Years between two date column = 'Timedelta' object has no attribute 'item'

Afternoon All,

Looking for the number of years between two dates to 4 decimal places. My Data:

df_Years = df[
            df['state'].str.contains('Done')

            ][[  
               'maturity_date'
              ]].copy()

df_Years['maturity_date'] = pd.to_datetime(df_Date['maturity_date'])
df_Years['Today'] = pd.to_datetime('today') 
display(df_Years.head(6))


maturity_date   Today
13  2022-12-15  2018-03-21
81  2028-02-15  2018-03-21
82  2045-12-01  2018-03-21
100 2025-08-18  2018-03-21
115 2019-01-16  2018-03-21
116 2018-12-21  2018-03-21

display(df_Years.dtypes)

maturity_date    datetime64[ns]
Today            datetime64[ns]
dtype: object
#Dataframe types

Attempt 1:

df_Years['Year_To_Maturity'] = df_Years['maturity_date'] - df_Years['Today']
df_Years['Year_To_Maturity'] = df_Years['Year_To_Maturity'].apply(lambda x: float(x.item().days)/365)

Error:

AttributeError: 'Timedelta' object has no attribute 'item'

Attempt 2:

df_Years['Year_To_Maturity'] = df_Years['maturity_date'] - df_Years['Today']
df_Years['Year_To_Maturity'] = df_Years['Year_To_Maturity'].apply(lambda x: float(x.item().days)/365)

Output:

maturity_date   Today   Year_To_Maturity
13  2022-12-15  2018-03-21  <map object at 0x00000000143F9C88>
81  2028-02-15  2018-03-21  <map object at 0x00000000143F9C88>
82  2045-12-01  2018-03-21  <map object at 0x00000000143F9C88>
100 2025-08-18  2018-03-21  <map object at 0x00000000143F9C88>
115 2019-01-16  2018-03-21  <map object at 0x00000000143F9C88>
116 2018-12-21  2018-03-21  <map object at 0x00000000143F9C88>

I'd like to know why both don't output the Year_To_Maturity?

1 answer

  • answered 2018-03-21 06:48 jezrael

    I think you need sub for subtract, convert timedeltas to days by dt.days, divide by div and last round:

    df_Years['Year_To_Maturity'] = (df_Years['maturity_date'].sub(df_Years['Today'])
                                                             .dt.days
                                                             .div(365)
                                                             .round(4))
    print (df_Years)
      maturity_date      Today  Year_To_Maturity
    0    2022-12-15 2018-03-21            4.7397
    1    2028-02-15 2018-03-21            9.9123
    2    2045-12-01 2018-03-21           27.7178
    3    2025-08-18 2018-03-21            7.4164
    4    2019-01-16 2018-03-21            0.8247
    5    2018-12-21 2018-03-21            0.7534
    

    Thanks @pir for better solution:

    df_Years['Year_To_Maturity'] = (df_Years['maturity_date'].sub(df_Years['Today'])
                                                             .dt.days
                                                             .div(365.25)
                                                             .round(4))
    print (df_Years)
      maturity_date      Today  Year_To_Maturity
    0    2022-12-15 2018-03-21            4.7365
    1    2028-02-15 2018-03-21            9.9055
    2    2045-12-01 2018-03-21           27.6988
    3    2025-08-18 2018-03-21            7.4114
    4    2019-01-16 2018-03-21            0.8241
    5    2018-12-21 2018-03-21            0.7529