how to add hour to pandas dataframe column

add hours to datetime column pandas
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I have a pandas dataframe time column like following.

 segments_data['time']
 Out[1585]: 
 0      04:50:00
 1      04:50:00
 2      05:00:00
 3      05:12:00
 4      06:04:00
 5      06:44:00
 6      06:44:00
 7      06:47:00
 8      06:47:00
 9      06:47:00

I want to add 5 hours and 30 mins to above time column. I am doing following in python.

pd.DatetimeIndex(segments_data['time']) + pd.DateOffset(hours=5,minutes=30)

But it gives me an error.

TypeError: object of type 'datetime.time' has no len()

please help.

This is a gnarly way of doing it, principally the problem here is the lack of vectorised support for time objects, so you first need to convert the time to datetime by using combine and then apply the offset and get the time component back:

In [28]:  
import datetime as dt  
df['new_time'] = df['time'].apply(lambda x: (dt.datetime.combine(dt.datetime(1,1,1), x,) + dt.timedelta(hours=3,minutes=30)).time())
df

Out[28]:
           time  new_time
index                    
0      04:50:00  08:20:00
1      04:50:00  08:20:00
2      05:00:00  08:30:00
3      05:12:00  08:42:00
4      06:04:00  09:34:00
5      06:44:00  10:14:00
6      06:44:00  10:14:00
7      06:47:00  10:17:00
8      06:47:00  10:17:00
9      06:47:00  10:17:00

How to properly add hours to a pandas.tseries.index.DatetimeIndex , DatetimeIndex? python pandas indexing time-series dataframe. I have a normal df.index that I would like to add some hours to  Pandas DataFrame – Add Column. To add a new column to the existing Pandas DataFrame, assign the new column values to the DataFrame, indexed using the new column name. In this tutorial, we shall learn how to add a column to DataFrame, with the help of example programs, that are going to be very detailed and illustrative. Syntax – Add Column

You can try importing timedelta:

from datetime import datetime, timedelta

and then:

segments_data['time'] = pd.DatetimeIndex(segments_data['time']) + timedelta(hours=5,minutes=30)

How to add/subtract time (hours, minutes, etc.) from a Pandas , datetime.time has replace but that'll only work on individual items of the Series? Here's an example of the index used: In  Now lets discuss different ways to add columns in this data frame. Add new columns in a DataFrame using [] operator Add a new column with values in list. Suppose we want to add a new column ‘Marks’ with default values from a list. Let’s see how to do this, # Add column with Name Marks dfObj['Marks'] = [10,20, 45, 33, 22, 11]

Pandas does not support vectorised operations with datetime.time objects. For efficient, vectorised operations, there is no requirement to use the datetime module from the standard library.

You have a couple of options to vectorise your calculation. Either use a Pandas timedelta series, if your times represent a duration. Or use a Pandas datetime series, if your times represent specific points in time.

The choice depends entirely on what your data represents.

timedelta series
df['time'] = pd.to_timedelta(df['time'].astype(str)) + pd.to_timedelta('05:30:00')

print(df['time'].head())

0   10:20:00
1   10:20:00
2   10:30:00
3   10:42:00
4   11:34:00
Name: 1, dtype: timedelta64[ns]
datetime series
df['time'] = pd.to_datetime(df['time'].astype(str)) + pd.DateOffset(hours=5, minutes=30)

print(df['time'].head())

0   2018-12-24 10:20:00
1   2018-12-24 10:20:00
2   2018-12-24 10:30:00
3   2018-12-24 10:42:00
4   2018-12-24 11:34:00
Name: 1, dtype: datetime64[ns]

Notice by default the current date is assumed.

pandas.Series.dt.hour, __iter__ · pandas.Series.items · pandas.Series.iteritems · pandas.Series.keys · pandas.Series.pop · pandas.Series.item · pandas.Series.xs · pandas.Series.add​  Let’s discuss how to add new columns to existing DataFrame in Pandas. There are multiple ways we can do this task. Method #1: By declaring a new list as a column.

as of '0.25.3' this is as simple as

df[column] = df[column] + pd.Timedelta(hours=1)

pandas.DatetimeIndex, Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without Create a DataFrame with a column containing the Index. Hi@akhtar, You can use the del command in Pandas to delete one column from your DataFrame. I have attached one example below for your reference. import pandas as pd df = pd.read_csv('my.csv')

pandas.DataFrame.between_time, start_timedatetime.time or str: end_timedatetime.time or str: include_startbool, default True: include_endbool, default True: axis{0 or 'index', 1 or 'columns'},  How to delete columns from a Python Pandas DataFrame. This Python programming tutorial video explains how to remove one or multiple columns using the drop function.

Time Series / Date functionality, You can also pass a DataFrame of integer or string columns to assemble into a Timestamp('2014-08-04 09:30:00') # Adding 2 business hours In [177]: pd. answered 3 hours ago by supriya (11.8k points) You can use .max(axis=1) function in pandas for it: Add new columns to pandas dataframe based on other dataframe.

Add Hours, minutes and seconds to timestamp in Pyspark , Add hour to timestamp in pyspark. import pyspark.sql.functions as F. df1 = df.​withColumn( 'birthdaytime' , df.birthdaytime + F.expr( 'INTERVAL 2 HOURS' )). Create a new column by assigning the output to the DataFrame with a new column name in between the []. Operations are element-wise, no need to loop over rows. Use rename with a dictionary or function to rename row labels or column names.

Comments
  • How to select time greater than 10am in above?
  • df[df['new_time'] > dt.time(10,0)] works for me or df[df.new_time.gt(dt.time(10))]
  • Interesting - how does this compare to the approach in this answer. Should we prefer pd.to_timedelta and co over pd.DatetimeIndex ?
  • @Mr_and_Mrs_D, No time complexity difference. They should be roughly equivalent. In general, I reserve DatetimeIndex when I'm creating an index (since it's in the name), to_datetime when creating a series.