![](/style/images/good.png)
![](/style/images/bad.png)
GitHub - vaaaaanquish/cloudia: Tools to easily create a word cloud
source link: https://github.com/vaaaaanquish/cloudia?
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
README.md
Cloudia
Tools to easily create a word cloud.
from string
from str or List[str]
from cloudia import Cloudia
text1 = "text data..."
text2 = "text data..."
# from str
Cloudia(text1).plot()
# from list
Cloudia([text1, text2]).plot()
example from : 20 Newsgroups
We can also make it from Tuple.
from cloudia import Cloudia
text1 = "text data..."
text2 = "text data..."
Cloudia([ ("cloudia 1", text1), ("cloudia 2", text2) ]).plot()
Tuple is ("IMAGE TITLE", "TEXT").
from pandas
We can use pandas.
df = pd.DataFrame({'wc1': ['sample1','sample2'], 'wc2': ['hoge hoge piyo piyo fuga', 'hoge']})
# plot from df
Cloudia(df).plot()
# add df method
df.wc.plot(dark_theme=True)
from pandas.DataFrame or pandas.Series.
We can use Tuple too.
Cloudia( ("IMAGE TITLE", pd.Series(['hoge'])) ).plot()
from japanese
We can process Japanese too.
text = "これはCloudiaのテストです。WordCloudをつくるには本来、形態素解析の導入が必要になります。Cloudiaはmecabのような形態素解析器の導入は必要はなくnagisaを利用した動的な生成を行う事ができます。nagisaとjapanize-matplotlibは、形態素解析を必要としてきたWordCloud生成に対して、Cloudiaに対して大きく貢献しました。ここに感謝の意を述べたいと思います。"
Cloudia(text).plot()
from japanese without morphological analysis module.
No need to introduce morphological analysis.
Install
pip install cloudia
Cloudia args.
Cloudia(
data, # text data
single_words=[], # It's not split word list, example: ["neural network"]
stop_words=STOPWORDS, # not count words, default is wordcloud.STOPWORDS
extract_postags=['名詞', '英単語', 'ローマ字文'], # part of speech for japanese
parse_func=None, # split text function, example: lambda x: x.split(',')
multiprocess=True, # Flag for using multiprocessing
individual=False # flag for ' '.join(word) with parse
)
plot method args.
Cloudia().plot(
dark_theme=False, # color theme
title_size=12, # title text size
row_num=3, # for example, 12 wordcloud, row_num=3 -> 4*3image
figsize_rate=2 # figure size rate
)
save method args.
Cloudia().save(
file_path, # save figure image path
dark_theme=False,
title_size=12,
row_num=3,
figsize_rate=2
)
pandas.DataFrame, pandas.Series wc.plot method args.
DataFrame.wc.plot(
single_words=[], # It's not split word list, example: ["neural network"]
stop_words=STOPWORDS, # not count words, default is wordcloud.STOPWORDS
extract_postags=['名詞', '英単語', 'ローマ字文'], # part of speech for japanese
parse_func=None, # split text function, example: lambda x: x.split(',')
multiprocess=True, # Flag for using multiprocessing
individual=False, # flag for ' '.join(word) with parse
dark_theme=False, # color theme
title_size=12, # title text size
row_num=3, # for example, 12 wordcloud, row_num=3 -> 4*3image
figsize_rate=2 # figure size rate
)
If we use wc.save, setting file_path args.
Thanks
Recommend
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK