manjusv
diff --git a/‎10-Subplots/data.csv
+39 b/‎10-Subplots/data.csv
+39
diff --git a/‎10-Subplots/demo.py
+35 b/‎10-Subplots/demo.py
+35
diff --git a/‎10-Subplots/fig1.png
31.5 KB b/‎10-Subplots/fig1.png
31.5 KB
diff --git a/‎10-Subplots/fig2.png
47.2 KB b/‎10-Subplots/fig2.png
47.2 KB
@@ -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
@@ -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')