Solving 100 Python NumPy Problems! (From easy to difficult)

NumPy is a foundational library for computation in Python. In this video we walk through exercises to learn the library in a hands-on manner. We learn skills such as array creation and manipulation, working with random numbers, performing mathematical operations, handling dates, dealing with various data types, and more. Should be a lot of fun! Link to GitHub repo: https://github.com/rougier/numpy-100 My solutions: https://github.com/KeithGalli/numpy-100 If you enjoy this video, make sure to throw it a like & subscribe if you haven't already 🫡 Here is a link to the similar video I did with the Python Pandas library!    • Solving 100 Python Pandas Problems! (from ...   Video timeline! 0:00 - Video Overview & Code Setup 4:18 - 1.) Import the numpy package under the name np 5:15 - 2.) Print the numpy version and the configuration 6:21 - 3.) Create a null vector of size 10 9:29 - 4.) How to get the memory size of any array 15:19 - 5.) How to get documentation of the numpy add function from the command line 18:51 - 6.) Create a null vector of size 10 but the fifth value which is 1 20:03 - 7.) Create a vector with values ranging from 10 to 49 21:48 - 8.) Reverse a vector (first number becomes last) 23:20 - 9.) Create a 3x3 Matrix with values ranging from 0 to 8 24:41 - 10.) Find indices of non-zero elements from array 26:24 - 11.) Create a 3x3 identity matrix 29:35 - 12.) Create a 3x3x3 array with random values. 30:48 - 13.) Create a 10x10 array with random values and find min/max values 33:17 - 14.) Create a random vector of size 30 and find the mean value 34:57 - 15.) Create a 2d array with 1 on the border and 0 inside 40:19 - 16.) How to add a border (filled with 0’s around an existing array? (np.pad) 43:41 - 17.) Evaluate some np.nan expressions 48:32 - 18.) Create a 5x5 matrix with values 1,2,3,4 just below the diagonal 56:01 - 19.) Create an 8x8 matrix and fill it with a checkerboard pattern 1:02:35 - 20.) Get the 100th element from a (6,7,8) shape array 1:07:09 - 21.) Create a checkerboard pattern 8x8 matrix using np.tile function 1:16:22 - 22.) Normalize a random 5x5 matrix 1:24:20 - 23.) Create a custom dtype that describes a color as four unsigned bytes (RGBA) 1:29:27 - 24.) Multiply a 5x3 matrix by a 3x2 matrix (real matrix product) 1:32:54 - 25.) Given a 1D array, negate all elements which are between 3 and 8, in place 1:37:16 - 26.) Default “range” function vs numpy “range” function 1:40:25 - 27.) Evaluate whether expressions are legal or not 1:55:41 - 28.) Evaluate divide by zero expressions / np.nan type casting 1:57:48 - 29.) How to round away from zero a float array? 1:59:22 - 30.) How to find common values between two arrays? 2:00:19 - 31.) How to ignore all numpy warnings? 2:03:24 - 32.) Is np.sqrt(-1) == np.emath.sqrt(-1) ?? 2:05:22 - 33.) Get the dates of yesterday, today, and tomorrow with numpy 2:19:39 - 34.) How to get all the dates corresponding to the month of July 2016? 2:27:27 - 35.) How to compute ((A+B)*(-A/2)) in place (without copy)? 2:35:00 - 36.) Extract the integer part of a random array of positive numbers using 4 different methods 2:40:47 - 37.) Create a 5x5 matrix with row values ranging from 0 to 4 2:43:07 - 38.) Use generator function that generates 10 integers and use it to build an array 2:43:58 - 39.) Create a vector of size 10 with values ranging from 0 to 1, both excluded. 2:48:49 - 40.) Create a random vector of size 10 and sort it. 2:51:07 - 41.) How to sum a small array faster than np.sum? 2:54:37 - 42.) Check if two random arrays A & B are equal 2:58:48 - 43.) Make an array immutable (read-only) 3:02:14 - Puppies are great 3:03:06 - 44.) Convert cartesian coordinates to polar coordinates 3:20:37 - 45.) Create a random vector of size 10 and replace the maximum value by 0 3:23:58 - 46.) Create a structured array with x and y coordinates covering the [0,1]x[0,1] area 3:26:25 - 47.) Given two arrays, X and Y, construct the Cauchy matrix C (Cij = 1/(xi-yj)) 3:34:31 - 48.) Print the min/max values for each numpy scalar type 3:36:50 - 49.) How to print all the values of an array? 3:39:23 - 50.) How to find the closest value (to a given scalar) in a vector? I got a little tired at the end, so not doing all 100 problems in this video. Will release the next 50 problems soon! #python #numpy -------------------- Follow me on social media! Instagram |   / keithgalli   Twitter |   / keithgalli   -------------------- Learn data skills with hands-on exercises & tutorials at Datacamp! https://datacamp.pxf.io/c/3588040/101... Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.