Grouping by Categorical Variables in a DataFrame for Creating a Line Chart with Matplotlib

In this article, we will explore how to group a Pandas DataFrame by categorical variables and create a line chart using Matplotlib. We will also delve into the process of calculating weighted averages within each group.

Introduction

Data analysis often involves grouping data points based on certain categories or variables. This can help us identify patterns, trends, and relationships between different groups in our dataset. In this article, we will focus on how to group a Pandas DataFrame by categorical variables using the groupby function and create a line chart using Matplotlib.

Choosing the Right Grouping Method

When grouping data points by categories, it is essential to choose the right method for your specific use case. There are several methods to consider, including:

• Hard Categorization: Assigning all data points within a certain category to a single label.
• Soft Categorization: Using a combination of numerical and categorical values to group data points.

For this example, we will focus on using the groupby function with categorical variables. This approach allows us to easily group data points based on specific categories while maintaining flexibility in our analysis.

Grouping by Categorical Variables

Let’s start by grouping our DataFrame df by categorical variables dealer, threshold, and protein_type. We will use the following code:

# Calculate comparable cost by using average threshold
dfg = df.groupby(['dealer', 'threshold']).apply(lambda x: pd.Series([x['Quantity Received'].mean() * (x['price'] + 1)]))

In this example, we are calculating the weighted average of Quantity Received and price for each group of dealer and threshold. The resulting DataFrame will have a single column named 'cats', containing categorical labels.

Shaping the DataFrame

To create a line chart with Matplotlib, we need to reshape our DataFrame into a long format. We can do this using the following code:

# Form the dataframe into a long form
dfl = dfg[['weighted_average', 'price']].stack().reset_index().rename(columns={'level_4': 'cats', 0: 'values'})

In this example, we are creating a new DataFrame dfl by stacking the columns 'weighted_average' and 'price' of our original DataFrame dfg. The resulting DataFrame will have three columns: 'cats', 'values', and an index.

Plotting the Line Chart

Now that we have reshaped our DataFrame, we can create a line chart using Matplotlib. We will use the following code:

# Plot with all dealers
markers = {"price": "s", "weighted_average": "X"}

for pt in dfl.protein_type.unique():
    for t in dfl.threshold.unique():
        data = dfl[(dfl.protein_type == pt) & (dfl.threshold == t)]
        if not data.empty:  # for some thresholds there's no data
            utc = len(data.threshold.unique())
            f, axes = plt.subplots(nrows=utc, ncols=1, figsize=(20, 7), squeeze=False)
            for j in range(utc):
                p = sns.scatterplot('date', 'values', data=data, hue='cats', markers=markers, style='cats', ax=axes[j, 0])
                p.set_title(f'Threshold: {t}\n{pt}')
                p.set_xlim(data.date.min() - timedelta(days=60), data.date.max() + timedelta(days=60))
                plt.legend(bbox_to_anchor=(1.04, 0.5), loc="center left", borderaxespad=0)
            plt.show()

In this example, we are creating a line chart for each combination of protein_type and threshold. The resulting plot will display multiple lines representing the weighted averages of 'Quantity Received' and 'price' for each group.

One Column of Plots

Alternatively, you can iterate through each unique combination of values for protein_type, threshold, and dealer to create a single column of plots:

# one column of plots
up = dfl.protein_type.unique()
ud = dfl.dealer.unique()
ut = dfl.threshold.unique()
date_min = dfl.date.min()
date_max = dfl.date.max()
years_fmt = mdates.DateFormatter('%Y-%m-%d')

for pt in up:
    for th in ut:
        for dl in ud: 
            data = dfl[(dfl.protein_type == pt) & (dfl.threshold == th) & (dfl.dealer == dl)]
            if not data.empty:  # for some thresholds there's no data
                price = data[data.cats == 'price']
                w_avg = data[data.cats == 'weighted_average']
                fig, ax = plt.subplots(figsize=(8, 5))
                p = sns.scatterplot('date', 'values', data=price, hue='cats', ax=ax)
                p.hlines(w_avg['values'].unique().tolist(), w_avg.date.min(), w_avg.date.max(), 'orange', label='weighted avg')
                p.set_title(f'{dl}\nThreshold: {th}\n{pt}')
                p.set_xlim(date_min - timedelta(days=60), date_max + timedelta(days=120))
                
                p.set_xticklabels(p.get_xticks(), rotation=90)
                p.xaxis.set_major_formatter(years_fmt)
                
                plt.legend(bbox_to_anchor=(1.04, 0.5), loc='center left', borderaxespad=0)        
                plt.show()

This approach creates a single column of plots for each unique combination of values.

By following these steps, you can effectively group your Pandas DataFrame by categorical variables and create a line chart using Matplotlib to visualize the relationships between different groups.

Last modified on 2023-07-14