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* Matplotlib Series Code * Fixed snippet * Python Threading Tutorial * Multiprocessing Snippets * Stack Overflow Survey Analysis * Pandas Tutorial 01 * Pandas Tutorial 01 * Pandas Tutorial 2 * Pandas Tutorial 3 * Pandas Tutorial 4 * Pandas Tutorial 5 * Pandas Tutorial 06 * Pandas Tutorial 7 * Pandas Tutorial 08 * Pandas Tutorial 09 * Pandas Tutorial 10 * Pandas Tutorial 11 * YouTube-API-Tutorial * Python YouTube API Tutorial Co-authored-by: Corey Schafer <CoreyMSchafer@gmail.com>
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‎Python/Matplotlib/01-Introduction/finished_code.py

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from matplotlib import pyplot as plt
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plt.xkcd()
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ages_x = [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
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36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]
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py_dev_y = [20046, 17100, 20000, 24744, 30500, 37732, 41247, 45372, 48876, 53850, 57287, 63016, 65998, 70003, 70000, 71496, 75370, 83640, 84666,
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84392, 78254, 85000, 87038, 91991, 100000, 94796, 97962, 93302, 99240, 102736, 112285, 100771, 104708, 108423, 101407, 112542, 122870, 120000]
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plt.plot(ages_x, py_dev_y, label='Python')
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js_dev_y = [16446, 16791, 18942, 21780, 25704, 29000, 34372, 37810, 43515, 46823, 49293, 53437, 56373, 62375, 66674, 68745, 68746, 74583, 79000,
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78508, 79996, 80403, 83820, 88833, 91660, 87892, 96243, 90000, 99313, 91660, 102264, 100000, 100000, 91660, 99240, 108000, 105000, 104000]
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plt.plot(ages_x, js_dev_y, label='JavaScript')
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dev_y = [17784, 16500, 18012, 20628, 25206, 30252, 34368, 38496, 42000, 46752, 49320, 53200, 56000, 62316, 64928, 67317, 68748, 73752, 77232,
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78000, 78508, 79536, 82488, 88935, 90000, 90056, 95000, 90000, 91633, 91660, 98150, 98964, 100000, 98988, 100000, 108923, 105000, 103117]
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plt.plot(ages_x, dev_y, color='#444444', linestyle='--', label='All Devs')
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plt.xlabel('Ages')
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plt.ylabel('Median Salary (USD)')
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plt.title('Median Salary (USD) by Age')
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plt.legend()
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plt.tight_layout()
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plt.savefig('plot.png')
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plt.show()

‎Python/Matplotlib/01-Introduction/plot.png

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‎Python/Matplotlib/01-Introduction/snippets.py

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# Median Developer Salaries by Age
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dev_x = [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
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dev_y = [38496, 42000, 46752, 49320, 53200,
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56000, 62316, 64928, 67317, 68748, 73752]
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# Median Python Developer Salaries by Age
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py_dev_x = [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
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py_dev_y = [45372, 48876, 53850, 57287, 63016,
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65998, 70003, 70000, 71496, 75370, 83640]
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# Median JavaScript Developer Salaries by Age
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js_dev_y = [37810, 43515, 46823, 49293, 53437,
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56373, 62375, 66674, 68745, 68746, 74583]
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# Ages 18 to 55
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ages_x = [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
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36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55]
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py_dev_y = [20046, 17100, 20000, 24744, 30500, 37732, 41247, 45372, 48876, 53850, 57287, 63016, 65998, 70003, 70000, 71496, 75370, 83640, 84666,
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84392, 78254, 85000, 87038, 91991, 100000, 94796, 97962, 93302, 99240, 102736, 112285, 100771, 104708, 108423, 101407, 112542, 122870, 120000]
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js_dev_y = [16446, 16791, 18942, 21780, 25704, 29000, 34372, 37810, 43515, 46823, 49293, 53437, 56373, 62375, 66674, 68745, 68746, 74583, 79000,
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78508, 79996, 80403, 83820, 88833, 91660, 87892, 96243, 90000, 99313, 91660, 102264, 100000, 100000, 91660, 99240, 108000, 105000, 104000]
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dev_y = [17784, 16500, 18012, 20628, 25206, 30252, 34368, 38496, 42000, 46752, 49320, 53200, 56000, 62316, 64928, 67317, 68748, 73752, 77232,
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78000, 78508, 79536, 82488, 88935, 90000, 90056, 95000, 90000, 91633, 91660, 98150, 98964, 100000, 98988, 100000, 108923, 105000, 103117]

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