scatter-plot

I'm trying to make three scatter plots in one figure. I tried it this way. Do an import first. import matplotlib.pyplot as plt Define the variables and make one figure that contains three scatter plots. c_pop = [0.410, -0.094, -0.111, -0.106, -0.090, 0.070, -0.043, -0.181, 0.221] c_labels = ['ll120', 'll123', 'll124', 'll27', 'll28', 'll30', 'll446, n12', 'll447, n86', 'll471'] pre_lo = [-0.493] post_lo = [0.145] lo_label = ['lo'] fig = plt.figure() ax1 = fig.add_subplot(111) ax1.scatter(c_labels, c_pop, color='black') ax1.scatter(lo_label, pre_lo, color='green', label='pre-SNEP') ax1.scatter

I have some basic car engine size, horsepower and body type data (sample shown below) body-style engine-size horsepower 0 convertible 130 111.0 2 hatchback 152 154.0 3 sedan 109 102.0 7 wagon 136 110.0 69 hardtop 183 123.0 Out of which I made a scatter plot with horsepower on x axis, engine size on y axis and using body-style as a color scheme to differentiate body classes and. I also used "compression ratio" of each car from a seperate dataframe to dictate the point point size This worked out well except I cant display color legends for my plot. Help needed as i'm a beginner. Here's my code:

I'm quite new to matplotlib and i would like to know how we can change color of points on a scatter plot based on the value in a list. In fact, I have a 2-D array that I want to plot and a list with the same number of rows containing, for each point, the color we want to use. #Example data = np.array([4.29488806,-5.34487081], [3.63116248,-2.48616998], [-0.56023222,-5.89586997], [-0.51538502,-2.62569576], [-4.08561754,-4.2870525 ], [-0.80869722,10.12529582]) colors = ['red','red','red','blue','red','blue'] ax1.plot(data[:,0],data[:,1],'o',picker=True) How to set the color parameter to fit my

I am running this program to find the distribution of characters in a specific text. # this is a paragraph from python documentation :) mytext = 'When a letter is first k encountered, it is missing from the mapping, so the default_factory function calls int() to supply a default count of zero. The increment operation then builds up the count for each letter.The function int() which always returns zero is just a special case of constant functions. A faster and more flexible way to create constant functions is to use a lambda function which can supply any constant value (not just zero):' d =

I am trying to do a scatter plot with speed over meters for each point where marker indicate different types, size indicate different weights and color indicate how old a point is over 10 minutes scale. However, I was only able to plot by size so far. Any help is highly appreciated. x = {'speed': [10, 15, 20, 18, 19], 'meters' : [122, 150, 190, 230, 300], 'type': ['phone', 'phone', 'gps', 'gps', 'car'], 'weight': [0.2, 0.3, 0.1, 0.85, 0.0], 'old': [1, 2, 4, 5, 8]} m = pd.DataFrame(x) plt.scatter(m.meters, m.speed, s = 30* m.weight) mkr_dict = {'gps': 'x', 'phone': '+', 'car': 'o'} meters speed

I have a time series of data for which I have the quantity, y, and its error, yerr. I would now like to create a plot that shows y against phase (i.e. time / period % 1) with vertical errorbars (yerr). For this, I typically use pyplot.errorbar(time, y, yerr=yerr, ...) However, I would like to use a colorbar/map to indicate the value of time in this same plot. What I thus do is the following: pylab.errorbar( phase, y, yerr=err, fmt=None, marker=None, mew=0 ) pylab.scatter( phase, y, c=time, cmap=cm ) Unfortunately, this will plot unicolored errorbars (default is blue). Since I have ~1600 points