Master Python NumPy Array & Broadcasting for Data Science : EP 04
In this video, we'll master Python NumPy Array and Broadcasting for data science.
Learn how to load and understand data with populations.txt file.
We have covered topics like transposing a matrix, calculating mean and standard deviation of population data along row and column axis.
Learn how to calculate the highest population each year, understand broadcasting of an array, and use np.tile() for broadcasting efficiently.
Learn how to transpose and broadcast arrays for efficient data manipulation and analysis. Add new axes and perform array operations with ease using NumPy!
Learn about flattening arrays, reshaping data, and assigning new values with shared memory locations.
Discover reshaping, adding a new dimension, and dimension shuffling techniques to work with 3D arrays efficiently!
We have covered resizing arrays, Sorting Data along the axis called in place sorting, sorting with fancy indexing, and more.
If you're looking to level up your data science skills, this video is for you!
⏰Timestamp⏰
00:00:00 : Intro and sharing populations.txt file
00:01:36 : Downloading populations.txt
00:02:23 : Discussing Population data
00:07:02 : Matrix Transpose
00:09:15 : Transposing populations data
00:10:55 : Mean and Standard Deviation of Population data along column axis
00:18:08 : Highest Population each year along column axis
00:21:02 : Broadcasting of an array
00:30:55 : np.tile() for Broadcasting
00:37:21 : Transpose of Broadcasted array
00:40:42 : Broadcast to add two array and adding new axis to an array
00:49:45 : Flattening and reshaping
00:58:39 : Assigning new values to an array a with shared memory location
01:03:15 : Reshape may also return a copy
01:08:44 : Adding a New Dimension to an array
01:11:45 : Dimension Shuffling and interpretation of 3D array
01:17:31 : Accessing value in a 3D array
01:19:21 : Resizing of an array referencing another array
01:28:01 : Place sorting - Sorting Data Along the axis
01:32:05 : Sorting with Fancy Indexing
01:36:55 : Committing code to gitHub
01:37:29 : Feedback from attendee
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📦 **Download class resources from GitHub** : https://github.com/anup-byte/Numpy_04...
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#numpy #dataanalysis #datascience #machinelearning
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