looping through columns to generate countplot() seaborn

Question

I am attempting to create a function I can reuse to loop through a list of categorical columns for any given pandas data frame and generate a series of plots to visually get the value_counts() for each column.

I have written a function for this

def get_count_plot(cols_list):
    for col in cols_list:
        fig = sns.countplot(data=df[col], x=df[col].value_counts())
    return fig

This function only returns one plot from a list of several columns. I get no errors on completion of the function.

I have tried adding enumerate() to this function with no luck. I have used enumerate to do this but with a list of dataframes instead of a list of column names.

def generate_box_plot(list_of_dfs):
    for idx, df in enumerate(list_of_dfs):
        fig = px.box(df, x='Year', y='Transportation_Cnt', color='Month')
        fig.update_xaxes(tickmode='linear')
        fig.show()

This script worked just fine, however I am not working with a list of pandas dataframes.

Answer 1

Here is the amended get_count_plot()

import seaborn as sns
import matplotlib.pyplot as plt

def get_count_plots(df, cols_list):
    for col in cols_list:
        plt.figure(figsize=(8, 6))
        sns.countplot(data=df, x=col)
        plt.title(f'Value Counts of {col}')
        plt.xticks(rotation=45)
        plt.show()

And this the amended get_count_plot()

import plotly.express as px

def generate_box_plot(dataframe, x_col, y_col, color_col):
    fig = px.box(dataframe, x=x_col, y=y_col, color=color_col)
    fig.update_xaxes(tickmode='linear')
    fig.show()

I hope you know to use them and Hope They work for you as expected.