Pandas Built-in Plotting (.plot())

Last Updated: 08 Nov 2025

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Pandas Support built-in plotting using .plot().I

Hinglish Tip: .plot() = "data dekho, graph dikhao" — no plt.show(), no import matplotlib!

Basic Syntax

df.plot(
    kind='line',      # chart type
    x='column',       # optional
    y='column',       # optional
    title="Title",
    figsize=(10,6),
    grid=True
)

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1. Line Plot (Time Series)

import pandas as pd
import numpy as np

# Sample sales data
dates = pd.date_range('2025-01-01', periods=10)
df = pd.DataFrame({
    'date': dates,
    'sales': [100, 120, 115, 140, 160, 155, 180, 200, 190, 220],
    'profit': [20, 25, 22, 30, 35, 32, 40, 45, 42, 50]
}, index=dates)

df['sales'].plot(
    title="Daily Sales Trend",
    figsize=(10,4),
    grid=True,
    color='green'
)

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2. Bar Plot (Categorical)

# City-wise customers
df_city = pd.DataFrame({
    'city': ['Delhi', 'Mumbai', 'Bangalore', 'Kolkata', 'Chennai'],
    'customers': [500, 420, 380, 290, 310]
})

df_city.plot(
    x='city',
    y='customers',
    kind='bar',
    title="Customers by City",
    color='skyblue',
    figsize=(8,5)
)

Horizontal Bar:

df_city.plot(x='city', y='customers', kind='barh', color='orange')

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3. Histogram (Distribution)

# Age distribution
ages = np.random.normal(35, 10, 1000)
df_age = pd.DataFrame({'age': ages})

df_age['age'].plot(
    kind='hist',
    bins=30,
    title="Age Distribution",
    color='purple',
    alpha=0.7,
    figsize=(8,5)
)

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4. Scatter Plot (Correlation)

df.plot(
    x='sales',
    y='profit',
    kind='scatter',
    title="Sales vs Profit",
    color='red',
    alpha=0.6,
    figsize=(8,6)
)

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5. Box Plot (Outliers)

# Exam scores
scores = pd.DataFrame({
    'Math': np.random.normal(75, 15, 100),
    'Science': np.random.normal(78, 12, 100),
    'English': np.random.normal(72, 18, 100)
})

scores.plot(
    kind='box',
    title="Exam Scores Distribution",
    figsize=(8,5),
    grid=True
)

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6. Area Plot (Stacked Trends)

df[['sales', 'profit']].plot(
    kind='area',
    title="Sales & Profit Over Time",
    alpha=0.6,
    figsize=(10,5)
)

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7. Pie Chart (Proportions)

dept_count = pd.Series({
    'IT': 50,
    'HR': 20,
    'Sales': 30,
    'Marketing': 25
})

dept_count.plot(
    kind='pie',
    title="Department Strength",
    autopct='%1.1f%%',
    figsize=(6,6),
    startangle=90
)

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Pro: Chaining with groupby()

# Top 5 cities by sales
(df.groupby('city')['sales']
   .sum()
   .sort_values(ascending=False)
   .head(5)
   .plot.bar(title="Top 5 Cities by Sales", color='gold'))

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All kind Options

kind	Use Case
'line'	Trends
'bar'	Category count
'barh'	Long labels
'hist'	Distribution
'box'	Outliers
'area'	Stacked
'pie'	Proportions
'scatter'	Correlation

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Save Plot

# Save as PNG
plot = df['sales'].plot(title="Sales")
plot.figure.savefig("sales_plot.png", dpi=300, bbox_inches='tight')

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Common Mistakes to Avoid

Mistake	Fix
No figsize	Add figsize=(10,6)
Forget kind=	Default is line
Pie chart messy	Use autopct='%1.1f%%'
Can't save	Use plot.figure.savefig()

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