Week 1: Numpy Arrays
CREATING ARRAYS
np.arange()
np.arange(1, 11.9, 2.1) à Starting point (1), End point (11.9) (Excluded) and the Steps taken (2.1)
np.linspace() à Array with an starting point, end point and amount of numbers. EQUALLY SPACED
np.linspace(0, 2, 9) à Starting point (0), End point (2) (Included) and the amount of numbers we
want (9)
np.zeros([])
np.zeros()
np.zeros(5) à Array with 5 zeros
np.ones() à Array with ones
np.random.random()
np.random.random(4) à 4 Random numbers between 0 and 1
np.random.randint()
np.random.randint(5, 10, 12) à Starting point (5), End point (10) (Excluded) and Number of Intergers
(12)
np.random.unifrom()
np.random.uniform(5, 10, 12) à Array with 12 random numbers with uniform distribution between 5 and
10
np.random.normal()
np.random.normal(5, 3, 10) à Array with 10 random numbers with normal distribution with mean 5 and
SD of 3
a = np.random.uniform(1, 100, 100000)
np.size() à Size of array in terms of numbers
np.size(a) = 100000
np.ndim() à Dimension of array
np.ndim(a) = 1
np.shape() à Shape of the array
np.shape(a) = (100000,)
CHECKING EQUIVALENCE
a = np.array([2, 4, 6, 8])
a = b
np.allclose() à Checks if two arrays are the same
np.allclose(a,b) = True
CHECK LOCATIONS OF TWO ARRAYS IN COMPUTER MEMORY
id() à Checks locations of arrays
id(a) == id(b) = True
, DATA TYPES IN ARRAYS
dtype() à Datatype of the array
The 6 basic data types of Numpy arrays are:
- float (float16, float32, or float64)
- integer (int8, int16, int32, or int64)
- unsigned integer: this number cannot be negative (uint8, uint16, uint32, or uint64)
- boolean (bool)
- complex (complex64 or complex128)
- string (for example <U3 or <U64, where the number indicates the maximum length of the strings)
X = np.array([1, 3, 5, 7])
x.dtype = int8
itemsize() à Checks how many items there are in the array
x.itemsize = 4
dtype=’ ‘ à Changing data type in the array
dtype=’float’ = [1. 3. 5. 7.]
INDEXING IN ARRAYS
a = np.arange(10)
a = [0 1 2 3 4 5 6 7 8 9]
print( a[0] ) = 0
print( a[3] ) = 3
print( a[9] ) = 9
print( a[-1] ) = 9
b = np.arange(1,11)
b = [1 2 3 4 5 6 7 8 9 10]
print( b[0] ) #First element of the list = 1
print( b[3] ) #Fourth element = 4
print( b[9] ) #Tenth element = 10
print( b[-1] ) #Last element. First of reversed order. = 10
print (b[1]) = 2
print( b[:] ) #All elements, from first to last = [1 2 3 4 5 6 7 8 9 10]
print( b[3:6] ) #Thrird element to fifth element (Excluded) = [4 5 6]
print( b[:4] ) #First to third = [1 2 3 4]
print( b[-4:] ) #Last four = [7 8 9 10]
CREATING ARRAYS
np.arange()
np.arange(1, 11.9, 2.1) à Starting point (1), End point (11.9) (Excluded) and the Steps taken (2.1)
np.linspace() à Array with an starting point, end point and amount of numbers. EQUALLY SPACED
np.linspace(0, 2, 9) à Starting point (0), End point (2) (Included) and the amount of numbers we
want (9)
np.zeros([])
np.zeros()
np.zeros(5) à Array with 5 zeros
np.ones() à Array with ones
np.random.random()
np.random.random(4) à 4 Random numbers between 0 and 1
np.random.randint()
np.random.randint(5, 10, 12) à Starting point (5), End point (10) (Excluded) and Number of Intergers
(12)
np.random.unifrom()
np.random.uniform(5, 10, 12) à Array with 12 random numbers with uniform distribution between 5 and
10
np.random.normal()
np.random.normal(5, 3, 10) à Array with 10 random numbers with normal distribution with mean 5 and
SD of 3
a = np.random.uniform(1, 100, 100000)
np.size() à Size of array in terms of numbers
np.size(a) = 100000
np.ndim() à Dimension of array
np.ndim(a) = 1
np.shape() à Shape of the array
np.shape(a) = (100000,)
CHECKING EQUIVALENCE
a = np.array([2, 4, 6, 8])
a = b
np.allclose() à Checks if two arrays are the same
np.allclose(a,b) = True
CHECK LOCATIONS OF TWO ARRAYS IN COMPUTER MEMORY
id() à Checks locations of arrays
id(a) == id(b) = True
, DATA TYPES IN ARRAYS
dtype() à Datatype of the array
The 6 basic data types of Numpy arrays are:
- float (float16, float32, or float64)
- integer (int8, int16, int32, or int64)
- unsigned integer: this number cannot be negative (uint8, uint16, uint32, or uint64)
- boolean (bool)
- complex (complex64 or complex128)
- string (for example <U3 or <U64, where the number indicates the maximum length of the strings)
X = np.array([1, 3, 5, 7])
x.dtype = int8
itemsize() à Checks how many items there are in the array
x.itemsize = 4
dtype=’ ‘ à Changing data type in the array
dtype=’float’ = [1. 3. 5. 7.]
INDEXING IN ARRAYS
a = np.arange(10)
a = [0 1 2 3 4 5 6 7 8 9]
print( a[0] ) = 0
print( a[3] ) = 3
print( a[9] ) = 9
print( a[-1] ) = 9
b = np.arange(1,11)
b = [1 2 3 4 5 6 7 8 9 10]
print( b[0] ) #First element of the list = 1
print( b[3] ) #Fourth element = 4
print( b[9] ) #Tenth element = 10
print( b[-1] ) #Last element. First of reversed order. = 10
print (b[1]) = 2
print( b[:] ) #All elements, from first to last = [1 2 3 4 5 6 7 8 9 10]
print( b[3:6] ) #Thrird element to fifth element (Excluded) = [4 5 6]
print( b[:4] ) #First to third = [1 2 3 4]
print( b[-4:] ) #Last four = [7 8 9 10]
