added exercise solutions

09_Scientific_Data_Analysis_Python/exercises/box_plot.py

+import matplotlib.pyplot as plt
+
+# data
+mutant = [1, 3, 2, 1, 5, 7, 3, 5]
+control = [6, 7, 1, 8, 3, 7, 8, 6]
+
+# draw a box plot
+data = [mutant, control]
+
+fig1, ax1 = plt.subplots()
+ax1.set_title('Basic Plot')
+ax1.boxplot(data)
+
+plt.show()
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09_Scientific_Data_Analysis_Python/exercises/stats_for_loop.py

+measurements = [1, 7, 3, 4, 0, 5, 4, 2, 8, 7]
+
+# determine sum of all measurements
+sum = 0
+
+for number in measurements:
+    sum = sum + number
+
+# determine mean of all measurements
+number_of_measurements = len(measurements)
+mean = sum / number_of_measurements
+
+print("Mean: " + str(mean))
+
+# go through measurements again and calculate standard deviation
+squared_sum = 0
+for number in measurements:
+    squared_sum += pow(number - mean, 2) / number_of_measurements
+
+from math import sqrt
+standard_deviation = sqrt(squared_sum)
+
+print("Standard deviation: " + str(standard_deviation))
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09_Scientific_Data_Analysis_Python/exercises/stats_numpy.py

+measurements = [1, 7, 3, 4, 0, 5, 4, 2, 8, 7]
+
+import numpy as np
+
+
+minimum = np.min(measurements)
+print("Mean: " + str(minimum))
+
+maximum = np.max(measurements)
+print("Mean: " + str(maximum))
+
+mean = np.mean(measurements)
+print("Median: " + str(mean))
+
+median = np.median(measurements)
+print("Mean: " + str(median))
+
+standard_deviation = np.std(measurements)
+print("Standard deviation: " + str(standard_deviation))
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