How I can get cartesian coordinate system in matplotlib?

Question

I am new to plotting with Python and can\'t really find an answer to the question: How can I get Cartesian coordinate plane in matplotlib? By this I mean perpendicular refe

Answer 1

The code below will give you a Cartesian plane.

import matplotlib.pyplot as plt


def build_cartesian_plane(max_quadrant_range):
    """ The quadrant range controls the range of the quadrants"""
    l = []
    zeros = []
    plt.grid(True, color='b', zorder=0,)
    ax = plt.axes()
    head_width = float(0.05) * max_quadrant_range
    head_length = float(0.1) * max_quadrant_range
    ax.arrow(0, 0, max_quadrant_range, 0, head_width=head_width, head_length=head_length, fc='k', ec='k',zorder=100)
    ax.arrow(0, 0, -max_quadrant_range, 0, head_width=head_width, head_length=head_length, fc='k', ec='k', zorder=100)
    ax.arrow(0, 0, 0, max_quadrant_range, head_width=head_width, head_length=head_length, fc='k', ec='k', zorder=100)
    ax.arrow(0, 0, 0, -max_quadrant_range, head_width=head_width, head_length=head_length, fc='k', ec='k', zorder=100)
    counter_dash_width = max_quadrant_range * 0.02
    dividers = [0,.1,.2,.3,.4, .5, .6, .7, .8, .9, 1]
    for i in dividers:
        plt.plot([-counter_dash_width, counter_dash_width], [i*max_quadrant_range, i*max_quadrant_range], color='k')
        plt.plot([i * max_quadrant_range, i*max_quadrant_range], [-counter_dash_width, counter_dash_width], color='k')
        plt.plot([-counter_dash_width, counter_dash_width], [-i * max_quadrant_range, -i * max_quadrant_range], color='k')
        plt.plot([-i * max_quadrant_range, -i * max_quadrant_range], [-counter_dash_width, counter_dash_width], color='k')
        l.append(i * max_quadrant_range)
        l.append(-i * max_quadrant_range)
        zeros.append(0)
        zeros.append(0)


build_cartesian_plane(10)
plt.show()

Example output from the code

Answer 2

If you just want to plot some dots, scatter is what you want

from pylab import *

x = [0,2,-3,-1.5]
y = [0,3,1,-2.5]
color=['m','g','r','b']

scatter(x,y, s=100 ,marker='o', c=color)

show()

For pretty printing ( with arrows and dashed lines ) :

from pylab import *
import matplotlib.pyplot as plt

x = [0,2,-3,-1.5]
y = [0,3,1,-2.5]
color=['m','g','r','b']

fig = plt.figure()
ax = fig.add_subplot(111)

scatter(x,y, s=100 ,marker='o', c=color)

[ plot( [dot_x,dot_x] ,[0,dot_y], '-', linewidth = 3 ) for dot_x,dot_y in zip(x,y) ] 
[ plot( [0,dot_x] ,[dot_y,dot_y], '-', linewidth = 3 ) for dot_x,dot_y in zip(x,y) ]

left,right = ax.get_xlim()
low,high = ax.get_ylim()
arrow( left, 0, right -left, 0, length_includes_head = True, head_width = 0.15 )
arrow( 0, low, 0, high-low, length_includes_head = True, head_width = 0.15 ) 

grid()

show()

There is still some work to do, but it is not far from the result :

Answer 3

Here is another way to draw a Cartesian coordinate system, built on the answers already given by others.

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(figsize=(10, 10))

# Enter x,y coordinates and colors of points
x = [0, 2, -3, -1.5]
y = [0, 3, 1, -2.5]
color = ['m', 'g', 'r', 'b']

# Select length of axes and the space between tick labels
xmin, xmax, ymin, ymax = -5, 5, -5, 5
ticks_frequency = 1

# Plot points
ax.scatter(x, y, c=color)

# Draw lines connecting points to axes
[ax.plot([dot_x, dot_x], [0, dot_y], c=c, ls='--', lw=1.5, alpha=0.5) for dot_x, dot_y, c in zip(x, y, color)] 
[ax.plot([0, dot_x], [dot_y, dot_y], c=c, ls='--', lw=1.5, alpha=0.5) for dot_x, dot_y, c in zip(x, y, color)]

# Set identical scales for both axes
ax.set(xlim=(xmin-1, xmax+1), ylim=(ymin-1, ymax+1), aspect='equal')

# Set bottom and left spines as x and y axes of coordinate system
ax.spines['bottom'].set_position('zero')
ax.spines['left'].set_position('zero')

# Remove top and right spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Create 'x' and 'y' labels placed at the end of the axes
ax.set_xlabel('x', size=14, labelpad=15)
ax.set_ylabel('y', size=14, labelpad=15, rotation=0)
ax.xaxis.set_label_coords(1.03, 0.512)
ax.yaxis.set_label_coords(0.5, 1.02)

# Create custom tick labels
x_ticks = np.arange(xmin, xmax+1, ticks_frequency)
x_ticks_major = x_ticks[x_ticks != 0]
y_ticks = np.arange(ymin, ymax+1, ticks_frequency)
y_ticks_major = y_ticks[y_ticks != 0]
ax.set_xticks(x_ticks_major)
ax.set_yticks(y_ticks_major)
ax.set_xticks(np.arange(xmin,xmax+1), minor=True)
ax.set_yticks(np.arange(ymin,ymax+1), minor=True)

# Draw grid lines
ax.grid(which='major', color='grey', linewidth=1, linestyle='-', alpha=0.2)
ax.grid(which='minor', color='grey', linewidth=1, linestyle='-', alpha=0.2)

# Draw arrows
ax.plot((1), (0), linestyle="", marker=">", markersize=4, color="k",
        transform=ax.get_yaxis_transform(), clip_on=False)
ax.plot((0), (1), linestyle="", marker="^", markersize=4, color="k",
        transform=ax.get_xaxis_transform(), clip_on=False)

plt.show()

Notice that I have not added annotations displaying the coordinates of the points as in my experience this requires a lot more code to place them nicely and with minimal overlapping. To get annotations, it is probably easier to use an interactive graphing library such as Plotly.

Answer 4

This is an old question, but I think with today's matplotlib versions, the keyword is spines. You would do:

ax = plt.gca()
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_position('zero')
ax.spines['left'].set_position('zero')
ax.spines['right'].set_color('none')

The link provides more examples.

Answer 5

I think this example in the matplotlib gallery should get you close enough: http://matplotlib.org/examples/axes_grid/demo_axisline_style.html