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Today, I will talk to you about how Python+wordcloud generates word clouds, which may not be well understood by many people. in order to make you understand better, the editor has summarized the following content for you. I hope you can get something according to this article.

What is a word cloud?

The word cloud, also known as the word cloud, is the visual prominent presentation of the "keywords" with high frequency in the text data, forming the rendering of the keywords to form a color picture similar to the cloud, so that you can appreciate the main expression meaning of the text data at a glance.

Preparatory work:

Python development environment, wordcloud, jieba, matplotlib, numpy, PIL and other library files are installed.

Pip installation method:

A brief introduction to the principle of generating word Cloud by wordcloud

The principle of generating word cloud by wordcloud is not complicated. It is generally divided into 5 steps (you can check the source code for details):

When 1.wordcloud makes a word cloud, we should first segment the text data and use the process_text () method. The main task of this step is to remove the stop words.

two。 The second step is to calculate the frequency of each word in the text and generate a hash table. Word frequency is used to determine the importance of a word.

3. The layout of a picture is generated proportionally according to the number of word frequency. IntegralOccupancyMap-like is the algorithm of the word cloud and the core of the data visualization of the word cloud. The color, position, direction, etc of a word.

4. Finally, the word is generated on the word cloud layout diagram according to the corresponding word frequency. The core method is generate_from_frequencies. Whether generate () or generate_from_text (), generate_from_frequencies is finally used.

Complete the coloring of each word on the word cloud. The default is random coloring.

5. Most of the enhancements to words can be achieved through the constructor of wordcloud, which provides 22 parameters and can be extended by itself.

Pip install xxx

Install directly using idea

# coding:utf-8 import matplotlib.pyplot as plt from wordcloud import WordCloud,ImageColorGenerator,STOPWORDSimport jiebaimport numpy as np from PIL import Image # read the background image abel_mask = np.array (Image.open ("filepath")) # read the file text_from_file_with_apath = open ('filepath'). Read () # use jieba participle to segment words and separate wordlist_after_jieba = jieba.cut by spaces (text_from_file_with_apath Cut_all = True) wl_space_split = ".join (wordlist_after_jieba) # my_wordcloud = WordCloud () .generate (wl_space_split) default constructor my_wordcloud = WordCloud (background_color='white', # sets background color mask = abel_mask, # sets background image max_words = 200, # sets maximum realistic number of words stopwords = STOPWORDS # set the deactivation word font_path = C _ Rank _ If you cannot display Chinese max_font_size = 50 without setting, # set the maximum font value random_state = 30, and # set how many randomly generated states there are That is, there are many color schemes scale=.5). Generate (wl_space_split) # generate the word cloud color from the picture image_colors = ImageColorGenerator (abel_mask) # my_wordcloud.recolor (color_func=image_colors) # the following code displays the picture plt.imshow (my_wordcloud) plt.axis ("off") plt.show ()

Example

Generate Prime Minister Li Keqiang's 2017 Government work report

1. Generate using the default constructor WordCloud (). Generate ()

Set some parameters in 2.WordCloud (). Set the mask parameter to add the following background pattern:

According to the background pattern above, the final word cloud looks like this:

After reading the above, do you have any further understanding of how Python+wordcloud generates word clouds? If you want to know more knowledge or related content, please follow the industry information channel, thank you for your support.

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