How do you create a new column in a dataframe based on multiple conditions/other variables in Python?

I am working with a large demographic dataset. I have a variable called "Ethnicity" which has: African American, White, Latino, and Asian. I also have a "Gender" variable where the genders for each person are "M" or "F".

What I want to do is create a new variable/column called "Gen_Eth" where it is coded based on gender AND ethnicity in the following manner:

African American male = 0

African American female = 1

White male = 2

White female = 3

Latino male = 4

Latino female = 5

Asian male = 6

Asian female = 7

How would I go about doing this? An if-else statement based on the "ethnicity" and "gender" variables, convert gender to 0's and 1's and then a function with the 2 variables, etc. Thank you so much!

Here is an example of what you can do.

# Data
d = {'ethnicity':['asian', 'african', 'white', 'latino', 'latino', 'african', 'white', 'asian'], 'gender':['M', 'F', 'M', 'F','M', 'F','M', 'F']}

df = pd.DataFrame(data=d) # creating pandas dataframe
df_int = df.copy(deep=True) # making a copy of dataframe

# Changing the labels to int
for idx, ethnicity in enumerate(['african', 'white', 'latino', 'asian']):
    df_int['ethnicity'].loc[df_int['ethnicity']==ethnicity] = idx

for idx, gender in enumerate(['M', 'F']):
    df_int['gender'].loc[df_int['gender']==gender] = idx

# Creating a new column in your original dataframe
df['Gen_Eth'] = 2*df_int['ethnicity'] + df_int['gender']

I particularly like using the map function for this. The rest of this answer is similar to @Jason Yu's answer

ethnic_mapper = {"African American":0,"White":1,"Latino":2,"Asian":3}
gender_mapper = {"M":0,"F":1}
df["ethnic_int"] = df.ethnicity.map(ethnic_mapper)
df["gender_int"] = df.gender.map(gender_mapper)

  gender         ethnicity  ethnic_int  gender_int
0      M  African American           0           0
1      M             White           1           0
2      F            Latino           2           1
3      M             Asian           3           0
4      F            Latino           2           1
5      F             Asian           3           1

df["gen_eth"] = 2*df["ethnic_int"] + df["gender_int"]
df = df.drop(columns=["ethnic_int","gender_int"])

  gender         ethnicity  gen_eth
0      M  African American        0
1      M             White        2
2      F            Latino        5
3      M             Asian        6
4      F            Latino        5
5      F             Asian        7

I would use a dictionary in conjunction with df.apply().

Example:

import pandas as pd

df = pd.DataFrame({
    "gender": ["M", "M", "F", "M", "F", "F"],
    "ethnicity": ["African American", "White", "Latino", "Asian", "Latino", "Asian"]
})

ge_map = {
    "M": 0,
    "F": 1,
    "African American": 0,
    "White": 2,
    "Latino": 4, 
    "Asian": 6
}

df["gen_eth"] = df.apply(lambda x: ge_map[x["gender"]] + ge_map[x["ethnicity"]], axis= 1)

print(df)

Result: