Using Plotly in Python to Create Line, Scatter, and Bar Charts

Data analysis often requires visualization, and while pandas, matplotlib, pyecharts, and Tableau are common, this article explores Plotly as an alternative for creating high‑quality interactive charts in Python.

Plotly is an online platform that supports many chart types (bar, scatter, pie, histogram, etc.) and multiple languages (Python, JavaScript, MATLAB, R). It can be installed with pip install plotly and works best in Jupyter notebooks, where the offline mode renders charts directly.

First, the necessary libraries are imported and offline mode is initialized:

from plotly.graph_objs import Scatter, Layout
import plotly
import plotly.offline as py
import numpy as np
import plotly.graph_objs as go
# setting offline mode
plotly.offline.init_notebook_mode(connected=True)

The article then demonstrates three chart types.

1. Line Chart – Random x values and three y series are generated, each plotted with different modes (markers, lines+markers, lines). The traces are combined into a list and displayed with py.iplot(data) .

N = 100
random_x = np.linspace(0,1,N)
random_y0 = np.random.randn(N)+5
random_y1 = np.random.randn(N)
random_y2 = np.random.randn(N)-5
# Create traces
trace0 = go.Scatter(x=random_x, y=random_y0, mode='markers', name='markers')
trace1 = go.Scatter(x=random_x, y=random_y1, mode='lines+markers', name='lines+markers')
trace2 = go.Scatter(x=random_x, y=random_y2, mode='lines', name='lines')
data = [trace0, trace1, trace2]
py.iplot(data)

The resulting line chart shows three distinct visual styles with automatically chosen colors.

2. Scatter Chart – A single trace with random y values is plotted as markers, customizing marker size, color scale, and showing a color legend.

trace1 = go.Scatter(
    y = np.random.randn(500),
    mode = 'markers',
    marker = dict(
        size = 16,
        color = np.random.randn(500),
        colorscale = 'Viridis',
        showscale = True
    )
)
data = [trace1]
py.iplot(data)

This produces a colorful scatter plot where each point’s color reflects its value.

3. Bar (Histogram) Chart – Two bar traces representing monthly data for two products are created and displayed together.

trace0 = go.Bar(
    x = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'],
    y = [20,14,25,16,18,22,19,15,12,16,14,17],
    name = 'Primary Product',
    marker=dict(color='rgb(49,130,189)')
)
trace1 = go.Bar(
    x = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'],
    y = [19,14,22,14,16,19,15,14,10,12,12,16],
    name = 'Secondary Product',
    marker=dict(color='rgb(204,204,204)')
)
data = [trace0, trace1]
py.iplot(data)

The bar chart compares two product series across months, illustrating differences in values.

These examples cover only the basics of Plotly; the library also offers many advanced visualizations such as candlestick charts for financial data, especially when combined with pandas.