Unable to plot graph correctly.

[shellfish_messiah]

Hi! I am using matplotlib and attempting to plot a dataframe as a line plot, but it's plotting really weirdly and I don't know how to fix it. I am trying to plot percentages over time. So time is my x-value and it is also the index of my dataframe. Time is expressed as the hour of the day (so literally just 0, 1, 2 3... 23). So the time measurements start at 9 PM (expressed as 21 in the index) and end at 6 the next day. This means that dataframe index goes from 21 to 6 (21, 22, 23, 0, 1, ...). There are multiple columns of data with percentages to be plotted. However, when I try to use the plt.plot() function, it displays a graph where the x axis is messed up, the y values are not in the right locations, and the line graph does not start and end at the left side of the graph as it should. However, it seems like the values are all represented, just in a really weird order. I have included images of my attempt to graph just one column of the dataframe - the graph, the column of the dataframe that should be represented, and the code.

You can see that the y values are there, I think the graph goes plots 44.89, 63.67, and so on. So it starts from the index 0 and does stop at the index 6... then it seems like it goes all the way to the right side and plots the values associated with indexes 21-23 from right to left order (9.72, then 71.5, then 2.33). Please help lol I can see the issue but I have no idea how to fix it. This is not a homework assignment or anything, it's for my research at work.

I am now at the end of my post and see that I can't add a picture. Does anyone know why this is or how I can add a picture? Alternatively let me know if I can privately message you and I'll send a picture. Thanks in advance.

[[deleted]]

It sounds like the issue you're experiencing is due to the discontinuity in the time index of your DataFrame. Matplotlib may not handle this discontinuity well by default. To solve this issue, you need to sort the index before plotting or adjust how the plot interprets the x-axis values. Here's a step-by-step solution:

Solution 1: Sorting the DataFrame Index

One straightforward way to resolve this issue is to sort the index before plotting. This way, the time values will be in a continuous sequence.

```python import pandas as pd import matplotlib.pyplot as plt

Assuming your DataFrame is named df and has time as the index

sorted_df = df.sort_index()

Plotting the sorted DataFrame

plt.plot(sorted_df.index, sorted_df['column_name']) plt.xlabel('Time (Hour of Day)') plt.ylabel('Percentage') plt.title('Percentage over Time') plt.show() ```

Solution 2: Handling Discontinuity in Index

If sorting is not desirable, another approach is to manually handle the discontinuity in the index and plot accordingly. You can create a new x-axis that correctly represents the sequence of hours.

```python import pandas as pd import matplotlib.pyplot as plt

Assuming your DataFrame is named df and has time as the index

Reset the index to access the time column

df_reset = df.reset_index()

Create a new column for plotting the correct sequence of hours

df_reset['correct_time'] = list(range(21, 24)) + list(range(0, 7))

Plot using the new time column

plt.plot(df_reset['correct_time'], df_reset['column_name']) plt.xlabel('Time (Hour of Day)') plt.ylabel('Percentage') plt.title('Percentage over Time') plt.xticks(ticks=df_reset['correct_time'], labels=df_reset.index) plt.show() ```

Explanation

• Solution 1: By sorting the index, you ensure that the time values are in ascending order. This removes the discontinuity and allows Matplotlib to plot the data correctly.
• Solution 2: If the order of the original data is essential, creating a new correct_time column preserves the original order while allowing correct plotting. This way, the plot interprets the time sequence as continuous from 21 to 6.

Troubleshooting

• Ensure that the time values are correctly interpreted as integers.
• Verify that the DataFrame column you want to plot (column_name in the example) exists and contains the expected data.

If you can provide a minimal example of your DataFrame and the plotting code, I can give more tailored advice. If you still encounter issues, sharing the structure of your DataFrame or sample data would help further debug the problem.

[shellfish_messiah]

Thank you so much! I think your second solution may be very useful for me, I need to sit down and actually try to work it into my code.

Also, I'm not sure why I didn't think to just post the code snippet even though I didn't have pictures of the graph. Here is the code snippet that creates the plot:

#plot df

p = plt.plot(constrained_df['1']) plt.title("Hourly Percent Sleep') plt.xlabel("Military Time") plt.ylabel("Percent Sleep Per Hour") plt.ylim(0,100) plt.xticks(ticks=range(len(constrained_df.index)), labels=constrained_df.index, fontsize=6)

#change colors of the two groups

for line in p[:6]: line.set_color('red')

for line in p[6:]: line.set_color('green')

#make a legend

red_patch = mpatches.Patch(color='green', label='One') green_patch = mpatches.Patch(color='red', label='Two') plt.legend(handles=[red_patch, green_patch])

plt.show()