| """ | |
| Ashley Wu 12/4/25 | |
| Creates visualizations for frontend of app | |
| """ | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import gradio as gr | |
| import math | |
| import utils | |
| def time_series(df, xcol, ycol): | |
| """ | |
| Creates a time series graph | |
| Args: | |
| df: dataframe | |
| xcol: column of x calues | |
| ycoL: column of y values | |
| Returns: | |
| fig: time series graph | |
| """ | |
| fig, ax = plt.subplots() | |
| if df[ycol].dtype.name == 'object': | |
| raise gr.Error("The Y value can not be a string or a category!", duration=10) | |
| x = df[xcol].to_numpy() | |
| y = df[ycol].to_numpy() | |
| ax.plot(x, y, color='red', linewidth=2, marker='o') | |
| ax.set_xlabel(xcol) | |
| ax.set_ylabel(ycol) | |
| ax.set_title(f"Time Series of {xcol} and {ycol}") | |
| return fig | |
| def bar_chart(df, xcol, ycol, aggmethod): | |
| """ | |
| Creates bar chart of information | |
| Args: | |
| df: dataframe | |
| xcol: column of x values | |
| ycol: column of y values | |
| aggmethod: aggregation method | |
| Return: | |
| fig: bar chart | |
| """ | |
| df = utils.detect_categories(df) | |
| fig, ax = plt.subplots() | |
| if df[ycol].dtype.name == 'category' or df[ycol].dtype.name == 'object': | |
| raise gr.Error("The Y variable can not be a string or a category!", duration=10) | |
| if df[xcol].dtype.name != 'category': | |
| raise gr.Error("The X variable must be a category!") | |
| if aggmethod == 'sum': | |
| y = df.groupby(xcol)[ycol].sum() | |
| elif aggmethod == 'mean': | |
| y = df.groupby(xcol)[ycol].mean() | |
| elif aggmethod == 'count': | |
| y = df[xcol].value_counts().tolist() | |
| elif aggmethod == 'median': | |
| y = df.groupby(xcol)[ycol].median() | |
| else: | |
| y = df[xcol].tolist() | |
| x = df[xcol].unique().tolist() | |
| ax.bar(x, y) | |
| ax.set_xlabel('Category') | |
| ax.set_ylabel(ycol) | |
| ax.set_title(f'{aggmethod} by Category') | |
| ax.set_xticks(ticks=x) | |
| return fig | |
| def histogram(df, col, bins): | |
| """ | |
| Creates a histogram | |
| Args: | |
| df: dataframe | |
| col: data for histogram | |
| bins: number of bins to put histogram data into | |
| Returns: | |
| fig: histogram created | |
| """ | |
| fig, ax = plt.subplots() | |
| bins = math.floor(bins) | |
| if df[col].dtype.name == 'object': | |
| raise gr.Error("The Y value can not be a string or a category!", duration=10) | |
| ax.hist(df[col], bins) | |
| ax.set_xlabel(f'{col}') | |
| ax.set_title(f'Histogram of {col}') | |
| return fig | |
| def scatter(df, xcol, ycol): | |
| """ | |
| Creates a scatter plot | |
| Args: | |
| df: dataframe | |
| xcol: column of x values | |
| ycol: column of y values | |
| Returns: | |
| fig: scatter plot | |
| """ | |
| fig, ax = plt.subplots() | |
| if df[ycol].dtype.name == 'object' or df[xcol].dtype.name == 'object': | |
| raise gr.Error("The X or Y values can not be a string or a category!", duration=10) | |
| x = df[xcol].to_numpy() | |
| y = df[ycol].to_numpy() | |
| ax.plot(x, y, color='red', marker='o') | |
| ax.set_xlabel(xcol) | |
| ax.set_ylabel(ycol) | |
| ax.set_title(f"Scatter Plot of {xcol} and {ycol}") | |
| return fig | |