100 Language Processing Knock-38 (using pandas): Histogram

Language processing 100 knocks 2015 "Chapter 4: Morphological analysis" This is the 38th "histogram" record of .ac.jp/nlp100/#ch4). It's easy as long as you get over the "tofu" that was knocked last time. If you don't put out the label, you don't have to deal with "tofu".

Reference link

Link Remarks
038.histogram.ipynb Answer program GitHub link
100 amateur language processing knocks:38 Copy and paste source of many source parts
MeCab Official The first MeCab page to look at

environment

type version Contents
OS Ubuntu18.04.01 LTS It is running virtually
pyenv 1.2.16 I use pyenv because I sometimes use multiple Python environments
Python 3.8.1 python3 on pyenv.8.I'm using 1
Packages are managed using venv
Mecab 0.996-5 apt-Install with get

In the above environment, I am using the following additional Python packages. Just install with regular pip.

type version
matplotlib 3.1.3
pandas 1.0.1

Chapter 4: Morphological analysis

content of study

Apply the morphological analyzer MeCab to Natsume Soseki's novel "I Am a Cat" to obtain the statistics of the words in the novel.

Morphological analysis, MeCab, part of speech, frequency of occurrence, Zipf's law, matplotlib, Gnuplot

Knock content

Using MeCab for the text (neko.txt) of Natsume Soseki's novel "I am a cat" Morphological analysis and save the result in a file called neko.txt.mecab. Use this file to implement a program that addresses the following questions.

For problems 37, 38, and 39, use matplotlib or Gnuplot.

38. Histogram

Draw a histogram of the frequency of occurrence of words (the horizontal axis represents the frequency of occurrence and the vertical axis represents the number of types of words that take the frequency of occurrence as a bar graph).

Answer

Answer Program [038. Histogram.ipynb](https://github.com/YoheiFukuhara/nlp100/blob/master/04.%E5%BD%A2%E6%85%8B%E7%B4%A0%E8%A7 % A3% E6% 9E% 90 / 038.% E3% 83% 92% E3% 82% B9% E3% 83% 88% E3% 82% B0% E3% 83% A9% E3% 83% A0.ipynb)

import matplotlib.pyplot as plt
import pandas as pd

plt.rcParams['font.family'] = 'IPAexGothic'

def read_text():
    # 0:Surface type(surface)
    # 1:Part of speech(pos)
    # 2:Part of speech subclassification 1(pos1)
    # 7:Uninflected word(base)
    df = pd.read_table('./neko.txt.mecab', sep='\t|,', header=None, 
                       usecols=[0, 1, 2, 7], names=['surface', 'pos', 'pos1', 'base'], 
                       skiprows=4, skipfooter=1 ,engine='python')
    return df[(df['pos'] != 'Blank') & (df['surface'] != 'EOS') & (df['pos'] != 'symbol')]

df = read_text()

hist = df['surface'].value_counts().plot.hist(bins=20, range=(1, 20))
hist.set_xlabel('Frequency of appearance')
hist.set_ylabel('Number of word types')

Answer commentary

Just use the plot of pandas. I also added a label.

python


hist = df['surface'].value_counts().plot.hist(bins=20, range=(1, 20))
hist.set_xlabel('Frequency of appearance')
hist.set_ylabel('Number of word types')

Output result (execution result)

When the program is executed, the following results will be output. Well, it looks like this.

image.png

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