This question already has answers here:
Add a sequential counter column on groups to a pandas dataframe
(4 answers)
Closed 4 years ago.
if I have a data set of time series and I want to estimate the number of the day of a groupby time series per each day as seen in the figure and act as a counter :
nothing special in my code yet, it is just reading the data and convert time and day into
import pandas as pd
df = pd.read_csv('*file location and name*',sep=",")
df.head()
df['Date'] =pd.to_datetime(df['Date']+" "+df['Time'])
df.set_index('Date', inplace=True)
See if answers your query:
df['dayOfMonth']= df.groupby('day').cumcount() + 1
Related
This question already has answers here:
How to change the datetime format in Pandas
(8 answers)
How can I format date time string which is not date time format so that I can use it with pd.to_datetime()?
(2 answers)
Convert DataFrame column type from string to datetime
(6 answers)
Closed 5 months ago.
I have a 'date' column that has a date value as 20170423 (yyyymmdd) how can i change it to 2017-04-23?
dataframe = df
column name 'date'
I read the below post, but none of the solutions worked
Fastest way to insert these dashes in python string?
Example
d = df['Date']
df['Date'] = '%s-%s-%s' % (d[:4], d[4:6], d[6:])
The output 0 0 20170711\n1 20170718\n2 20170718\n3... when i exported in .csv
This question already has answers here:
Extract day and month from a datetime object
(4 answers)
Closed 12 months ago.
I recently started using python.
I have a series of dates in excel
01-05-2021
02-05-2021
.
.
29-05-2021
Now, I want to load this column and convert it into individual strings based on rows. So i can extract the day, month and year separately for each dates
Can someone help me how to do that??
you can do:
df = pd.read_excel("filename.xlsx")
# let's imagine your date column name is "date"
df["day"] = df["date"].apply(lambda elem:elem.split("-")[0])
df["month"] = df["date"].apply(lambda elem:elem.split("-")[1])
df["year"] = df["date"].apply(lambda elem:elem.split("-")[2])
from datetime import datetime
str_time = 01-05-2021
time_convert = datetime.strptime(str_time, '%d-%m-%Y')
print (time_convert, time_convert.day, time_convert.month, time_convert.year)
in your case, make the convert in looping for each data you got from the excel file
This question already has answers here:
datetime to string with series in pandas
(3 answers)
Closed 2 years ago.
I'm converting a datetime column (referred to as DATE) in my Pandas dataframe df to a string of the form 'Ymd' (e.g. '20191201' for December 1st 2019). My current way of doing that is:
import datetime as dt
df['DATE'] = df['DATE'].apply(lambda x: dt.datetime.strftime(x, '%Y%m%d'))
But this is surprisingly inefficient and slow when run on large dataframes with millions of rows. Is there a more efficient alternative I am not seeing? That would be extremely helpful. Thanks.
In pandas you do not need apply
df['Date']=df['DATE'].dt.strftime('%Y%m%d')
This question already has answers here:
Pandas Timedelta in Days
(5 answers)
Closed 3 years ago.
So, I have a pandas dataframe with a lot of variables including start/end date of loans.
I subtract these two in order to get their difference in days.
The result I get is of the type i.e. 349 days 00:00:00.
How can I keep only for example the number 349 from this column?
Check this format,
df['date'] = pd.to_timedelta(df['date'], errors='coerce').days
also, check .normalize() function in pandas.
This question already has answers here:
Pandas Datetime: Calculate Number of Weeks Between Dates in Two Columns
(2 answers)
Closed 4 years ago.
Hi, I have a dataframe with date columns. I want to add a column to calculate how many weeks since the contact? For example, today's date is 20-Sep-18, and use this date to calculate with the column.
Can anyone help me with this questions? Thanks!
You can do like this.
df['Contact Date']= pd.to_datetime(df['Contact Date'])
import datetime
df['How Many days'] = datetime.datetime.now() - df['Contact Date']