![]() ![]() This is the mot simplest way of adding legends. The plt.legend() command will add these labels as a legend on the graph. Then, pass the co-ordinates of the graph you want to plot along with the label indicating what does that line represent to the plot() function. Matplotlib has a legend() method which is used to describe what the different lines on the graph represent.įirst of all, import the matplotlib library and define the values for x and y co-ordinates. Lets see in detail how can you create, display and perform different action with legends. If you want to learn more about Python Programming, visit Python Programming Tutorials. In this tutorial, we will learn about how to add legend to a matplotlib in python to draw the graphs. To identify what these different colored lines represent, we use legends. Suppose you’ve a graph consisting of multiple lines of different colors. It is a 2D plotting library which take an array of numbers as an input and plot the data in form of different graphical representations such as bar graph, histogram, scatter plots, line plot etc. Returns : is the most commonly used python package for data visualization. Other keyword arguments are passed down to If False, no legend data is added and no legend is drawn. If “auto”,Ĭhoose between brief or full representation based on number of levels. If “full”, every group will get an entry in the legend. Variables will be represented with a sample of evenly spaced values. ![]() Specified order for appearance of the style variable levels ![]() You can pass a list of markers or a dictionary mapping levels of the Setting to True will use default markers, or Object determining how to draw the markers for different levels of the Normalization in data units for scaling plot objects when the Otherwise they are determined from the data. Specified order for appearance of the size variable levels, Which forces a categorical interpretation. List or dict arguments should provide a size for each unique data value, sizes list, dict, or tupleĪn object that determines how sizes are chosen when size is used. Or an object that will map from data units into a interval. hue_norm tuple or Įither a pair of values that set the normalization range in data units Specify the order of processing and plotting for categorical levels of the Similar to the plot method, they take at least two arguments, the x- and y-positions of the data points. xxxxxxxxxx 1 import matplotlib.pyplot as plt 2 from numpy.random import random 3 4 colors b, c, y, m, r 5 6 lo plt. Imply categorical mapping, while a colormap object implies numeric mapping. Scatter plots are drawn with the Axes.scatter method. Other answers seem a bit complex, you can just add a parameter label in scatter function and that will be the legend for your plot. String values are passed to color_palette(). Method for choosing the colors to use when mapping the hue semantic. Grouping variable that will produce points with different markers.Ĭan have a numeric dtype but will always be treated as categorical. Grouping variable that will produce points with different sizes.Ĭan be either categorical or numeric, although size mapping willīehave differently in latter case. ![]() Grouping variable that will produce points with different colors.Ĭan be either categorical or numeric, although color mapping willīehave differently in latter case. Variables that specify positions on the x and y axes. Either a long-form collection of vectors that can beĪssigned to named variables or a wide-form dataset that will be internally Parameters : data pandas.DataFrame, numpy.ndarray, mapping, or sequence This behavior can be controlled through various parameters, asĭescribed and illustrated below. In particular, numeric variablesĪre represented with a sequential colormap by default, and the legendĮntries show regular “ticks” with values that may or may not exist in theĭata. Represent “numeric” or “categorical” data. Semantic, if present, depends on whether the variable is inferred to The default treatment of the hue (and to a lesser extent, size) Hue and style for the same variable) can be helpful for making Using all three semantic types, but this style of plot can be hard to It is possible to show up to three dimensions independently by Parameters control what visual semantics are used to identify the different Of the data using the hue, size, and style parameters. The relationship between x and y can be shown for different subsets scatterplot ( data = None, *, x = None, y = None, hue = None, size = None, style = None, palette = None, hue_order = None, hue_norm = None, sizes = None, size_order = None, size_norm = None, markers = True, style_order = None, legend = 'auto', ax = None, ** kwargs ) #ĭraw a scatter plot with possibility of several semantic groupings. ![]()
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