Random Forest Feature Importance Chart using Python

Load the feature importances into a pandas series indexed by your column names, then use its plot method. e.g. for an sklearn RF classifier/regressor model trained using df:

feat_importances = pd.Series(model.feature_importances_, index=df.columns)
feat_importances.nlargest(4).plot(kind='barh')

Here is an example using the iris data set.

>>> from sklearn.datasets import load_iris
>>> iris = load_iris()
>>> rnd_clf = RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)
>>> rnd_clf.fit(iris["data"], iris["target"])
>>> for name, importance in zip(iris["feature_names"], rnd_clf.feature_importances_):
...     print(name, "=", importance)

sepal length (cm) = 0.112492250999
sepal width (cm) = 0.0231192882825
petal length (cm) = 0.441030464364
petal width (cm) = 0.423357996355

Plotting feature importance

>>> features = iris['feature_names']
>>> importances = rnd_clf.feature_importances_
>>> indices = np.argsort(importances)

>>> plt.title('Feature Importances')
>>> plt.barh(range(len(indices)), importances[indices], color='b', align='center')
>>> plt.yticks(range(len(indices)), [features[i] for i in indices])
>>> plt.xlabel('Relative Importance')
>>> plt.show()