pandas series repeat n time and change column value

Question

I have input data like this.

NAME | PLACE | DATE
  A  |   X   | 2020-04-30
  B  |   Y   | 2019-04-30

i want to duplicate 5 times and change date by increasing years

NAME | PLACE | DATE
  A  |   X   | 2020-04-30
  A  |   X   | 2021-04-30
  A  |   X   | 2022-04-30
  A  |   X   | 2023-04-30
  A  |   X   | 2024-04-30
  A  |   X   | 2025-04-30
  B  |   Y   | 2019-04-30
  B  |   Y   | 2020-04-30
  B  |   Y   | 2021-04-30
  B  |   Y   | 2022-04-30
  B  |   Y   | 2023-04-30
  B  |   Y   | 2024-04-30

Is this possible to do using pandas repeat ?.

Answer 1

Use:

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

y = np.array([pd.offsets.DateOffset(years=_) for _ in np.tile(range(6), len(df.index))])
df = df.reindex(df.index.repeat(6)).assign(Date=lambda x: x['Date'] + y)

Details:

Create a np.array of DateOffset objects that needs to be added to the Date column to get the desired year offset.

print(y)
array([<DateOffset: years=0>, <DateOffset: years=1>,
       <DateOffset: years=2>, <DateOffset: years=3>,
       <DateOffset: years=4>, <DateOffset: years=5>,
       <DateOffset: years=0>, <DateOffset: years=1>,
       <DateOffset: years=2>, <DateOffset: years=3>,
       <DateOffset: years=4>, <DateOffset: years=5>], dtype=object)

Use reindex to reindex the dataframe as required and use assign to add the Date with the years.

print(df)
  Name Place       Date
0    A     X 2020-04-30
0    A     X 2021-04-30
0    A     X 2022-04-30
0    A     X 2023-04-30
0    A     X 2024-04-30
0    A     X 2025-04-30
1    B     Y 2019-04-30
1    B     Y 2020-04-30
1    B     Y 2021-04-30
1    B     Y 2022-04-30
1    B     Y 2023-04-30
1    B     Y 2024-04-30

Answer 2

Let's try this, convert single date to an array of dates for the given range & will utilize DataFrame.explode to transform each element of a list-like to a row.

import pandas as pd

df = pd.DataFrame({
    "Name": ["A", "B"],
    "Place": ["X", "Y"],
    "Date": ["2020-04-30", "2020-04-30"]
})

expand = 5
print(
    df.assign(
        Date=pd.to_datetime(df.Date)
            .apply(lambda x: [x.replace(x.year + i) for i in range(0, expand + 1)])
    ).explode("Date").reset_index(drop=True)
)

---

   Name Place       Date
0     A     X 2020-04-30
1     A     X 2021-04-30
2     A     X 2022-04-30
3     A     X 2023-04-30
4     A     X 2024-04-30
5     A     X 2025-04-30
6     B     Y 2020-04-30
7     B     Y 2021-04-30
8     B     Y 2022-04-30
9     B     Y 2023-04-30
10    B     Y 2024-04-30
11    B     Y 2025-04-30

Answer 3

Here is a way to do it:

df_out = df.reindex(df.index.repeat(6))

df_out['DATE'] += pd.Series([pd.DateOffset(years=i) 
                              for i in df_out.groupby('AME').cumcount()], 
                            index=df_out.index)    
df_out.reset_index(drop=True)

Output:

      AME    PLACE       DATE
0     A       X    2020-04-30
1     A       X    2021-04-30
2     A       X    2022-04-30
3     A       X    2023-04-30
4     A       X    2024-04-30
5     A       X    2025-04-30
6     B       Y    2019-04-30
7     B       Y    2020-04-30
8     B       Y    2021-04-30
9     B       Y    2022-04-30
10    B       Y    2023-04-30
11    B       Y    2024-04-30