matplotlib: Plot Feature Importance with feature names

Quick answer for data scientists that ain't got no time to waste:

Load the feature importances into a pandas series indexed by your column names, then use its plot method. For a classifier model trained using X:

feat_importances = pd.Series(model.feature_importances_, index=X.columns)
feat_importances.nlargest(20).plot(kind='barh')

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Slightly more detailed answer with a full example:

Assuming you trained your model with data contained in a pandas dataframe, this is fairly painless if you load the feature importance into a panda's series, then you can leverage its indexing to get the variable names displayed easily. The plot argument kind='barh' gives us a horizontal bar chart, but you could easily substitute this argument for kind='bar' for a traditional bar chart with the feature names along the x-axis if you prefer.

nlargest(n) is a pandas Series method which will return a subset of the series with the largest n values. This is useful if you've got lots of features in your model and you only want to plot the most important.

A quick complete example using the classic Kaggle Titanic dataset...

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
%matplotlib inline            # don't forget this if you're using jupyter!

X = pd.read_csv("titanic_train.csv")
X = X[['Pclass', 'Age', 'Fare', 'Parch', 'SibSp', 'Survived']].dropna()
y = X.pop('Survived')

model = RandomForestClassifier()
model.fit(X, y)

(pd.Series(model.feature_importances_, index=X.columns)
   .nlargest(4)
   .plot(kind='barh'))        # some method chaining, because it's sexy!

Which will give you this:

Not exactly sure what you are looking for. Derived a example from here. As mentioned in the comment: you can change indices to a list of labels at line plt.yticks(range(X.shape[1]), indices) if you want to customize feature labels.

import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import make_classification
from sklearn.ensemble import ExtraTreesClassifier

# Build a classification task using 3 informative features
X, y = make_classification(n_samples=1000,
                           n_features=10,
                           n_informative=3,
                           n_redundant=0,
                           n_repeated=0,
                           n_classes=2,
                           random_state=0,
                           shuffle=False)

# Build a forest and compute the feature importances
forest = ExtraTreesClassifier(n_estimators=250,
                              random_state=0)

forest.fit(X, y)
importances = forest.feature_importances_
std = np.std([tree.feature_importances_ for tree in forest.estimators_],
             axis=0)
indices = np.argsort(importances)

# Plot the feature importances of the forest
plt.figure()
plt.title("Feature importances")
plt.barh(range(X.shape[1]), importances[indices],
       color="r", xerr=std[indices], align="center")
# If you want to define your own labels,
# change indices to a list of labels on the following line.
plt.yticks(range(X.shape[1]), indices)
plt.ylim([-1, X.shape[1]])
plt.show()

It's possible to just pass df.columns as the parameter for plt.xticks(),i have written a sample implementation.

plt.bar( range(len(model.feature_importances_)), model.feature_importances_)
plt.xticks(range(len(model.feature_importances_)), train_features.columns)
plt.show()