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We are given a binary array which can store digits 1’s and 0’s of any given size and an integer variable let’s say, base. The task is to calculate the minimum 1’s that can lend power to other elements of a binary array such that the entire array becomes powerful. An element can lend power to its adjacent element or any other elements within the distance less than base.Let us see various input output scenarios for this -In − int arr[] = {1, 1, 0, 1, 1, 0, 1}, int base = 7Out −Minimum 1s to lend power to make ... Read More
The Latin square is a matrix that has a special pattern. Let's see different examples to examine the pattern.1 2 2 1 1 2 3 3 1 2 2 3 1 1 2 3 4 4 1 2 3 3 4 1 2 2 3 4 1 The Latin square that you get will be of different size as you notice in the above examples. But, if you carefully observe the above matrices' pattern, you will find out that the last number of the previous row comes as the first element of the next row.That's the pattern ... Read More
The Latin square is a matrix that has a special pattern. Let's see different examples to examine the pattern.1 2 2 1 1 2 3 3 1 2 2 3 1 1 2 3 4 4 1 2 3 3 4 1 2 2 3 4 1 The Latin square that you get will be of different size as you notice in the above examples. But, if you carefully observe the above matrices' pattern, you will find out that the last number of the previous row comes as the first element of the next row.That's the pattern ... Read More
Move to the next business day using the BusinessHour.next_bday property in Pandas. At first, import the required libraries −import datetime import pandas as pdCreate the BusinessHour Offset. BusinessHour is the DateOffset subclass −bhOffset = pd.tseries.offsets.BusinessHour(offset = datetime.timedelta(days = 3, hours = 3)) Display the BusinessHour Offset −print("BusinessHour Offset...", bhOffset)Set the timestamp object in Pandas −timestamp = pd.Timestamp('2021-9-30 06:50:20') Display the next business day −print("The next business day...", timestamp + bhOffset.next_bday)ExampleFollowing is the code −import datetime import pandas as pd # Set the timestamp object in Pandas timestamp = pd.Timestamp('2021-9-30 06:50:20') # Display the Timestamp print("Timestamp...", timestamp) # ... Read More
To display the end time of the custom business hour in 24h format from the BusinessHour offset object, use the BusinessHour.end property.At first, import the required libraries −import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-9-30 06:50:20') Create the BusinessHour Offset. Here, "start" is the start time of your custom business hour in 24h format. The "end" is the end time of your custom business hour in 24h format −bhOffset = pd.tseries.offsets.BusinessHour(start="09:30", end = "18:00", n = 8)Display the Updated Timestamp −print("Updated Timestamp...", timestamp + bhOffset) Display the end time of the custom business hour −print("The end ... Read More
To display the start time of the custom business hour in 24h format from the BusinessHour offset object, use the BusinessHour.start property.At first, import the required libraries −import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-9-30 06:50:20') Create the BusinessHour Offset. Here, "start" is the start time of your custom business hour in 24h format. The "end" is the end time of your custom business hour in 24h format −bhOffset = pd.tseries.offsets.BusinessHour(start="09:30", end = "18:00", n = 8)Display the Updated Timestamp − Display the start time of the custom business hour −print("The start time of the custom ... Read More
Let's take an example to see how to get the same effect as MatLab's surf(x, y, z, c) in Matplotlib. steps −Set the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure.Add an 'ax' to the figure as part of a subplot arrangement.Create r, u, v, x, y and z data points using Numpy.Create a surface plot.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt import numpy as np plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.add_subplot(projection='3d') r = 0.05 u, v ... Read More
To return the count of increments applied on the BusinessHour offset, use the BusinessHour.n property in Pandas.At first, import the required libraries −import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-1-1 01:55:30')Create the BusinessHour Offset. Here, "start" is the start time of your custom business hour in 24h format. The "end" is the end time of your custom business hour in 24h format −bhOffset = pd.tseries.offsets.BusinessHour(start="09:30", end = "18:00", n = 8)Display the Updated Timestamp −print("Updated Timestamp...", timestamp + bhOffset)Return the count of increments on the given BusinessHour object −print("The count of increments on the BusinessHour object..", ... Read More
To return the rule code applied on the given BusinessHour object, use the BusinessHour.rule_code property in Pandas.At first, import the required libraries −import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-1-1 01:55:30') Create the BusinessHour Offset. BusinessHour is the DateOffset subclass −bhOffset = pd.tseries.offsets.BusinessHour(start="09:30", end = "18:00")Display the Updated Timestamp −print("Updated Timestamp...", timestamp + bhOffset) Return the rule code of the frequency applied on the given BusinessHour Offset −print("The rule code of the BusinessHour object..", bhOffset.rule_code)ExampleFollowing is the code −import pandas as pd # Set the timestamp object in Pandas timestamp = pd.Timestamp('2021-1-1 01:55:30') # ... Read More
To check whether the BusinessHour Offset has been normalized or not, use the BusinessHour.normalize property in Pandas.At first, import the required libraries −import pandas as pdSet the timestamp object in Pandas −timestamp = pd.Timestamp('2021-1-1 01:55:30') Create the BusinessHour Offset. We have normalized the BusinessHour using the "normalize" parameter −bhOffset = pd.tseries.offsets.BusinessHour(start="09:30", end = "18:00", normalize=True)Display the Updated Timestamp −print("Updated Timestamp...", timestamp + bhOffset) Check whether the BusinessHour Offset is normalized or not −print("The BusinessHour Offset is normalized ?", bhOffset.normalize)ExampleFollowing is the code −import pandas as pd # Set the timestamp object in Pandas timestamp = pd.Timestamp('2021-1-1 01:55:30') # ... Read More