano', 0], [37, 43700, 'performer', 14, 'voice', 1], [35, 37162, 'educator', 17, 'voice', 1], [31, 25220, 'performer', 16, 'drums', 1], [37, 31216, 'producer', 30, 'guitar', 1], [49, 29417, 'educator', 17, 'piano', 1], [58, 51682, 'performer', 25, 'guitar', 0], [38, 30383, 'performer', 13, 'guitar', 0], [38, 45996, 'producer', 16, 'guitar', 1], [29, 29576, 'performer', 13, 'guitar', 1], [35, 63889, 'manager', 36, 'guitar', 0],
[50, 54892, 'performer', 39, 'piano', 0], [30, 71131, 'performer', 45, 'drums', 0],
[31, 42709, 'performer', 19, 'drums', 0], [44, 34082, 'educator', 19, 'drums', 1],
[32, 40700, 'performer', 34, 'voice', 1], [40, 61084, 'performer', 38, 'voice', 0],
[53, 22003, 'performer', 15, 'drums', 1], [56, 35558, 'composer', 30, 'piano', 0]]
display_statistics(dataset)
correlation_performer_income = find_correlations('performer', 'guitar', 'composer', 'piano', dataset)
print("\nCorrelation between Performer's Income (Playing Guitar) and Composer's Income (Playing Piano):", correlation_performer_income)
correlation_educator_experience = find_correlations('educator', '', 'performer', 'guitar', dataset)
print("Correlation between Educator's Income and Performer's Income (Playing Guitar):", correlation_educator_experience)