[FIXED] How do I turn the X-ticks and Y-ticks on a matplotlib plot into a tuppled list in python?

Issue

I would like to turn the xticks (feature names) and yticks (feature values) into a tuppled list in python so I can eventually export the pairs to a csv. How would I do this? here is the code for the plot below. Thanks in advance.

from sklearn import svm
import matplotlib.pyplot as plt
def feature_plot(classifier, feature_names, top_features=25):
 coef = classifier.coef_.ravel()
 top_positive_coefficients = np.argsort(coef)[-top_features:]
 #top_negative_coefficients = np.argsort(coef)[:top_features]
 #top_coefficients = np.hstack([top_negative_coefficients, top_positive_coefficients])
 plt.figure(figsize=(18, 7))
 colors = ['green' if c < 0 else 'blue' for c in coef[top_positive_coefficients]]
 plt.bar(np.arange(top_features), coef[top_positive_coefficients], color=colors)
 feature_names = np.array(feature_names)
 plt.xticks(np.arange(top_features), feature_names[top_positive_coefficients], rotation=45, ha='right')
 plt.show()

#print(pandasdfx.drop(columns=['target_label'], axis = 1).columns.values)

trainedsvm = svm.LinearSVC(C=0.001, max_iter=10000, dual=False).fit(Xx_train2, yx_train)
feature_plot(trainedsvm, pandasdfx.drop(columns=['target_label'], axis = 1).columns.values)

Solution

 classifier = svm.LinearSVC(C=0.01, max_iter=10000, dual=False).fit(Xx_train2, yx_train)
feature_names=pandasdfx.drop(columns=['target_label', axis = 1).columns.values
def feature_plot(classifier, feature_names, top_features=25):
 coef = classifier.coef_.ravel()
 top_positive_coefficients = np.argsort(coef)[-top_features:]
 return np.array((feature_names[top_positive_coefficients], coef[top_positive_coefficients])).T

var=feature_plot(classifier, feature_names, top_features=25)
print(var)

Answered By - jw32022