numpy.ndarray syntax understanding for confirmation

Question

I am referring the code example here (http://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html), and specifically confused by this line iris.data[:, :2], since iris.data is 150 (row) * 4 (column) dimensional I think it means, select all rows, and the first two columns. I ask here to confirm if my understanding is correct, since I take time but cannot find such syntax definition official document.

Another question is, I am using the following code to get # of rows and # of columns, not sure if better more elegant ways? My code is more Python native style and not sure if numpy has better style to get the related values.

print len(iris.data) # for number of rows
print len(iris.data[0]) # for number of columns

Using Python 2.7 with miniconda interpreter.

print(__doc__)


# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause

import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model, datasets

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
Y = iris.target

h = .02  # step size in the mesh

logreg = linear_model.LogisticRegression(C=1e5)

# we create an instance of Neighbours Classifier and fit the data.
logreg.fit(X, Y)

# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(4, 3))
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)

# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')

plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())

plt.show()

regards, Lin

You are right. The first syntax selects the first 2 columns/features. Another way to query dimensions is to look at iris.data.shape. This will return a n-dimensional tuple with the length. — Pankaj Daga
Thanks @PankajDaga, do you know if there are any official document how to refer dimensions, and select sub-array (my example) of ndarray of numpy? — Lin Ma
You can look here: docs.scipy.org/doc/numpy/reference/arrays.indexing.html — Pankaj Daga

Pankaj Daga Pankaj Daga · Accepted Answer · 2016-08-23T06:26:06

You are right. The first syntax selects the first 2 columns/features. Another way to query dimensions is to look at iris.data.shape. This will return a n-dimensional tuple with the length. You can find some documentation here: http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html

import numpy as np
x = np.random.rand(100, 200)
# Select the first 2 columns
y = x[:, :2]
# Get the row length
print (y.shape[0])
# Get the column length
print (y.shape[1])
# Number of dimensions
print (len(y.shape))

numpy.ndarray syntax understanding for confirmation

1 Answers