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Module Group

src/eda1

Project Stage ID

4[^2]

Purpose

The purpose of this module is to provide the user with the ability to visualise each numerical columns in a pandas dataframe in a two dimensional figure relative to other numerical columns, the module revolves around the utilisation of plotly express

Location

Here are the locations of the relevant files associated with the module

module information:

/src/eda/meda_pplot.json

module activation functions:

/src/eda/meda_pplot.py

Requirements

Module import information

Selection

Activation functions need to be assigned a unique label. Here's the process of label & activation function selection

Activation Functions

Here you will find the relevant activation functions available in class meda_scplot

plscatter

description:

Used to create scatter plots. Scatter plots are used to visualize the relationship between two numerical variables.

code:

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# plotly basic scatter plot(plscatter)
def plotly_scatterplot(self,args:dict):

    fig = px.scatter(args['data'],
                     x=args['x'],
                     y=args['y'],
                     color=args['hue'],
                     facet_col=args['col'],
                     facet_row=args['row'],
                     opacity=nlpi.pp['alpha'],
                     facet_col_wrap=args['col_wrap'],
                     template=nlpi.pp['template'],
                     width=nlpi.pp['figsize'][0],
                     height=nlpi.pp['figsize'][1],
                     title=nlpi.pp['title'])

    # Plot Adjustments

    fig.update_traces(marker={'size':nlpi.pp['s'],"line":{'width':nlpi.pp['mew'],'color':nlpi.pp['mec']}},
                      selector={'mode':'markers'})

    if(nlpi.pp['background'] is False):
        fig.update_layout({
        'plot_bgcolor': 'rgba(0, 0, 0, 0)',
        'paper_bgcolor': 'rgba(0, 0, 0, 0)',
        })

    fig.show()

plbox

description:

Used to create boxplots. Box plots are used to visualize the distribution of numerical variables and display summary statistics such as the median, quartiles, and outliers

code:

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# plotly basic box plot (plbox)
def plotly_boxplot(self,args:dict):

    col_wrap = self.convert_str('col_wrap')
    nbins = self.convert_str('nbins')

    fig = px.box(args['data'],
                 x=args['x'],
                 y=args['y'],
                 color=args['hue'],
                 nbins=nbins,
                 facet_col=args['col'],
                 facet_row=args['row'],
                 facet_col_wrap=col_wrap,
                 template=nlpi.pp['template'],
                 width=nlpi.pp['figsize'][0],
                 height=nlpi.pp['figsize'][1],
                 title=nlpi.pp['title'])

    fig.show()

plhist

description:

Used to create a histogram plot. A histogram is a graphical representation of the distribution of a dataset. It displays the frequency of occurrence of data points within specified intervals, called bins

code:

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# plotly basic histogram plot (plhist)
def plotly_histogram(self,args:dict):

    col_wrap = self.convert_str('col_wrap')
    nbins = self.convert_str('nbins')

    fig = px.histogram(args['data'],
                       x=args['x'],
                       y=args['y'],
                       color=args['hue'],
                       facet_col=args['col'],
                       facet_row=args['row'],
                       facet_col_wrap=col_wrap,
                       nbins=nbins,
                       template=nlpi.pp['template'],
                       width=nlpi.pp['figsize'][0],
                       height=nlpi.pp['figsize'][1],
                       title=nlpi.pp['title'])

    fig.show()

plline

description:

Used to create a histogram plot. A histogram is a graphical representation of the distribution of a dataset. It displays the frequency of occurrence of data points within specified intervals, called bins

code:

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# plotly basic histogram plot (plline)
def plotly_line(self,args:dict):

    col_wrap = self.convert_str('col_wrap')

    fig = px.line(args['data'],
                   x=args['x'],
                   y=args['y'],
                   color=args['hue'],
                   facet_col=args['col'],
                   facet_row=args['row'],
                   facet_col_wrap=col_wrap,
                   template=nlpi.pp['template'],
                   width=nlpi.pp['figsize'][0],
                   height=nlpi.pp['figsize'][1],
                   title=nlpi.pp['title'])

    fig.show()

plviolin

description:

A violin plot displays the distribution of a continuous variable across different categories or groups. It consists of a series of vertical or horizontal violin-shaped curves, where the width of each curve represents the density or frequency of data points at different values. The wider parts of the curve indicate areas with higher density, while the narrower parts represent areas with lower density.

code:

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# [plotly] Violin plot (plviolin)

def plotly_violin(self,args:dict):

    col_wrap = self.convert_str('col_wrap')

    fig = px.violin(args['data'],
                   x=args['x'],
                   y=args['y'],
                   color=args['hue'],
                   facet_col=args['col'],
                   facet_row=args['row'],
                   facet_col_wrap=col_wrap,
                   box=True,
                   template=nlpi.pp['template'],
                   width=nlpi.pp['figsize'][0],
                   height=nlpi.pp['figsize'][1],
                   title=nlpi.pp['title'])

    fig.show()

plbarplot

description:

A bar chart displays the distribution or comparison of data across different categories or groups. Each category is represented by a bar, where the length or height of the bar corresponds to the value or frequency of the data in that category

code:

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# [plotly] Bar Plot (plbarplot)

def plotly_bar(self,args:dict):

    fig = px.bar(args['data'],
                 x=args['x'],
                 y=args['y'],
                 color=args['hue'],
                 facet_col=args['col'],
                 facet_row=args['row'],
                 facet_col_wrap=col_wrap,
                 template=nlpi.pp['template'],
                 width=nlpi.pp['figsize'][0],
                 height=nlpi.pp['figsize'][1],
                 title=nlpi.pp['title'])

    fig.show()

plbarplot

description:

A density heatmap uses color to represent the density or frequency of data points in different regions of the 2D space. The intensity of the color corresponds to the density of data points, with darker colors indicating higher densities.

code:

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# [plotly] Heatmap (plheatmap)

def plotly_heatmap(self,args:dict):

    col_wrap = self.convert_str('col_wrap')

    fig = px.density_heatmap(args['data'],
                             x=args['x'],
                             y=args['y'],
                             facet_col=args['col'],
                             facet_row=args['row'],
                             facet_col_wrap=col_wrap,
                             template=nlpi.pp['template'],
                             width=nlpi.pp['figsize'][0],
                             height=nlpi.pp['figsize'][1],
                             title=nlpi.pp['title'])

    fig.show()

  1. Reference to the sub folder in src ↩