In matplotlib, when your y-axis values appear in an unexpected, non-ordered form, it is likely due to the data type of these values. Oftentimes, they are being plotted as strings instead of numbers.
To rectify this issue, convert your y-axis data to floats by explicitly casting them during list comprehension:
<code class="python">Solar = [float(line[1]) for line in I020]</code>
Beyond data conversion, it is also advisable to utilize matplotlib's auto formatting capability for the x-axis when working with dates and times. This feature automatically adjusts label rotation and other aspects to enhance the graph's readability:
<code class="python">plt.gcf().autofmt_xdate()</code>
For illustration purposes, let's revise the provided code:
<code class="python">I020 = [line.strip('\n').split(",") for line in open('PV5sdata1.csv')][1:] Time = [datetime.datetime.strptime(line[0], "%H%M%S%f") for line in I020] Time1 = [mdates.date2num(line) for line in Time] Solar = [float(line[1]) for line in I020] fig, ax = plt.subplots() ax.set_title('Solar data') ax.set_xlabel('Time') ax.set_ylabel('Solar') ax.plot_date(Time1, Solar, 'k-') hfmt = mdates.DateFormatter('%H:%M:%S') ax.xaxis.set_major_formatter(hfmt) plt.gcf().autofmt_xdate() plt.show()</code>
In this modified code, we declare the figure and axes as separate objects (fig, ax). This approach offers more control and flexibility when customizing plot attributes.
The resulting graph displays well-ordered y-axis values and improved x-axis labeling:
[Image of ordered y-axis and improved x-axis labeling]
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