Let's say I have a dataframe with columns: "Date, Price".

I wish to achieve this:
If the value of the date in the "Date" column of the dataframe falls between April and October, I want to create a new column:

df['Price_new'] = df[Price]   0.01

And if it falls between November and March:

df['Price_new'] = df['Price']   0.12

CodePudding user response：

First, you need to have your dates in datetime format. If they're not already datetimes, you can convert them with pd.to_datetime.

df = pd.DataFrame({'Date':['17-2-21', '1-4-21', '21-6-21', '15-9-21', '17-2-22'], 'Price': [10.2, 12.5, 7.0, 3.8, 8.8]})
df.Date = pd.to_datetime(df.Date)
df

Then you can use apply in a similar way than here:

df.loc[(df['Date'].dt.month >= 4) & (df['Date'].dt.month <= 10),'Price_new'] = df.loc[(df['Date'].dt.month >= 4) & (df['Date'].dt.month <= 10),'Price'].apply(lambda x: x   0.01)
df.loc[(df['Date'].dt.month <= 3) | (df['Date'].dt.month >= 11),'Price_new'] = df.loc[(df['Date'].dt.month <= 3) | (df['Date'].dt.month >= 11),'Price'].apply(lambda x: x   0.12)
df