To plot basic visualizations using pandas in Python | S-Logix

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How to Plot Basic Visualization Using Pandas in Python

Basic Visualization Using Pandas

Condition for Basic Visualization Using Pandas in Python

Description:
Pandas provides built-in data visualization capabilities via the plot() method, which is based on Matplotlib. This allows for quick and simple creation of basic plots directly from a DataFrame or Series.

Pandas supports various types of plots such as line, bar, histogram, scatter, and box plots.

Step-by-Step Process

Import Libraries:
Import pandas and matplotlib.pyplot.
Load or Create Data:
Prepare the dataset as a DataFrame or Series.
Choose the Plot Type:
Use the .plot() method with the appropriate kind argument to specify the type of plot.
Customize the Plot:
Add titles, labels, legends, or modify styles.
Display the Plot:
Use plt.show() to render the visualization.

Sample Source Code

# Code for basic visualization

import pandas as pd
import matplotlib.pyplot as plt

data = {
'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
'Sales': [200, 300, 250, 400, 350],
'Profit': [50, 80, 60, 100, 90]
}
df = pd.DataFrame(data)

# Line plot for Sales
df.plot(x='Month', y='Sales', kind='line', title='Monthly Sales', marker='o', color='blue')
plt.ylabel('Sales Amount')
plt.show()

# Bar plot for Sales and Profit
df.plot(x='Month', kind='bar', title='Sales and Profit by Month')
plt.ylabel('Amount')
plt.show()

# Scatter plot for Sales vs Profit
df.plot(x='Sales', y='Profit', kind='scatter', title='Sales vs Profit', color='green')
plt.ylabel('Profit')
plt.xlabel('Sales')
plt.show()

# Histogram for Sales
df['Sales'].plot(kind='hist', title='Distribution of Sales', bins=5, color='purple')
plt.xlabel('Sales')
plt.show()

# Box plot for Profit
df['Profit'].plot(kind='box', title='Profit Box Plot', patch_artist=True, color='red')
plt.show()

Screenshots

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