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Articles by AmitDiwan
Page 83 of 840
Python Pandas – Can we use & Operator to find common columns between two DataFrames?
Yes, we can use the & operator to find the common columns between two DataFrames. The & operator performs a set intersection operation on DataFrame column indexes, returning only the columns that exist in both DataFrames. Creating Two DataFrames Let's create two DataFrames with some overlapping columns − import pandas as pd # Creating dataframe1 dataFrame1 = pd.DataFrame({ "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], }) print("Dataframe1...", dataFrame1) # Creating dataframe2 dataFrame2 = pd.DataFrame({ ...
Read MorePython Program to print all distinct uncommon digits present in two given numbers
When it is required to print all the distinct uncommon digits that are present in two numbers, a method is defined that takes two integers as parameters. The method symmetric_difference() is used to get the uncommon digits that exist in one number but not in both. What are Uncommon Digits? Uncommon digits are digits that appear in one number but not in the other. For example, in numbers 567234 and 87953573214, the uncommon digits are 1, 6, 8, and 9. Example Below is a demonstration of finding distinct uncommon digits ? def distinct_uncommon_nums(val_1, val_2): ...
Read MorePython Program to Split joined consecutive similar characters
When working with strings containing consecutive similar characters, we often need to split them into groups. Python's groupby function from the itertools module provides an efficient way to group consecutive identical characters. Syntax The groupby() function groups consecutive equal elements from an iterable ? itertools.groupby(iterable, key=None) Example Let's split a string with consecutive similar characters into separate groups ? from itertools import groupby my_string = 'pppyyytthhhhhhhoooooonnn' print("The string is:") print(my_string) my_result = ["".join(grp) for elem, grp in groupby(my_string)] print("The result is:") print(my_result) The string is: ...
Read MorePython - Fetch columns between two Pandas DataFrames by Intersection
To fetch columns between two DataFrames by intersection, use the intersection() method. This method returns the common column names present in both DataFrames. Syntax dataframe.columns.intersection(other_dataframe.columns) Creating Sample DataFrames Let's create two DataFrames with some common and different columns ? import pandas as pd # Creating dataframe1 dataFrame1 = pd.DataFrame({ "Car": ['Bentley', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000], "Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000] }) print("Dataframe1...") print(dataFrame1) Dataframe1... ...
Read MorePython - Index Ranks of Elements
When working with data structures, you might need to determine the index rank of elements. Index ranking assigns a numerical rank to each element based on its relative position when sorted, where smaller values get lower ranks. This tutorial shows how to calculate index ranks using a custom Python function. What is Index Ranking? Index ranking assigns ranks to elements based on their sorted order: Smallest element gets rank 1 Second smallest gets rank 2, and so on For duplicate elements, the average rank is assigned Implementation Here's how to calculate index ranks ...
Read MorePython - Remove non-increasing elements
When working with lists, sometimes we need to remove elements that break an increasing sequence. This means keeping only elements that are greater than or equal to the previous element, creating a non-decreasing subsequence. Understanding Non-Increasing Elements Non-increasing elements are those that are smaller than the previous element in the sequence. By removing them, we create a monotonically increasing or non-decreasing subsequence. Method: Using Iteration and Comparison We can iterate through the list and keep only elements that maintain the increasing order ? my_list = [5, 23, 45, 11, 45, 67, 89, 99, 10, ...
Read MoreHow to append a list to a Pandas DataFrame using append() in Python?
To append a list to a Pandas DataFrame, we can use the append() method. However, note that append() is deprecated as of Pandas 1.4.0, and pd.concat() is now the recommended approach. Creating the Initial DataFrame Let's start by creating a DataFrame with team rankings ? import pandas as pd # Data in the form of list of team rankings team_data = [['India', 1, 100], ['Australia', 2, 85], ['England', 3, 75], ['New Zealand', 4, 65], ['South Africa', 5, 50]] # Creating a DataFrame and adding columns dataFrame = pd.DataFrame(team_data, columns=['Country', 'Rank', 'Points']) print("Original DataFrame:") print(dataFrame) ...
Read MorePython - Consecutive Ranges of K greater than N
When you need to find consecutive ranges of a specific value K that appear at least N times in a row, you can use enumerate() to track positions and identify these ranges. Problem Understanding Given a list, find all consecutive sequences where: The value equals K The sequence length is at least N Return the start and end indices of each valid range Example Below is a demonstration of finding consecutive ranges ? my_list = [3, 65, 33, 23, 65, 65, 65, 65, ...
Read MoreHow to encode multiple strings that have the same length using Tensorflow and Python?
Multiple strings of same length can be encoded using tf.Tensor as an input value. When encoding multiple strings of varying lengths, a tf.RaggedTensor should be used as an input. If a tensor contains multiple strings in padded/sparse format, it needs to be converted to a tf.RaggedTensor before calling unicode_encode. Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks? Let us understand how to represent Unicode strings using Python, and manipulate those using Unicode equivalents. We separate the Unicode strings into tokens based on script detection with the help of the Unicode equivalents ...
Read MoreHow can Tensorflow be used to create a pair using a file path for the flower dataset?
TensorFlow can process image datasets by creating (image, label) pairs from file paths. The flowers dataset contains thousands of flower images organized in subdirectories, where each subdirectory represents a different flower class. Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks? We are using Google Colaboratory to run the below code. Google Colab helps run Python code over the browser and requires zero configuration with free access to GPUs. Setting Up the Dataset First, let's set up the basic variables and import required libraries ? import tensorflow as ...
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