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Articles by AmitDiwan
Page 60 of 840
Python Pandas - Return an Index of formatted strings specified by date format
The DateTimeIndex.strftime() method in Pandas returns an Index of formatted strings based on a specified date format. This method is useful for converting datetime objects into human-readable string representations. Syntax DateTimeIndex.strftime(date_format) Parameters: date_format − A string specifying the format using strftime directives Creating a DateTimeIndex First, let's create a DateTimeIndex with timezone information − import pandas as pd # Create DatetimeIndex with period 7 and frequency as 2 days datetimeindex = pd.date_range('2021-10-30 02:30:50', periods=7, tz='Australia/Adelaide', freq='2D') print("DateTimeIndex...") print(datetimeindex) DateTimeIndex... DatetimeIndex(['2021-10-30 02:30:50+10:30', '2021-11-01 02:30:50+10:30', ...
Read MorePython Pandas - Convert times to midnight in DateTimeIndex
To convert times to midnight in DateTimeIndex, use the DateTimeIndex.normalize() method in Pandas. This method sets the time component of all datetime values to 00:00:00 while preserving the date and timezone information. What is normalize()? The normalize() method converts the time component of datetime values to midnight (00:00:00). This is useful when you want to work with dates only, ignoring the time portion. Creating a DateTimeIndex First, let's create a DateTimeIndex with various times ? import pandas as pd # Create DateTimeIndex with period 7 and frequency as 10H (10 hours) # The ...
Read MorePython Pandas - Return index locations of values between particular time of day including start time in DateTimeIndex
To return index locations of values between particular time of day in DateTimeIndex, use the DateTimeIndex.indexer_between_time() method. Set the include_start parameter to True for including the start time. Syntax DateTimeIndex.indexer_between_time(start_time, end_time, include_start=True, include_end=True) Parameters Parameter Description start_time Start time as string in format 'HH:MM:SS' end_time End time as string in format 'HH:MM:SS' include_start Whether to include start time (default: True) include_end Whether to include end time (default: True) Example Let's create a DatetimeIndex and find index locations between specific times ...
Read MorePython Pandas - Return index locations of values between particular time of day in DateTimeIndex
To return index locations of values between particular time of day in DateTimeIndex, use the DateTimeIndex.indexer_between_time() method. This method is useful for filtering time-series data based on specific time ranges. Syntax DateTimeIndex.indexer_between_time(start_time, end_time, include_start=True, include_end=True) Parameters The method accepts the following parameters: start_time − The start time in 'HH:MM:SS' format end_time − The end time in 'HH:MM:SS' format include_start − Boolean to include start time (default: True) include_end − Boolean to include end time (default: True) Creating a DateTimeIndex First, let's create a DateTimeIndex with timezone information − ...
Read MorePython Pandas - Return index locations of values at particular time of day in DateTimeIndex
To return index locations of values at particular time of day in DateTimeIndex, use the DateTimeIndex.indexer_at_time() method. This method returns an array of integer positions where the time component matches the specified time. Syntax DateTimeIndex.indexer_at_time(time, asof=False) Parameters The key parameters are ? time ? Time as a time object or string asof ? Return the latest index location if exact time not found (default: False) Creating DateTimeIndex First, let's create a DateTimeIndex with timezone-aware timestamps ? import pandas as pd # Create DatetimeIndex with 20-minute intervals ...
Read MorePython Pandas - Detect the frequency of the given DatetimeIndex object
To detect the frequency of a given DatetimeIndex object, use the DatetimeIndex.inferred_freq property. This property automatically infers the frequency pattern from the datetime values. Creating a DatetimeIndex First, let's create a DatetimeIndex with a specific frequency ? import pandas as pd # Create DatetimeIndex with 5 periods and 3-year frequency # Using Australia/Adelaide timezone datetimeindex = pd.date_range('2021-10-30 02:30:50', periods=5, tz='Australia/Adelaide', freq='3Y') print("DateTimeIndex...") print(datetimeindex) DateTimeIndex... DatetimeIndex(['2021-12-31 02:30:50+10:30', '2024-12-31 02:30:50+10:30', '2027-12-31 02:30:50+10:30', '2030-12-31 02:30:50+10:30', ...
Read MorePython Pandas - Indicate whether the date in DateTimeIndex is the last day of the year
To check whether dates in a DateTimeIndex fall on the last day of the year (December 31st), use the DateTimeIndex.is_year_end property. This returns a boolean array indicating which dates are year-end dates. Syntax DateTimeIndex.is_year_end Creating a DateTimeIndex First, let's create a DateTimeIndex with dates around year-end ? import pandas as pd # Create DateTimeIndex with period 6 and frequency as 2 days datetimeindex = pd.date_range('2021-12-25 02:30:50', periods=6, tz='Australia/Adelaide', freq='2D') print("DateTimeIndex...") print(datetimeindex) DateTimeIndex... DatetimeIndex(['2021-12-25 02:30:50+10:30', '2021-12-27 02:30:50+10:30', ...
Read MorePython Pandas - Indicate whether the date in DateTimeIndex is the first day of the year
To check whether the date in DateTimeIndex is the first day of the year, use the DateTimeIndex.is_year_start property. This property returns a boolean array indicating which dates are January 1st. Syntax DateTimeIndex.is_year_start Creating a DateTimeIndex First, let's create a DateTimeIndex that spans across a year boundary to demonstrate the property ? import pandas as pd # Create DateTimeIndex spanning year boundary datetimeindex = pd.date_range('2021-12-30 02:30:50', periods=6, tz='Australia/Adelaide', freq='1D') print("DateTimeIndex...") print(datetimeindex) DateTimeIndex... DatetimeIndex(['2021-12-30 02:30:50+10:30', '2021-12-31 02:30:50+10:30', ...
Read MorePython Pandas - Indicate whether the date in DateTimeIndex is the last day of the quarter
To check whether the date in DateTimeIndex is the last day of the quarter, use the DateTimeIndex.is_quarter_end property. This property returns a boolean array indicating which dates fall on the last day of their respective quarters. Understanding Quarter Divisions Before using is_quarter_end, it's important to understand how pandas divides the year into quarters ? Quarter 1: January 1st to March 31st Quarter 2: April 1st to June 30th Quarter 3: July 1st to September 30th Quarter 4: October 1st to December 31st Basic Example Let's create a DateTimeIndex and check which dates are ...
Read MorePython Pandas - Indicate whether the date in DateTimeIndex is the first day of the quarter
To check whether the date in DateTimeIndex is the first day of the quarter, use the DateTimeIndex.is_quarter_start property. This property returns a boolean array indicating which dates fall on the first day of their respective quarters. Understanding Quarters In Pandas, a year is divided into four quarters ? Quarter 1: January 1 to March 31 Quarter 2: April 1 to June 30 Quarter 3: July 1 to September 30 Quarter 4: October 1 to December 31 Syntax DateTimeIndex.is_quarter_start Example Let's create a DateTimeIndex and check which dates are the ...
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