Splitting coef into arrays applicable for multi class

I use this function to plot the best and worst features (coef) for each label.

 def plot_coefficients(classifier, feature_names, top_features=20):
     coef = classifier.coef_.ravel()
     for i in np.split(coef,6): 
        top_positive_coefficients = np.argsort(i)[-top_features:]
        top_negative_coefficients = np.argsort(i)[:top_features]
        top_coefficients = np.hstack([top_negative_coefficients, top_positive_coefficients])
     # create plot
     plt.figure(figsize=(15, 5))
     colors = ["red" if c < 0 else "blue" for c in i[top_coefficients]]
     plt.bar(np.arange(2 * top_features), i[top_coefficients], color=colors)
     feature_names = np.array(feature_names)
     plt.xticks(np.arange(1, 1 + 2 * top_features), feature_names[top_coefficients], rotation=60, ha="right")
     plt.show()

Applying it to sklearn.LinearSVC:

if (name == "LinearSVC"):   
    print(clf.coef_)
    print(clf.intercept_)
    plot_coefficients(clf, cv.get_feature_names())

The CountVectorizer used has a dimension of (15258, 26728). It's a multi-class decision problem with 6 labels. Using .ravel returns a flat array with a length of 6*26728=160368. Meaning that all indicies that are higher than 26728 are out of bound for axis 1. Here are the top and bottom indices for one label:

i[ 0. 0. 0.07465654 ... -0.02112607  0. -0.13656274]
Top [39336 35593 29445 29715 36418 28631 28332 40843 34760 35887 48455 27753
 33291 54136 36067 33961 34644 38816 36407 35781]

i[ 0. 0. 0.07465654 ... -0.02112607  0. -0.13656274]
Bot [39397 40215 34521 39392 34586 32206 36526 42766 48373 31783 35404 30296
 33165 29964 50325 53620 34805 32596 34807 40895]

The first entry in the "top" list has the index 39336. This is equal to the entry 39337-26728=12608 in the vocabulary. What would I need to change in the code to make this applicable?

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