Fundamentals 5 min read

Python Data Visualization: Line, Scatter, Bar, Stacked Bar, Pie, Histogram, Heatmap, Boxplot, Interactive Plotly, and DataFrame Charts

This guide demonstrates how to install common Python plotting libraries and provides ready-to-use functions for creating line, scatter, bar, stacked bar, pie, histogram, heatmap, boxplot, interactive Plotly scatter, and pandas DataFrame visualizations with example code snippets.

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Test Development Learning Exchange
Test Development Learning Exchange
Python Data Visualization: Line, Scatter, Bar, Stacked Bar, Pie, Histogram, Heatmap, Boxplot, Interactive Plotly, and DataFrame Charts

Installation

pip install matplotlib seaborn plotly panda

Simple Line Chart

import matplotlib.pyplot as plt
def plot_line_chart(x, y, title, xlabel, ylabel):
    plt.plot(x, y)
    plt.title(title)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.show()

x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
plot_line_chart(x, y, 'Simple Line Chart', 'X-axis', 'Y-axis')

Scatter Chart

import matplotlib.pyplot as plt
def plot_scatter_chart(x, y, title, xlabel, ylabel):
    plt.scatter(x, y)
    plt.title(title)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.show()

x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
plot_scatter_chart(x, y, 'Scatter Chart', 'X-axis', 'Y-axis')

Bar Chart

import matplotlib.pyplot as plt
def plot_bar_chart(labels, values, title):
    plt.bar(labels, values)
    plt.title(title)
    plt.show()

labels = ['A', 'B', 'C', 'D', 'E']
values = [10, 20, 15, 25, 18]
plot_bar_chart(labels, values, 'Bar Chart')

Stacked Bar Chart

import matplotlib.pyplot as plt
def plot_stacked_bar_chart(labels, data, title):
    plt.bar(labels, data[0], label='Data 1')
    plt.bar(labels, data[1], bottom=data[0], label='Data 2')
    plt.title(title)
    plt.legend()
    plt.show()

labels = ['A', 'B', 'C', 'D', 'E']
data = [[10, 20, 15, 25, 18], [5, 10, 7, 12, 9]]
plot_stacked_bar_chart(labels, data, 'Stacked Bar Chart')

Pie Chart

import matplotlib.pyplot as plt
def plot_pie_chart(labels, sizes, title):
    plt.pie(sizes, labels=labels, autopct='%1.1f%%')
    plt.title(title)
    plt.show()

labels = ['A', 'B', 'C', 'D', 'E']
sizes = [15, 30, 45, 10, 15]
plot_pie_chart(labels, sizes, 'Pie Chart')

Histogram

import matplotlib.pyplot as plt
def plot_histogram(data, title):
    plt.hist(data, bins=10)
    plt.title(title)
    plt.show()

data = [21, 22, 23, 4, 5, 6, 77, 8, 9, 10, 31, 32, 33, 34, 35, 36, 37, 18, 49, 50, 51]
plot_histogram(data, 'Histogram')

Heatmap

import seaborn as sns
import numpy as np
def plot_heatmap(data, title):
    sns.heatmap(data, annot=True, fmt="d")
    plt.title(title)
    plt.show()

data = np.random.rand(5, 5)
plot_heatmap(data, 'Heatmap')

Boxplot

import matplotlib.pyplot as plt
def plot_boxplot(data, title):
    plt.boxplot(data)
    plt.title(title)
    plt.show()

data = [np.random.normal(0, std, 100) for std in range(1, 4)]
plot_boxplot(data, 'Boxplot')

Interactive Scatter Plot (Plotly)

import plotly.express as px
def plot_interactive_scatter(x, y, title):
    fig = px.scatter(x=x, y=y, title=title)
    fig.show()

x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]
plot_interactive_scatter(x, y, 'Interactive Scatter Plot')

Pandas DataFrame Plot

import pandas as pd
import matplotlib.pyplot as plt
def plot_dataframe(df, x_col, y_col, title):
    df.plot(x=x_col, y=y_col, kind='line')
    plt.title(title)
    plt.show()

data = {'Year': [2000, 2001, 2002, 2003, 2004],
        'Sales': [150, 200, 250, 300, 350]}

df = pd.DataFrame(data)
plot_dataframe(df, 'Year', 'Sales', 'DataFrame Plot')
PythonData VisualizationMatplotlibpandasPlotlyseaborn
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