Automate the process of visualization
Project description
Visualizer:
Visualizer is a Python package that automates the process of visualization and facilitates the plotting of any individual relationship between multiple-columns.
Visualizer package allows you to do 2 types of plotting:
-
Visualize by an individual column:
- Count Plot.
- Pie Plot.
- Histogram plot.
- KDE plot.
- WordCloud plot.
- Histogram for high cardinality columns.
- Line plot with index.
- Point plot with index.
- Clustered-bar Plot.
- Bubble plot.
- Scatter plot.
- Density plot.
- Box plot.
- Violin plot.
- Ridge plot.
- Parallel plot.
- Radar plot.
-
Visualize by a relationship (multiple-columns):
- Uni-vairate Target.
- Uni-variate Categorical (Cat).
- Uni-variate Numerical (Num).
- Bi-variate Num with Index.
- Bi-variate Cat with Index.
- Bi-variate Num with Num.
- Bi-variate Num with Cat.
- Bi-variate Cat with Cat.
- Bi-variate Cat with Target.
- Bi-variate Num with Target.
- Multi-variate Nums with Cat.
Installation:
pip install -U visualizer
Usage:
- To use the first type Individual Plotting, all the methods starts with create_, and you can use them as follows:
# Import the library
from visualizer import Visualize
# Create a count plot
Visualizer.create_count_plot(df=df, cat_col="cat_col")
- To use the second type Automatic Visualization, all the methods starts with visualize_, and you can them as follows:
# Import the library
from visualizer import Visualizer
autoVis = Visualizer(df=df, # df: (dataframe)
num_cols=num_cols, # num_cols: (list) of numerical columns.
cat_cols=cat_cols, # cat_cols: (list) of categorical columns.
target_col=target_col, # target_col: (string) your target column.
ignore_cols=ignore_cols, # ignore_cols: (list) of columns to ignore.
problem_type='classification') # problem_type: (string) ['classification', 'regression']
# Visualize all the relationships between the selected columns,
# whether it's uni-variate, Bi-variate, or even multi-variate.
# This methods saves the generated figures into folder named "visualizer"
# into the current directory.
autoVis.visualize_all()
To know more, you can see the docs.
Further Ideas/Developments:
The following ideas are under construction and it will be added soon in upcoming versions:
-
Visualize Sparse Columns, to see if they have a pattern.
-
Visualize NaN/Infinite/Large numeric values across the whole dataframe, to see the pattern of the whole dataframe.
-
Visualize Text columns.
-
Add the functionality to arrange the structure of the folders to be by columns, so each column has all the relationships for a specific column.
Contribute:
If you've found a bug or something that you would like to improve, don't hesitate to create an issue and create a pull request.
License:
MIT License.
Authors:
Project details
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