The plot() Method¶

Pandas provides built-in plotting capabilities through the plot() method available on both Series and DataFrame objects. This method is a convenient wrapper around matplotlib that simplifies common visualization tasks.

Basic Usage¶

Series.plot()¶

```python import pandas as pd import matplotlib.pyplot as plt import numpy as np

Create a Series¶

s = pd.Series([1, 3, 2, 4, 3, 5], index=pd.date_range('2024-01-01', periods=6))

Simple line plot¶

s.plot() plt.show() ```

DataFrame.plot()¶

```python

Create a DataFrame¶

df = pd.DataFrame({ 'A': np.random.randn(10).cumsum(), 'B': np.random.randn(10).cumsum(), 'C': np.random.randn(10).cumsum() }, index=pd.date_range('2024-01-01', periods=10))

Plot all columns¶

df.plot() plt.show() ```

Each column becomes a separate line with automatic legend.

Selecting Columns to Plot¶

Single Column¶

```python df['A'].plot()

or¶

df.plot(y='A') ```

Multiple Columns¶

```python df[['A', 'B']].plot()

or¶

df.plot(y=['A', 'B']) ```

Real-World Example: Stock Prices¶

```python import yfinance as yf

Download stock data¶

ticker = 'WMT' df = yf.Ticker(ticker).history(start='2020-01-01', end='2020-12-31')

Plot High and Low prices¶

fig, ax = plt.subplots(figsize=(12, 4)) df[['High', 'Low']].plot(title=f'{ticker} High/Low Prices', ax=ax) plt.show() ```

Using x and y Parameters¶

For DataFrames with non-index x-axis data:

```python import yfinance as yf

Get options data¶

ticker = 'AAPL' company = yf.Ticker(ticker) maturity = company.options[0] calls = company.option_chain(maturity).calls

Plot strike price vs last price¶

fig, ax = plt.subplots(figsize=(10, 4)) calls.plot(x='strike', y='lastPrice', ax=ax) ax.set_title(f'{ticker} Call Options - {maturity}') plt.show() ```

Index as X-Axis¶

By default, the DataFrame index is used as the x-axis:

```python

DatetimeIndex becomes x-axis automatically¶

df = pd.DataFrame({ 'price': [100, 102, 101, 105, 103] }, index=pd.date_range('2024-01-01', periods=5))

df.plot() # Dates on x-axis plt.show() ```

Multiple Subplots with subplots=True¶

Plot each column in a separate subplot:

python fig, axes = plt.subplots(2, 2, figsize=(12, 8)) df[['Open', 'High', 'Low', 'Close']].plot(subplots=True, ax=axes.flatten()) plt.tight_layout() plt.show()

Or let pandas create the subplots:

python df[['Open', 'High', 'Low', 'Close']].plot(subplots=True, figsize=(12, 8), layout=(2, 2)) plt.tight_layout() plt.show()

Why Use pandas plot()?¶

Use pandas plot() for:

• Quick data exploration
• Simple visualizations
• Prototyping before detailed plots

Use matplotlib directly for:

• Publication-quality figures
• Complex layouts
• Fine-grained control

Method Signature¶

python DataFrame.plot( x=None, # Column for x-axis y=None, # Column(s) for y-axis kind='line', # Plot type ax=None, # Matplotlib axes object subplots=False, # Separate subplot per column figsize=None, # Figure size (width, height) title=None, # Plot title grid=False, # Show grid legend=True, # Show legend **kwargs # Additional matplotlib kwargs )

Summary¶

The plot() method provides quick visualization directly from pandas objects:

```python

Series¶

s.plot()

DataFrame - all columns¶

df.plot()

DataFrame - selected columns¶

df[['A', 'B']].plot()

DataFrame - specify x and y¶

df.plot(x='col1', y='col2')

Multiple subplots¶

df.plot(subplots=True) ```

Exercises¶

Exercise 1. Write code that creates a line plot from a DataFrame using df.plot(). Explain what the default x-axis is.

Exercise 2. Create a plot with a specific column as the x-axis using df.plot(x='col_x', y='col_y').

Exercise 3. Write code that plots multiple columns on the same axes using df[['a', 'b']].plot().

Exercise 4. Explain how df.plot() integrates with Matplotlib. How do you access the underlying Axes object?