YouTipMatplotlib Ref Plot
* * Matplotlib Reference](#)
`plot()` is the most core function in Matplotlib, used to draw line charts.
It creates lines by connecting given x and y coordinate points, and supports customizing color, line style, marker, and line width.
## Function Definition
### pyplot Interface
matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)
### Axes Interface
Axes.plot(*args, scalex=True, scaley=True, data=None, **kwargs)
## Parameter Description
| Parameter | Type | Description |
| --- | --- | --- |
| *args | Variable-length argument | Supports multiple calling methods: plot(y), plot(x, y), plot(x, y, 'fmt'), plot(x1, y1, 'fmt1', x2, y2, 'fmt2', ...) |
| scalex / scaley | bool | Whether to automatically scale the x/y axis to fit the data, default is True |
| data | Indexable object | If provided, string can be used as parameters to reference its data columns |
| color or c | color | Line color, such as 'red', '#ff0000', 'tab:blue' |
| linestyle or ls | str | Line style: '-' (solid), '--' (dashed), '-.' (dash-dot), ':' (dotted), '' (no line) |
| linewidth or lw | float | Line width, default is about 1.5 |
| marker | str | Data point marker: '.', 'o', 's', '^', '*', '+' (plus), 'x', etc. |
| markersize or ms | float | Marker size |
| label | str | Legend label, used with legend() |
| alpha | float | Transparency, from 0 (fully transparent) to 1 (fully opaque) |
| zorder | float | Layer order, larger values are placed on top |
> The fmt format string consists of three parts: `''`, for example, `'ro--'` means red, circle marker, dashed line. The order of each part can be arranged arbitrarily.
## Common Color Abbreviations
| Character | Color | Character | Color |
| --- | --- | --- | --- |
| 'b' | blue | 'r' | red |
| 'g' | green | 'c' | cyan |
| 'm' | magenta | 'y' | yellow |
| 'k' | black | 'w' | white |
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## Usage Examples
### Example 1: The Simplest Line Chart
Only pass in y values, x automatically uses indices starting from 0.
## Instance
import matplotlib.pyplot as plt
import numpy as np
# Generate 50 data points between 0-10
x = np.linspace(0,10,50)
y = np.sin(x)# Sine function
# Simplest call: x, y
plt.plot(x, y)
plt.title('Simple Line Plot')
plt.xlabel('x')
plt.ylabel('sin(x)')
plt.grid(True, alpha=0.3)
plt.show()
### Example 2: Multiple Curves + Format Strings
Draw multiple curves on the same plot, using different colors, line styles, and markers.
## Instance
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,10,50)
y1 = np.sin(x)
y2 = np.cos(x)
y3 = np.sin(x) * np.exp(-x / 3)# Damped sine
# Multiple curves, each using a different format
plt.plot(x, y1,'b-o', label='sin(x)')# Blue solid line + circle
plt.plot(x, y2,'r--s', label='cos(x)')# Red dashed line + square
plt.plot(x, y3,'g-.^', label='damped sin(x)')# Green dash-dot line + triangle
plt.title('Multiple Lines with Different Styles')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()
### Example 3: Customizing Styles Using Keyword Arguments
Use keyword arguments to more finely control the appearance of lines.
## Instance
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,2 * np.pi,100)
fig, ax = plt.subplots(figsize=(8,4), layout='constrained')
# Use keyword arguments instead of format strings, more readable
ax.plot(x, np.sin(x),
color='steelblue',# Color name
linestyle='-',# Solid line
linewidth=2.5,# Line width
marker='o',# Circle marker
markersize=4,# Marker size
markerfacecolor='red',# Marker fill color
markeredgecolor='black',# Marker edge color
markeredgewidth=0.5,# Marker edge width
alpha=0.8,# Overall transparency
label='sin(x)')
# Contrast: dashed line without markers
ax.plot(x, np.cos(x),
color='#ff6600',# Hexadecimal color
linestyle='--',
linewidth=2,
label='cos(x)')
ax.set_title('Customized Line Styles', fontsize=14)
ax.set_xlabel('x (radians)')
ax.set_ylabel('Amplitude')
ax.legend(loc='upper right')
ax.grid(True, alpha=0.2)
plt.show()
print("tutorial: customized plot displayed")
### Example 4: Using the data Parameter (DataFrame Support)
When the data is a dict or pandas DataFrame, string column names can be used.
## Instance
import matplotlib.pyplot as plt
import numpy as np
# Organize data as a dictionary
data ={
'time': np.arange(0,10,0.5),
'voltage': np.sin(np.arange(0,10,0.5)) * 5 + 10,
'current': np.cos(np.arange(0,10,0.5)) * 2 + 5,
}
fig, ax = plt.subplots(layout='constrained')
# Reference data via the data parameter + string column names
ax.plot('time','voltage','b-', data=data, label='Voltage (V)')
ax.plot('time','current','r--', data=data, label='Current (A)')
ax.set_title('Using data Parameter with Column Names')
ax.set_xlabel('Time (s)')
ax.set_ylabel('Value')
ax.legend()
ax.grid(True, alpha=0.3)
plt.show()
### Example 5: Calling on an Axes Object (Recommended Method)
Using the object-oriented Axes interface is clearer in multi-subplot scenarios.
## Instance
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0,10,100)
# Create two side-by-side subplots
fig,(ax1, ax2)= plt.subplots(1,2, figsize=(10,4),
layout='constrained')
# Left subplot: Math functions
ax1.plot(x, np.sin(x), label='sin(x)')
ax1.plot(x, np.cos(x), label='cos(x)')
ax1.set_title('Math Functions')
ax1.legend()
ax1.grid(True, alpha=0.3)
# Right subplot: Data trend
data = np.random.randn(100).cumsum()# Random walk
ax2.plot(data, color='teal', linewidth=1.5)
ax2.set_title('Random Walk (cumulative sum)')
ax2.set_xlabel('Step')
ax2.set_ylabel('Position')
ax2.grid(True, alpha=0.3)
plt.show()
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## Frequently Asked Questions
### Order of x and y in plot()
The standard call is `plot(x, y)`, with x first and y last.
If only one array is passed, such as `plot(y)`, x automatically takes `range(0, len(y))`.
### How to draw discontinuous line segments?
For data containing NaN, plot() will automatically break the line at NaN, which can be used to draw segmented lines.
### Can fmt strings and keyword arguments be used at the same time?
Yes, keyword arguments will override the corresponding settings in the fmt string. For example, in `plot(x, y, 'ro', color='blue')`, marker='o' comes from fmt, but color will be overridden to blue.
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