📝 nlplot

Description

Requirement

Installation

pip install nlplot

Quick start - Data Preparation

# sample data
target_col = "text"
texts = [
 "Think rich look poor",
 "When you come to a roadblock, take a detour",
 "When it is dark enough, you can see the stars",
 "Never let your memories be greater than your dreams",
 "Victory is sweetest when you’ve known defeat"
 ]
df = pd.DataFrame({target_col: texts})
df.head()

Quick start - Python API

import nlplot
import pandas as pd
import plotly
from plotly.subplots import make_subplots
from plotly.offline import iplot
import matplotlib.pyplot as plt
%matplotlib inline
# target_col as a list type or a string separated by a space.
npt = nlplot.NLPlot(df, target_col='text')
# Stopword calculations can be performed.
stopwords = npt.get_stopword(top_n=30, min_freq=0)
# 1. N-gram bar chart
fig_unigram = npt.bar_ngram(
 title='uni-gram',
 xaxis_label='word_count',
 yaxis_label='word',
 ngram=1,
 top_n=50,
 width=800,
 height=1100,
 color=None,
 horizon=True,
 stopwords=stopwords,
 verbose=False,
 save=False,
)
fig_unigram.show()
fig_bigram = npt.bar_ngram(
 title='bi-gram',
 xaxis_label='word_count',
 yaxis_label='word',
 ngram=2,
 top_n=50,
 width=800,
 height=1100,
 color=None,
 horizon=True,
 stopwords=stopwords,
 verbose=False,
 save=False,
)
fig_bigram.show()
# 2. N-gram tree Map
fig_treemap = npt.treemap(
 title='Tree map',
 ngram=1,
 top_n=50,
 width=1300,
 height=600,
 stopwords=stopwords,
 verbose=False,
 save=False
)
fig_treemap.show()
# 3. Histogram of the word count
fig_histgram = npt.word_distribution(
 title='word distribution',
 xaxis_label='count',
 yaxis_label='',
 width=1000,
 height=500,
 color=None,
 template='plotly',
 bins=None,
 save=False,
)
fig_histgram.show()
# 4. wordcloud
fig_wc = npt.wordcloud(
 width=1000,
 height=600,
 max_words=100,
 max_font_size=100,
 colormap='tab20_r',
 stopwords=stopwords,
 mask_file=None,
 save=False
)
plt.figure(figsize=(15, 25))
plt.imshow(fig_wc, interpolation="bilinear")
plt.axis("off")
plt.show()
# 5. co-occurrence networks
npt.build_graph(stopwords=stopwords, 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
fig_co_network = npt.co_network(
 title='Co-occurrence network',
 sizing=100,
 node_size='adjacency_frequency',
 color_palette='hls',
 width=1100,
 height=700,
 save=False
)
iplot(fig_co_network)
# 6. sunburst chart
fig_sunburst = npt.sunburst(
 title='sunburst chart',
 colorscale=True,
 color_continuous_scale='Oryel',
 width=1000,
 height=800,
 save=False
)
fig_sunburst.show()
# other
# The original data frame of the co-occurrence network can also be accessed
display(
 npt.node_df.head(), npt.node_df.shape,
 npt.edge_df.head(), npt.edge_df.shape
)

Document

Test

cd tests
pytest

Other