Suppose we have a list of numbers called piles and a value k. The piles[i] represents, the number of stones on the pile i. On each hour, we select any pile and remove r number of stones from that pile. If we pick a pile with fewer than r stones, it still takes an hour to clear the pile. We have to find the minimum value of r, such that we can remove all the stones in less than or equal to k hours.So, if the input is like piles = [3, 6, 4] k = 5, then the output ... Read More

To extract the day of week from the DateTimeIndex with specific time series frequency, use the DateTimeIndex.dayofweek propertyAt first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 6 and frequency as D i.e. day −datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=6, tz='Australia/Sydney', freq='3D') Display DateTimeIndex frequency −print("DateTimeIndex frequency...", datetimeindex.freq)Get the day of week. It is assumed the week starts on Monday, which is denoted by 0 and ends on Sunday which is denoted by 6 −print("Get the day of week..", datetimeindex.dayofweek) ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 6 and frequency as ... Read More

To extract the ordinal day of year from the DateTimeIndex with specific time series frequency, use the DateTimeIndex.dayofyear propertyAt first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 6 and frequency as D i.e. day −datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=6, tz='Australia/Sydney', freq='D') Display DateTimeIndex −print("DateTimeIndex...", datetimeindex)Get the day of year −print("Get the ordinal day of year..", datetimeindex.dayofyear) ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 6 and frequency as D i.e. day # The timezone is Australia/Sydney datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=6, tz='Australia/Sydney', freq='D') # display DateTimeIndex print("DateTimeIndex...", datetimeindex) ... Read More

Suppose we have two lists nums1 and nums2, they are representing sensor metrics. Each list contains unique values, so a ≠ b. One of these two lists are holding accurate sensor metrics but the other one contains faulty. In the faulty list one value, that is not the last value was dropped and a wrong value was placed to the end of that list. We have to find the actual value that was dropped.So, if the input is like nums1 = [5, 10, 15] nums2 = [10, 15, 8], then the output will be 5, as first list nums1 holds ... Read More

To return numpy array of python datetime.time objects with timezone information, use the datetimeindex.timetz property.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 5 and frequency as us i.e. nanoseconds −datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=5, tz='Australia/Sydney', freq='ns') Display DateTimeIndex −print("DateTimeIndex...", datetimeindex)Returns only the time part of Timestamp with timezone information −print("The numpy array (time part with timezone)..", datetimeindex.timetz) ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 5 and frequency as us i.e. nanoseconds # The timezone is Australia/Sydney datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=5, tz='Australia/Sydney', freq='ns') # display DateTimeIndex print("DateTimeIndex...", ... Read More

To return numpy array of python datetime.time objects, use the datetimeindex.time property in Pandas.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 3 and frequency as us i.e. nanoseconds. The timezone is Australia/Sydney −datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=3, tz='Australia/Sydney', freq='ns') Display DateTimeIndex −print("DateTimeIndex...", datetimeindex)Returns only the time part of Timestamps without timezone information −print("The numpy array (time part)..", datetimeindex.time) ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 3 and frequency as us i.e. nanoseconds # The timezone is Australia/Sydney datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=3, tz='Australia/Sydney', freq='ns') # display DateTimeIndex ... Read More

To extract the nanoseconds from the DateTimeIndex with specific time series frequency, use the DateTimeIndex.nanosecond property.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 6 and frequency as ns i.e. nanoseconds −datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=6, tz='Australia/Sydney', freq='ns') Display DateTimeIndex −print("DateTimeIndex...", datetimeindex)Get the nanoseconds −print("Getting the nanoseconds..", datetimeindex.nanosecond) ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 6 and frequency as ns i.e. nanoseconds # The timezone is Australia/Sydney datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=6, tz='Australia/Sydney', freq='ns') # display DateTimeIndex print("DateTimeIndex...", datetimeindex) # display DateTimeIndex frequency print("DateTimeIndex frequency...", datetimeindex.freq) ... Read More

Suppose we have a value h and a list of numbers called blacklist. We are currently at height h, and are playing a game to move a small ball down to height 0. Now, in even rounds (starting from 0) we can move the ball 1, 2, or 4 stairs down. And in odd rounds, we can move the ball 1, 3, or 4 stairs down. Some levels are blacklisted. So if the ball reach there, it will die immediately. We have to find the number of ways the ball can move down at height 0. If the answer is ... Read More

To return numpy array of python datetime.date objects, use the datetimeindex.date property in Pandas.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 3 and frequency as us i.e. nanoseconds −datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=3, tz='Australia/Sydney', freq='ns') Display DateTimeIndex −print("DateTimeIndex...", datetimeindex)Returns only the date part of Timestamps without timezone information −print("The numpy array (date part)..", datetimeindex.date) ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 3 and frequency as us i.e. nanoseconds # The timezone is Australia/Sydney datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=3, tz='Australia/Sydney', freq='ns') # display DateTimeIndex print("DateTimeIndex...", datetimeindex) # ... Read More

To extract the microsecond from the DateTimeIndex with specific time series frequency, use the DateTimeIndex.microsecond property.At first, import the required libraries −import pandas as pdCreate a DatetimeIndex with period 6 and frequency as us i.e. microseconds. The timezone is Australia/Sydney −datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=6, tz='Australia/Sydney', freq='us') Display DateTimeIndex −print("DateTimeIndex...", datetimeindex)Get the microsecond −print("Getting the microseconds..", datetimeindex.microsecond) ExampleFollowing is the code −import pandas as pd # DatetimeIndex with period 6 and frequency as us i.e. microseconds # The timezone is Australia/Sydney datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=6, tz='Australia/Sydney', freq='us') # display DateTimeIndex print("DateTimeIndex...", datetimeindex) # display DateTimeIndex frequency ... Read More