NumPy Cheat Sheet
Python For Data Science 

Learn NumPy online at www.DataCamp.com
Saving & Loading On Disk
Saving & Loading Text Files
>>> np.save( , a)

>>> np.savez( ', a, b)

>>> np.load( )
'my_array'
'array.npz
'my_array.npy'
>>> np.loadtxt( )

>>> np.genfromtxt( , delimiter= )

>>> np.savetxt( , a, delimiter= )
"myfile.txt"
"my_file.csv" ','
"myarray.txt" " "
> I/O
Numpy
>>> np.int64
>>> np.float32
>>> np.complex
>>> np.bool
>>> np.object
>>> np.string_
>>> np.unicode_
#Signed 64-bit integer types

#Standard double-precision floating point

#Complex numbers represented by 128 floats

#Boolean type storing TRUE and FALSE values

#Python object type

#Fixed-length string type

#Fixed-length unicode type
> Data Types
>>> np.info(np.ndarray.dtype)
> Asking For Help
Arithmetic Operations
Comparison
Aggregate Functions
>>> g = a b
array([[-0.5, 0. , 0. ],

[-3. , -3. , -3. ]])

>>> np.subtract(a,b) 

>>> b a 

array([[ 2.5, 4. , 6. ],

[ 5. , 7. , 9. ]])

>>> np.add(b,a) 

>>> a b
array([[ 0.66666667, 1. , 1. ],

[ 0.25 , 0.4 , 0.5 ]])

>>> np.divide(a,b) 

>>> a b 

array([[ 1.5, 4. , 9. ],

[ 4. , 10. , 18. ]])

>>> np.multiply(a,b) 

>>> np.exp(b) 

>>> np.sqrt(b)
>>> np.sin(a)
>>> np.cos(b)
>>> np.log(a) 

>>> e.dot(f) 

array([[ 7., 7.],

[ 7., 7.]])
-
+
/
*
#Subtraction

#Subtraction
#Addition
Addition
#Division

#Division
#Multiplication
#Multiplication
#Exponentiation
#Square root

#Print sines of an array

#Element-wise cosine

#Element-wise natural logarithm
#Dot product
>>> a == b
array([[ , , ],

[ , , ]], dtype=bool)

>>> a < 2
array([ , , ], dtype=bool)

>>> np.array_equal(a, b)
#Element-wise comparison

#Element-wise comparison

#Array-wise comparison
False True True
False False False
True False False
>>> a.sum()
>>> a.min()
>>> b.max(axis=0)
>>> b.cumsum(axis=1)
>>> a.mean()
>>> np.median(b)
>>> np.corrcoef(a)
>>> np.std(b)
#Array-wise sum

#Array-wise minimum value

#Maximum value of an array row

#Cumulative sum of the elements

#Mean

#Median

#Correlation coefficient

#Standard deviation
> Array Mathematics
Numpy
The NumPy library is the core library for scientific computing in
 Python. 

It provides a high-performance multidimensional array
 object, and tools for
working with these arrays
NumPy Arrays
>>> import numpy as np
Use the following import convention:
Initial Placeholders
>>> a = np.array([1,2,3])

>>> b = np.array([(1.5,2,3), (4,5,6)], dtype = float)

>>> c = np.array([[(1.5,2,3), (4,5,6)],[(3,2,1), (4,5,6)]], dtype = float)
>>> np.zeros((3,4)) 

>>> np.ones((2,3,4),dtype=np.int16)
>>> d = np.arange(10,25,5)
>>> np.linspace(0,2,9)
>>> e = np.full((2,2),7)
>>> f = np.eye(2)
>>> np.random.random((2,2))
>>> np.empty((3,2))
#Create an array of zeros
#Create an array of ones

#Create an array of evenly spaced values (step value)

#Create an array of evenly
spaced values (number of samples)

#Create a constant array

#Create a 2X2 identity matrix

#Create an array with random values

#Create an empty array
> Creating Arrays
>>> h = a.view()
>>> np.copy(a)
>>> h = a.copy()
#Create a view of the array with the same data

#Create a copy of the array

#Create a deep copy of the array
>>> a[2]
3
>>> b[1,2]

6.0
#Select the element at the 2nd index 



#Select the element at row 1 column 2 (equivalent to b[1][2])

>>> a[0:2]
array([1, 2])

>>> b[0:2,1]
array([ 2., 5.])


>>> b[:1]
array([[1.5, 2., 3.]]) 

>>> c[1,...] 

array([[[ 3., 2., 1.],

[ 4., 5., 6.]]])

>>> a[ : :-1]
#Select items at index 0 and 1

#Select items at rows 0 and 1 in column 1

#Select all items at row 0
(equivalent to b[0:1, :])

#Same as [1,:,:]
#Reversed array a array([3, 2, 1])
>>> a[a<2]
array([1])
#Select elements from a less than 2

>>> b[[1, 0, 1, 0],[0, 1, 2, 0]]
array([ 4. , 2. , 6. , 1.5])

>>> b[[1, 0, 1, 0]][:,[0,1,2,0]]
array([[ 4. ,5. , 6. , 4. ], 

[ 1.5, 2. , 3. , 1.5],

[ 4. , 5. , 6. , 4. ],

[ 1.5, 2. , 3. , 1.5]])
#Select elements (1,0),(0,1),(1,2) and (0,0)

#Select a subset of the matrix’s rows
and columns

>>> a.sort()
>>> c.sort(axis=0)
#Sort an array

#Sort the elements of an array's axis
>>> a.shape
>>> len(a) 

>>> b.ndim 

>>> e.size
>>> b.dtype
>>> b.dtype.name
>>> b.astype(int)
#Array dimensions

#Length of array
#Number of array dimensions
#Number of array elements

#Data type of array elements

#Name of data type

#Convert an array to a different type
> Inspecting Your Array
> Copying Arrays
> Sorting Arrays
> Subsetting, Slicing, Indexing
Subsetting
Slicing
Boolean Indexing
Fancy Indexing
>>> i = np.transpose(b)
>>> i.T
#Permute array dimensions

#Permute array dimensions
>>> b.ravel()
>>> g.reshape(3,-2)
#Flatten the array

#Reshape, but don’t change data
>>> h.resize((2,6))
>>> np.append(h,g)
>>> np.insert(a, 1, 5)
>>> np.delete(a,[1])
#Return a new array with shape (2,6)

#Append items to an array

#Insert items in an array

#Delete items from an array
>>> np.concatenate((a,d),axis=0)
array([ 1, 2, 3, 10, 15, 20])

>>> np.vstack((a,b))
array([[ 1. , 2. , 3. ],

[ 1.5, 2. , 3. ],

[ 4. , 5. , 6. ]])

>>> np.r_[e,f]
>>> np.hstack((e,f))
array([[ 7., 7., 1., 0.],

[ 7., 7., 0., 1.]])

>>> np.column_stack((a,d))
array([[ 1, 10],

[ 2, 15],

[ 3, 20]])

>>> np.c_[a,d]
#Concatenate arrays

#Stack arrays vertically (row-wise)

#Stack arrays vertically (row-wise)

#Stack arrays horizontally (column-wise)

#Create stacked column-wise arrays

#Create stacked column-wise arrays
>>> np.hsplit(a,3)
[array([1]),array([2]),array([3])]

>>> np.vsplit(c,2)
[array([[[ 1.5, 2. , 1. ],

[ 4. , 5. , 6. ]]]),

array([[[ 3., 2., 3.],

[ 4., 5., 6.]]])]
#Split the array horizontally at the 3rd
index

#Split the array vertically at the 2nd index

> Array Manipulation
Transposing Array
Changing Array Shape
Adding/Removing Elements
Combining Arrays
Splitting Arrays
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1 2 3
1 2 3
1 2 3
1.5
4
2
5
3
6
1.5
4
2
5
3
6
1.5
4
2
5
3
6