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Pandas Articles
Page 15 of 42
Python Pandas - Get the timedelta in nanoseconds for internal compatibility
The timedelta.delta property in Pandas returns the underlying timedelta value in nanoseconds, which is useful for internal compatibility and precise time calculations. Syntax timedelta_object.delta Creating a Timedelta Object First, let's create a Timedelta object with various time components ? import pandas as pd # Create a Timedelta object with days, minutes, seconds, and nanoseconds timedelta = pd.Timedelta('5 days 1 min 45 s 40 ns') print("Timedelta:", timedelta) Timedelta: 5 days 00:01:45.000000040 Getting Nanoseconds Using delta Property The delta property returns the total duration in nanoseconds ? ...
Read MorePython Pandas - Get the number of days from TimeDelta
To get the number of days from a TimeDelta object in Pandas, use the timedelta.days property. This property returns only the day component as an integer, excluding hours, minutes, and seconds. Syntax timedelta.days Creating a TimeDelta Object First, create a TimeDelta object using pd.Timedelta() ? import pandas as pd # Create a Timedelta object timedelta = pd.Timedelta('5 days 1 min 45 s') print("Timedelta:", timedelta) Timedelta: 5 days 00:01:45 Extracting Days Use the .days property to get only the number of days ? import ...
Read MorePython Pandas - Get the UTC Offset Time
To get the UTC offset time in Pandas, use the timestamp.utcoffset() method. The UTC offset represents the time difference between a timezone and Coordinated Universal Time (UTC). What is UTC Offset? UTC offset shows how many hours a timezone is ahead or behind UTC. For example, EST is UTC-5, while IST is UTC+5:30. Basic Usage First, let's create a timestamp and get its UTC offset ? import pandas as pd # Creating a timestamp with UTC timezone timestamp = pd.Timestamp('2021-10-16T15:12:34.261811624', tz='UTC') print("Timestamp:", timestamp) # Get the UTC offset time offset = timestamp.utcoffset() ...
Read MorePython Pandas - Construct a naive UTC datetime from a POSIX timestamp
To construct a naive UTC datetime from a POSIX timestamp, use the pd.Timestamp.utcfromtimestamp() method. A POSIX timestamp represents the number of seconds since January 1, 1970, 00:00:00 UTC. What is a POSIX Timestamp? A POSIX timestamp (also called Unix timestamp) is the number of seconds that have elapsed since the Unix epoch (January 1, 1970, 00:00:00 UTC). It's a standard way to represent time across different systems. Basic Syntax pd.Timestamp.utcfromtimestamp(posix_timestamp) Example Let's create a Pandas timestamp and use it to construct a naive UTC datetime from a POSIX timestamp ? ...
Read MorePython Pandas - Convert Timestamp to another time zone
Converting timestamps between time zones is essential when working with global data. Pandas provides the tz_convert() method to easily convert timezone-aware timestamps to different time zones. Import Required Libraries First, import the pandas library ? import pandas as pd Creating a Timezone-Aware Timestamp Create a timestamp object with an initial timezone. The timezone parameter accepts standard timezone names ? import pandas as pd # Create timestamp with US/Eastern timezone timestamp = pd.Timestamp('2021-10-14T15:12:34.261811624', tz='US/Eastern') print("Original timestamp:", timestamp) Original timestamp: 2021-10-14 15:12:34.261811624-04:00 Converting to Another Time Zone ...
Read MorePython Pandas - Return proleptic Gregorian ordinal
To return proleptic Gregorian ordinal, use the timestamp.toordinal() method. The proleptic Gregorian ordinal is the number of days since January 1 of year 1, where January 1 of year 1 is day 1. Understanding Proleptic Gregorian Ordinal The proleptic Gregorian calendar extends the Gregorian calendar backward to dates before its introduction in 1582. The ordinal represents the total number of days elapsed since the theoretical date January 1, 1 AD. Basic Usage First, import the required library and create a timestamp object ? import pandas as pd # Create the timestamp object timestamp ...
Read MorePython Pandas - Get the current date and time from Timestamp object
To get the current date and time from a Pandas Timestamp object, use the timestamp.today() method. This method returns the current system date and time, regardless of the original timestamp value. Import Required Libraries First, import the necessary libraries ? import pandas as pd import datetime Creating a Timestamp Object Create a Pandas Timestamp object with a specific date ? # Create a timestamp with a specific date timestamp = pd.Timestamp(datetime.datetime(2021, 10, 10)) print("Original Timestamp:", timestamp) print("Day Name from Timestamp:", timestamp.day_name()) Original Timestamp: 2021-10-10 00:00:00 Day Name from ...
Read MorePandas GroupBy – Count the occurrences of each combination
When analyzing data, we often need to count how many times each combination of values appears across multiple columns. In Pandas, we can use DataFrame.groupby() with size() to count occurrences of each unique combination. Creating a Sample DataFrame Let's start by creating a DataFrame with car sales data ? import pandas as pd # Create sample data data = { 'Car': ['BMW', 'Mercedes', 'Lamborghini', 'Audi', 'Mercedes', 'Porsche', 'RollsRoyce', 'BMW'], 'Place': ['Delhi', 'Hyderabad', 'Chandigarh', 'Bangalore', 'Hyderabad', 'Mumbai', 'Mumbai', 'Delhi'], 'Sold': [95, 80, 80, ...
Read MoreCreate a legend with Pandas and Matplotlib.pyplot
To create a legend with Pandas and matplotlib.pyplot, we can plot DataFrame data and enable the legend parameter. The legend helps identify different data series in the plot. Steps to Create a Legend Set the figure size and adjust the padding between and around the subplots. Create a DataFrame with multiple columns for plotting. Plot the DataFrame using plot() method with legend=True. Display the figure using show() method. Example Let's create a bar chart with a legend showing two data series ? import pandas as pd from matplotlib import pyplot as plt ...
Read MoreHow to plot a stacked event duration using Python Pandas?
To plot a stacked event duration using Python Pandas, you create horizontal lines that represent different events over time periods. This visualization is useful for displaying timelines, project schedules, or any data with start and end times. Steps to Create a Stacked Event Duration Plot Set the figure size and adjust the padding between and around the subplots Create a DataFrame with lists of xmin (start times) and corresponding xmax (end times) Use hlines() method to plot horizontal lines representing event durations Display the figure using show() method Example Here's how to create a ...
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