Kwargs are used to specify properties like a line label (for auto legends), linewidth, antialiasing, marker face color. The values are passed on to autoscale_view. These parameters determine if the view limits are adapted to the data limits. Other Parameters scalex, scaleybool, default: True Returns list of Line2DĪ list of lines representing the plotted data. You may suppress the warning by adding an empty format string plot('n', 'o', '', data=obj). In such cases, the former interpretation is chosen, but a warning is issued. plot('n', 'o', data=obj) could be plt(x, y) or plt(y, fmt). Technically there's a slight ambiguity in calls where the second label is a valid fmt. If given, provide the label names to plot in x and y. dataindexable object, optionalĪn object with labelled data. This argument cannot be passed as keyword. All of these and more can also be controlled by keyword arguments. Thin line scatter plot matplotlib full#See the Notes section for a full description of the format strings.įormat strings are just an abbreviation for quickly setting basic line properties. These arguments cannot be passed as keywords. They can also be scalars, or two-dimensional (in that case, the columns represent separate data sets). x values are optional and default to range(len(y)).Ĭommonly, these parameters are 1D arrays. The horizontal / vertical coordinates of the data points. Alternatively, you can also change the style cycle using rcParams (default: cycler('color', )). The fmt and line property parameters are only necessary if you want explicit deviations from these defaults. Also this syntax cannot be combined with the data parameter.īy default, each line is assigned a different style specified by a 'style cycle'. In this case, any additional keyword argument applies to all datasets. The third way is to specify multiple sets of, y, groups: > plot(x1, y1, 'g^', x2, y2, 'g-') Is equivalent to: > for col in range(y.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.Įxample: > x = > y = np.array(,, ]) > plot(x, y) If both x and y are 2D, they must have the same shape. If x and/or y are 2D arrays a separate data set will be drawn for every column. The most straight forward way is just to call plot multiple times. There are various ways to plot multiple sets of data. be a dict, a pandas.DataFrame or a structured numpy array. 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)Īll indexable objects are supported. There's a convenient way for plotting objects with labelled data (i.e. When conflicting with fmt, keyword arguments take precedence. The following two calls yield identical results: > plot(x, y, 'go-', linewidth= 2, markersize= 12) > plot(x, y, color= 'green', marker= 'o', linestyle= 'dashed'. You can use Line2D properties as keyword arguments for more control on the appearance. > 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 It's a shortcut string notation described in the Notes section below. The optional parameter fmt is a convenient way for defining basic formatting like color, marker and linestyle. The coordinates of the points or line nodes are given by x, y. ( *args, scalex=True, scaley=True, data=None, **kwargs) Ĭall signatures: plot(, y,, *, data=None, **kwargs)
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