matplotlib.pyplot.plot#
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.
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 外匯交易教學 圖表 iq option yield identical results:
When conflicting with fmt, keyword arguments take precedence.
Plotting labelled data
There's 外匯交易教學 圖表 iq option 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:
All indexable objects are supported. This could e.g. be a dict , a pandas.DataFrame 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:
If x and/or y are 2D arrays a separate data set will be drawn for every column. If both x and y are 2D, they must have the same shape. If only one of them is 2D with shape (N, m) the other must have length N and will be used for every data set m.
is equivalent to:
The third way is to specify multiple sets 外匯交易教學 圖表 iq option of [x], y, [fmt] groups:
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 rcParams["axes.prop_cycle"] (default: cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']) ).
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 外匯交易教學 圖表 iq option 外匯交易教學 圖表 iq option 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 外匯交易教學 圖表 iq option 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.
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 外匯交易教學 圖表 iq option plot('n', 'o', '', data=obj) .
A list of lines representing the plotted data.
Other Parameters scalex, scaley bool, default: True
These parameters determine 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:
If you specify multiple lines with one plot call, the kwargs apply to all those lines. In case the label object is iterable, each element is used as labels for each set of data.
Here is a list of available Line2D properties:
a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image
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Matplotlib: Visualization with Python
Matplotlib 外匯交易教學 圖表 iq option 外匯交易教學 圖表 iq option is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.
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A large number of third party packages extend and build on Matplotlib functionality, including several higher-level plotting interfaces (seaborn, HoloViews, ggplot, . ), and a projection and mapping toolkit (Cartopy).
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Cartopy is a Python package designed for geospatial 外匯交易教學 圖表 iq option data processing in order to produce maps and other geospatial data analyses.
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DNA Features Viewer is a Python library to visualize DNA features, e.g. from GenBank or Gff files, or Biopython SeqRecords.
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plotnine is an implementation of a grammar of graphics in Python. The grammar allows users to compose plots by explicitly mapping data to the visual objects that make up the plot.
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Matplotlib is the result of development efforts by John Hunter (1968–2012) and the project's many contributors.
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