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.