Loop through arrays in dataframe and plot by group

Question

I have a dataframe that contains X & Y position data and has 3 grouping variables:

obsScenario (0, 1 or 2)
startPos (1 or 2)
targetPos (1, 2, or 3).

Thus there are 18 combinations of these grouping variables: 3 x 2 x 3

The X and Y data is approx 300-500 data points in length per participant (it varies).

The dataframe looks like this:

              X         Y  participantNum  obsScenario  startPos  targetPos
0    -16.000000  5.000000         6432024            0         1          1
1    -16.000000  5.000000         6432024            0         1          1
2    -15.833450  5.000000         6432024            0         1          1
3    -15.667200  5.000000         6432024            0         1          1
4    -15.500100  5.000000         6432024            0         1          1
        ...       ...             ...          ...       ...        ...
2185  -1.572058 -3.982638         7830381            2         2          2
2186  -1.406996 -3.958967         7830381            2         2          2
2187  -1.242231 -3.935339         7830381            2         2          2
2188  -1.077516 -3.911718         7830381            2         2          2
2189  -0.912604 -3.888069         7830381            2         2          2

I need to plot the X, Y data separately for each of these 18 combinations.

Im trying to use something like this, but this just plots all the XY trajectories on the same plot:

for aid, grp in df.groupby(['obsScenario', 'startPos', 'targetPos']):
    plt.plot(grp[0].values, grp[1].values) 
plt.show()

And using something like this doesnt take the different combinations of grouping variables into account:

fig, axs = plt.subplots(nrows=3, ncols=6)

for ax in axs.flat:
    plotxy(ax,x,y)

If you will put plt.show() inside the loop then you will get plot for each row. else use subplots — Pygirl
– Pygirl, Commented Feb 2, 2021 at 8:23
Does this answer your question? How to plot a list of image in loop using matplotlib? — Pygirl
– Pygirl, Commented Feb 2, 2021 at 8:23
No, as it doesnt take the different combinations of grouping variables into account. And i dont want to plot each row as that would output thousands of plots, and I need the XY trajectories separated into the 18 diff combinations of grouping variables. My full dataset actually has 106 total combinations. — CentauriAurelius
– CentauriAurelius, Commented Feb 2, 2021 at 8:34
may be this will help a bit: stackoverflow.com/a/48358380/6660373 — Pygirl
– Pygirl, Commented Feb 2, 2021 at 8:43
You are so close. You just have to combine both strategies - define the number of subplots, group by variables, zip the ax object with the groupby variables, then plot into different subplots with ax.plot(grp[0].values, grp[1].values) — Mr. T
– Mr. T, Commented Feb 2, 2021 at 10:06

CentauriAurelius · Accepted Answer · 2021-02-02 10:29:07Z

2

This does what I was looking for:

fig, axs = plt.subplots(6,3)
grp = df.groupby(['obsScenario', 'startPos', 'targetPos'])
for (name, df), ax in zip(grp, axs.flat):
    df.plot(x='X',y='Y', ax=ax)

answered Feb 2, 2021 at 10:29

CentauriAurelius

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2 Comments

Mr. T Over a year ago

Yup, that's what I meant. However, in the long run, it might be preferable to plot with ax.plot(gf.X, gf.Y) (don't reuse df - it will overwrite your original df). The reason is that pandas and matplotlib sometimes conflict in their axis treatment - so, if you need additional functionality from matplotlib, it is better to not use the pandas plot functions. They are great for what they are - making life easier for standard plots, though.

CentauriAurelius Over a year ago

Yes i noticed that was happening! Thanks again.

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