Plotting Bar Charts in Python Using Specific Values

In this article, we will explore how to plot bar charts using specific values in Python. We will start by understanding the basics of bar charts and then move on to plotting them using popular libraries like matplotlib.

Understanding Bar Charts

A bar chart is a type of chart that uses bars to represent data. Each bar represents a category or group, and its height corresponds to the value of that category. The x-axis typically shows the categories, while the y-axis shows the values.

Types of Bar Charts

There are several types of bar charts, including:

Simple Bar Chart: A basic bar chart where each bar represents a single data point.
Grouped Bar Chart: A bar chart where multiple bars represent different categories within a group.
Stacked Bar Chart: A bar chart where multiple bars are stacked on top of each other to represent different categories.

Choosing the Right Library

There are several libraries available in Python for plotting bar charts, including:

matplotlib: A popular and widely-used library for creating static, animated, and interactive visualizations.
seaborn: A visualization library built on top of matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.
plotly: An interactive visualization library that allows you to create web-based interactive plots.

Plotting Bar Charts with Matplotlib

In this section, we will explore how to plot bar charts using matplotlib. We will start by importing the necessary libraries and creating a sample dataset.

## Importing Libraries

We will need the following libraries for plotting bar charts:

```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

Creating a Sample Dataset

Let’s create a sample dataset with two columns: item and values.

# Create a sample dataset
np.random.seed(0)
df = pd.DataFrame({
    'item': np.random.choice([*'abcdef'], 1000),
    'values': np.random.randint(0, 2, 1000)
})

Plotting Bar Charts

Now that we have our dataset, let’s plot a bar chart using matplotlib.

# Plot a bar chart
crosstab_df = pd.crosstab(df['item'], df['values'], normalize='index').mul(100).sort_values(by=1, ascending=False)
ax = crosstab_df[1].plot.bar()
ax.bar_label(ax.containers[0], fmt='%.2f %%')
ax.margins(y=0.1)
plt.show()

This code will create a bar chart where the x-axis represents the categories (item), and the y-axis represents the values (normalized to 100%).

Plotting Bar Charts with Specific Values

In the original question, we were asked how to plot bar charts using specific values. We can achieve this by selecting the desired column from the crosstab DataFrame.

## Selecting Specific Columns

Let's select only the `1` column from the crosstab DataFrame and plot a bar chart.

```python
# Select only the 1 column
specific_column = df['values'] == 1

# Plot a bar chart for the specific column
crosstab_df_specific = pd.crosstab(df['item'], specific_column, normalize='index').mul(100).sort_values(by=0, ascending=False)
ax = crosstab_df_specific[0].plot.bar()
ax.bar_label(ax.containers[0], fmt='%.2f %%')
ax.margins(y=0.1)
plt.show()

This code will create a bar chart where the x-axis represents the categories (item), and the y-axis represents the values (normalized to 100%) for only the rows where values is equal to 1.

Plotting Bar Charts with Seaborn

Seaborn provides a high-level interface for drawing attractive and informative statistical graphics. We can use seaborn’s barplot() function to plot bar charts.

## Importing Seaborn

We will need to import seaborn in addition to matplotlib.

```python
import seaborn as sns

Plotting Bar Charts with Seaborn

Let’s create a sample dataset and plot a bar chart using seaborn.

# Create a sample dataset
np.random.seed(0)
df = pd.DataFrame({
    'item': np.random.choice([*'abcdef'], 1000),
    'values': np.random.randint(0, 2, 1000)
})

# Plot a bar chart with seaborn
crosstab_df_seaborn = pd.crosstab(df['item'], df['values'], normalize='index').mul(100).sort_values(by=1, ascending=False)
sns.set()
plt.figure(figsize=(8,6))
sns.barplot(x=crosstab_df_seaborn.columns, y=crosstab_df_seaborn[0], data=crosstab_df_seaborn)
ax.text(0.5, 1.05, "Values", ha='center', transform=ax.transAxes)
plt.title("Bar Chart with Seaborn")
plt.xlabel("Item")
plt.ylabel("Normalized Values")
plt.xticks(rotation=90)
plt.show()

This code will create a bar chart where the x-axis represents the categories (item), and the y-axis represents the values (normalized to 100%). The chart is created using seaborn’s barplot() function.

Plotting Bar Charts with Plotly

Plotly provides an interactive interface for creating web-based visualizations. We can use plotly’s bar() function to plot bar charts.

## Importing Plotly

We will need to import plotly in addition to matplotlib.

```python
import plotly.graph_objs as go

Plotting Bar Charts with Plotly

Let’s create a sample dataset and plot a bar chart using plotly.

# Create a sample dataset
np.random.seed(0)
df = pd.DataFrame({
    'item': np.random.choice([*'abcdef'], 1000),
    'values': np.random.randint(0, 2, 1000)
})

# Plot a bar chart with plotly
crosstab_df_plotly = pd.crosstab(df['item'], df['values'], normalize='index').mul(100).sort_values(by=1, ascending=False)

fig = go.Figure(data=[go.Bar(x=crosstab_df_plotly.columns, y=crosstab_df_plotly[0])])
fig.update_layout(title="Bar Chart with Plotly", xaxis_title="Item", yaxis_title="Normalized Values")
fig.show()

This code will create a bar chart where the x-axis represents the categories (item), and the y-axis represents the values (normalized to 100%). The chart is created using plotly’s bar() function.

Conclusion

In this tutorial, we explored how to plot bar charts using matplotlib, seaborn, and plotly. We discussed how to select specific columns from the crosstab DataFrame to create bar charts with specific values.

Last modified on 2023-09-06