Iterating Over Timestamp In Python

The portion of pandas dataframe is given below: timestamp quantity price Dates Time store_price 2016-07-01 09:15:55 750 1237.50 2016-07-01 09:15:55 nan

Solution 1:

Solution

df['timestamp'] = pd.to_datetime(df['timestamp']) # Only if needed
condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)
df.loc[condition, "store_price"] = df.loc[condition, "price"]

Explanation

First, make sure timestamp column is of type datetime:

df['timestamp'].dtypes

If it returns dtype('O'), you need to cast it to datetime using pd.to_datetime, as below:

df['timestamp'] = pd.to_datetime(df['timestamp'])
df['timestamp'].dtypes
>>> dtype('<M8[ns]')

Now you can access the hour and the minute of the column with the .dt accessor and write a mask as below:

condition = (df['timestamp'].dt.hour == 9) & (df['timestamp'].dt.minute >= 20) & (df['timestamp'].dt.minute <= 30)

Finally, you can override the store_price column with price only for the rows that match the condition using .loc:

df.loc[condition, "store_price"] = df.loc[condition, "price"]

Obtaining the results you want.