Initialer Import der Synology Scripts

Python/UdemyPythonIntro-master/Chapter3_Libraries/Main.py

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+from MyModule import list_max
+from MyModule import list_min
+
+
+list1 = [-2, 1, 2, -10, 22, -10]
+list_max(list1)
+list_min(list1)
+list2 = [-20, 123, 112, -10, 22, -120]
+list_max(list2)
+list_min(list2)

Python/UdemyPythonIntro-master/Chapter3_Libraries/MainFunction.py

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+from MyModule import list_max
+from MyModule import list_min
+
+
+def main():
+    list1 = [-2, 1, 2, -10, 22, -10]
+    list_max(list1)
+    list_min(list1)
+    list2 = [-20, 123, 112, -10, 22, -120]
+    list_max(list2)
+    list_min(list2)
+
+
+if __name__ == "__main__":
+    main()

Python/UdemyPythonIntro-master/Chapter3_Libraries/MatplotlibIntro.py

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+import matplotlib.pyplot as plt
+
+
+grades_jan = [56, 64, 78, 100]
+grades_ben = [86, 94, 98, 90]
+
+
+plt.plot(range(len(grades_jan)), grades_jan, color="blue")
+plt.plot(range(len(grades_ben)), grades_ben, color="red")
+plt.xlabel("Courses")
+plt.ylabel("Grade (in %)")
+plt.legend(["Jan", "Ben"])
+plt.title("Jan vs. Ben")
+plt.show()
+
+
+plt.scatter(range(len(grades_jan)), grades_jan, c="blue")
+plt.scatter(range(len(grades_ben)), grades_ben, c="red")
+plt.xlabel("Courses")
+plt.ylabel("Grade (in %)")
+plt.legend(["Jan", "Ben"])
+plt.title("Jan vs. Ben")
+plt.show()

Python/UdemyPythonIntro-master/Chapter3_Libraries/MyModule.py

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+def list_max(input_list):
+    max_value = input_list[0]
+
+    for i in range(1, len(input_list)):
+        if input_list[i] > max_value:
+            max_value = input_list[i]
+
+    print(max_value)
+
+
+def list_min(input_list):
+    max_value = input_list[0]
+
+    for i in range(1, len(input_list)):
+        if input_list[i] < max_value:
+            max_value = input_list[i]
+
+    print(max_value)
+
+
+if __name__ == "__main__":
+    print("Hello from MyModule")

Python/UdemyPythonIntro-master/Chapter3_Libraries/NumpyIntro.py

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+import numpy as np
+
+
+vec = np.array([1, 2, 3, 4, 5])
+print(np.max(vec))
+print(np.min(vec))
+print(np.median(vec))
+print(np.mean(vec))
+print(vec.shape)
+
+matrix = np.array([[1, 2], [3, 4]])
+print(np.max(matrix))
+print(np.min(matrix))
+print(np.median(matrix))
+print(np.mean(matrix))
+print(matrix.shape)