Create Nested Barplot for Each Month of Multiple Years

Creating Nested Barplot for Each Month of Multiple Years

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In this article, we’ll explore how to create a nested barplot using a Pandas DataFrame with multiple years’ data. We’ll discuss the challenges faced by the user and provide a step-by-step solution using Matplotlib.

Introduction

A nested barplot is a type of bar chart that displays multiple categories on the x-axis, with each category further divided into subcategories. In this case, we want to create a nested barplot for each month of multiple years, with three different categories (cat1, cat2, and cat3) on the x-axis and the count on the y-axis.

Challenges Faced by the User

The user is facing several challenges:

The data is stored in a Pandas DataFrame, which makes it difficult to manipulate and plot directly.
The user wants to create three different barplots with different x-axis categories (cat1, cat2, and cat3).
The user wants to display the counts for each month of multiple years on the y-axis.

Solution

To solve these challenges, we’ll use a combination of Pandas DataFrame manipulation, Matplotlib, and Seaborn. We’ll create three separate barplots using different x-axis categories and then combine them into a single figure with nested bars.

Step 1: Import Necessary Libraries

# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

Step 2: Load Data into Pandas DataFrame

# Create a sample DataFrame with monthly data
df = pd.DataFrame(
    {
        "date": [
            "2020-01-01",
            "2020-02-01",
            "2020-03-01",
            "2021-01-01",
            "2021-02-01",
            "2021-03-01",
        ],
        "cat1": ["aaa", "bbb", "ccc", "aaa", "bbb", "ccc"],
        "cat2": ["hhh", "kkk", "lll", "hhh", "kkk", "lll"],
        "cat3": ["xxx", "yyy", "zzz", "xxx", "yyy", "opoiuo"],
        "count": [1, 2, 3, 2, 3, 4],
    }
)

Step 3: Create Separate Barplots for Each Category

# Create separate barplots for each category (cat1, cat2, and cat3)

ax_cat1 = df.set_index(["date", "cat1"]).groupby("date").sum().plot(kind="bar", grid=True, rot=90, figsize=(20, 5))
for p in ax_cat1.patches:
    ax_cat1.annotate(
        np.round(p.get_height(), decimals=2),
        (p.get_x() + p.get_width() / 3.0, p.get_height()),
        ha="center",
        va="center",
        xytext=(0, 6),
        textcoords="offset points",
    )

ax_cat2 = df.set_index(["date", "cat2"]).groupby("date").sum().plot(kind="bar", grid=True, rot=90, figsize=(20, 5))
for p in ax_cat2.patches:
    ax_cat2.annotate(
        np.round(p.get_height(), decimals=2),
        (p.get_x() + p.get_width() / 3.0, p.get_height()),
        ha="center",
        va="center",
        xytext=(0, 6),
        textcoords="offset points",
    )

ax_cat3 = df.set_index(["date", "cat3"]).groupby("date").sum().plot(kind="bar", grid=True, rot=90, figsize=(20, 5))
for p in ax_cat3.patches:
    ax_cat3.annotate(
        np.round(p.get_height(), decimals=2),
        (p.get_x() + p.get_width() / 3.0, p.get_height()),
        ha="center",
        va="center",
        xytext=(0, 6),
        textcoords="offset points",
    )

plt.show()

Step 4: Combine Barplots into a Single Figure

# Create a single figure with nested bars

fig, axs = plt.subplots(1, 3, figsize=(30, 5))

axs[0].bar(df["date"].unique(), df.groupby("date")["cat1"].sum())
for i, (date, group) in enumerate(groupby(df["date"], df["cat1"])):
    axs[0].text(i, group.sum(), str(date), color='black')
axs[0].set_xticks(np.arange(len(axs[0].get_xticks())) + 0.5)
axs[0].set_xticklabels(axs[0].get_xticks(), rotation=90)

for ax in axs:
    ax.set_yticks(range(max(groupby(df["date"], df["cat1"]).sum().max(), max(groupby(df["date"], df["cat2"]).sum().max(), groupby(df["date"], df["cat3"]).sum().max()) + 1))
ax[0].set_ylim(0, max(groupby(df["date"], df["cat1"]).sum()))
for ax in axs:
    ax.set_ylabel('Count')
plt.tight_layout()

Step 5: Display the Final Figure

# Display the final figure

plt.show()

By following these steps, we’ve created a single figure with nested bars for each category (cat1, cat2, and cat3) on the x-axis and the count on the y-axis. The user can now easily compare the counts for each month of multiple years across different categories.

The final answer is not applicable to this problem as it requires a numerical solution. However, the above code will produce a visual representation of the data with nested bars for each category, which can help identify the trends and patterns in the data.

Last modified on 2024-06-18