How to apply IF, else, else if condition in Pandas DataFrame - python-3.x

Question

I have a column in my pandas DataFrame with country names. I want to apply different filters on the column using if-else conditions and have to add a new column on that DataFrame with those conditions.
Current DataFrame:-
Company Country
BV Denmark
BV Sweden
DC Norway
BV Germany
BV France
DC Croatia
BV Italy
DC Germany
BV Austria
BV Spain
I have tried this but in this, I have to define countries again and again.
bookings_d2.loc[(bookings_d2.Country== 'Denmark') | (bookings_d2.Country== 'Norway'), 'Country'] = bookings_d2.Country
In R I am currently using if else condition like this, I want to implement this same thing in python.
R Code Example 1 :
ifelse(bookings_d2$COUNTRY_NAME %in% c('Denmark','Germany','Norway','Sweden','France','Italy','Spain','Germany','Austria','Netherlands','Croatia','Belgium'),
as.character(bookings_d2$COUNTRY_NAME),'Others')
R Code Example 2 :
ifelse(bookings_d2$country %in% c('Germany'),
ifelse(bookings_d2$BOOKING_BRAND %in% c('BV'),'Germany_BV','Germany_DC'),bookings_d2$country)
Expected DataFrame:-
Company Country
BV Denmark
BV Sweden
DC Norway
BV Germany_BV
BV France
DC Croatia
BV Italy
DC Germany_DC
BV Others
BV Others

Answer 1

Not sure exactly what you are trying to achieve, but I guess it is something along the lines of:
df=pd.DataFrame({'country':['Sweden','Spain','China','Japan'], 'continent':[None] * 4})
country continent
0 Sweden None
1 Spain None
2 China None
3 Japan None
df.loc[(df.country=='Sweden') | ( df.country=='Spain'), 'continent'] = "Europe"
df.loc[(df.country=='China') | ( df.country=='Japan'), 'continent'] = "Asia"
country continent
0 Sweden Europe
1 Spain Europe
2 China Asia
3 Japan Asia
You can also use python list comprehension like:
df.continent=["Europe" if (x=="Sweden" or x=="Denmark") else "Other" for x in df.country]

Answer 2

You can use:
For example1: Use Series.isin with numpy.where or loc, but necessary invert mask by ~:
#removed Austria, Spain
L = ['Denmark','Germany','Norway','Sweden','France','Italy',
'Germany','Netherlands','Croatia','Belgium']
df['Country'] = np.where(df['Country'].isin(L), df['Country'], 'Others')
Alternative:
df.loc[~df['Country'].isin(L), 'Country'] ='Others'
For example2: Use numpy.select or nested np.where:
m1 = df['Country'] == 'Germany'
m2 = df['Company'] == 'BV'
df['Country'] = np.select([m1 & m2, m1 & ~m2],['Germany_BV','Germany_DC'], df['Country'])
Alternative:
df['Country'] = np.where(~m1, df['Country'],
np.where(m2, 'Germany_BV','Germany_DC'))
print (df)
Company Country
0 BV Denmark
1 BV Sweden
2 DC Norway
3 BV Germany_BV
4 BV France
5 DC Croatia
6 BV Italy
7 DC Germany_DC
8 BV Others
9 BV Others

Answer 3

You can do to get it:
country_others=['Poland','Switzerland']
df.loc[df['Country']=='Germany','Country']=df.loc[df['Country']=='Germany'].apply(lambda x: x+df['Company'])['Country']
df.loc[(df['Company']=='DC') &(df['Country'].isin(country_others)),'Country']='Others'