Add Column with Number of Days Between Dates in Dataframe Pandas

Add column with number of days between dates in DataFrame pandas

Assuming these were datetime columns (if they're not apply to_datetime) you can just subtract them:

df['A'] = pd.to_datetime(df['A'])
df['B'] = pd.to_datetime(df['B'])

In [11]: df.dtypes  # if already datetime64 you don't need to use to_datetime
Out[11]:
A    datetime64[ns]
B    datetime64[ns]
dtype: object

In [12]: df['A'] - df['B']
Out[12]:
one   -58 days
two   -26 days
dtype: timedelta64[ns]

In [13]: df['C'] = df['A'] - df['B']

In [14]: df
Out[14]:
             A          B        C
one 2014-01-01 2014-02-28 -58 days
two 2014-02-03 2014-03-01 -26 days

Note: ensure you're using a new of pandas (e.g. 0.13.1), this may not work in older versions.

Pandas: Subtracting two date columns and the result being an integer

How about:

df_test['Difference'] = (df_test['First_Date'] - df_test['Second Date']).dt.days

This will return difference as int if there are no missing values(NaT) and float if there is.

Pandas have a rich documentation on Time series / date functionality and Time deltas

How to get number of days between two dates using pandas

For performance you can subtract values without apply for avoid loops use Series.rsub for subtract from rigth side:

df['date'] = pd.to_datetime(df.date)
df['days'] = df['date'].rsub(pd.Timestamp('today')).dt.days

What working like:

df['days'] = (pd.Timestamp('today') - df['date']).dt.days

If want use your solution:

df['date'] = pd.to_datetime(df.date)

def calculate_days(date):
    today = pd.Timestamp('today')
    return (today - date).days

df['days'] = df['date'].apply(lambda x: calculate_days(x))

Or:

df['date'] = pd.to_datetime(df.date)

def calculate_days(date):
    today = pd.Timestamp('today')
    return (today - date)

df['days'] = df['date'].apply(lambda x: calculate_days(x)).dt.days

Pandas: using column of date to calculate number of days

You can try this:

def add_host_days(df):    
    df['host_days'] = (pd.Timestamp.now() - pd.to_datetime(df['host_since'], dayfirst=True)).dt.days

    # If you original date fields have invalid dates and would like this number of days to be in integer:
    df['host_days'] = df['host_days'].astype('Int64')   

    return df

Demo

Suppose you have a dataframe like this:

  guest_id  host_since
0    A0001  24/09/2008
1    A0002  25/09/2008
2    A0003  29/09/2008
3    A8788  20/05/2021

Then you run the code:

new_df = add_host_days(df)

Result:

print(new_df)

  guest_id  host_since  host_days
0    A0001  24/09/2008       4629
1    A0002  25/09/2008       4628
2    A0003  29/09/2008       4624
3    A8788  20/05/2021          8

How to make new column with string value if it is between two dates? Pandas

It is very easy

import numpy as np
df['new']= np.select([df.date.between(date1, date2)], ['person a'], 'person b')

np.select method is very easy and you can read more about it.

Also you can use a for loop for this but it is not optimum solution

Python: get count of days between dates from two columns in DataFrame without using apply()

Don't use apply:

df['c'] = (df['b'] - df['a']).dt.days

Python: Create new column that counts the days between current date and a lag date

I came up with the solution of using a for loop and zip function, to simply subtract each pair like so...

from datetime import datetime
import pandas as pd
 
dates = ['2012-08-01', '2012-08-15', '2012-09-01', '2012-08-15']
dates2 = ['2012-08-01', '2012-09-01', '2012-10-01', '2012-11-01']
diff = []

for i, x in zip(dates, dates2):
    i = datetime.strptime(i, '%Y-%m-%d')
    x = datetime.strptime(x, '%Y-%m-%d')
    diff.append(i - x)

df = {'--col1--': dates, '--col2--': dates2, '--difference--': diff}
df = pd.DataFrame(df)
print(df)

Ouput:

     --col1--    --col2-- --difference--
0  2012-08-01  2012-08-01         0 days
1  2012-08-15  2012-09-01       -17 days
2  2012-09-01  2012-10-01       -30 days
3  2012-08-15  2012-11-01       -78 days

Process finished with exit code 0

I hope that solves your problem.

Pandas - Number of Months Between Two Dates

Here is a very simple answer my friend:

df['nb_months'] = ((df.date2 - df.date1)/np.timedelta64(1, 'M'))

and now:

df['nb_months'] = df['nb_months'].astype(int)

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