< Ordenando elementos de un arreglo | Contenido | Manipulación de datos con Pandas >

In [1]:

import numpy as np

In [2]:

name = ['Alice', 'Bob', 'Cathy', 'Doug']
age = [25, 45, 37, 19]
weight = [55.0, 85.5, 68.0, 61.5]

In [3]:

x = np.zeros(4, dtype=int)

In [4]:

# Use a compound data type for structured arrays
data = np.zeros(4, dtype={'names':('name', 'age', 'weight'),
                          'formats':('U10', 'i4', 'f8')})
print(data.dtype)

[('name', '<U10'), ('age', '<i4'), ('weight', '<f8')]

In [5]:

data['name'] = name
data['age'] = age
data['weight'] = weight
print(data)

[('Alice', 25, 55.0) ('Bob', 45, 85.5) ('Cathy', 37, 68.0)
 ('Doug', 19, 61.5)]

In [6]:

# Get all names
data['name']

Out[6]:

array(['Alice', 'Bob', 'Cathy', 'Doug'], 
      dtype='<U10')

In [7]:

# Get first row of data
data[0]

Out[7]:

('Alice', 25, 55.0)

In [8]:

# Get the name from the last row
data[-1]['name']

Out[8]:

'Doug'

In [9]:

# Get names where age is under 30
data[data['age'] < 30]['name']

Out[9]:

array(['Alice', 'Doug'], 
      dtype='<U10')

Creando Arrays Estructurados¶

In [10]:

np.dtype({'names':('name', 'age', 'weight'),
          'formats':('U10', 'i4', 'f8')})

Out[10]:

dtype([('name', '<U10'), ('age', '<i4'), ('weight', '<f8')])

In [11]:

np.dtype({'names':('name', 'age', 'weight'),
          'formats':((np.str_, 10), int, np.float32)})

Out[11]:

dtype([('name', '<U10'), ('age', '<i8'), ('weight', '<f4')])

In [12]:

np.dtype([('name', 'S10'), ('age', 'i4'), ('weight', 'f8')])

Out[12]:

dtype([('name', 'S10'), ('age', '<i4'), ('weight', '<f8')])

In [13]:

np.dtype('S10,i4,f8')

Out[13]:

dtype([('f0', 'S10'), ('f1', '<i4'), ('f2', '<f8')])

Character	Description	Example
`'b'`	Byte	`np.dtype('b')`
`'i'`	Signed integer	`np.dtype('i4') == np.int32`
`'u'`	Unsigned integer	`np.dtype('u1') == np.uint8`
`'f'`	Floating point	`np.dtype('f8') == np.int64`
`'c'`	Complex floating point	`np.dtype('c16') == np.complex128`
`'S'`, `'a'`	String	`np.dtype('S5')`
`'U'`	Unicode string	`np.dtype('U') == np.str_`
`'V'`	Raw data (void)	`np.dtype('V') == np.void`

Tipos Compuestos mas avanzados¶

In [14]:

tp = np.dtype([('id', 'i8'), ('mat', 'f8', (3, 3))])
X = np.zeros(1, dtype=tp)
print(X[0])
print(X['mat'][0])

(0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
[[ 0.  0.  0.]
 [ 0.  0.  0.]
 [ 0.  0.  0.]]

Record Arrays¶

In [15]:

data['age']

Out[15]:

array([25, 45, 37, 19], dtype=int32)

In [16]:

data_rec = data.view(np.recarray)
data_rec.age

Out[16]:

array([25, 45, 37, 19], dtype=int32)

In [17]:

%timeit data['age']
%timeit data_rec['age']
%timeit data_rec.age

1000000 loops, best of 3: 241 ns per loop
100000 loops, best of 3: 4.61 µs per loop
100000 loops, best of 3: 7.27 µs per loop

A Pandas¶

Para el dia a dia el paquete Pandas es mucho mejor

< Ordenando elementos de un arreglo | Contenido | Manipulación de datos con Pandas >