1.1 Explanation

You can plot both eyes on the same axes by splitting your original DataFrame by the eye column.

• We call eyemovementDF.groupby("eye") to get two groups: “left” and “right.”
• Looping over each group gives you the subset of rows for that eye.
• Then you simply plot its px (or py) against timestamp with a label.

Matplotlib will draw both lines on the same figure, and you add a legend so you can tell them apart.

1.2 Code Example

import matplotlib.pyplot as plt
create a new figure + axis

fig, ax = plt.subplots()

loop through left/right groups

for eye_label, df_eye in eyemovementDF.groupby("eye"):
# plot x‐coordinate
ax.plot(
df_eye["timestamp"],
df_eye["px"],
label=f"{eye_label.capitalize()} Eye – X",
linewidth=2
)
# plot y‐coordinate with a dashed line
ax.plot(
df_eye["timestamp"],
df_eye["py"],
label=f"{eye_label.capitalize()} Eye – Y",
linestyle="--"
)

labels and legend

ax.set_xlabel("Timestamp")
ax.set_ylabel("Pixel Position")
ax.legend()
plt.show()

2.1 Explanation

If you prefer one wide DataFrame where left- and right-eye positions are side by side, use pivot:

• index="timestamp" makes each timepoint a row.
• columns="eye" creates two sub-columns (“left” and “right”).
• values=["px","py"] fills the table with those measurements.

You get a DataFrame with a MultiIndex on the columns, letting you call e.g. df_pivot["px"]["left"] and df_pivot["px"]["right"] in one go.

2.2 Code Example

import matplotlib.pyplot as plt
pivot the DataFrame

df_pivot = eyemovementDF.pivot(
index="timestamp",
columns="eye",
values=["px", "py"]
)

plot left vs. right X‐positions over time

fig, ax = plt.subplots()
ax.plot(
df_pivot.index,
df_pivot["px"]["left"],
label="Left Eye – X",
linewidth=2
)
ax.plot(
df_pivot.index,
df_pivot["px"]["right"],
label="Right Eye – X",
linewidth=2
)
ax.set_xlabel("Timestamp")
ax.set_ylabel("X Position")
ax.legend()
plt.show()

and similarly for Y if you wish

fig, ay = plt.subplots()

ay.plot(df_pivot.index, df_pivot["py"]["left"], label="Left – Y")

ay.plot(df_pivot.index, df_pivot["py"]["right"], label="Right – Y", linestyle="--")

ay.set_xlabel("Timestamp")

ay.set_ylabel("Y Position")

ay.legend()

plt.show()

3.1 Explanation

Since you already have merge from pd.merge(leftEye, rightEye, on="timestamp"), you can plot its suffixed columns directly:

• px_x is the left-eye X (from leftEye).
• px_y is the right-eye X (from rightEye).
• Same story for py_x and py_y.

3.2 Code Example

import matplotlib.pyplot as plt
fig, ax = plt.subplots()

left‐eye X vs right‐eye X

ax.plot(
merge["timestamp"],
merge["px_x"],
label="Left Eye – X",
linewidth=2
)
ax.plot(
merge["timestamp"],
merge["px_y"],
label="Right Eye – X",
linewidth=2
)

ax.set_xlabel("Timestamp")
ax.set_ylabel("X Position")
ax.legend()
plt.show()