seaborn-analyzer 0.1.3

seaborn-analyzer: data visualization of regression, classification and distribution

seaborn-analyzer

A data analysis and visualization tool using Seaborn library.

Usage

An example of using CustomPairPlot class

from seaborn_analyzer import CustomPairPlot
import seaborn as sns

titanic = sns.load_dataset("titanic")
cp = CustomPairPlot()
cp.pairanalyzer(titanic, hue='survived')

If you want to know usage of other classes, see API Reference and Examples

Requirements

seaborn-analyzer 0.1.2 requires

• Python >=3.6

• Numpy >=1.20.3

• Pandas >=1.2.4

• Matplotlib >=3.3.4

• Scipy >=1.6.3

• Scikit-learn >=0.24.2

Installing seaborn-analyzer

Use pip to install the binary wheels on PyPI

$ pip install seaborn-analyzer

Support

Bugs may be reported at https://github.com/c60evaporator/seaborn-analyzer/issues

API Reference

The following classes and methods are included in seaborn-analyzer

CustomPairPlot class

Method name	Summary	API Documentation	Example
pairanalyzer	Plotting pair plot including scatter plot and correlation coefficient matrix simultaneously	CustomPairPlot.pairanalyzer	example

hist class

Method name	Summary	API Documentation	Example
plot_normality	Plotting normality test and QQ plot	hist.plot_normality	example
fit_dist	Fitting distributions and calculating fitting scores	hist.fit_dist	example

classplot class

Method name	Summary	API Documentation	Example
class_separator_plot	Plotting class separation lines of any scikit-learn classification models	hist.class_separator_plot	example
class_proba_plot	Plotting class prediction probability of any scikit-learn classification models	hist.class_proba_plot	example

regplot class

Method name	Summary	API Documentation	Example
linear_plot	Plotting linear regression line and calculating Pearson correlation coefficient	regplot.linear_plot	example
regression_pred_true	Plotting prediction value vs. true value scatter plots	regplot.regression_pred_true	example
regression_plot_1d	Plotting 1d regression lines of any scikit-learn regression models	regplot.regression_plot_1d	example
regression_heat_plot	Plotting 2 to 4d regression heat maps of any scikit-learn regression models	regplot.regression_heat_plot	example

Examples

CustomPairPlot.pairanalyzer

from seaborn_analyzer import CustomPairPlot
import seaborn as sns
titanic = sns.load_dataset("titanic")
cp = CustomPairPlot()
cp.pairanalyzer(titanic, hue='survived')

hist.plot_normality

from seaborn_analyzer import hist
from sklearn.datasets import load_boston
import pandas as pd
df = pd.DataFrame(load_boston().data, columns= load_boston().feature_names)
hist.plot_normality(df, x='LSTAT', norm_hist=False, rounddigit=5)

hist.fit_dist

from seaborn_analyzer import hist
from sklearn.datasets import load_boston
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats
df = pd.DataFrame(load_boston().data, columns= load_boston().feature_names)
all_params, all_scores = hist.fit_dist(df, x='LSTAT', dist=['norm', 'gamma', 'lognorm', 'uniform'])
df_scores = pd.DataFrame(all_scores).T
df_scores

classplot.class_separator_plot

import seaborn as sns
from sklearn.svm import SVC
from seaborn_analyzer import classplot
iris = sns.load_dataset("iris")
model = SVC()
classplot.class_separator_plot(model, ['petal_width', 'petal_length'], 'species', iris)

classplot.class_proba_plot

import seaborn as sns
from sklearn.svm import SVC
from seaborn_analyzer import classplot
iris = sns.load_dataset("iris")
model = SVC()
classplot.class_proba_plot(model, ['petal_width', 'petal_length'], 'species', iris,
                           proba_type='imshow')

regplot.linear_plot

from seaborn_analyzer import regplot
import seaborn as sns
iris = sns.load_dataset("iris")
regplot.linear_plot(x='petal_length', y='sepal_length', data=iris)

regplot.regression_pred_true

import pandas as pd
from seaborn_analyzer import regplot
import seaborn as sns
from sklearn.linear_model import LinearRegression
df_temp = pd.read_csv(f'./sample_data/temp_pressure.csv')
regplot.regression_pred_true(LinearRegression(), x=['altitude', 'latitude'], y='temperature', data=df_temp)

regplot.regression_plot_1d

from seaborn_analyzer import regplot
import seaborn as sns
from sklearn.svm import SVR
iris = sns.load_dataset("iris")
regplot.regression_plot_1d(SVR(), x='petal_length', y='sepal_length', data=iris)

regplot.regression_heat_plot

import pandas as pd
from sklearn.linear_model import LinearRegression
from seaborn_analyzer import regplot
df_temp = pd.read_csv(f'./sample_data/temp_pressure.csv')
regplot.regression_heat_plot(LinearRegression(), x=['altitude', 'latitude'], y='temperature', data=df_temp)

Contact

If you have any questions or comments about seaborn-analyzer, please feel free to contact me via eMail: [e-mail] or Twitter: https://twitter.com/c60evaporator This project is hosted at https://github.com/c60evaporator/seaborn-analyzer