Article Categories
- All Categories
-
Data Structure
-
Networking
-
RDBMS
-
Operating System
-
Java
-
MS Excel
-
iOS
-
HTML
-
CSS
-
Android
-
Python
-
C Programming
-
C++
-
C#
-
MongoDB
-
MySQL
-
Javascript
-
PHP
-
Economics & Finance
Programming Articles
Page 255 of 2547
Python Pandas - Extract the ordinal day of year from the DateTimeIndex with specific time series frequency
To extract the ordinal day of year from the DateTimeIndex with specific time series frequency, use the DateTimeIndex.dayofyear property. The ordinal day of year represents which day of the year it is (1-365 or 1-366 for leap years). Creating DateTimeIndex with Time Series Frequency First, let's create a DateTimeIndex with daily frequency and extract the day of year ? import pandas as pd # Create DatetimeIndex with period 6 and frequency as D (daily) # 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...") print(datetimeindex) # Display ...
Read MoreProgram to find dropped correct sensor value from the faulty list in Python
Suppose we have two lists nums1 and nums2 representing sensor metrics. Each list contains unique values where no two elements are equal. One list holds accurate sensor metrics while the other contains faulty data. In the faulty list, one value (not the last) was dropped and a wrong value was placed at the end. We need to find the actual value that was dropped. For example, if nums1 = [5, 10, 15] and nums2 = [10, 15, 8], the output will be 5. The first list nums1 holds the actual values [5, 10, 15], while in the second array, ...
Read MorePython Pandas - Return numpy array of python datetime.time objects
To return numpy array of python datetime.time objects, use the datetimeindex.time property in Pandas. This property extracts only the time component from datetime objects, discarding date and timezone information. Syntax DatetimeIndex.time This property returns a numpy array containing datetime.time objects. Creating a DatetimeIndex First, let's create a DatetimeIndex with timezone information ? import pandas as pd # Create DatetimeIndex with nanosecond frequency datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=3, tz='Australia/Sydney', freq='ns') print("DateTimeIndex...") print(datetimeindex) DateTimeIndex... DatetimeIndex(['2021-10-20 02:30:50+11:00', ...
Read MorePython Pandas - Extract the nanoseconds from the DateTimeIndex with specific time series frequency
To extract the nanoseconds from the DateTimeIndex with specific time series frequency, use the DateTimeIndex.nanosecond property. This property returns an Int64Index containing the nanosecond values for each datetime in the index. Syntax DateTimeIndex.nanosecond Creating DateTimeIndex with Nanosecond Frequency First, let's create a DateTimeIndex with nanosecond frequency to demonstrate the nanosecond extraction ? import pandas as pd # Create DatetimeIndex with period 6 and frequency as ns (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) DateTimeIndex... DatetimeIndex(['2021-10-20 ...
Read MoreProgram to count number of ways ball can drop to lowest level by avoiding blacklisted steps in Python
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 MorePython Pandas - Return numpy array of python datetime.date objects
To return a numpy array of Python datetime.date objects from a Pandas DatetimeIndex, use the date property. This property extracts only the date part from timestamps, removing timezone information and returning standard Python date objects. Syntax datetimeindex.date Where datetimeindex is a Pandas DatetimeIndex object. Creating a DatetimeIndex First, let's create a DatetimeIndex with timezone information − import pandas as pd # Create DatetimeIndex with 3 periods and nanosecond frequency datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=3, tz='Australia/Sydney', freq='ns') print("DateTimeIndex...") print(datetimeindex) DateTimeIndex... DatetimeIndex(['2021-10-20 02:30:50+11:00', ...
Read MorePython Pandas - Extract the microseconds from the DateTimeIndex with specific time series frequency
To extract the microseconds from a DateTimeIndex with specific time series frequency, use the DateTimeIndex.microsecond property. This property returns an Index containing the microsecond component of each datetime. Creating DateTimeIndex with Microsecond Frequency First, let's create a DateTimeIndex with microsecond frequency ? import pandas as pd # Create DatetimeIndex with period 6 and frequency as 'us' (microseconds) # The timezone is Australia/Sydney datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=6, tz='Australia/Sydney', freq='us') print("DateTimeIndex...") print(datetimeindex) DateTimeIndex... DatetimeIndex(['2021-10-20 02:30:50+11:00', '2021-10-20 02:30:50.000001+11:00', ...
Read MorePython Pandas - Extract the seconds from the DateTimeIndex with specific time series frequency
To extract the seconds from the DateTimeIndex with specific time series frequency, use the DateTimeIndex.second property. This property returns an Int64Index containing the second component of each datetime in the index. Syntax DateTimeIndex.second Creating a DateTimeIndex First, let's create a DateTimeIndex with a seconds frequency to demonstrate the extraction ? import pandas as pd # Create DatetimeIndex with period 6 and frequency as S (seconds) # The timezone is Australia/Sydney datetimeindex = pd.date_range('2021-10-20 02:30:50', periods=6, tz='Australia/Sydney', freq='S') # Display DateTimeIndex print("DateTimeIndex...", datetimeindex) DateTimeIndex... DatetimeIndex(['2021-10-20 02:30:50+11:00', '2021-10-20 02:30:51+11:00', ...
Read MorePython Pandas - Extract the minute from DateTimeIndex with specific time series frequency
To extract the minute from the DateTimeIndex with specific time series frequency, use the DateTimeIndex.minute property. This property returns an Int64Index containing the minute values for each timestamp in the DateTimeIndex. Syntax DateTimeIndex.minute Creating a DateTimeIndex First, let's create a DateTimeIndex with a specific frequency. We'll use period 6 and frequency as 'T' (minute) with Australia/Sydney timezone − import pandas as pd # Create DatetimeIndex with period 6 and frequency as T i.e. minute # The timezone is Australia/Sydney datetimeindex = pd.date_range('2021-10-20 02:30:55', periods=6, tz='Australia/Sydney', freq='T') # Display DateTimeIndex print("DateTimeIndex...") ...
Read MoreProgram to count number of operations needed to make string as concatenation of same string twice in Python
Suppose we have a lowercase string s. We need to find the minimum number of operations (delete, insert, or update) required to make s equal to the concatenation of some string t with itself (s = t + t). So, if the input is like s = "pqrxqsr", then the output will be 2. We can update the "x" with "p" and delete "s", making s = "pqrpqr", which is t + t where t = "pqr". Algorithm We solve this by trying all possible split points and using edit distance ? For each position ...
Read More