_Python Data Science Cheat Sheet NumPy Basics 4 .pdf

🚀 Elevate your Python skills: Easy manipulation of NumPy Arrays
NumPy arrays themselves are made specifically for easily manipulating data via high-powered functions. Talking big data or building workload optimizations with fewer resources, then chances are, you really need to absorb these.

With NumPy, you can:

🔄 Transpose arrays giving you the ability to switch dimensions immediately.
📐 Reshape and resize arrays to suit your desired analysis.
🔗 Merging arrays with the help of concatenate, vstack, and hstack.
✂ Splitting arrays into specific parts with hsplit and vsplit commands.
➕➖ Adding and deleting elements with the append and delete commands, respectively.
🔍 Uses a function such as ravel to flatten arrays into a single dimension.

💡 Pro Tip: Doing this in just one or two lines of code can turn complex data structures into data ready for insights!

✅ Which Numpy function do you find the most powerful? Let us know in the comments!

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NUMPY BASICS
PYTHON FORPYTHON FOR
DATA SCIENCEDATA SCIENCE
CHEAT SHEET PART- 4

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>>> i = np.transpose(b)
>>> b.ravel()
>>> h.resize((2,6))
>>> g.reshape(3,-2)
>>> np.append(h,g)
>>> np.delete(a,[1])
>>> i.T
Permute array dimensions
Flatten the array
Return a new array with
shape (2,6)
Reshape, but don’t change
data
Append items to an array
Delete items from an array
Permute array dimensions
Array Manipulation
Transposing Array
Changing Array Shape
Adding/Removing Elements

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>>> np.concatenate((a,d),axis=0)
array([ 1, 2, 3, 10, 15, 20])
Concatenate arrays
>>> np.r_[e,f] Stack arrays vertically
(row-wise)
>>> np.vstack((a,b))
array([[ 1. , 2. , 3. ],
[ 1.5, 2. , 3. ],[ 4. , 5. , 6. ]])
Stack arrays vertically
(row-wise)
>>> np.hstack((e,f))
array([[ 7., 7., 1., 0.],
[ 7., 7., 0., 1.]])
Stack arrays horizontally
(column-wise)
>>> np.column_stack((a,d))
array([[ 1, 10],[ 2, 15],[ 3, 20]])
Create stacked
column-wise arrays
>>> np.c_[a,d] Create stacked
column-wise arrays
Array Manipulation
Combining Arrays

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>>> np.hsplit(a,3)
[array([1]),array([2]),array([3])]
Split the array horizontally
at the 3rd
>>> np.vsplit(c,2)
[array([[[ 1.5, 2. , 1. ],
[ 4. , 5. , 6. ]]]),
array([[[ 3., 2., 3.],
[ 4., 5., 6.]]])]
Split the array vertically at
the 2nd index
Array Manipulation
Splitting Arrays

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