The following code is going to enable you to create a very pretty bar plot, in an attractive color scheme, with the bars lining up in ascending order AND labeled with the corresponding % of the data.
Always remember, a spot on analysis is very important...and your presentation of that analysis is that cool refreshing mint iced-tea on a very hot summer day!
def my_labeled_barplot(data, feature, perc=False, n=None, order=None, palette='viridis', ascending=False): """ Creates a bar plot with labels showing counts or percentages, with sorting options. Args: data: The DataFrame containing the data. feature: The name of the categorical column to plot. perc: If True, display percentages on labels, otherwise counts. n: (Optional) Width of the plot figure. order: (Optional) 'asc' for ascending order, 'desc' for descending order, or None for no specific order. palette: (Optional) Color palette for the bars. ascending: (Optional) Used when 'order' is not specified. Sorts bars in ascending order if True, descending if False. """ plt.figure(figsize=(n if n else len(data.columns) + 1, 5)) if order == 'asc': order_index = data[feature].value_counts(ascending=True).index elif order == 'desc': order_index = data[feature].value_counts(ascending=False).index else: order_index = data[feature].value_counts(ascending=not ascending).index # Sort based on ascending parameter ax = sns.countplot(data=data, x=feature, order=order_index, palette=palette) if perc: total = len(data[feature]) for p in ax.patches: percentage = '{:.1f}%'.format(100 * p.get_height() / total) x = p.get_x() + p.get_width() / 2 - 0.1 y = p.get_y() + p.get_height() ax.annotate(percentage, (x, y), size=10) else: for p in ax.patches: x = p.get_x() + p.get_width() / 2 - 0.1 y = p.get_y() + p.get_height() ax.annotate(p.get_height(), (x, y), size=10) plt.xticks(rotation=90, fontsize=12) # Example usage: # my_labeled_barplot(data, 'Club', perc=True, order='asc') # Ascending order # my_labeled_barplot(data, 'Club', perc=True, order='desc') # Descending order # my_labeled_barplot(data, 'Club', perc=True, ascending=False) # Descending order without using 'order' parameter 
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