< Cálculos con Arrays 2. Broadcasting | Contenido | Indexado elegante >

Ejemplo: Contando días¶

In [1]:

import numpy as np
import pandas as pd

# use pandas to extract rainfall inches as a NumPy array
rainfall = pd.read_csv('data/Seattle2014.csv')['PRCP'].values
inches = rainfall / 254.0  # 1/10mm -> inches
inches.shape

Out[1]:

(365,)

In [2]:

%matplotlib inline
import matplotlib.pyplot as plt
import seaborn; seaborn.set()  # set plot styles

In [3]:

plt.hist(inches, 40);

Operadores de comparación como ufuncs¶

En Cálculos con Arrays 1. Ufuncs introducimos ufuncs en particular operadores aritméticos +, -, *, /, y otros
NumPy implementa operadores de comparación < (menor que) y > (mayor que)

In [4]:

x = np.array([1, 2, 3, 4, 5])

In [5]:

x < 3  # less than

Out[5]:

array([ True,  True, False, False, False], dtype=bool)

In [6]:

x > 3  # greater than

Out[6]:

array([False, False, False,  True,  True], dtype=bool)

In [7]:

x <= 3  # less than or equal

Out[7]:

array([ True,  True,  True, False, False], dtype=bool)

In [8]:

x >= 3  # greater than or equal

Out[8]:

array([False, False,  True,  True,  True], dtype=bool)

In [9]:

x != 3  # not equal

Out[9]:

array([ True,  True, False,  True,  True], dtype=bool)

In [10]:

x == 3  # equal

Out[10]:

array([False, False,  True, False, False], dtype=bool)

In [11]:

(2 * x) == (x ** 2)

Out[11]:

array([False,  True, False, False, False], dtype=bool)

As in the case of arithmetic operators, the comparison operators are implemented as ufuncs in NumPy; for example, when you write x < 3, internally NumPy uses np.less(x, 3). A summary of the comparison operators and their equivalent ufunc is shown here:

Operator	Equivalent ufunc	Operator	Equivalent ufunc
`==`	`np.equal`	`!=`	`np.not_equal`
`<`	`np.less`	`<=`	`np.less_equal`
`>`	`np.greater`	`>=`	`np.greater_equal`

In [12]:

rng = np.random.RandomState(0)
x = rng.randint(10, size=(3, 4))
x

Out[12]:

array([[5, 0, 3, 3],
       [7, 9, 3, 5],
       [2, 4, 7, 6]])

In [13]:

x < 6

Out[13]:

array([[ True,  True,  True,  True],
       [False, False,  True,  True],
       [ True,  True, False, False]], dtype=bool)

Usando Arrays Booleanos¶

In [14]:

print(x)

[[5 0 3 3]
 [7 9 3 5]
 [2 4 7 6]]

Contando entradas¶

In [15]:

# how many values less than 6?
np.count_nonzero(x < 6)

Out[15]:

In [16]:

np.sum(x < 6)

Out[16]:

In [17]:

# how many values less than 6 in each row?
np.sum(x < 6, axis=1)

Out[17]:

array([4, 2, 2])

In [18]:

# are there any values greater than 8?
np.any(x > 8)

Out[18]:

True

In [19]:

# are there any values less than zero?
np.any(x < 0)

Out[19]:

False

In [20]:

# are all values less than 10?
np.all(x < 10)

Out[20]:

True

In [21]:

# are all values equal to 6?
np.all(x == 6)

Out[21]:

False

np.all and np.any can be used along particular axes as well. For example:

In [22]:

# are all values in each row less than 8?
np.all(x < 8, axis=1)

Out[22]:

array([ True, False,  True], dtype=bool)

Operadores Booleanos¶

In [23]:

np.sum((inches > 0.5) & (inches < 1))

Out[23]:

In [24]:

np.sum(~( (inches <= 0.5) | (inches >= 1) ))

Out[24]:

Operator	Equivalent ufunc		Operator	Equivalent ufunc
`&`	`np.bitwise_and`		\|	`np.bitwise_or`
`^`	`np.bitwise_xor`		`~`	`np.bitwise_not`

In [25]:

print("Number days without rain:      ", np.sum(inches == 0))
print("Number days with rain:         ", np.sum(inches != 0))
print("Days with more than 0.5 inches:", np.sum(inches > 0.5))
print("Rainy days with < 0.2 inches  :", np.sum((inches > 0) &
                                                (inches < 0.2)))

Number days without rain:       215
Number days with rain:          150
Days with more than 0.5 inches: 37
Rainy days with < 0.2 inches  : 75

Máscaras de Arrays Booleanos¶

In [26]:

Out[26]:

array([[5, 0, 3, 3],
       [7, 9, 3, 5],
       [2, 4, 7, 6]])

In [27]:

x < 5

Out[27]:

array([[False,  True,  True,  True],
       [False, False,  True, False],
       [ True,  True, False, False]], dtype=bool)

In [28]:

x[x < 5]

Out[28]:

array([0, 3, 3, 3, 2, 4])

In [29]:

# construct a mask of all rainy days
rainy = (inches > 0)

# construct a mask of all summer days (June 21st is the 172nd day)
days = np.arange(365)
summer = (days > 172) & (days < 262)

print("Median precip on rainy days in 2014 (inches):   ",
      np.median(inches[rainy]))
print("Median precip on summer days in 2014 (inches):  ",
      np.median(inches[summer]))
print("Maximum precip on summer days in 2014 (inches): ",
      np.max(inches[summer]))
print("Median precip on non-summer rainy days (inches):",
      np.median(inches[rainy & ~summer]))

Median precip on rainy days in 2014 (inches):    0.194881889764
Median precip on summer days in 2014 (inches):   0.0
Maximum precip on summer days in 2014 (inches):  0.850393700787
Median precip on non-summer rainy days (inches): 0.200787401575

Usar las palabras reservadas `and`/`or` contra usar los operadores `&`/`|`¶

In [30]:

bool(42), bool(0)

Out[30]:

(True, False)

In [31]:

bool(42 and 0)

Out[31]:

False

In [32]:

bool(42 or 0)

Out[32]:

True

In [33]:

bin(42)

Out[33]:

'0b101010'

In [34]:

bin(59)

Out[34]:

'0b111011'

In [35]:

bin(42 & 59)

Out[35]:

'0b101010'

In [36]:

bin(42 | 59)

Out[36]:

'0b111011'

In [37]:

A = np.array([1, 0, 1, 0, 1, 0], dtype=bool)
B = np.array([1, 1, 1, 0, 1, 1], dtype=bool)
A | B

Out[37]:

array([ True,  True,  True, False,  True,  True], dtype=bool)

In [38]:

A or B

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-38-5d8e4f2e21c0> in <module>()
----> 1 A or B

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

In [39]:

x = np.arange(10)
(x > 4) & (x < 8)

Out[39]:

array([False, False, False, False, False,  True,  True,  True, False, False], dtype=bool)

In [40]:

(x > 4) and (x < 8)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-40-3d24f1ffd63d> in <module>()
----> 1 (x > 4) and (x < 8)

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()