populate column using loop based on value in row index 0

Question

I have a DataFrame where I want to populate a column which currently has a date only in the first row, all other rows in the column are blank (None).

Using the value in index row 0 of df['date'] column, I have written a loop that gives me a date in each subsequent row which is 7 days previous of the date in the previous row.

import numpy as pd

first_date = pd.to_datetime(df['date'].loc[0])


date_list = [first_date]
count = 0
while count < (len(df) -1):
    count +=1
    print(count)
    subtract_days = count * 7
    latest_date = first_date  - timedelta(days=subtract_days)
    print(latest_date)
    date_list.append(latest_date)

print(date_list)

df['date_1'] = date_list

However, this requires creating a new column df['date_1'], deleting the existing df['date'] and renaming the new.

What would be a more efficient way of doing the same?

check pd.date_range

Irfanuddin
– Irfanuddin

2022-01-30 11:48:01 +00:00
Commented Jan 30, 2022 at 11:48 — Irfanuddin
– Irfanuddin, Commented Jan 30, 2022 at 11:48

Irfanuddin · Accepted Answer · 2022-01-30 12:01:14Z

1

You can try to use pd.date_range:

# set your date column as index
df.set_index('date', inplace=True)

# generate dates for 7 days descending for periods equal to length of the dataframe
df.index = pd.date_range(start=df.index[0], freq='-7d', periods=df.shape[0])

This can be done without setting as an index as well.

df['date'] = pd.date_range(start=df.iloc[0]['date'], freq='-7d', periods=df.shape[0])

edited Jan 30, 2022 at 12:01

answered Jan 30, 2022 at 11:52

Irfanuddin

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