matplotlib.pyplot.plot   matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)[source]
 
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.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: >>> plot(x1, y1, 'bo')
>>> plot(x2, y2, 'go')
  
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. Example: >>> x = [1, 2, 3]
>>> y = np.array([[1, 2], [3, 4], [5, 6]])
>>> plot(x, y)
 is equivalent to: >>> for col in range(y.shape[1]):
...     plot(x, y[:, col])
  
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 rcParams["axes.prop_cycle"] (default: cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'])).  Parameters 
 
x, yarray-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.  
fmtstr, 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.  
dataindexable 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).     Returns 
 list of Line2D


A list of lines representing the plotted data.    Other Parameters 
 
scalex, scaleybool, default: True


These parameters determine if the view limits are adapted to the data limits. The values are passed on to autoscale_view.  
**kwargsLine2D 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 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:   
Property Description   
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 scalar or None  
animated bool  
antialiased or aa bool  
clip_box Bbox  
clip_on bool  
clip_path Patch or (Path, Transform) or None  
color or c color  
dash_capstyle CapStyle or {'butt', 'projecting', 'round'}  
dash_joinstyle JoinStyle or {'miter', 'round', 'bevel'}  
dashes sequence of floats (on/off ink in points) or (None, None)  
data (2, N) array or two 1D arrays  
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 string, Path or MarkerStyle  
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) or list[bool]  
path_effects AbstractPathEffect  
picker float or callable[[Artist, Event], tuple[bool, dict]]  
pickradius float  
rasterized bool  
sketch_params (scale: float, length: float, randomness: float)  
snap bool or None  
solid_capstyle CapStyle or {'butt', 'projecting', 'round'}  
solid_joinstyle JoinStyle or {'miter', 'round', 'bevel'}  
transform unknown  
url str  
visible bool  
xdata 1D array  
ydata 1D array  
zorder float        See also  scatter

XY scatter plot with markers of varying size and/or color ( sometimes also called bubble chart).    Notes Format Strings A format string consists of a part for color, marker and line: fmt = '[marker][line][color]'
 Each of them is optional. If not provided, the value from the style cycle is used. Exception: If line is given, but no marker, the data will be a line without markers. Other combinations such as [color][marker][line] are also supported, but note that their parsing may be ambiguous. Markers   
character description   
'.' point marker  
',' pixel marker  
'o' circle marker  
'v' triangle_down marker  
'^' triangle_up marker  
'<' triangle_left marker  
'>' triangle_right marker  
'1' tri_down marker  
'2' tri_up marker  
'3' tri_left marker  
'4' tri_right marker  
'8' octagon marker  
's' square marker  
'p' pentagon marker  
'P' plus (filled) marker  
'*' star marker  
'h' hexagon1 marker  
'H' hexagon2 marker  
'+' plus marker  
'x' x marker  
'X' x (filled) marker  
'D' diamond marker  
'd' thin_diamond marker  
'|' vline marker  
'_' hline marker   Line Styles   
character description   
'-' solid line style  
'--' dashed line style  
'-.' dash-dot line style  
':' dotted line style   Example format strings: 'b'    # blue markers with default shape
'or'   # red circles
'-g'   # green solid line
'--'   # dashed line with default color
'^k:'  # black triangle_up markers connected by a dotted line
 Colors The supported color abbreviations are the single letter codes   
character color   
'b' blue  
'g' green  
'r' red  
'c' cyan  
'm' magenta  
'y' yellow  
'k' black  
'w' white   and the 'CN' colors that index into the default property cycle. If the color is the only part of the format string, you can additionally use any matplotlib.colors spec, e.g. full names ('green') or hex strings ('#008000'). 
  Examples using matplotlib.pyplot.plot
 
   Plotting masked and NaN values   

   Scatter Masked   

   Stairs Demo   

   Step Demo   

   Custom Figure subclasses   

   Managing multiple figures in pyplot   

   Shared Axis   

   Multiple subplots   

   Controlling style of text and labels using a dictionary   

   Title positioning   

   Infinite lines   

   plot() format string   

   Pyplot Mathtext   

   Pyplot Simple   

   Pyplot Three   

   Pyplot Two Subplots   

   Dolphins   

   Solarized Light stylesheet   

   Frame grabbing   

   Coords Report   

   Customize Rc   

   Findobj Demo   

   Multipage PDF   

   Print Stdout   

   Set and get properties   

   transforms.offset_copy   

   Zorder Demo   

   Custom scale   

   Placing date ticks using recurrence rules   

   Rotating custom tick labels   

   Tool Manager   

   Buttons   

   Slider   

   Snapping Sliders to Discrete Values   

   Basic Usage   

   Pyplot tutorial   

   Customizing Matplotlib with style sheets and rcParams   

   Path effects guide