How to assign a season to a country on a particular day in Python?

I am working with a big dataset that looks like this -

My goal is to create a new column called season, which has the season for each country on each day. To do so I have to check if the country is in the northern or southern hemisphere, then assign them the season based on this criteria -

> If Country is Northern Hemisphere -
>
> Season = Spring if month is March, April, May
>
> Season = Summer if month is June, July, August
>
> Season = Fall if month is September, October, November
>
> Season = Winter if month is December, January, February

> If Country is Southern Hemisphere -
>
> Season = Spring if month is September, October, November
>
> Season = Summer if month is December, January, February
>
> Season = Fall if month is March, April, May
>
> Season = Winter if month is June, July, August

I am given the list of all nothern and southern hemisphere countries in the form of 2 lists named north_hem_list and south_hem_list.

I tried to solve this by finding the month from the date, create a new column called season, then try to iterate through the dataset to assign season. I tried to solve this by creating a new columns with months, then using iterrows and iteritems to iterate through the dataframe. However, it is not working.

Would appreciate any and all help with this.

You can try this solution (The input df was not provided so I couldn't show the result..)

season_dict = {
    'Northern Hemisphere': {
        'Spring': [3, 4, 5],
        'Summer': [6, 7, 8],
        'Fall': [9, 10, 11],
        'Winter': [12, 1, 2]
    },
    'Southern Hemisphere': {
        'Spring': [9, 10, 11],
        'Summer': [12, 1, 2],
        'Fall': [3, 4, 5],
        'Winter': [6, 7, 8]
    }
}

# create a function to assign season based on hemisphere and month
def get_season(row):
    date = pd.to_datetime(row['Date'])
    month = date.month
    hemisphere = ''
    if row['Country'] in north_hem_list:
        hemisphere = 'Northern Hemisphere'
    elif row['Country'] in south_hem_list:
        hemisphere = 'Southern Hemisphere'
    for season, months in season_dict[hemisphere].items():
        if month in months:
            return season

# apply the function to the dataframe to create the season column
df['Season'] = df.apply(get_season, axis=1)

You can use:

# Sample
df = pd.DataFrame({'Code': ['AFG', 'ZWE'], 'Day': ['2020-02-24', '2023-02-01']})
df['Day'] = pd.to_datetime(df['Day'])

# Detection based on Code
north_hem_list = ['AFG']
south_hem_list = ['ZWE']

# Seasons
north_seasons = ['Winter', 'Spring', 'Summer', 'Fall']
south_seasons = ['Summer', 'Fall', 'Winter', 'Spring']

season = (df['Day'].dt.month % 12 + 3) // 3 - 1
df['Season'] = np.where(df['Code'].isin(north_hem_list),
                        np.array(north_seasons)[season],
                        np.array(south_seasons)[season])

Output:

>>> df
  Code        Day  Season
0  AFG 2020-02-24  Winter
1  ZWE 2023-02-01  Summer