cloudia

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

Args

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

• japanize-matplotlib
• nagisa