Metadata-Version: 2.1
Name: nlplot
Version: 1.0.5
Summary: Visualization Module for Natural Language Processing
Home-page: https://github.com/takapy0210/nlplot
Author: Takanobu Nozawa
Author-email: takanobu.030210@gmail.com
License: MIT License
Description: # nlplot
        Visualization Module for Natural Language Processing
        
        ## Description
        Facilitates the visualization of natural language processing and provides quicker analysis
        
        You can draw the following graph
        
        1. N-gram bar chart
        2. N-gram tree Map
        3. Histogram of the word count
        4. wordcloud
        5. co-occurrence networks
        6. sunburst chart
        7. pyLDAvis
        
        （Tested in English and Japanese）
        
        ## Requirement
        - [python package](https://github.com/takapy0210/nlplot/blob/master/requirements.txt)
        
        ## Install
        ```sh
        pip install nlplot
        ```
        
        ## Usage
        
        sample df
        
        ```python
        df.head()
        ```
        
        |    |  text  |
        | ---- | ---- |
        |  0  |  Think rich look poor |
        |  1  |  When you come to a roadblock, take a detour |
        |  2  |  When it is dark enough, you can see the stars |
        |  3  |  Never let your memories be greater than your dreams  |
        |  4  |  Victory is sweetest when you’ve known defeat  |
        
        
        ```python
        import nlplot
        
        # taget_col as a list type or a string separated by a space.
        npt = nlplot.NLPlot(df, taget_col='text')
        
        # 1. N-gram bar chart
        npt.bar_ngram(title='uni-gram', ngram=1, top_n=50)
        npt.bar_ngram(title='bi-gram', ngram=2, top_n=50)
        
        # 2. N-gram tree Map
        npt.treemap(title='Tree of Most Common Words', ngram=1,top_n=30)
        
        # 3. Histogram of the word count
        npt.word_distribution(title='number of words distribution')
        
        # 4. wordcloud
        npt.wordcloud()
        
        # 5. co-occurrence networks
        npt.build_graph(min_edge_frequency=10)
        # The number of nodes and edges to which this output is plotted.
        # If this number is too large, plotting will take a long time, so adjust the [min_edge_frequency] well.
        >> node_size:70, edge_size:166
        npt.co_network(title='Co-occurrence network')
        
        # 6. sunburst chart
        npt.sunburst(title='sunburst chart', colorscale=True)
        
        # 7. pyLDAvis
        npt.ldavis(num_topics=5, passes=5, save=False)
        
        
        ```
        
        ## Document
        TBD
        
        ## Test
        TBD
        
        ## Other
        
        - Plotly is used to plot the figure
            - https://plotly.com/python/
        
        - co-occurrence networks is used to calculate the co-occurrence network
            - https://networkx.github.io/documentation/stable/tutorial.html
        
        - The following is used to plot pyLDAvis
            - https://github.com/bmabey/pyLDAvis
        
        - wordcloud uses the following fonts
            - https://mplus-fonts.osdn.jp/about.html
Platform: UNKNOWN
Requires-Python: ~=3.6
Description-Content-Type: text/markdown
