Python_Data_Structures | Code from Youtube Tutorial Series | Dataset library
kandi X-RAY | Python_Data_Structures Summary
kandi X-RAY | Python_Data_Structures Summary
Code from Youtube Tutorial Series. Each lesson begins by introducing the idea behind the data structure then, after explaining some basic concepts, moves over to coding the actual Python class. All the code in this repository is implemented in Python 2, for Python 3 see 'Python3_Data_Structures'. The AVL Tree is an improvement upon the traditional Binary Search Tree (BST) that implements an auto-balancing feature, with the hopes to keep tree operations closer to O(logn) rather than O(n). After insertions and deletions that cause the tree to become unbalanced, special functions are called to manage the situation by rebalancing any nodes they find to be unbalanced. If you don't have experience with traditional BSTs you should start with the tutorial covering those. The BST validator is a function whose purpose is to check whether the input BST adheres to the rules of a binary search tree, namely that for every node, the subtree rooted at its left child contains only smaller values, and the subtree rooted at its right child contains only larger values. The code for this lesson is simple once explained but is not that easy to come up with from scratch. The most popular data structure, the binary search tree is an intuitive way of storing sortable data that provides faster-than-linear search capabilities. Values in a BST are wrapped in a 'node' class used to link the tree together. Given a certain node 'n' with value 'v', all nodes to the right of 'n' contain values larger than 'v', and all nodes to the left contain values smaller than 'v'. The linked list is a useful data structure providing similar functionality to an array, but in a dynamic package. Albeit, not as useful in Python as in statically typed languages, the linked list is still an interesting project to undertake. Similar to BST, values in a linked list are wrapped in an instance of a 'node' class containing a pointer, allowing the whole list to be linked together by distributed references.
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- delete the given node
- Return the string representation of the node .
- Insert node at given index .
- Insert node at index .
- Validate a BST tree .
- Find the node with the given value .
- initialize node
- Print the tree .
Python_Data_Structures Key Features
Python_Data_Structures Examples and Code Snippets
Community Discussions
Trending Discussions on Dataset
QUESTION
I have two dataframes one with the dates (converted in months) of multiple survey replicates for a given grid cell and the other one with snow data for each month for the same grid cell, they have a matching ID column to identify the cells. What I would like to do is to replace in the first dataframe, the one with months of survey replicates, the month value with the snow value for that month considering the grid cell ID. Thank you
...ANSWER
Answered 2022-Apr-14 at 14:50df3 <- df1
df3[!is.na(df1)] <- df2[!is.na(df1)]
# CellID sampl1 sampl2 sampl3
# 1 1 0.1 0.4 0.6
# 2 2 0.1 0.5 0.7
# 3 3 0.1 0.4 0.8
# 4 4 0.1
# 5 5
# 6 6
QUESTION
I was taking a look at Hub—the dataset format for AI—and noticed that hub integrates with GCP and AWS. I was wondering if it also supported integrations with MinIO.
I know that Hub allows you to directly stream datasets from cloud storage to ML workflows but I’m not sure which ML workflows it integrates with.
I would like to use MinIO over S3 since my team has a self-hosted MinIO instance (aka it's free).
...ANSWER
Answered 2022-Mar-19 at 16:28Hub allows you to load data from anywhere. Hub works locally, on Google Cloud, MinIO, AWS as well as Activeloop storage (no servers needed!). So, it allows you to load data and directly stream datasets from cloud storage to ML workflows.
You can find more information about storage authentication in the Hub docs.
Then, Hub allows you to stream data to PyTorch or TensorFlow with simple dataset integrations as if the data were local since you can connect Hub datasets to ML frameworks.
QUESTION
I have a map-stype dataset, which is used for instance segmentation tasks. The dataset is very imbalanced, in the sense that some images have only 10 objects while others have up to 1200.
How can I limit the number of objects per batch?
A minimal reproducible example is:
...ANSWER
Answered 2022-Mar-17 at 19:22If what you are trying to solve really is:
QUESTION
I'm tackling a exercise which is supposed to exactly benchmark the time complexity of such code.
The data I'm handling is made up of pairs of strings like this hbFvMF,PZLmRb, each string is present two times in the dataset, once on position 1 and once on position 2 . so the first string would point to zvEcqe,hbFvMF for example and the list goes on....
I've been able to produce code which doesn't have much problem sorting these datasets up to 50k pairs, where it takes about 4-5 minutes. 10k gets sorted in a matter of seconds.
The problem is that my code is supposed to handle datasets of up to 5 million pairs. So I'm trying to see what more I can do. I will post my two best attempts, initial one with vectors, which I thought I could upgrade by replacing vector with unsorted_map because of the better time complexity when searching, but to my surprise, there was almost no difference between the two containers when I tested it. I'm not sure if my approach to the problem or the containers I'm choosing are causing the steep sorting times...
Attempt with vectors:
...ANSWER
Answered 2022-Feb-22 at 07:13You can use a trie data structure, here's a paper that explains an algorithm to do that: https://people.eng.unimelb.edu.au/jzobel/fulltext/acsc03sz.pdf
But you have to implement the trie from scratch because as far as I know there is no default trie implementation in c++.
QUESTION
I'm trying to load the DomainNet dataset into a tensorflow dataset.
Each of the domains contain two .txt files for the training and test data respectively, which is structured as follows:
ANSWER
Answered 2022-Feb-09 at 08:09You can use tf.data.TextLineDataset to load and process multiple txt files at a time:
QUESTION
I have a csv dataset with the values 0-1 for the features of the elements. I want to iterate each cell and replace the values 1 with the name of its column. There are more than 500 thousand rows and 200 columns and, because the table is exported from another annotation tool which I update often, I want to find a way in Python to do it automatically. This is not the table, but a sample test which I was using while trying to write a code I tried some, but without success. I would really appreciate it if you can share your knowledge with me. It will be a huge help. The final result I want to have is of the type: (abonojnë, token_pos_verb). If you know any method that I can do this in Excel without the help of Python, it would be even better. Thank you, Brikena
...ANSWER
Answered 2022-Jan-31 at 10:08Using pandas, this is quite easy:
QUESTION
I want to download only person class and binary segmentation from COCO dataset. How can I do it?
...ANSWER
Answered 2022-Jan-06 at 05:04use pycocotools .
- import library
QUESTION
I have a vector of words, like the below. This is an oversimplification, my real vector is over 600 words:
...ANSWER
Answered 2021-Dec-16 at 23:33Update: If a list is preferred: Using str_extract_all:
QUESTION
I have an image dataset that looks like this: Dataset
The timestep of each image is 15 minutes (as you can see, the timestamp is in the filename).
Now I would like to group those images in 3hrs long sequences and save those sequences inside subfolders that would contain respectively 12 images(=3hrs). The result would ideally look like this: Sequences
I have tried using os.walk and loop inside the folder where the image dataset is saved, then I created a dataframe using pandas because I thought I could handle the files more easily but I think I am totally off target here.
ANSWER
Answered 2021-Dec-08 at 15:10The timestep of each image is 15 minutes (as you can see, the timestamp is in the filename).
Now I would like to group those images in 3hrs long sequences and save those sequences inside subfolders that would contain respectively 12 images(=3hrs)
I suggest exploiting datetime built-in libary to get desired result, for each file you have
- get substring which is holding timestamp
- parse it into
datetime.datetimeinstance usingdatetime.datetime.strptime - convert said instance into seconds since epoch using
.timestampmethod - compute number of seconds integer division (
//)10800(number of seconds inside 3hr) - convert value you got into
strand use it as target subfolder name
QUESTION
I've got a huge CSV file, which looks like this:
...ANSWER
Answered 2021-Nov-15 at 21:33You can use a regular expression for this:
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You can use Python_Data_Structures like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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