matplotlib
Trame di base

Grafici a dispersione

Una trama a dispersione semplice

import matplotlib.pyplot as plt

# Data
x = [43,76,34,63,56,82,87,55,64,87,95,23,14,65,67,25,23,85]
y = [34,45,34,23,43,76,26,18,24,74,23,56,23,23,34,56,32,23]

fig, ax = plt.subplots(1, figsize=(10, 6))
fig.suptitle('Example Of Scatterplot')

# Create the Scatter Plot
ax.scatter(x, y,
            color="blue",    # Color of the dots
            s=100,           # Size of the dots
            alpha=0.5,       # Alpha/transparency of the dots (1 is opaque, 0 is transparent)
            linewidths=1)    # Size of edge around the dots

# Show the plot
plt.show()

Un piano di dispersione con punti etichettati

import matplotlib.pyplot as plt

# Data
x = [21, 34, 44, 23]
y = [435, 334, 656, 1999]
labels = ["alice", "bob", "charlie", "diane"]

# Create the figure and axes objects
fig, ax = plt.subplots(1, figsize=(10, 6))
fig.suptitle('Example Of Labelled Scatterpoints')

# Plot the scatter points
ax.scatter(x, y,
           color="blue",  # Color of the dots
           s=100,         # Size of the dots
           alpha=0.5,     # Alpha of the dots
           linewidths=1)  # Size of edge around the dots

# Add the participant names as text labels for each point
for x_pos, y_pos, label in zip(x, y, labels):
    ax.annotate(label,             # The label for this point
                xy=(x_pos, y_pos), # Position of the corresponding point
                xytext=(7, 0),     # Offset text by 7 points to the right
                textcoords='offset points', # tell it to use offset points
                ha='left',         # Horizontally aligned to the left
                va='center')       # Vertical alignment is centered

# Show the plot
plt.show()

Piazzole ombreggiate

Regione ombreggiata sotto una linea

import matplotlib.pyplot as plt

# Data
x =  [0,1,2,3,4,5,6,7,8,9]
y1 = [10,20,40,55,58,55,50,40,20,10]

# Shade the area between y1 and line y=0
plt.fill_between(x, y1, 0,
                 facecolor="orange", # The fill color
                 color='blue',       # The outline color
                 alpha=0.2)          # Transparency of the fill

# Show the plot
plt.show()

Regione ombreggiata tra due linee

import matplotlib.pyplot as plt

# Data
x =  [0,1,2,3,4,5,6,7,8,9]
y1 = [10,20,40,55,58,55,50,40,20,10]
y2 = [20,30,50,77,82,77,75,68,65,60]

# Shade the area between y1 and y2
plt.fill_between(x, y1, y2,
                 facecolor="orange", # The fill color
                 color='blue',       # The outline color
                 alpha=0.2)          # Transparency of the fill

# Show the plot
plt.show()

Line plot

Trama semplice

import matplotlib.pyplot as plt

# Data
x = [14,23,23,25,34,43,55,56,63,64,65,67,76,82,85,87,87,95]
y = [34,45,34,23,43,76,26,18,24,74,23,56,23,23,34,56,32,23]

# Create the plot
plt.plot(x, y, 'r-')
# r- is a style code meaning red solid line

# Show the plot
plt.show()

Nota che in generale y non è una funzione di x e anche che i valori in x non hanno bisogno di essere ordinati. Ecco come appare un grafico a linee con valori x non ordinati:

# shuffle the elements in x
np.random.shuffle(x)
plt.plot(x, y, 'r-')
plt.show()

Trama dei dati

È simile a un grafico a dispersione , ma utilizza invece la funzione plot() . L'unica differenza nel codice qui è l'argomento di stile.

plt.plot(x, y, 'b^')
# Create blue up-facing triangles

Dati e linea

L'argomento di stile può assumere simboli sia per i marcatori che per lo stile della linea:

plt.plot(x, y, 'go--')
# green circles and dashed line

Mappa di calore

Le Heatmap sono utili per visualizzare le funzioni scalari di due variabili. Forniscono un'immagine "piatta" di istogrammi bidimensionali (che rappresentano ad esempio la densità di una determinata area).

Il seguente codice sorgente illustra mappe di calore usando numeri distribuiti normalmente bivariati centrati a 0 in entrambe le direzioni (significa [0.0, 0.0] ) e una con una data matrice di covarianza. I dati sono generati usando la funzione numpy numpy.random.multivariate_normal ; viene quindi hist2d funzione hist2d di pyplot matplotlib.pyplot.hist2d .

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

# Define numbers of generated data points and bins per axis.
N_numbers = 100000
N_bins = 100

# set random seed 
np.random.seed(0)

# Generate 2D normally distributed numbers.
x, y = np.random.multivariate_normal(
        mean=[0.0, 0.0],      # mean
        cov=[[1.0, 0.4],
             [0.4, 0.25]],    # covariance matrix
        size=N_numbers
        ).T                   # transpose to get columns


# Construct 2D histogram from data using the 'plasma' colormap
plt.hist2d(x, y, bins=N_bins, normed=False, cmap='plasma')

# Plot a colorbar with label.
cb = plt.colorbar()
cb.set_label('Number of entries')

# Add title and labels to plot.
plt.title('Heatmap of 2D normally distributed data points')
plt.xlabel('x axis')
plt.ylabel('y axis')

# Show the plot.
plt.show()

Ecco gli stessi dati visualizzati come un istogramma 3D (qui usiamo solo 20 contenitori per l'efficienza). Il codice è basato su questa demo di matplotlib .

from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

# Define numbers of generated data points and bins per axis.
N_numbers = 100000
N_bins = 20

# set random seed 
np.random.seed(0)

# Generate 2D normally distributed numbers.
x, y = np.random.multivariate_normal(
        mean=[0.0, 0.0],      # mean
        cov=[[1.0, 0.4],
             [0.4, 0.25]],    # covariance matrix
        size=N_numbers
        ).T                   # transpose to get columns

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
hist, xedges, yedges = np.histogram2d(x, y, bins=N_bins)

# Add title and labels to plot.
plt.title('3D histogram of 2D normally distributed data points')
plt.xlabel('x axis')
plt.ylabel('y axis')

# Construct arrays for the anchor positions of the bars.
# Note: np.meshgrid gives arrays in (ny, nx) so we use 'F' to flatten xpos,
# ypos in column-major order. For numpy >= 1.7, we could instead call meshgrid
# with indexing='ij'.
xpos, ypos = np.meshgrid(xedges[:-1] + 0.25, yedges[:-1] + 0.25)
xpos = xpos.flatten('F')
ypos = ypos.flatten('F')
zpos = np.zeros_like(xpos)

# Construct arrays with the dimensions for the 16 bars.
dx = 0.5 * np.ones_like(zpos)
dy = dx.copy()
dz = hist.flatten()

ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', zsort='average')

# Show the plot.
plt.show()