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To convert the DateTimeIndex to Series, use the DateTimeIndex.to_series() method.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 5 and frequency as S i.e. seconds. The timezone is Australia/Adelaide −datetimeindex = pd.date_range('2021-10-18 07:20:32.261811624', periods=5, tz='Australia/Adelaide', freq='40S')Convert DateTimeIndex to Series −print("DateTimeIndex to series...", datetimeindex.to_series())ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 5 and frequency as S i.e. seconds # timezone is Australia/Adelaide datetimeindex = pd.date_range('2021-10-18 07:20:32.261811624', periods=5, tz='Australia/Adelaide', freq='40S') # display DateTimeIndex print("DateTimeIndex...", datetimeindex) # display DateTimeIndex frequency print("DateTimeIndex frequency...", datetimeindex.freq) # Convert DateTimeIndex to Series print("DateTimeIndex to ... Read More
Suppose we have a binary matrix. We can first rearrange the columns as many times as we want, then find return the area of the largest submatrix containing only 1s.So, if the input is like100111101then the output will be 4, because we can arrange is like −100111110To solve this, we will follow these steps −n := row count of matrixm := column count of matrixans := 0for i in range 1 to n - 1, dofor j in range 0 to m - 1, doif matrix[i, j] is 1, thenmatrix[i, j] := matrix[i, j] + matrix[i-1, j]for each row in ... Read More
To return DatetimeIndex as object ndarray of datetime.datetime objects, use the datetimeindex.to_pydatetime() method.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 5 and frequency as Y i.e. year −datetimeindex = pd.date_range('2021-10-18 07:20:32.261811624', periods=5, freq='2Y') Display DateTimeIndex −print("DateTimeIndex...", datetimeindex)Return DatetimeIndex as object ndarray −print("Return DatetimeIndex as object ndarray of datetime.datetime objects...", datetimeindex.to_pydatetime())ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 5 and frequency as Y i.e. year datetimeindex = pd.date_range('2021-10-18 07:20:32.261811624', periods=5, freq='2Y') # display DateTimeIndex print("DateTimeIndex...", datetimeindex) # display DateTimeIndex frequency print("DateTimeIndex frequency...", datetimeindex.freq) # Return DatetimeIndex as ... Read More
To calculate TimedeltaArray of difference between index values and index converted to PeriodArray at specified freq, use the datetimeindex.to_perioddelta() method. Set the frequency using the freq parameter.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 7 and frequency as Y i.e. year −datetimeindex = pd.date_range('2021-10-18 07:20:32.261811624', periods=5, freq='2Y') Display DateTimeIndex −print("DateTimeIndex...", datetimeindex)Calculate TimedeltaArray of difference between index values and index converted to PeriodArray. We have set the Period frequency using the "freq" parameter with value 'M' −print("Convert DateTimeIndex to PeriodDelta...", datetimeindex.to_perioddelta(freq='M'))ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 7 and ... Read More
To convert DateTimeIndex to Period, use the datetimeindex.to_period() method in Pandas. The frequency is set using the freq parameter.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 5 and frequency as Y i.e. year −datetimeindex = pd.date_range('2021-10-18 07:20:32.261811624', periods=5, freq='2Y') Display DateTimeIndex −print("DateTimeIndex...", datetimeindex)Convert DateTimeIndex to Period. We have set the frequency as Month using the "freq" parameter with value 'M' −print("Convert DateTimeIndex to Period...", datetimeindex.to_period(freq='M'))ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 5 and frequency as Y i.e. year # timezone is Australia/Adelaide datetimeindex = pd.date_range('2021-10-18 07:20:32.261811624', periods=5, freq='2Y') ... Read More
Suppose we have three values n, total, and k. Now consider a list of size n whose sum is same as total and where the absolute difference between any two consecutive elements is at most 1. We have to find the maximum value at index k of such a list.So, if the input is like n = 5 total = 15 k = 3, then the output will be 4, because one possible list is like [3, 2, 3, 4, 3], maximum element that is found at index 3 is 4.To solve this, we will follow these steps −x := ... Read More
To perform ceil operation on the DateTimeIndex with specified frequency, use the DateTimeIndex.ceil() method. For frequency, use the freq parameter.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 5 and frequency as S i.e. seconds. The timezone is Australia/Adelaide −datetimeindex = pd.date_range('2021-10-18 07:20:32.261811624', periods=5, tz='Australia/Adelaide', freq='40S')Display DateTimeIndex −print("DateTimeIndex...", datetimeindex) Ceil operation on DateTimeIndex date with specified frequency −print("Performing ceil operation...", datetimeindex.ceil(freq='us'))ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 5 and frequency as S i.e. seconds # timezone is Australia/Adelaide datetimeindex = pd.date_range('2021-10-18 07:20:32.261811624', periods=5, tz='Australia/Adelaide', freq='40S') # display DateTimeIndex ... Read More
Suppose we have a list of numbers called nums, we also have another value k, we have to find the kth (starting from 0) smallest element in the list. We have to solve this problem in O(n) time on average.So, if the input is like nums = [6, 4, 9, 3, 1] k = 2, then the output will be 4, as after sorting the list will be like [1, 3, 4, 6, 9], the kth smallest element is 4.To solve this, we will follow these steps −maxHeap := a new empty heapfor i in range 0 to k, doinsert ... Read More
To perform ceil operation on the DateTimeIndex with microseconds frequency, use the DateTimeIndex.ceil() method. For microseconds frequency, use the freq parameter with value ‘us’.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 5 and frequency as S i.e. seconds −datetimeindex = pd.date_range('2021-10-18 07:20:32.261811624', periods=5, tz='Australia/Adelaide', freq='40S')Display DateTimeIndex −print("DateTimeIndex...", datetimeindex) Perform Ceil operation on DateTimeIndex date with microseconds frequency. For microseconds frequency, we have used 'us' −print("Performing ceil operation with microseconds frequency...", datetimeindex.ceil(freq='us'))ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 5 and frequency as S i.e. seconds # timezone is Australia/Adelaide ... Read More
To perform ceil operation on the DateTimeIndex with milliseconds frequency, use the DateTimeIndex.ceil() method. For milliseconds frequency, use the freq parameter with value ‘ms’.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 5 and frequency as S i.e. seconds. The timezone is Australia/Adelaide −datetimeindex = pd.date_range('2021-10-18 07:20:32.261811624', periods=5, tz='Australia/Adelaide', freq='40S')Ceil operation on DateTimeIndex date with milliseconds frequency. For milliseconds frequency, we have used 'ms' −print("Performing ceil operation with milliseconds frequency...", datetimeindex.ceil(freq='ms'))ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 5 and frequency as S i.e. seconds # timezone is Australia/Adelaide datetimeindex ... Read More