Files

82 lines
2.4 KiB
Python

from typing import Any, Dict
from matplotlib import pyplot as plt
import pandas as pd
import seaborn as sns
from .util import plot_post_steps
from .util import set_up_sns_style_palette
from ..pandas_wrapper.utils import column_label_string_to_target_type
class SNSPairplot:
"""
SNS Pairplot:
Generates a pairplot from a Pandas DataFrame using Seaborn.
Specify a column name for the hue parameter to color the data points based on that column.
category: Plot
"""
@classmethod
def INPUT_TYPES(cls) -> Dict[str, Any]:
"""
Defines the input types for the `bar_chart` function.
Returns:
Dict[str, Any]: A dictionary specifying required input types.
"""
return {
"required": {
"dataframe": ("DATAFRAME", {}),
"hue_column_name": ("STRING", {"default": ""}),
"title": ("STRING", {"default": ""}),
"style": ("STRING", {"default": ""}),
"palette": ("STRING", {"default": ""})
}
}
RETURN_TYPES: tuple = ("IMAGE",)
FUNCTION: str = "f"
CATEGORY: str = "Data Analysis"
def f(self,
dataframe: pd.DataFrame,
hue_column_name: str,
title: str,
style: str=None,
palette: str=None) -> tuple:
"""
Generates a pairplot from a pandas DataFrame.
Args:
dataframe (DataFrame): The DataFrame.
hue_column_name (str): The category (variable) column name.
title (str): The title of the plot.
style (str): Style of the plot
palette (str): Palette of the plot
Returns:
tuple: A tuple containing the image tensor representation of the plot.
"""
df = dataframe
set_up_sns_style_palette(style, palette)
# Create the plot
hue_column_name = column_label_string_to_target_type(df, hue_column_name, return_none=True)
if hue_column_name:
pp = sns.pairplot(df, hue=hue_column_name)
else:
pp = sns.pairplot(df)
pp.figure.suptitle(title, y=0.95)
pp.figure.tight_layout(rect=[0, 0, 1, 0.95])
fig = pp.figure
return plot_post_steps(
fig,
None,
plt,
title,
None,
None,
None)