Matplotlib Series Code

Python/Matplotlib/01-Introduction/finished_code.py

@@ -0,0 +1,31 @@
+
+from matplotlib import pyplot as plt
+
+plt.xkcd()
+
+ages_x = [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]
+
+py_dev_y = [20046, 17100, 20000, 24744, 30500, 37732, 41247, 45372, 48876, 53850, 57287, 63016, 65998, 70003, 70000, 71496, 75370, 83640, 84666,
+            84392, 78254, 85000, 87038, 91991, 100000, 94796, 97962, 93302, 99240, 102736, 112285, 100771, 104708, 108423, 101407, 112542, 122870, 120000]
+plt.plot(ages_x, py_dev_y, label='Python')
+
+js_dev_y = [16446, 16791, 18942, 21780, 25704, 29000, 34372, 37810, 43515, 46823, 49293, 53437, 56373, 62375, 66674, 68745, 68746, 74583, 79000,
+            78508, 79996, 80403, 83820, 88833, 91660, 87892, 96243, 90000, 99313, 91660, 102264, 100000, 100000, 91660, 99240, 108000, 105000, 104000]
+plt.plot(ages_x, js_dev_y, label='JavaScript')
+
+dev_y = [17784, 16500, 18012, 20628, 25206, 30252, 34368, 38496, 42000, 46752, 49320, 53200, 56000, 62316, 64928, 67317, 68748, 73752, 77232,
+         78000, 78508, 79536, 82488, 88935, 90000, 90056, 95000, 90000, 91633, 91660, 98150, 98964, 100000, 98988, 100000, 108923, 105000, 103117]
+plt.plot(ages_x, dev_y, color='#444444', linestyle='--', label='All Devs')
+
+plt.xlabel('Ages')
+plt.ylabel('Median Salary (USD)')
+plt.title('Median Salary (USD) by Age')
+
+plt.legend()
+
+plt.tight_layout()
+
+plt.savefig('plot.png')
+
+plt.show()

Python/Matplotlib/01-Introduction/snippets.py

@@ -0,0 +1,31 @@
+
+# Median Developer Salaries by Age
+dev_x = [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
+
+dev_y = [38496, 42000, 46752, 49320, 53200,
+         56000, 62316, 64928, 67317, 68748, 73752]
+
+
+# Median Python Developer Salaries by Age
+py_dev_x = [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
+py_dev_y = [45372, 48876, 53850, 57287, 63016,
+            65998, 70003, 70000, 71496, 75370, 83640]
+
+
+# Median JavaScript Developer Salaries by Age
+js_dev_y = [37810, 43515, 46823, 49293, 53437,
+            56373, 62375, 66674, 68745, 68746, 74583]
+
+
+# Ages 18 to 55
+ages_x = [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]
+
+py_dev_y = [20046, 17100, 20000, 24744, 30500, 37732, 41247, 45372, 48876, 53850, 57287, 63016, 65998, 70003, 70000, 71496, 75370, 83640, 84666,
+            84392, 78254, 85000, 87038, 91991, 100000, 94796, 97962, 93302, 99240, 102736, 112285, 100771, 104708, 108423, 101407, 112542, 122870, 120000]
+
+js_dev_y = [16446, 16791, 18942, 21780, 25704, 29000, 34372, 37810, 43515, 46823, 49293, 53437, 56373, 62375, 66674, 68745, 68746, 74583, 79000,
+            78508, 79996, 80403, 83820, 88833, 91660, 87892, 96243, 90000, 99313, 91660, 102264, 100000, 100000, 91660, 99240, 108000, 105000, 104000]
+
+dev_y = [17784, 16500, 18012, 20628, 25206, 30252, 34368, 38496, 42000, 46752, 49320, 53200, 56000, 62316, 64928, 67317, 68748, 73752, 77232,
+         78000, 78508, 79536, 82488, 88935, 90000, 90056, 95000, 90000, 91633, 91660, 98150, 98964, 100000, 98988, 100000, 108923, 105000, 103117]

Python/Matplotlib/02-BarCharts/finished_code.py

@@ -0,0 +1,36 @@
+import csv
+import numpy as np
+import pandas as pd
+from collections import Counter
+from matplotlib import pyplot as plt
+
+plt.style.use("fivethirtyeight")
+
+data = pd.read_csv('data.csv')
+ids = data['Responder_id']
+lang_responses = data['LanguagesWorkedWith']
+
+language_counter = Counter()
+
+for response in lang_responses:
+    language_counter.update(response.split(';'))
+
+languages = []
+popularity = []
+
+for item in language_counter.most_common(15):
+    languages.append(item[0])
+    popularity.append(item[1])
+
+languages.reverse()
+popularity.reverse()
+
+plt.barh(languages, popularity)
+
+plt.title("Most Popular Languages")
+# plt.ylabel("Programming Languages")
+plt.xlabel("Number of People Who Use")
+
+plt.tight_layout()
+
+plt.show()

Python/Matplotlib/02-BarCharts/starting_code.py

@@ -0,0 +1,27 @@
+from matplotlib import pyplot as plt
+
+plt.style.use("fivethirtyeight")
+
+ages_x = [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]
+
+dev_y = [38496, 42000, 46752, 49320, 53200,
+         56000, 62316, 64928, 67317, 68748, 73752]
+plt.plot(ages_x, dev_y, color="#444444", label="All Devs")
+
+# py_dev_y = [45372, 48876, 53850, 57287, 63016,
+#             65998, 70003, 70000, 71496, 75370, 83640]
+# plt.plot(ages_x, py_dev_y, color="#008fd5", label="Python")
+
+# js_dev_y = [37810, 43515, 46823, 49293, 53437,
+#             56373, 62375, 66674, 68745, 68746, 74583]
+# plt.plot(ages_x, js_dev_y, color="#e5ae38", label="JavaScript")
+
+plt.legend()
+
+plt.title("Median Salary (USD) by Age")
+plt.xlabel("Ages")
+plt.ylabel("Median Salary (USD)")
+
+plt.tight_layout()
+
+plt.show()

Python/Matplotlib/03-PieCharts/finished_code.py

@@ -0,0 +1,15 @@
+from matplotlib import pyplot as plt
+
+plt.style.use("fivethirtyeight")
+
+slices = [59219, 55466, 47544, 36443, 35917]
+labels = ['JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java']
+explode = [0, 0, 0, 0.1, 0]
+
+plt.pie(slices, labels=labels, explode=explode, shadow=True,
+        startangle=90, autopct='%1.1f%%',
+        wedgeprops={'edgecolor': 'black'})
+
+plt.title("My Awesome Pie Chart")
+plt.tight_layout()
+plt.show()

Python/Matplotlib/03-PieCharts/snippets.txt

@@ -0,0 +1,4 @@
+
+# Language Popularity
+slices = [59219, 55466, 47544, 36443, 35917, 31991, 27097, 23030, 20524, 18523, 18017, 7920, 7331, 7201, 5833]
+labels = ['JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java', 'Bash/Shell/PowerShell', 'C#', 'PHP', 'C++', 'TypeScript', 'C', 'Other(s):', 'Ruby', 'Go', 'Assembly']

Python/Matplotlib/03-PieCharts/starting_code.py

@@ -0,0 +1,14 @@
+from matplotlib import pyplot as plt
+
+plt.style.use("fivethirtyeight")
+
+
+plt.title("My Awesome Pie Chart")
+plt.tight_layout()
+plt.show()
+
+# Colors:
+# Blue = #008fd5
+# Red = #fc4f30
+# Yellow = #e5ae37
+# Green = #6d904f

Python/Matplotlib/04-StackPlots/finished_code.py

@@ -0,0 +1,27 @@
+from matplotlib import pyplot as plt
+
+plt.style.use("fivethirtyeight")
+
+
+minutes = [1, 2, 3, 4, 5, 6, 7, 8, 9]
+
+player1 = [8, 6, 5, 5, 4, 2, 1, 1, 0]
+player2 = [0, 1, 2, 2, 2, 4, 4, 4, 4]
+player3 = [0, 1, 1, 1, 2, 2, 3, 3, 4]
+
+labels = ['player1', 'player2', 'player3']
+colors = ['#6d904f', '#fc4f30', '#008fd5']
+
+plt.stackplot(minutes, player1, player2, player3, labels=labels, colors=colors)
+
+plt.legend(loc=(0.07, 0.05))
+
+plt.title("My Awesome Stack Plot")
+plt.tight_layout()
+plt.show()
+
+# Colors:
+# Blue = #008fd5
+# Red = #fc4f30
+# Yellow = #e5ae37
+# Green = #6d904f

Python/Matplotlib/04-StackPlots/snippets.txt

@@ -0,0 +1,3 @@
+player1 = [8, 6, 5, 5, 4, 2, 1, 1, 0]
+player2 = [0, 1, 2, 2, 2, 4, 4, 4, 4]
+player3 = [0, 1, 1, 1, 2, 2, 3, 3, 4]

Python/Matplotlib/04-StackPlots/starting_code.py

@@ -0,0 +1,22 @@
+from matplotlib import pyplot as plt
+
+plt.style.use("fivethirtyeight")
+
+
+minutes = [1, 2, 3, 4, 5, 6, 7, 8, 9]
+
+player1 = [1, 2, 3, 3, 4, 4, 4, 4, 5]
+player2 = [1, 1, 1, 1, 2, 2, 2, 3, 4]
+player3 = [1, 1, 1, 2, 2, 2, 3, 3, 3]
+
+plt.pie([1, 1, 1], labels=["Player 1", "Player2", "Player3"])
+
+plt.title("My Awesome Stack Plot")
+plt.tight_layout()
+plt.show()
+
+# Colors:
+# Blue = #008fd5
+# Red = #fc4f30
+# Yellow = #e5ae37
+# Green = #6d904f

Python/Matplotlib/05-Fill_Betweens/data.csv

@@ -0,0 +1,39 @@
+Age,All_Devs,Python,JavaScript
+18,17784,20046,16446
+19,16500,17100,16791
+20,18012,20000,18942
+21,20628,24744,21780
+22,25206,30500,25704
+23,30252,37732,29000
+24,34368,41247,34372
+25,38496,45372,37810
+26,42000,48876,43515
+27,46752,53850,46823
+28,49320,57287,49293
+29,53200,45000,53437
+30,56000,50000,56373
+31,62316,55000,62375
+32,64928,70000,66674
+33,67317,71496,68745
+34,68748,75370,68746
+35,73752,83640,74583
+36,77232,84666,79000
+37,78000,84392,78508
+38,78508,78254,79996
+39,79536,85000,80403
+40,82488,87038,83820
+41,88935,91991,88833
+42,90000,100000,91660
+43,90056,94796,87892
+44,95000,97962,96243
+45,90000,93302,90000
+46,91633,99240,99313
+47,91660,102736,91660
+48,98150,112285,102264
+49,98964,100771,100000
+50,100000,104708,100000
+51,98988,108423,91660
+52,100000,101407,99240
+53,108923,112542,108000
+54,105000,122870,105000
+55,103117,120000,104000

Python/Matplotlib/05-Fill_Betweens/finished_code.py

@@ -0,0 +1,33 @@
+import pandas as pd
+from matplotlib import pyplot as plt
+
+data = pd.read_csv('data.csv')
+ages = data['Age']
+dev_salaries = data['All_Devs']
+py_salaries = data['Python']
+js_salaries = data['JavaScript']
+
+plt.plot(ages, dev_salaries, color='#444444',
+         linestyle='--', label='All Devs')
+
+plt.plot(ages, py_salaries, label='Python')
+
+overall_median = 57287
+
+plt.fill_between(ages, py_salaries, dev_salaries,
+                 where=(py_salaries > dev_salaries),
+                 interpolate=True, alpha=0.25, label='Above Avg')
+
+plt.fill_between(ages, py_salaries, dev_salaries,
+                 where=(py_salaries <= dev_salaries),
+                 interpolate=True, color='red', alpha=0.25, label='Below Avg')
+
+plt.legend()
+
+plt.title('Median Salary (USD) by Age')
+plt.xlabel('Ages')
+plt.ylabel('Median Salary (USD)')
+
+plt.tight_layout()
+
+plt.show()

Python/Matplotlib/05-Fill_Betweens/starting_code.py

@@ -0,0 +1,25 @@
+import pandas as pd
+from matplotlib import pyplot as plt
+
+data = pd.read_csv('data.csv')
+ages = data['Age']
+dev_salaries = data['All_Devs']
+py_salaries = data['Python']
+js_salaries = data['JavaScript']
+
+plt.plot(ages, dev_salaries, color='#444444',
+         linestyle='--', label='All Devs')
+
+plt.plot(ages, py_salaries, label='Python')
+
+# overall_median = 57287
+
+plt.legend()
+
+plt.title('Median Salary (USD) by Age')
+plt.xlabel('Ages')
+plt.ylabel('Median Salary (USD)')
+
+plt.tight_layout()
+
+plt.show()

Python/Matplotlib/06-Histograms/finished_code.py

@@ -0,0 +1,27 @@
+import pandas as pd
+from matplotlib import pyplot as plt
+
+plt.style.use('fivethirtyeight')
+
+data = pd.read_csv('data.csv')
+ids = data['Responder_id']
+ages = data['Age']
+
+bins = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
+
+plt.hist(ages, bins=bins, edgecolor='black', log=True)
+
+median_age = 29
+color = '#fc4f30'
+
+plt.axvline(median_age, color=color, label='Age Median', linewidth=2)
+
+plt.legend()
+
+plt.title('Ages of Respondents')
+plt.xlabel('Ages')
+plt.ylabel('Total Respondents')
+
+plt.tight_layout()
+
+plt.show()

Python/Matplotlib/06-Histograms/starting_code.py

@@ -0,0 +1,23 @@
+import pandas as pd
+from matplotlib import pyplot as plt
+
+plt.style.use('fivethirtyeight')
+
+ages = [18, 19, 21, 25, 26, 26, 30, 32, 38, 45, 55]
+
+# data = pd.read_csv('data.csv')
+# ids = data['Responder_id']
+# ages = data['Age']
+
+# median_age = 29
+# color = '#fc4f30'
+
+# plt.legend()
+
+plt.title('Ages of Respondents')
+plt.xlabel('Ages')
+plt.ylabel('Total Respondents')
+
+plt.tight_layout()
+
+plt.show()

Python/Matplotlib/07-ScatterPlots/2019-05-31-data.csv

@@ -0,0 +1,201 @@
+view_count,likes,ratio
+8036001,324742,96.91
+9378067,562589,98.19
+2182066,273650,99.38
+6525864,94698,96.25
+9481284,582481,97.22
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Python/Matplotlib/07-ScatterPlots/finished_code.py

@@ -0,0 +1,27 @@
+
+import pandas as pd
+from matplotlib import pyplot as plt
+
+plt.style.use('seaborn')
+
+data = pd.read_csv('2019-05-31-data.csv')
+view_count = data['view_count']
+likes = data['likes']
+ratio = data['ratio']
+
+plt.scatter(view_count, likes, c=ratio, cmap='summer',
+            edgecolor='black', linewidth=1, alpha=0.75)
+
+cbar = plt.colorbar()
+cbar.set_label('Like/Dislike Ratio')
+
+plt.xscale('log')
+plt.yscale('log')
+
+plt.title('Trending YouTube Videos')
+plt.xlabel('View Count')
+plt.ylabel('Total Likes')
+
+plt.tight_layout()
+
+plt.show()

Python/Matplotlib/07-ScatterPlots/starting_code.py

@@ -0,0 +1,27 @@
+
+import pandas as pd
+from matplotlib import pyplot as plt
+
+plt.style.use('seaborn')
+
+x = [5, 7, 8, 5, 6, 7, 9, 2, 3, 4, 4, 4, 2, 6, 3, 6, 8, 6, 4, 1]
+y = [7, 4, 3, 9, 1, 3, 2, 5, 2, 4, 8, 7, 1, 6, 4, 9, 7, 7, 5, 1]
+
+
+# colors = [7, 5, 9, 7, 5, 7, 2, 5, 3, 7, 1, 2, 8, 1, 9, 2, 5, 6, 7, 5]
+
+# sizes = [209, 486, 381, 255, 191, 315, 185, 228, 174,
+#          538, 239, 394, 399, 153, 273, 293, 436, 501, 397, 539]
+
+# data = pd.read_csv('2019-05-31-data.csv')
+# view_count = data['view_count']
+# likes = data['likes']
+# ratio = data['ratio']
+
+# plt.title('Trending YouTube Videos')
+# plt.xlabel('View Count')
+# plt.ylabel('Total Likes')
+
+plt.tight_layout()
+
+plt.show()

Python/Matplotlib/08-TimeSeries/data.csv

@@ -0,0 +1,15 @@
+Date,Open,High,Low,Close,Adj Close,Volume
+2019-05-18,7266.080078,8281.660156,7257.259766,8193.139648,8193.139648,723011166
+2019-05-19,8193.139648,8193.139648,7591.850098,7998.290039,7998.290039,637617163
+2019-05-20,7998.290039,8102.319824,7807.770020,7947.930176,7947.930176,357803946
+2019-05-21,7947.930176,8033.759766,7533.660156,7626.890137,7626.890137,424501866
+2019-05-22,7626.890137,7971.259766,7478.740234,7876.500000,7876.500000,386766321
+2019-05-23,7876.500000,8165.450195,7801.569824,7996.399902,7996.399902,413162746
+2019-05-24,7996.399902,8140.819824,7948.680176,8059.129883,8059.129883,179206342
+2019-05-25,8059.129883,8779.000000,7894.529785,8726.230469,8726.230469,483663699
+2019-05-26,8726.230469,8931.530273,8668.459961,8785.169922,8785.169922,507164714
+2019-05-27,8785.169922,8818.709961,8562.200195,8718.849609,8718.849609,360752199
+2019-05-28,8718.849609,8760.480469,8444.099609,8664.559570,8664.559570,380343928
+2019-05-29,8664.559570,9065.889648,8027.209961,8276.250000,8276.250000,815525590
+2019-05-30,8276.250000,8570.780273,8116.000000,8560.080078,8560.080078,500141087
+2019-05-31,8550.629883,8576.339844,8459.650391,8504.980469,8504.980469,69915456

Python/Matplotlib/08-TimeSeries/finished_code.py

@@ -0,0 +1,26 @@
+import pandas as pd
+from datetime import datetime, timedelta
+from matplotlib import pyplot as plt
+from matplotlib import dates as mpl_dates
+
+plt.style.use('seaborn')
+
+data = pd.read_csv('data.csv')
+
+data['Date'] = pd.to_datetime(data['Date'])
+data.sort_values('Date', inplace=True)
+
+price_date = data['Date']
+price_close = data['Close']
+
+plt.plot_date(price_date, price_close, linestyle='solid')
+
+plt.gcf().autofmt_xdate()
+
+plt.title('Bitcoin Prices')
+plt.xlabel('Date')
+plt.ylabel('Closing Price')
+
+plt.tight_layout()
+
+plt.show()

Python/Matplotlib/08-TimeSeries/starting_code.py

@@ -0,0 +1,30 @@
+import pandas as pd
+from datetime import datetime, timedelta
+from matplotlib import pyplot as plt
+from matplotlib import dates as mpl_dates
+
+plt.style.use('seaborn')
+
+dates = [
+    datetime(2019, 5, 24),
+    datetime(2019, 5, 25),
+    datetime(2019, 5, 26),
+    datetime(2019, 5, 27),
+    datetime(2019, 5, 28),
+    datetime(2019, 5, 29),
+    datetime(2019, 5, 30)
+]
+
+y = [0, 1, 3, 4, 6, 5, 7]
+
+# data = pd.read_csv('data.csv')
+# price_date = data['Date']
+# price_close = data['Close']
+
+# plt.title('Bitcoin Prices')
+# plt.xlabel('Date')
+# plt.ylabel('Closing Price')
+
+plt.tight_layout()
+
+plt.show()

Python/Matplotlib/09-LiveData/data_gen.py

@@ -0,0 +1,35 @@
+
+import csv
+import random
+import time
+
+x_value = 0
+total_1 = 1000
+total_2 = 1000
+
+fieldnames = ["x_value", "total_1", "total_2"]
+
+
+with open('data.csv', 'w') as csv_file:
+    csv_writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
+    csv_writer.writeheader()
+
+while True:
+
+    with open('data.csv', 'a') as csv_file:
+        csv_writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
+
+        info = {
+            "x_value": x_value,
+            "total_1": total_1,
+            "total_2": total_2
+        }
+
+        csv_writer.writerow(info)
+        print(x_value, total_1, total_2)
+
+        x_value += 1
+        total_1 = total_1 + random.randint(-6, 8)
+        total_2 = total_2 + random.randint(-5, 6)
+
+    time.sleep(1)

Python/Matplotlib/09-LiveData/finished_code.py

@@ -0,0 +1,34 @@
+
+import random
+from itertools import count
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+
+plt.style.use('fivethirtyeight')
+
+x_vals = []
+y_vals = []
+
+index = count()
+
+
+def animate(i):
+    data = pd.read_csv('data.csv')
+    x = data['x_value']
+    y1 = data['total_1']
+    y2 = data['total_2']
+
+    plt.cla()
+
+    plt.plot(x, y1, label='Channel 1')
+    plt.plot(x, y2, label='Channel 2')
+
+    plt.legend(loc='upper left')
+    plt.tight_layout()
+
+
+ani = FuncAnimation(plt.gcf(), animate, interval=1000)
+
+plt.tight_layout()
+plt.show()

Python/Matplotlib/09-LiveData/snippets.txt

@@ -0,0 +1,26 @@
+# Another way to do it without clearing the Axis
+
+def animate(i):
+    data = pd.read_csv('data.csv')
+    x = data['x_value']
+    y1 = data['total_1']
+    y2 = data['total_2']
+
+    line1.set_data(x, y1)
+    line2.set_data(x, y2)
+
+    ax = plt.gca()
+    xlim_low, xlim_high = ax.get_xlim()
+    ylim_low, ylim_high = ax.get_ylim()
+
+    ax.set_xlim(xlim_low, (x.max() + 5))
+
+    y1max = y1.max()
+    y2max = y2.max()
+    current_ymax = y1max if (y1max > y2max) else y2max
+
+    y1min = y1.min()
+    y2min = y2.min()
+    current_ymin = y1min if (y1min < y2min) else y2min
+
+    ax.set_ylim((current_ymin - 5), (current_ymax + 5))

Python/Matplotlib/09-LiveData/starting_code.py

@@ -0,0 +1,29 @@
+
+import random
+from itertools import count
+import pandas as pd
+import matplotlib.pyplot as plt
+
+plt.style.use('fivethirtyeight')
+
+x_vals = [0, 1, 2, 3, 4, 5]
+y_vals = [0, 1, 3, 2, 3, 5]
+
+plt.plot(x_vals, y_vals)
+
+
+# index = count()
+
+# def animate(i):
+#     x_vals.append(next(index))
+#     y_vals.append(random.randint(0, 5))
+
+
+plt.tight_layout()
+plt.show()
+
+
+# data = pd.read_csv('data.csv')
+# x = data['x_value']
+# y1 = data['total_1']
+# y2 = data['total_2']

Python/Matplotlib/10-Subplots/data.csv

@@ -0,0 +1,39 @@
+Age,All_Devs,Python,JavaScript
+18,17784,20046,16446
+19,16500,17100,16791
+20,18012,20000,18942
+21,20628,24744,21780
+22,25206,30500,25704
+23,30252,37732,29000
+24,34368,41247,34372
+25,38496,45372,37810
+26,42000,48876,43515
+27,46752,53850,46823
+28,49320,57287,49293
+29,53200,63016,53437
+30,56000,65998,56373
+31,62316,70003,62375
+32,64928,70000,66674
+33,67317,71496,68745
+34,68748,75370,68746
+35,73752,83640,74583
+36,77232,84666,79000
+37,78000,84392,78508
+38,78508,78254,79996
+39,79536,85000,80403
+40,82488,87038,83820
+41,88935,91991,88833
+42,90000,100000,91660
+43,90056,94796,87892
+44,95000,97962,96243
+45,90000,93302,90000
+46,91633,99240,99313
+47,91660,102736,91660
+48,98150,112285,102264
+49,98964,100771,100000
+50,100000,104708,100000
+51,98988,108423,91660
+52,100000,101407,99240
+53,108923,112542,108000
+54,105000,122870,105000
+55,103117,120000,104000

Python/Matplotlib/10-Subplots/finished_code.py

@@ -0,0 +1,35 @@
+
+import pandas as pd
+from matplotlib import pyplot as plt
+
+plt.style.use('seaborn')
+
+data = pd.read_csv('data.csv')
+ages = data['Age']
+dev_salaries = data['All_Devs']
+py_salaries = data['Python']
+js_salaries = data['JavaScript']
+
+fig1, ax1 = plt.subplots()
+fig2, ax2 = plt.subplots()
+
+ax1.plot(ages, dev_salaries, color='#444444',
+         linestyle='--', label='All Devs')
+
+ax2.plot(ages, py_salaries, label='Python')
+ax2.plot(ages, js_salaries, label='JavaScript')
+
+ax1.legend()
+ax1.set_title('Median Salary (USD) by Age')
+ax1.set_ylabel('Median Salary (USD)')
+
+ax2.legend()
+ax2.set_xlabel('Ages')
+ax2.set_ylabel('Median Salary (USD)')
+
+plt.tight_layout()
+
+plt.show()
+
+fig1.savefig('fig1.png')
+fig2.savefig('fig2.png')

Python/Matplotlib/10-Subplots/starting_code.py

@@ -0,0 +1,27 @@
+
+import pandas as pd
+from matplotlib import pyplot as plt
+
+plt.style.use('seaborn')
+
+data = pd.read_csv('data.csv')
+ages = data['Age']
+dev_salaries = data['All_Devs']
+py_salaries = data['Python']
+js_salaries = data['JavaScript']
+
+plt.plot(ages, py_salaries, label='Python')
+plt.plot(ages, js_salaries, label='JavaScript')
+
+plt.plot(ages, dev_salaries, color='#444444',
+         linestyle='--', label='All Devs')
+
+plt.legend()
+
+plt.title('Median Salary (USD) by Age')
+plt.xlabel('Ages')
+plt.ylabel('Median Salary (USD)')
+
+plt.tight_layout()
+
+plt.show()