How to create a word frequency plot using matplotlib Python?

Word frequency analysis is an important stage in text mining and NLP research. It is because it identifies the most used and common words in a text corpus. We can use the words to display the text sample to reveal broad trends in the textual data. We can plot the word frequency distributions using the Matplotlib library. And the graph type "Graph Word Frequency." 

Types of word frequency plots: 

The types of word frequency plots are as follows:

Graph Word Frequency: 

A graph word frequency plot uses a bar graph or a line graph to display the frequency of each word in a text corpus. 

Top 10 Most Frequent Words: 

You can list the frequently used words. A plot bar chart displays a text corpus's most frequently used words. 

Word Frequency Distributions: 

Using a histogram or a line graph, a word frequency distribution. This plot depicts the distribution of word frequencies in a text corpus. 

Word Cloud: 

A word cloud is a plot that uses a visual representation to show the frequency of each term in a text corpus. 

Vocabulary Items: 

A vocabulary items plot displays the number of unique words in a text corpus. This style of visualization is handy for comparing the size of various texts. 

General procedure for creating a word frequency plot: 

We can open a programming environment like Jupyter Notebook or Python Prompt. It can create a new Python file or script. Then we must import the required packages, which include Matplotlib, nltk, and stop-words. Stop words are genuine in the text with no special meaning. We can filter out of the analysis. 

We can import the text data or sample from an input or many text files. We can enter the text data as plain text documents or plain text files. Then we can use nltk to tokenize the text into individual words or many words. We can find the occurrences of those words. 

After we get the word counts, we can use Matplotlib to plot the data using a sorted dictionary or list. The result can provide insights into the vocabulary items utilized in the text. It can identify the specific terms important for text analysis. We can plot the word frequency distribution and label the plot with title and axis labels. 

In the code below, we have used two main libraries - pandas and matplotlib. 

plt.plot(pd.Series(s).value_counts(), linestyle = '-'): 

This line plots the frequency counts of the words in 's' using Matplotlib. The linestyle argument sets the style of the line to a solid line (-). 

Preview of the word frequency plot using matplotolib

Code

plt.plot(pd.Series(s).value_counts(), linestyle = '-'): This line plots the frequency counts of the words in 's' using Matplotlib. The linestyle argument sets the style of the line to a solid line (-).

Follow the steps carefully to get the output easily.

• Install Visual Studio Code in your computer.
• Install the required library by using the following command -

pip install matplotlib

pip install pandas

• If your system is not reflecting the installation, try running the above command by opening windows powershell as administrator.
• Open the folder in the code editor, copy and paste the above kandi code snippet in the python file.
• Add the lines in the beginning

import pandas as pd
import matplotlib as plt

• Run the code using the run command.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for "how to create a word frequency plot using matplotlib python" in kandi. You can try any such use case!

Dependent libraries

If you do not have matplotlib and pandas that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the pages in kandi.

You can search for any dependent library on kandi like matplotlib.

Environment tested

1. This code had been tested using python version 3.8.0
2. matplotlib version 3.7.1 has been used.
3. pandas version 1.5.3 has been used.

FAQ 

What is word frequency analysis, and how can we use it in Python?  

Word frequency analysis is an NLP technique that counts the frequency of words in a text corpus. Word frequency analysis seeks to find a text's frequently used words and phrases. It can provide insight into language trends and usage. We can use the Python packages such as NLTK, Pandas, and Matplotlib to analyze word frequency. 

How can I read a Python file to generate a word frequency plot?  

It helps create a word frequency plot from the Python file. You must extract the text data from the file to count the frequency of every word before processing it. Once you have the word frequency data, you may plot it using several packages. 

Are there any limitations when creating a word frequency plot with different datatypes?  

We can standardize the data preprocessing methods, picking acceptable thresholds or cutoffs. We can limit the data preprocessing, vocabulary size, contextual characteristics, and visualization techniques. It will happen when constructing a plot from files containing different data forms. It will take contextual characteristics and select appropriate visualization approaches. It can all help to reduce these restrictions. 

Are there any libraries or packages available? Could I visualize my results from the word frequency plot Python program more?  

Yes, there are various Python modules and packages available. It will help you develop more effective word-frequency plot visualizations. Matplotlib, seaborn, wordcloud, and plotly are popular solutions. They offer a variety of customization possibilities for making informative and beautiful charts. 

How can I use this information from my results to draw useful conclusions about the dataset?  

You should identify the occurring words, patterns, and co-occurrence to extract inferences. It is also necessary to consider domain-specific knowledge. It will interpret the results within the context of the issue. It might also interpret the domain under consideration. 

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