如何使用matplotlib以图形形式显示结果？

Question

我正在使用matplotlib学习python中的twitter情绪分析。目前，控制台中会显示结果（正数为1，中性为0，负数为-1）。我想使用matplotlib以图形形式显示控制台数据结果。我希望有人可以帮助我使用matplotlib以图形形式显示数据。有人请帮帮我吗？ 我的代码在下面列出。

from tweepy import API
from tweepy import Cursor
from tweepy.streaming import StreamListener
from tweepy import OAuthHandler
from tweepy import Stream
from textblob import TextBlob

import twitter_credentials
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re


## # # TWITTER CLIENT # # # #

class TwitterClient:

    def __init__(self, twitter_user=None):
        self.auth = TwitterAuthenticator().authenticate_twitter_app()
        self.twitter_client = API(self.auth)

        self.twitter_user = twitter_user

    def get_twitter_client_api(self):
        return self.twitter_client

    def get_user_timeline_tweets(self, num_tweets):
        tweets = []
        for tweet in Cursor(self.twitter_client.user_timeline,
                            id=self.twitter_user).items(num_tweets):
            tweets.append(tweet)
        return tweets

    def get_friend_list(self, num_friends):
        friend_list = []
        for friend in Cursor(self.twitter_client.friends,
                             id=self.twitter_user).items(num_friends):
            friend_list.append(friend)
        return friend_list

    def get_home_timeline_tweets(self, num_tweets):
        home_timeline_tweets = []
        for tweet in Cursor(self.twitter_client.home_timeline,
                            id=self.twitter_user).items(num_tweets):
            home_timeline_tweets.append(tweet)
        return home_timeline_tweets


## # # TWITTER AUTHENTICATER # # # #

class TwitterAuthenticator:

    def authenticate_twitter_app(self):
        auth = OAuthHandler(twitter_credentials.CONSUMER_KEY,
                            twitter_credentials.CONSUMER_SECRET)
        auth.set_access_token(twitter_credentials.ACCESS_TOKEN,
                              twitter_credentials.ACCESS_TOKEN_SECRET)
        return auth


## # # TWITTER STREAMER # # # #

class TwitterStreamer:

    """
    Class for streaming and processing live tweets.
    """

    def __init__(self):
        self.twitter_autenticator = TwitterAuthenticator()

    def stream_tweets(self, fetched_tweets_filename, hash_tag_list):

        # This handles Twitter authetification and the connection to Twitter Streaming API

        listener = TwitterListener(fetched_tweets_filename)
        auth = self.twitter_autenticator.authenticate_twitter_app()
        stream = Stream(auth, listener)

        # This line filter Twitter Streams to capture data by the keywords:

        stream.filter(track=hash_tag_list)


## # # TWITTER STREAM LISTENER # # # #

class TwitterListener(StreamListener):

    """
    This is a basic listener that just prints received tweets to stdout.
    """

    def __init__(self, fetched_tweets_filename):
        self.fetched_tweets_filename = fetched_tweets_filename

    def on_data(self, data):
        try:
            print data
            with open(self.fetched_tweets_filename, 'a') as tf:
                tf.write(data)
            return True
        except BaseException, e:
            print 'Error on_data %s' % str(e)
        return True

    def on_error(self, status):
        if status == 420:

            # Returning False on_data method in case rate limit occurs.

            return False
        print status


class TweetAnalyzer:

    """
    Functionality for analyzing and categorizing content from tweets.
    """

    def clean_tweet(self, tweet):
        return ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"
                        , ' ', tweet).split())

    def analyze_sentiment(self, tweet):
        analysis = TextBlob(self.clean_tweet(tweet))

        if analysis.sentiment.polarity > 0:
            return 1
        elif analysis.sentiment.polarity == 0:
            return 0
        else:
            return -1

    def tweets_to_data_frame(self, tweets):
        df = pd.DataFrame(data=[tweet.text for tweet in tweets],
                          columns=['tweets'])

        df['id'] = np.array([tweet.id for tweet in tweets])
        df['len'] = np.array([len(tweet.text) for tweet in tweets])
        df['date'] = np.array([tweet.created_at for tweet in tweets])
        df['source'] = np.array([tweet.source for tweet in tweets])
        df['likes'] = np.array([tweet.favorite_count for tweet in
                               tweets])
        df['retweets'] = np.array([tweet.retweet_count for tweet in
                                  tweets])

        return df


if __name__ == '__main__':

    twitter_client = TwitterClient()
    tweet_analyzer = TweetAnalyzer()

    api = twitter_client.get_twitter_client_api()

    tweets = api.user_timeline(screen_name='realDonaldTrump', count=200)

    df = tweet_analyzer.tweets_to_data_frame(tweets)
    df['sentiment'] = np.array([tweet_analyzer.analyze_sentiment(tweet)
                               for tweet in df['tweets']])

    print df.head(10)