Understanding Nationality Distribution: Creating Horizontal Barplots with Pandas and Matplotlib

Understanding Pandas and Creating Horizontal Barplots for Nationality Distribution

In this article, we will delve into the world of pandas data frames and explore how to create two horizontal barplots to show the distribution of different values in a ’nationality’ column. We will also discuss alternative methods to achieve this, including using seaborn’s countplot function.

Introduction to Pandas Data Frames

Pandas is a powerful library for data manipulation and analysis in Python. It provides a high-level interface for working with structured data, such as tabular data. A pandas data frame is a two-dimensional table of data with rows and columns.

The following code snippet demonstrates how to create a simple data frame:

import pandas as pd

# Create a dictionary with data
data = {
    'nationality': ['Canadian', 'American', 'American', 'Chinese', 'Australian'],
    'age': [12, 24, 20, 12, 11],
    'sex': ['Male', 'Male', 'Female', 'Female', 'Male'],
    'surname': ['Smith', 'Taylor', 'Smith', 'Jones', 'Norman']
}

# Create a data frame
df = pd.DataFrame(data)

print(df)

Output:

     nationality  age   sex      surname
0        Canadian   12   Male      Smith
1         American   24   Male     Taylor
2         American   20  Female      Smith
3           Chinese   12  Female      Jones
4       Australian    11   Male     Norman

Counting the Number of Different Values in a ’nationality’ Column

To count the number of different values in a ’nationality’ column, we can use the value_counts() function provided by pandas.

import pandas as pd

# Create a data frame
df = pd.DataFrame({
    'nationality': ['Canadian', 'American', 'American', 'Chinese', 'Australian'],
    'age': [12, 24, 20, 12, 11],
    'sex': ['Male', 'Male', 'Female', 'Female', 'Male'],
    'surname': ['Smith', 'Taylor', 'Smith', 'Jones', 'Norman']
})

# Count the number of different values in the 'nationality' column
nationalities = df['nationality'].value_counts()

print(nationalities)

Output:

American      2
Canadian     1
Chinese       1
Australian    1
Name: nationality, dtype: int64

Creating Horizontal Barplots for Nationality Distribution

We can create horizontal barplots to show the distribution of different values in a ’nationality’ column using matplotlib’s barh() function.

import pandas as pd
import matplotlib.pyplot as plt

# Create a data frame
df = pd.DataFrame({
    'nationality': ['Canadian', 'American', 'American', 'Chinese', 'Australian'],
    'age': [12, 24, 20, 12, 11],
    'sex': ['Male', 'Male', 'Female', 'Female', 'Male'],
    'surname': ['Smith', 'Taylor', 'Smith', 'Jones', 'Norman']
})

# Count the number of different values in the 'nationality' column
nationalities = df['nationality'].value_counts()

# Create a figure and axis object
fig, ax = plt.subplots(figsize=(10, 6))

# Create horizontal barplots for male and female
ax.barh(nationalities.index, nationalities.values, color='blue', label='Male')
ax.barh(nationalities.index, nationalities.values, bottom=0, color='red', label='Female')

# Set the title and labels
ax.set_title('Nationality Distribution')
ax.set_xlabel('Count')
ax.set_ylabel('Nationality')
ax.set_yticks(range(len(nationalities)))
ax.set_yticklabels(nationalities.index)
ax.legend()

# Show the plot
plt.show()

Output:

This code will create a horizontal barplot showing the distribution of different values in the ’nationality’ column. The blue bars represent males, and the red bars represent females.

Alternative Method Using Seaborn’s Countplot Function

Seaborn is a visualization library built on top of matplotlib that provides a high-level interface for creating attractive and informative statistical graphics.

We can use seaborn’s countplot() function to create horizontal barplots for nationality distribution.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Create a data frame
df = pd.DataFrame({
    'nationality': np.random.choice(['American', 'Canadian', 'Chinese', 'Australian'], 1000),
    'sex': np.random.choice(['male', 'female'], 1000)
})

# Create horizontal barplots for male and female
sns.countplot(data=df, x='nationality', hue='sex')

# Show the plot
plt.show()

Output:

This code will create a horizontal barplot showing the distribution of different values in the ’nationality’ column. The bars are colored according to the ‘sex’ column.

Creating Horizontal Barplots with Mirror Images

To create horizontal barplots where one image is the mirror image of the other, we can use matplotlib’s barh() function and adjust the x-coordinates accordingly.

import pandas as pd
import matplotlib.pyplot as plt

# Create a data frame
df = pd.DataFrame({
    'nationality': ['Canadian', 'American', 'American', 'Chinese', 'Australian'],
    'age': [12, 24, 20, 12, 11],
    'sex': ['Male', 'Male', 'Female', 'Female', 'Male'],
    'surname': ['Smith', 'Taylor', 'Smith', 'Jones', 'Norman']
})

# Count the number of different values in the 'nationality' column
nationalities = df['nationality'].value_counts()

# Create a figure and axis object
fig, ax = plt.subplots(figsize=(10, 6))

# Create horizontal barplots for male and female
ax.barh(nationalities.index, nationalities.values, color='blue', label='Male')
ax.barh(nationalities.index, nationalities.values, bottom=0, color='red', label='Female')

# Invert the x-axis of the female bars to create a mirror image
for i in range(len(nationalities)):
    ax.barh(nationalities.index[i], nationalities.values[i] + 1, bottom=nationalities.index[i], color='red')
ax.invert_yaxis()

# Set the title and labels
ax.set_title('Nationality Distribution')
ax.set_xlabel('Count')
ax.set_ylabel('Nationality')
ax.set_yticks(range(len(nationalities)))
ax.set_yticklabels(nationalities.index)
ax.legend()

# Show the plot
plt.show()

Output:

This code will create a horizontal barplot showing the distribution of different values in the ’nationality’ column. The blue bars represent males, and the red bars represent females. The female bars are inverted to create a mirror image.

Conclusion

In this article, we have explored how to create horizontal barplots to show the distribution of different values in a ’nationality’ column using pandas and matplotlib. We have also discussed alternative methods to achieve this, including using seaborn’s countplot function. Additionally, we have shown how to create horizontal barplots with mirror images by adjusting the x-coordinates accordingly.

Last modified on 2024-09-08