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Suppose we have a list of numbers called counts where counts[i] represents the number of items are of type i. We also have another value k. We have to find the maximum number of groups of size k we can find, such that each group must have items of distinct types.So, if the input is like counts = [2, 3, 5, 3] k = 2, then the output will be 6, because let four types of items are represented by a, b, c, d respectively. We can have the following groups of k = 2, where all elements are of ... Read More
To perform ceil operation on the DateTimeIndex with seconds frequency, use the DateTimeIndex.ceil() method. For seconds frequency, use the freq parameter with value ‘S’.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) Peform Ceil operation on DateTimeIndex date with seconds frequency. For seconds frequency, we have used 'S' −print("Performing ceil operation with seconds frequency...", datetimeindex.ceil(freq='S'))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 minutely frequency, use the DateTimeIndex.ceil() method. For minutely frequency, use the freq parameter with value ‘T’.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) Performing Ceil operation on DateTimeIndex date with minute frequency. For minute frequency, we have used 'T −print("Performing ceil operation with minute frequency...", datetimeindex.ceil(freq='T'))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 hourly frequency, use the DateTimeIndex.ceil() method. For hourly frequency, use the freq parameter with value ‘H’.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 5 and frequency as min i.e. minutes −datetimeindex = pd.date_range('2021-09-29 07:20:32.261811624', periods=5, tz='Australia/Adelaide', freq='20min')Performing Ceil operation on DateTimeIndex date with hourly frequency. For hourly frequency, we have used 'H' −print("Performing ceil operation with hourly frequency...", datetimeindex.ceil(freq='H'))ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 5 and frequency as min i.e. minutes # timezone is Australia/Adelaide datetimeindex = pd.date_range('2021-09-29 07:20:32.261811624', ... Read More
To perform floor operation on the DateTimeIndex with microseconds frequency, use the DateTimeIndex.floor() method. For microseconds frequency, use the freq parameter with value ‘us’.At first, import the required libraries −import pandas as pdDatetimeIndex with period 7 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')Floor operation on DateTimeIndex date with microseconds frequency. For microseconds frequency, we have used 'us' −print("Performing floor operation with microseconds frequency...", datetimeindex.floor(freq='us'))ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 7 and frequency as S i.e. seconds # timezone is Australia/Adelaide datetimeindex = pd.date_range('2021-10-18 ... Read More
To perform floor operation on the DateTimeIndex with milliseconds frequency, use the DateTimeIndex.floor() method. For milliseconds frequency, use the freq parameter with value ‘ms’.At first, import the required libraries −import pandas as pdDatetimeIndex with period 7 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) Floor operation on DateTimeIndex date with milliseconds frequency. For milliseconds frequency, we have used 'ms' −print("Performing floor operation with milliseconds frequency...", datetimeindex.floor(freq='ms'))ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 7 and frequency as S i.e. seconds # timezone is Australia/Adelaide datetimeindex = pd.date_range('2021-10-18 ... Read More
To perform floor operation on the DateTimeIndex with seconds frequency, use the DateTimeIndex.floor() method. For seconds frequency, use the freq parameter with value ‘S’At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 7 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) Floor operation on DateTimeIndex date with seconds frequency. For seconds frequency, we have used 'S' −print("Performing floor operation with seconds frequency...", datetimeindex.floor(freq='S'))ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 7 and frequency as S i.e. seconds # timezone is Australia/Adelaide datetimeindex ... Read More
To perform floor operation on the DateTimeIndex with minutely frequency, use the DateTimeIndex.floor() method. For minutely frequency, use the freq parameter with value ‘T’.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 7 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) Floor operation on DateTimeIndex date with minute frequency. For minute frequency, we have used 'T' −print("Performing floor operation with minute frequency...", datetimeindex.floor(freq='T'))ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 7 and frequency as s i.e. seconds # timezone is Australia/Adelaide datetimeindex ... Read More
To perform floor operation on the DateTimeIndex with hourly frequency, use the DateTimeIndex.floor() method. For hourly frequency, use the freq parameter with value ‘H’.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 5 and frequency as min i.e. minutes −datetimeindex = pd.date_range('2021-09-29 07:20:32.261811624', periods=5, tz='Australia/Adelaide', freq='20min')Display DateTimeInde −print("DateTimeIndex...", datetimeindex)Floor operation on DateTimeIndex date with hourly frequency, For hourly frequency, we have used 'H' −print("Performing floor operation with hourly frequency...", datetimeindex.floor(freq='H'))ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 5 and frequency as min i.e. minutes # timezone is Australia/Adelaide datetimeindex = ... Read More
To round the DateTimeIndex with microseconds frequency, use the DateTimeIndex.round() 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('2021-09-29 07:20:32.261811624', periods=5, tz='Australia/Adelaide', freq='28s')Round operation on DateTimeIndex date with microseconds frequency. For microseconds frequency, we have used 'us' −print("Performing round operation with microseconds frequency...", datetimeindex.round(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('2021-09-29 07:20:32.261811624', periods=5, tz='Australia/Adelaide', freq='28s') ... Read More