Visualizing Automation Data
Data visualization is a vital skill for automation engineers because it helps you quickly identify trends, patterns, and anomalies in your data. When monitoring devices or analyzing process logs, visualizing the data makes it easier to spot issues that might be missed in raw tables or text outputs. Charts and plots can turn complex datasets into clear, actionable insights, which is essential for troubleshooting, reporting, and optimizing automated systems.
1234567891011import matplotlib.pyplot as plt # Example device readings over time time = ["08:00", "09:00", "10:00", "11:00", "12:00"] readings = [20, 22, 21, 23, 24] plt.plot(time, readings) plt.title("Device Readings Over Time") plt.xlabel("Time") plt.ylabel("Reading") plt.show()
In the plot above, several elements help you interpret the data. The title ("Device Readings Over Time") describes the purpose of the chart. The x-axis label ("Time") and y-axis label ("Reading") clarify what each axis represents. These labels make your plots easier to understand at a glance. You can also add a legend if you are plotting multiple lines, which helps distinguish between different data series.
1234567891011121314import matplotlib.pyplot as plt # Multiple device readings time = ["08:00", "09:00", "10:00", "11:00", "12:00"] device1 = [20, 22, 21, 23, 24] device2 = [19, 21, 20, 22, 23] plt.plot(time, device1, color="blue", marker="o", label="Device 1") plt.plot(time, device2, color="green", marker="s", label="Device 2") plt.title("Device Readings Comparison") plt.xlabel("Time") plt.ylabel("Reading") plt.legend() plt.show()
1. What is matplotlib used for in Python?
2. Why visualize automation data?
3. Fill in the blank to add markers to the plot using matplotlib.
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Data visualization is a vital skill for automation engineers because it helps you quickly identify trends, patterns, and anomalies in your data. When monitoring devices or analyzing process logs, visualizing the data makes it easier to spot issues that might be missed in raw tables or text outputs. Charts and plots can turn complex datasets into clear, actionable insights, which is essential for troubleshooting, reporting, and optimizing automated systems.
1234567891011import matplotlib.pyplot as plt # Example device readings over time time = ["08:00", "09:00", "10:00", "11:00", "12:00"] readings = [20, 22, 21, 23, 24] plt.plot(time, readings) plt.title("Device Readings Over Time") plt.xlabel("Time") plt.ylabel("Reading") plt.show()
In the plot above, several elements help you interpret the data. The title ("Device Readings Over Time") describes the purpose of the chart. The x-axis label ("Time") and y-axis label ("Reading") clarify what each axis represents. These labels make your plots easier to understand at a glance. You can also add a legend if you are plotting multiple lines, which helps distinguish between different data series.
1234567891011121314import matplotlib.pyplot as plt # Multiple device readings time = ["08:00", "09:00", "10:00", "11:00", "12:00"] device1 = [20, 22, 21, 23, 24] device2 = [19, 21, 20, 22, 23] plt.plot(time, device1, color="blue", marker="o", label="Device 1") plt.plot(time, device2, color="green", marker="s", label="Device 2") plt.title("Device Readings Comparison") plt.xlabel("Time") plt.ylabel("Reading") plt.legend() plt.show()
1. What is matplotlib used for in Python?
2. Why visualize automation data?
3. Fill in the blank to add markers to the plot using matplotlib.
Дякуємо за ваш відгук!
