Creating Interactive Line and Bar Charts

Line and bar charts are commonly used in business analysis. Plotly makes both interactive, allowing users to inspect values and compare categories.

Interactive Line Charts

Line charts help reveal changes over time or across an ordered sequence.

import pandas as pd

import plotly.express as px

df = pd.DataFrame({

"Month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun"],

"Sales": [15000, 18000, 16000, 22000, 25000, 28000]

})

fig = px.line(

df,

x="Month",

y="Sales",

title="Monthly Sales Performance",

markers=True,

labels={

"Month": "Month",

"Sales": "Sales Revenue"

}

)

fig.show()

The markers=True option displays individual observations. Hovering over a point reveals its associated values.

Comparing Multiple Categories

You can use the color parameter to display multiple categories in the same chart.

df = pd.DataFrame({

"Month": ["Jan", "Feb", "Mar", "Jan", "Feb", "Mar"],

"Region": ["North", "North", "North", "South", "South", "South"],

"Sales": [12000, 15000, 17000, 10000, 14000, 16000]

})

fig = px.line(

df,

x="Month",

y="Sales",

color="Region",

markers=True,

title="Regional Sales Comparison"

)

fig.show()

Separate lines make it easier to compare regional trends. For meaningful comparisons, ensure the time periods and measurement units are consistent.

Interactive Bar Charts

Bar charts are useful for comparing numerical values across categories.

df = pd.DataFrame({

"Product": ["Laptop", "Phone", "Tablet"],

"Units_Sold": [120, 250, 180]

})

fig = px.bar(

df,

x="Product",

y="Units_Sold",

title="Product Sales Comparison",

color="Product",

text="Units_Sold"

)

fig.show()

The text parameter displays values on the bars, while color distinguishes product categories. Users can hover over bars for additional information.