nlplot: Analysis and visualization module for Natural Language Processing 📈
Facilitates the visualization of natural language processing and provides quicker analysis
You can draw the following graph
- N-gram bar chart
- N-gram tree Map
- Histogram of the word count
- wordcloud
- co-occurrence networks
- sunburst chart
（Tested in English and Japanese）
pip install nlplotI've posted on this blog about the specific use. (Japanese)
And, The sample code is also available in the kernel of kaggle. (English)
The column to be analyzed must be a space-delimited string
# 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()| 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 |
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
)TBD
cd tests
pytest-
Plotly is used to plot the figure
-
co-occurrence networks is used to calculate the co-occurrence network
-
wordcloud uses the following fonts
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent
