Python - Select 2 columns in a Dataframe and classify them - python-3.x

Question

I'm new in the programming world, and I'm doing some studies to gain knowledge in the area of Data Science.
I have a Dataframe with a lot of information, among it gender and age. I want to bring the amount of lines of each gender (male and female) and classify them as children (0 to> 12 years), young (12 to> 18 years) and adults (18+ years).
The result would be something like:
Children Female: x amount
Young Female: y amount
Adult Female: z amount
Children Male: n amount
Young Male: k amount
Adult Male: j amount
I'm lost to the point of not knowing if I have started correctly... I have created another dataframe with the two columns I need.
df2 = df[["Sex", "Age"]].copy()
Little stuck from here
EDIT (sorry about some terms in the code, they are in portugues but the code is understandable
I could solve the problem.
Here what I did from begining:
Creation of a new DF with only the informations I need:
df2 = df[["Sex", "Age"]].copy()
Creation of a function to classify the values:
def definition(age):
if age >= 18:
return 'Adulto'
elif age >= 12:
return 'Jovem'
return 'Criança'
Add the new column to the DF
df2['Classification'] = df2['Age'].map(definition)
and PRINT
print("A quantidade de crianças do sexo masculino é de {}".format(len(df2.loc[df2['Classification'] == 'Criança'].loc[df2['Sex'] == 'male'])))
print("A quantidade de crianças do sexo feminino é de {}".format(len(df2.loc[df2['Classification'] == 'Criança'].loc[df2['Sex'] == 'female'])))
print("A quantidade de jovens do sexo masculino é de {}".format(len(df2.loc[df2['Classification'] == 'Jovem'].loc[df2['Sex'] == 'male'])))
print("A quantidade de jovens do sexo feminino é de {}".format(len(df2.loc[df2['Classification'] == 'Jovem'].loc[df2['Sex'] == 'female'])))
print("A quantidade de adultos do sexo masculino é de {}".format(len(df2.loc[df2['Classification'] == 'Adulto'].loc[df2['Sex'] == 'male'])))
print("A quantidade de adultos do sexo feminino é de {}".format(len(df2.loc[df2['Classification'] == 'Adulto'].loc[df2['Sex'] == 'female'])))
Result:
A quantidade de crianças do sexo masculino é de 36
A quantidade de crianças do sexo feminino é de 32
A quantidade de jovens do sexo masculino é de 22
A quantidade de jovens do sexo feminino é de 23
A quantidade de adultos do sexo masculino é de 519
A quantidade de adultos do sexo feminino é de 259

Answer 1

I would create the classes for age using pandas.cut and then group by the two columns and check the size. Let me know if I can elaborate on anything.
bins = [1, 5, 10, 15]
group_names = ['Children', 'Young', 'Adult']
age_groups = pandas.cut(df2.Age, bins, labels=group_names)
df2['Age Groups'] = age_groups.tolist()
df2.groupby(['Gender','Age Groups']).size()
My familiarity with the nuances of pandas.cut is a bit rusty so the above bins are not exactly what you want. I suggest playing around with the data in a notebook to get them where you want them. The docs here are helpful https://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html