Plotly Python Tutorial: Scatter, Bar, Histogram, Express, 3D, and Density Map Visualizations

First, install Plotly via pip.

pip install plotly

Import the required libraries: plotly.graph_objects for low‑level chart construction, plotly.offline for offline rendering, and pandas for data handling.

import plotly.graph_objects as go
import plotly.offline as of  # offline mode
import pandas as pd

Example 1 – Scatter Plot : Load a New Zealand weather CSV, create two go.Scatter traces for Auckland and Wellington, set chart titles and axis labels, and display the figure with of.plot(fig) .

data = pd.read_csv(r'C:\Users\Administrator\Desktop\data\nz_weather.csv')
line1 = go.Scatter(y=data['Auckland'], x=data['DATE'], name='Auckland')
line2 = go.Scatter(y=data['Wellington'], x=data['DATE'], name='Wellington')
fig = go.Figure([line1, line2])
fig.update_layout(title='New Zealand Weather',
                  xaxis_title='DATE',
                  yaxis_title='Weather')
of.plot(fig)

Example 2 – Bar Chart : Filter the data for the year 2010, create two go.Bar traces, add text labels positioned outside the bars, and render the chart.

data_2010 = data[(data['DATE'] >= '2010-01') & (data['DATE'] < '2011-01')]
bar1 = go.Bar(y=data_2010['Auckland'], x=data_2010['DATE'],
              text=data_2010['Auckland'], textposition='outside', name='Auckland')
bar2 = go.Bar(y=data_2010['Wellington'], x=data_2010['DATE'],
              text=data_2010['Wellington'], textposition='outside', name='Wellington')
fig = go.Figure([bar1, bar2])
fig.update_layout(title='New Zealand Weather',
                  xaxis_title='DATE',
                  yaxis_title='Weather')
of.plot(fig)

Example 3 – Histogram : Use go.Histogram to count occurrences of weather values for Auckland and Wellington, adjust bin size, and display the result.

hist1 = go.Histogram(x=data['Auckland'], xbins={'size': 10}, name='Auckland')
hist2 = go.Histogram(x=data['Wellington'], xbins={'size': 10}, name='Wellington')
fig = go.Figure([hist1, hist2])
fig.update_layout(title='New Zealand Weather',
                  xaxis_title='Weather',
                  yaxis_title='count',
                  bargap=0.1)
of.plot(fig)

Example 4 – Plotly Express (Scatter) : Load the Iris dataset and create a scatter plot with color encoding using px.scatter , then render it.

import plotly.express as px
data = pd.read_csv(r'C:\Users\Administrator\Desktop\data\iris.csv')
fig = px.scatter(data, x='SepalLength', y='SepalWidth', color='Name')
of.plot(fig)

Example 5 – Pie Chart : Read a CSV containing label/value pairs and generate a pie chart with px.pie .

data = pd.read_csv(r'C:\Users\Administrator\Desktop\data\pie-charts-with-excel.csv')
fig = px.pie(data, names=data['Label'], values=data['Values'], title='pie-charts-with-excel')
of.plot(fig)

Example 6 – 3D Scatter Plot : Show two ways to create a 3D scatter plot—first with go.Scatter3d , then with px.scatter_3d .

# Using graph_objects
line1 = go.Scatter3d(x=data1['x'], y=data1['y'], z=data1['z'],
                      mode='markers', marker={'size': 3, 'color': data1['color']})
fig4 = go.Figure(line1)
of.plot(fig4)

# Using plotly.express
fig5 = px.scatter_3d(data1, x='x', y='y', z='z', color='color')
of.plot(fig5)

Example 7 – Density Mapbox : Load earthquake data, create a density map with go.Densitymapbox , set the map style, and display it.

data = pd.read_csv(r'C:\Users\Administrator\Desktop\data\earthquakes.csv')
my_map = go.Densitymapbox(lat=data['Latitude'], lon=data['Longitude'],
                         z=data['Magnitude'], radius=4)
fig = go.Figure(my_map)
fig.update_layout(mapbox_style='open-street-map')
of.plot(fig)

Each code block can be executed after installing the required packages, and the resulting figures are displayed inline using Plotly's offline mode.