102 lines
3.5 KiB
Python
102 lines
3.5 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 SNSBoxplot:
|
|
"""
|
|
SNS Boxplot:
|
|
Generates a boxplot from a Pandas DataFrame using Seaborn.
|
|
|
|
You can generate a boxplot from either a single column or two columns.
|
|
* For two columns, the first column should contain the category, and the second column should contain the corresponding numerical values.
|
|
* For a wide-format table, use the Pandas Melt node to create a category column first. This column is referred to as the "variable" column in melting.
|
|
* For a single-column dataset, leave the category field blank.
|
|
|
|
For example, if you have a DataFrame tracking apple and banana prices at various stores:
|
|
```
|
|
Fruit Price
|
|
apple 5
|
|
apple 3
|
|
apple 2
|
|
appel 2.5
|
|
banana 4
|
|
banana 2
|
|
banana 2
|
|
```
|
|
If you specify `Fruit` as the category and `Price` as the value, two boxplots will be generated: one for apples and one for bananas.
|
|
|
|
|
|
|
|
Refer to [Seaborn documentation](https://seaborn.pydata.org/generated/seaborn.boxplot.html) for more information.
|
|
|
|
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", {}),
|
|
"category_column_name": ("STRING", {"default": ""}),
|
|
"value_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,
|
|
category_column_name: str,
|
|
value_column_name: str,
|
|
title: str,
|
|
style: str=None,
|
|
palette: str=None) -> tuple:
|
|
"""
|
|
Generates a bar chart from a pandas DataFrame.
|
|
|
|
Args:
|
|
dataframe (DataFrame): The DataFrame.
|
|
category_column_name (str): The category (variable) column name.
|
|
value_column_name (str): The column name for the value data.
|
|
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)
|
|
category_column_name = column_label_string_to_target_type(df, category_column_name, return_none=True)
|
|
value_column_name = column_label_string_to_target_type(df, value_column_name)
|
|
|
|
# Create the plot
|
|
fig, ax = plt.subplots()
|
|
if category_column_name:
|
|
sns.boxplot(x=df[category_column_name],y=df[value_column_name], ax=ax)
|
|
else:
|
|
sns.boxplot(y=df[value_column_name], ax=ax)
|
|
|
|
return plot_post_steps(
|
|
fig,
|
|
ax,
|
|
plt,
|
|
title,
|
|
None,
|
|
None,
|
|
None)
|