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Articles on Trending Technologies
Technical articles with clear explanations and examples
Program to implement run length string decoding iterator class in Python
Run-length encoding compresses data by representing consecutive identical characters as a count followed by the character. This article implements an iterator class that decodes such strings character by character using next() and hasnext() methods. Problem Understanding Given a run-length encoded string like "2b1a", we need to create an iterator that can decode it step by step. The string "2b1a" represents "bba" (2 b's followed by 1 a). Iterator Methods next() − Returns the next character in the decoded sequence hasnext() − Checks if more characters are available Algorithm Steps The solution follows ...
Read MoreProgram to count operations to remove consecutive identical bits in Python
Suppose we have a binary string s, now let us consider an operation where we select a bit and flip its value from 0 to 1 or vice-versa. We have to find the minimum number of operations needed to get a string with no three identical consecutive bits. So, if the input is like s = "10011100", then the output will be 1, because we can flip 1 to 0 the bit at index 4 to make the string "10010100" where there are no three consecutive identical bits. Algorithm To solve this, we will follow these steps ...
Read MoreProgram to define data structure that supports rate limiting checking for user in Python
Rate limiting is a technique used to control the frequency of requests from users. We need to design a data structure that tracks user requests and determines if a new request should be allowed based on an expiration time window. The data structure should support checking whether a user's request fails based on their previous successful request. A request fails (returns True) only when the user had a successful request within the expire time window. Problem Understanding Given an expire time, we need to: Track the last successful request timestamp for each user Check if a ...
Read MoreProgram to define data structure that supports range sum in Python
Suppose we want to develop a data structure that can build up with a list of integers, and there is a function to find sum of elements from index i to index j-1 whenever we require in an efficient way. There are two functions: Constructor that constructs a new instance with the integer array. get_sum(i, j) returns the sum of integers of array elements from starting index i and ending index j-1. So, if the input is like array = [5, 2, 3, 6, 4, 7, 8, 9, ...
Read MorePython Pandas - Calculate the left slice bound that corresponds to given label
In Pandas, the get_slice_bound() method calculates the slice bound position for a given label. To find the left slice bound, set the side parameter to 'left'. Syntax index.get_slice_bound(label, side='left', kind='loc') Parameters label − The value to find the slice bound for side − Either 'left' or 'right' to specify which bound kind − Either 'loc' or 'getitem' for different indexing behaviors Creating a Pandas Index First, let's create a Pandas Index with numeric values − import pandas as pd # Creating Pandas index index = pd.Index([10, 20, ...
Read MorePython Pandas - Calculate the right slice bound that corresponds to given label
To calculate the right slice bound that corresponds to a given label in Pandas, use the index.get_slice_bounds() method. Set the side parameter to right to get the right boundary index position. What is get_slice_bounds()? The get_slice_bounds() method returns the integer position that corresponds to a label in the index. When side='right', it returns the right boundary position for slicing operations. Syntax index.get_slice_bounds(label, side='right', kind='getitem') Parameters label − The label to find the slice bound for side − Either 'left' or 'right' to specify which boundary kind − The kind of slicing ...
Read MorePython Pandas - Get integer location for requested label and find the nearest index value if no exact match
The get_loc() method in Pandas returns the integer location of a label in an index. When an exact match isn't found, you can use the method="nearest" parameter to find the closest index value. Syntax index.get_loc(key, method=None, tolerance=None) Parameters key − The label to find method − Method to use for inexact matches ('nearest', 'pad', 'backfill') tolerance − Maximum distance for inexact matches Creating a Pandas Index First, let's create a Pandas index with some values ? import pandas as pd # Creating Pandas index index = pd.Index([10, ...
Read MorePython Pandas - Get integer location for requested label and find the previous index value if no exact match
To get the integer location for a requested label and find the previous index value if no exact match, use the get_loc() method with the method parameter set to "ffill" (forward fill). Syntax index.get_loc(key, method='ffill') Creating a Pandas Index First, let's create a Pandas Index with some integer values − import pandas as pd # Creating Pandas index index = pd.Index([10, 20, 30, 40, 50, 60, 70]) print("Pandas Index...", index) Pandas Index... Int64Index([10, 20, 30, 40, 50, 60, 70], dtype='int64') Finding Exact Matches When the ...
Read MorePython Pandas - Get integer location for requested label
To get the integer location for a requested label in Pandas, use the index.get_loc() method. This method returns the zero-based position of a label within the index. Syntax index.get_loc(key) Parameters: key − The label for which to find the integer location Basic Example Let's create a Pandas index and find integer locations for specific labels ? import pandas as pd # Create Pandas index object index = pd.Index(list('pqrstuvwxyz')) # Display the Pandas index print("Pandas Index...") print(index) # Get integer location from the given index print("Integer location ...
Read MorePython Pandas - Getting values from a specific level in Multiindex
To get values from a specific level in MultiIndex, use the get_level_values() method in Pandas. This method extracts all values from a particular level of a hierarchical index structure. Creating a MultiIndex First, let's create a MultiIndex with multiple levels using from_arrays() ? import pandas as pd # Create a multi-index with 4 levels multiIndex = pd.MultiIndex.from_arrays([[5, 10], [15, 20], [25, 30], [35, 40]], ...
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