How to make a word cloud with Python-a word cloud analysis of 1000 ancient poems

Today I will introduce how to use Python to make a word cloud .

A word cloud is also called a word cloud. It can count the more frequent words in the text and visualize these words so that we can intuitively understand the key words in the text.

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The higher the frequency of the word, the larger the size of the word display.

1. The wordcloud module

wordcloud is a word cloud generator. It is not only a Python library, but also a command line tool . We can learn how to use it through the official wordcloud documentation and sample library .

Before using wordcloud , you need to install it first:

pip install wordcloud

2. WordCloud class

The WordCloud class is used to create 词云对象 , let's take a look at its prototype:

WordCloud(font_path=None, 
  width=400, height=200, 
  margin=2, ranks_only=None, 
  prefer_horizontal=0.9, 
  mask=None, scale=1, 
  color_func=None, max_words=200, 
  min_font_size=4, stopwords=None, 
  random_state=None, 
  background_color='black', 
  max_font_size=None, 
  font_step=1, mode='RGB', 
  relative_scaling='auto', 
  regexp=None, collocations=True, 
  colormap=None, normalize_plurals=True, 
  contour_width=0, contour_color='black', 
  repeat=False, include_numbers=False, 
  min_word_length=0, 
  collocation_threshold=30)

As you can see, the WordCloud class has many parameters that can be set. Here are some commonly used parameters:

font_path : Set the font file path to the font file .ttf suffix.
- If the analyzed text is Chinese, you need to set the Chinese font, otherwise it will be garbled.
background_color : Set the background color of the picture, the default is black , or it can be set to white, etc.
mask : Set the background image.
max_words : Set the maximum number of words, the default is 200.
stopwords : Set stop words.
max_font_size : Set the maximum font size.
width : Set the width of the canvas, the default is 400.
height : Set the height of the canvas, the default is 200.
random_state : How many random states are set, that is, how many colors.

After creation 词云对象 , you can use the generate method to generate the word cloud, and use the to_file method to save the word cloud image in the file.

generate The prototype of the method is as follows:

generate(text)

The parameter text is a 空格 separated text string. If the analysis is in Chinese, you need to use jieba for word segmentation first , you can refer to here .

In addition to saving the word cloud image in a file, you can also use the Matplotlib module to display the word cloud image. The sample code is as follows:

import matplotlib.pyplot as plt

plt.imshow(wordcloud)  # wordcloud 是词云对象
plt.axis("off")        # 用于关闭坐标轴
plt.show()

3. A simple example

Here is a simple example to see how to use wordcloud .

First create 词云对象 :

from wordcloud import WordCloud
wc = WordCloud()

Generate word cloud:

text = "Python is a programming language, it is easy to use."
wc.generate(text)

The words_ attribute of the word cloud object stores the (normalized) weight of each word, and the range of the weight is (0, 1] .

words_ The attribute is a dictionary type, and the maximum number of keys it stores is max_words , that is WordCloud , the parameters of the class.

as follows:

>>> wc.words_
{'Python': 1.0, 'programming': 1.0, 'language': 1.0, 'easy': 1.0, 'use': 1.0}
# 示例中的这些单词出现的频率都相等（均为 1），
# 所以它们的权重都是 1。

Use Matplotlib to display word cloud images:

import matplotlib.pyplot as plt

plt.imshow(wc) 
plt.axis("off")
plt.show()

The word cloud image is as follows:

4. Do word cloud analysis of ancient poems

I have prepared a case here , which is a word cloud analysis of 1,000 ancient poems.

The code directory is as follows:

wordcloud/
├── SimHei.ttf
├── gushi.txt
└── gushi_wordcloud.py

among them:

SimHei.ttf : is a font file to avoid Chinese garbled characters in word cloud analysis.
gushi.txt : This file contains 1000 ancient poems .
gushi_wordcloud.py : is the word cloud analysis code .

I also put the code here for easy viewing:

#!/usr/bin/env python
# coding=utf-8

import os
import sys
import jieba
from wordcloud import WordCloud

if sys.version.startswith('2.'):
    reload(sys)
    sys.setdefaultencoding('utf-8')

# 去掉一些作者的名字
STOPWORDS = [
        u'李白', u'杜甫', u'辛弃疾', u'李清照', u'苏轼',
        u'李商隐', u'王维', u'白居易', u'李煜', u'杜牧',
        ]

def load_file(file_path):
    if sys.version.startswith('2.'):
        with open(file_path) as f:
            lines = f.readlines()
    else:
        with open(file_path, encoding='utf-8') as f:
            lines = f.readlines()

    content = ''

    for line in lines:
        line = line.encode('unicode-escape').decode('unicode-escape')
        line = line.strip().rstrip('\n')

        content += line

    words = jieba.cut(content)

    l = []
    for w in words:
        # 如果词的长度小于 2，则舍去
        if len(w) < 2: continue

        l.append(w)

    return ' '.join(l)


if __name__ == '__main__':
    file_path = './gushi.txt'
    content = load_file(file_path)

    wc = WordCloud(
            font_path="./SimHei.ttf",
            stopwords=STOPWORDS,
            width=2000, height=1200)

    wc.generate(content)
    wc.to_file("wordcloud.jpg")

among them:

STOPWORDS The stop word list is the names of some authors.
load_file The method is used to load text, which uses jieba word segmentation.

Finally, the word cloud image is saved in the wordcloud.jpg file, as follows:

We can also words_ view the weight of each word from the attributes of the word cloud object . Here I list the top ten:

('Ming Moon', 1.0) 
('Today', 0.9130434782608695) 
('I don't know', 0.8405797101449275) 
('Where', 0.8260869565217391) 
('No See', 0.8115942028985508) 
('Spring Breeze', 0.7536231884057971) 
('Nobody', 0.7536231884057971 ) 
('Not possible', 0.7536231884057971) 
('Wanli', 0.7536231884057971) 
('Modern', 0.6666666666666666)

(End of this section.)