繪圖函數
plt.plot()函數可以通過相應的參數設置繪圖風格。
plt.plot(*args, scalex=True, scaley=True, data=None, **kwargs)
Docstring:
Plot y versus x as lines and/or markers.
Call signatures::
plot([x], y, [fmt], *, data=None, **kwargs)
plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)
The coordinates of the points or line nodes are given by *x*, *y*.
The optional parameter *fmt* is a convenient way for defining basic
formatting like color, marker and linestyle. It's a shortcut string
notation described in the *Notes* section below.
>>> plot(x, y) # plot x and y using default line style and color
>>> plot(x, y, 'bo') # plot x and y using blue circle markers
>>> plot(y) # plot y using x as index array 0..N-1
>>> plot(y, 'r+') # ditto, but with red plusses
You can use `.Line2D` properties as keyword arguments for more
control on the appearance. Line properties and *fmt* can be mixed.
The following two calls yield identical results:
>>> plot(x, y, 'go--', linewidth=2, markersize=12)
>>> plot(x, y, color='green', marker='o', linestyle='dashed',
... linewidth=2, markersize=12)
When conflicting with *fmt*, keyword arguments take precedence.
**Plotting labelled data**
There's a convenient way for plotting objects with labelled data (i.e.
data that can be accessed by index ``obj['y']``). Instead of giving
the data in *x* and *y*, you can provide the object in the *data*
parameter and just give the labels for *x* and *y*::
>>> plot('xlabel', 'ylabel', data=obj)
All indexable objects are supported. This could e.g. be a `dict`, a
`pandas.DataFame` or a structured numpy array.
**Plotting multiple sets of data**
There are various ways to plot multiple sets of data.
- The most straight forward way is just to call `plot` multiple times.
Example:
>>> plot(x1, y1, 'bo')
>>> plot(x2, y2, 'go')
- Alternatively, if your data is already a 2d array, you can pass it
directly to *x*, *y*. A separate data set will be drawn for every
column.
Example: an array ``a`` where the first column represents the *x*
values and the other columns are the *y* columns::
>>> plot(a[0], a[1:])
- The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*
groups::
>>> plot(x1, y1, 'g^', x2, y2, 'g-')
In this case, any additional keyword argument applies to all
datasets. Also this syntax cannot be combined with the *data*
parameter.
By default, each line is assigned a different style specified by a
'style cycle'. The *fmt* and line property parameters are only
necessary if you want explicit deviations from these defaults.
Alternatively, you can also change the style cycle using the
'axes.prop_cycle' rcParam.
Parameters
----------
x, y : array-like or scalar
The horizontal / vertical coordinates of the data points.
*x* values are optional and default to `range(len(y))`.
Commonly, these parameters are 1D arrays.
They can also be scalars, or two-dimensional (in that case, the
columns represent separate data sets).
These arguments cannot be passed as keywords.
fmt : str, optional
A format string, e.g. 'ro' for red circles. See the *Notes*
section for a full description of the format strings.
Format strings are just an abbreviation for quickly setting
basic line properties. All of these and more can also be
controlled by keyword arguments.
This argument cannot be passed as keyword.
data : indexable object, optional
An object with labelled data. If given, provide the label names to
plot in *x* and *y*.
.. note::
Technically there's a slight ambiguity in calls where the
second label is a valid *fmt*. `plot('n', 'o', data=obj)`
could be `plt(x, y)` or `plt(y, fmt)`. In such cases,
the former interpretation is chosen, but a warning is issued.
You may suppress the warning by adding an empty format string
`plot('n', 'o', '', data=obj)`.
Other Parameters
----------------
scalex, scaley : bool, optional, default: True
These parameters determined if the view limits are adapted to
the data limits. The values are passed on to `autoscale_view`.
**kwargs : `.Line2D` properties, optional
*kwargs* are used to specify properties like a line label (for
auto legends), linewidth, antialiasing, marker face color.
Example::
>>> plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)
>>> plot([1,2,3], [1,4,9], 'rs', label='line 2')
If you make multiple lines with one plot command, the kwargs
apply to all those lines.
Here is a list of available `.Line2D` properties:
agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
alpha: float
animated: bool
antialiased or aa: bool
clip_box: `.Bbox`
clip_on: bool
clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None]
color or c: color
contains: callable
dash_capstyle: {'butt', 'round', 'projecting'}
dash_joinstyle: {'miter', 'round', 'bevel'}
dashes: sequence of floats (on/off ink in points) or (None, None)
drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'
figure: `.Figure`
fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}
gid: str
in_layout: bool
label: object
linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
linewidth or lw: float
marker: marker style
markeredgecolor or mec: color
markeredgewidth or mew: float
markerfacecolor or mfc: color
markerfacecoloralt or mfcalt: color
markersize or ms: float
markevery: None or int or (int, int) or slice or List[int] or float or (float, float)
path_effects: `.AbstractPathEffect`
picker: float or callable[[Artist, Event], Tuple[bool, dict]]
pickradius: float
rasterized: bool or None
sketch_params: (scale: float, length: float, randomness: float)
snap: bool or None
solid_capstyle: {'butt', 'round', 'projecting'}
solid_joinstyle: {'miter', 'round', 'bevel'}
transform: `matplotlib.transforms.Transform`
url: str
visible: bool
xdata: 1D array
ydata: 1D array
zorder: float
Returns
-------
lines
A list of `.Line2D` objects representing the plotted data.
顏色設置
通過color參數設置。
#標准顏色名稱
plt.plot(x, np.sin(x-0), color='blue')
#縮寫顏色代碼（rgbcmyk）
plt.plot(x, np.sin(x-1), color='g')
#范圍在0~1的灰度值
plt.plot(x, np.sin(x-2), color='0.75')
#十六進制（RRGGBB, 00~FF)
plt.plot(x, np.sin(x-3), color='#FFDD44')
#RGB元組，范圍在0~1
plt.plot(x, np.sin(x-4), color=(1.0, 0.2, 0.3))
#HTML顏色名稱
plt.plot(x, np.sin(x-5), color='chartreuse')
線條風格設置
通過linesyle設置線條風格。
#實線
plt.plot(x, np.sin(x-0), linestyle='solid')
# plt.plot(x, np.sin(x-0), linestyle='-')
#虛線
plt.plot(x, np.sin(x-1), linestyle='dashed')
# plt.plot(x, np.sin(x-0), linestyle='--')
#點划線
plt.plot(x, np.sin(x-2), linestyle='dashdot')
# plt.plot(x, np.sin(x-0), linestyle='-.')
#實點線
plt.plot(x, np.sin(x-3), linestyle='dotted')
# plt.plot(x, np.sin(x-0), linestyle=':')
組合設置
將linestyle和color編碼組合起來。
#綠色實線
plt.plot(x, x + 0, '-g')
#青色虛線
plt.plot(x, x + 1, '--c')
#黑色點划線
plt.plot(x, x + 2, '-.k')
#紅色實點線
plt.plot(x, x + 3, ':r')
