NumPy is a library for the Python programming language which is used for working with multi-dimensional arrays and matrices. This is very useful in large scientific computing. Because NumPy ndarrays is way faster compared to a regular python list. Arrays are very frequently used in data science too, where speed and resources are very important. That’s why NumPy is a very handy tool in data-science.
But remembering all the NumPy commands might be overwhelming for both beginners and professionals. So Datalators makes the complex simple. It’s also a good idea to check the official NumPy documentation from time to time. Even if you can find what you need in the cheat sheet. Reading documentation is a skill every data professional needs. Also, the documentation goes into a lot more detail than we can fit in a single sheet anyway!
Creating Arrays:
np.array([1.3, 2, 3], dtype = float)
Creating a 1d array
np.array([1, 2, 3], [2, 3, 4], [3, 4, 5])
Creating a 3d array
np.arange(2, 8, 2)
Array of evenly spaced values
np.zeros((2, 3))
2×3 array consists of zeros
np.ones((3, 4))
3×4 array consists of ones
np.random.random((3, 3))
Array consists of random values
np.empty((2, 3))
Empty array
np.full((3,2), 5)
Constant array
Import Export:
np.save(‘saved_array’, a)
Save ‘a’ array as on disk
np.savez(‘array.npz’, a, b)
Save 2 arrays
np.savetxt(‘array.txt’, a, delimiter= ” “)
Saving as text file
np.genformatxt(‘array.csv’, a, delimiter= “,”)
Saving as CSV file
np.load(‘array.npz’)
Load from disk
np.loadtxt(‘array.txt’)
Load from text file
Inspecting Array:
a.shape
Dimensions of ‘a’ array
len(a)
Length of array
a.ndim
Array dimensions
a.size
Number of elements
a.dtype
Data type of elements
a == b
Element-wise comparison
a > 1
Element-wise comparison
np.array_equal(a, b)
Array-wise comparison
Data Types:
np.int64
Signed integer types
np.float32
Floating point
np.complex
Complex number
np.bool
Boolean type
np.object
Python object type
np.string_
String type
np.unicode
Unicode type
a.astype(int)
Convert to int type
Array Mathematics:
np.subtract(a, b)
Subtraction
np.add(a, b)
Addition
np.devide(a, b)
Division
np.multiply(a, b)
Multiplication
a+b, a-b, a*b, a/b
Operation – arithmetic sign
np.sin(a), np.cos(a), np.log(a)
Mathematical operation
a.dot(b)
Dot product
np.exp(a), np.sqrt(a)
Exponentiation and Square root
Statistics on NumPy:
a.sum()
Array-wise sum
a.min(), a.max(axis = 0)
Minimum and Maximum value
a.mean(), a.median()
Mean and Median
a.corrcoef()
Correlation coefficient
np.std(a)
Standard deviation
b.cumsum(axis = 1)
Cumulative sum of elements
a.sort(), a.sort(axis = 0)
Sort an array
Indexing and Slicing
b = a.view() / a.copy()
Create a copy of array
a[1, 2]
Subsetting
a[0:2]
Slicing
a[0:2, :-1]
Slicing
a[a<2]
Boolean Indexing
Array Manipulation:
b = np.transpose(a)
Permute array dimensions
a.reshape(3,-2)
Reshape but don’t change data
h.resize((2,4))
Return a new array with shape (2,4)
np.append(a,b)
Append items to an array
np.insert(a, 1, 5)
Insert items in an array
np.delete(a,[1])
Delete items from an array
np.hsplit(a,3)
Split the array horizontally at the 3rd index
I hope this cheat sheet will be useful to you. No matter you are new to python who is learning python for data science or a data professional. Happy Programming.
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