plot() Keywords¶

The pandas plot() method accepts many keyword arguments to customize visualizations. This document covers the most commonly used parameters.

ax - Specify Axes¶

The ax parameter allows plotting on an existing matplotlib axes object, enabling complex layouts.

```python import matplotlib.pyplot as plt import pandas as pd import yfinance as yf

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

Create figure with multiple subplots¶

fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(12, 3))

Plot on specific axes¶

df[['High', 'Low']].plot(ax=ax0, title='Price') df['Volume'].plot(ax=ax1, title='Volume')

plt.tight_layout() plt.show() ```

Why Use ax?¶

• Combine multiple plots in one figure
• Control layout precisely
• Add annotations to specific subplots
• Reuse axes from other libraries

title - Plot Title¶

```python

Single title¶

df['Close'].plot(title='Stock Price')

With ax¶

fig, ax = plt.subplots() df['Close'].plot(ax=ax, title='WMT Closing Price 2020') plt.show() ```

figsize - Figure Size¶

Control the figure dimensions (width, height) in inches:

```python

Wider figure¶

df.plot(figsize=(12, 4))

Square figure¶

df.plot(figsize=(6, 6))

Tall figure¶

df.plot(figsize=(6, 10)) ```

subplots - Separate Plots per Column¶

When subplots=True, each column gets its own subplot:

```python import matplotlib.pyplot as plt import pandas as pd import yfinance as yf

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

Each column in separate subplot¶

fig, axes = plt.subplots(1, 2, figsize=(12, 3)) df[['High', 'Low']].plot(ax=axes, subplots=True) plt.tight_layout() plt.show() ```

With layout Parameter¶

```python

2x2 grid of subplots¶

df[['Open', 'High', 'Low', 'Close']].plot( subplots=True, layout=(2, 2), figsize=(10, 8) ) plt.tight_layout() plt.show() ```

x and y - Specify Columns¶

Use x and y to plot specific columns against each other:

```python import yfinance as yf

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

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

Multiple y Columns¶

python df.plot(x='date', y=['open', 'close'])

label - Legend Label¶

Customize the legend label:

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

rot - Rotate Tick Labels¶

Rotate x-axis tick labels to prevent overlap:

```python import pandas as pd

url = 'https://raw.githubusercontent.com/theJollySin/scipy_con_2019/master/modern_time_series_analysis/ModernTimeSeriesAnalysis/StateSpaceModels/global_temps.csv' df = pd.read_csv(url) df = df.pivot(index='Date', columns='Source', values='Mean')

Rotate labels 30 degrees¶

df['GCAG'].plot(rot=30) plt.show() ```

Common Rotation Values¶

grid - Show Grid Lines¶

python df.plot(grid=True)

legend - Control Legend¶

```python

Show legend (default)¶

df.plot(legend=True)

Hide legend¶

df.plot(legend=False)

Legend position¶

df.plot().legend(loc='upper left') ```

color / c - Line Colors¶

```python

Single color¶

df['A'].plot(color='red')

Multiple colors for multiple columns¶

df.plot(color=['red', 'blue', 'green'])

Using color codes¶

df['A'].plot(color='#FF5733') ```

style - Line Style¶

```python

Dashed line¶

df['A'].plot(style='--')

With markers¶

df['A'].plot(style='o-') # Line with circle markers

Different styles per column¶

df.plot(style=['--', '-.', ':']) ```

Style Codes¶

alpha - Transparency¶

```python

50% transparent¶

df.plot(alpha=0.5) ```

linewidth / lw - Line Width¶

python df.plot(linewidth=2)

xlim and ylim - Axis Limits¶

python df.plot(xlim=(0, 100), ylim=(0, 50))

Complete Example¶

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

Create sample data¶

df = pd.DataFrame({ 'A': np.random.randn(100).cumsum(), 'B': np.random.randn(100).cumsum() }, index=pd.date_range('2024-01-01', periods=100))

Plot with multiple customizations¶

fig, ax = plt.subplots(figsize=(12, 5))

df.plot( ax=ax, title='Cumulative Random Walk', color=['steelblue', 'coral'], linewidth=1.5, alpha=0.8, grid=True, rot=45 )

ax.set_xlabel('Date') ax.set_ylabel('Value') plt.tight_layout() plt.show() ```

Summary Table¶

Exercises¶

Exercise 1. Write code demonstrating the figsize, title, xlabel, ylabel, grid, and legend keywords in df.plot().

Exercise 2. Explain the style parameter in df.plot(). Write code using style=['r-', 'b--'] for two columns.

Exercise 3. Write code that uses subplots=True in df.plot() to create a separate subplot for each column.

Exercise 4. Create a plot using df.plot() and pass ax to plot on an existing Matplotlib axes object.