Problem With Plotting Graphs In 1 Row Using Plot Method From Pandas

Suppose I want to plot 3 graphs in 1 row: dependencies cnt from other 3 features. Code: fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10)) for idx, feature in enumerate(

Solution 1:

The problem is not related to pandas. The index error you see comes from ax= axes[0, idx]. This is because you have a single row. [0, idx] would work when you have more than one row.

For just one row, you can skip the first index and use

fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10))
for idx, feature inenumerate(min_regressors):
    df_shuffled.plot(feature, "cnt", subplots=True, kind="scatter", ax= axes[idx])
plt.show()

As a recap

Correct

fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(8, 3))
axes[0].plot([1,2], [1,2])

Incorrect

fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(8, 3))
axes[0, 0].plot([1,2], [1,2])

Correct

fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(8, 3))
axes[0,0].plot([1,2], [1,2])

Solution 2:

For you to learn and understand what is happening, I suggest you check the size of axes in both of these situations. You will see that when either nrows or ncols is 1, the axes variable will be 1-dimensional, and otherwise it'll be 2 dimensional.

You cannot index a 1-dimensional object the way you are doing (ax= axes[0, idx]).

What you can do is use numpy's atleast_2d to make the axes 2D.

Alternatively, a better solution would be to iterate over the features and axes directly:

fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10))
for ax, feature inzip(axes, min_regressors):
    df_shuffled.plot(feature, "cnt", subplots=True, kind="scatter", ax=ax)
plt.show()