Getting Type Error Trying to create a month-year column from date ranges in pandas
Question:
I’m trying to follow the solution provided Find all months between two date columns and generate row for each month and I’m hitting a wall as I’m getting an error. What I want to do is create a Year-Month column for each year-month that exists in the startdate and enddate range for each row. When I tried to follow the above linked Stack, I get the error
TypeError: Cannot convert input … Name: ServiceStartDate, dtype: datetime64[ns]] of type <class ‘pandas.core.series.Series’> to Timestamp
I have no idea how to fix this. Please help!
Sample Data
ID
StartDate
EndDate
1
311566
2021-10-01
2024-09-30
2
235216
2020-11-01
2020-11-30
3
157054
2021-10-01
2023-09-30
4
159954
2021-01-01
2023-12-31
5
255815
2019-11-01
2022-10-31
Answers:
I have found a solution to my problem (sorry for the long response delay). The problem was that my data had a time stamp associated with it. I needed to change the date field to y/m/-01 format using the following code.
df['date] = df['date'].apply(lambda x: x.strftime('%Y-%m-01'))
Then I used the solution below to get all the months/years that exist between the min and max dates as a single column.
df.merge(df.apply(lambda s: pd.date_range(df['date'].min(),
df['date'].max(), freq='MS'), 1).explode("").rename('Month'),
left_index=True, right_index=True)
I’m trying to follow the solution provided Find all months between two date columns and generate row for each month and I’m hitting a wall as I’m getting an error. What I want to do is create a Year-Month column for each year-month that exists in the startdate and enddate range for each row. When I tried to follow the above linked Stack, I get the error
TypeError: Cannot convert input … Name: ServiceStartDate, dtype: datetime64[ns]] of type <class ‘pandas.core.series.Series’> to Timestamp
I have no idea how to fix this. Please help!
Sample Data
| ID | StartDate | EndDate | |
|---|---|---|---|
| 1 | 311566 | 2021-10-01 | 2024-09-30 |
| 2 | 235216 | 2020-11-01 | 2020-11-30 |
| 3 | 157054 | 2021-10-01 | 2023-09-30 |
| 4 | 159954 | 2021-01-01 | 2023-12-31 |
| 5 | 255815 | 2019-11-01 | 2022-10-31 |
I have found a solution to my problem (sorry for the long response delay). The problem was that my data had a time stamp associated with it. I needed to change the date field to y/m/-01 format using the following code.
df['date] = df['date'].apply(lambda x: x.strftime('%Y-%m-01'))
Then I used the solution below to get all the months/years that exist between the min and max dates as a single column.
df.merge(df.apply(lambda s: pd.date_range(df['date'].min(),
df['date'].max(), freq='MS'), 1).explode("").rename('Month'),
left_index=True, right_index=True)