100 Numpy Exercises - Hints, Discussions & Help

Data Analysis with Python: Zero to Pandas
#1

This is the discussion thread for Numpy 100 - an optional set of exercises on numerical computing with Numpy. You can find the notebook here:

Hints: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_hints.md

Use this thread for:

  • Getting help if you are stuck and need help with a problem
  • Discussing & sharing your approach or code for a problems
  • Working with the community to find the shortest or most efficient solution
  • Helping others to fix errors and improve their solutions

IMPORTANT NOTE: Solving these 100 problems is not necessary for getting the certification. This is an optional exercise.

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Assignment 2: Numpy Array Operations - Share Your Work & Get Help
#2
#3

Use the ā€œEmbed Cellā€ option from Jovian, to share a single cell from your notebook within a forum post. Example:

Learn more: https://medium.com/jovianml/share-and-embed-jupyter-notebooks-online-with-jovian-ml-df709a03064e

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#4

some one please help how to solve thisā€¦
Create a 2d array with 1 on the border and 0 inside ?

#6

arr = np.ones((5,5))
arr[1:-1,1:-1]=0

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#7

Help with this question:

  1. Create a 8x8 matrix and fill it with a checkerboard pattern

I found an answer but donā€™t understand one thing:

import numpy as np
x = np.ones((3,3))
print("Checkerboard pattern:")
x = np.zeros((8,8),dtype=int)
x[1::2,::2] = 1
x[::2,1::2] = 1
print(x)

I understand the first part of x argument, that is, 1::2 (which means every 2nd row), but how does the second argument, ::2, function?

Thanks.

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#8

in question 17 what to do is not understandable

#9

Thanks for this exercise. Iā€™d say this is both more interesting and exciting than the assignment 2. Moreover, Iā€™m sure I can learn a lot from this. Even better, I hope I can look up for more creative answers here.

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#10

You already have the answer yourself. See if this helps: x[0:] = x[:].

Pls try this:

x = np.arange(12).reshape(2,6)
print(x[0,::2])
print(x[1,1::2])
# play with this array whatever you want
#11

Google is your best friend.

3x3 Identity matrix is
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]

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#12

Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?
please help how to think on this question to solve

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#13

Letā€™s put it this way: thereā€™s a big bag of balls, say 336 (678) balls. Thereā€™re 6 cartons, which can hold 7 boxes each, and each of these boxes, thereā€™re 8 partitions in it to store these balls. Number these balls from 0 to 335, and store them into paritions in ascending order. Now, you need to find where the Ball 99 (this is the 100th ball) is stored.

We know 1 box carries 8 balls, and 1 carton carries 7 boxes. In math, we know 1 carton carries 7*8 balls. So, we basically know the Ball 99 is in Carton 1 (the first Carton is Carton 0). From here, I bet you have an idea how to look for Ball 99 with the help of // and %.

Hope this helps.

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#14

Is there any resource online where I can find the explanations to these exercises?

Googling these problems returns plenty of solutions for the questions, but not explanations. For problems after the first 20 or so, Iā€™m unable to find explanations (specially ones where the code is already given).

Iā€™d post the problems here but they may end up being too many eventually.

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#15

You can find some hints here: https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises_with_hints.md

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#17 
x = np.ones((6,6))
x[1:-1, 1:-1] = 0
x

Note: First 1:-1 means 2nd row to (last row - 1) as -1 indicates last one. Second one refers to column.

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#18

@cfran064

x[1::2,::2] means row1 to last row with step 2, col0 to last col with step 2
x[::2,1::2] means row0 to last row with step 2, col1 to last col with step 2
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#19

On question no. 27 i.e. `

27. Consider an integer vector Z, which of these expressions are legal? (ā˜…ā˜†ā˜†)

Z**Z
2 << Z >> 2
Z <- Z
1j*Z
Z/1/1
Z<Z>Z

What does Legal actually means? I am not able to understand the question does it means runable. Please help.
`

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#20

On Question number
35. How to compute ((A+B)*(-A/2)) in place (without copy)? (ā˜…ā˜…ā˜†)
I tried dividing A = np.array([1,2,3,4]) on numpy 1.19.1

np.divide(A,4,out=A)

TypeError Traceback (most recent call last)
in
----> 1 np.divide(A,4,out=A)
TypeError: No loop matching the specified signature and casting was found for ufunc true_divide

Then doing google it was said to use np.true_divide(A,4,out=A) but in my case using that gave same error. But doing simple A=A/4 is giving answer. Please help

#21

Legal means which can run without giving any error.

#22 
arr = np.random.randint(420, size=(6, 7, 8))
print(arr.reshape(-1)[99])

arr.reshape(-1) puts all the elements of arr into 1-D array.

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