import
pandas as pd
df
=
pd.DataFrame({
'Name'
: [
'John'
,
'Sammy'
,
'Stephan'
,
'Joe'
,
'Emily'
,
'Tom'
],
'Gender'
: [
'Male'
,
'Female'
,
'Male'
,
'Female'
,
'Female'
,
'Male'
],
'Age'
: [
45
,
6
,
4
,
36
,
12
,
43
]})
print
(
"Dataset"
)
print
(df)
print
(
"-"
*
40
)
def
age_bucket(age):
if
age <
=
18
:
return
"<18"
else
:
return
">18"
df[
'Age Group'
]
=
df[
'Age'
].
apply
(age_bucket)
gender
=
pd.DataFrame(df.Gender.value_counts(normalize
=
True
)
*
100
).reset_index()
gender.columns
=
[
'Gender'
,
'%Gender'
]
df
=
pd.merge(left
=
df, right
=
gender, how
=
'inner'
, on
=
[
'Gender'
])
table
=
pd.pivot_table(df, index
=
[
'Gender'
,
'%Gender'
,
'Age Group'
],
values
=
[
'Name'
], aggfunc
=
{
'Name'
:
'count'
,})
print
(
"Table"
)
print
(table)