[Pandas] Pandas04 - Student Alcohol Consumption 풀이

Student Alcohol Consumption

Introduction:

This time you will download a dataset from the UCI.

Step 1. Import the necessary libraries

In [12]:

import pandas as pd
import numpy as np

Step 2. Import the dataset from this address.

Step 3. Assign it to a variable called df.

In [3]:

url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/Students_Alcohol_Consumption/student-mat.csv'
df = pd.read_csv(url,sep=',')
df.head()

Out[3]:

	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	Fjob	...	famrel	freetime	goout	Dalc	Walc	health	absences	G1	G2	G3
0	GP	F	18	U	GT3	A	4	4	at_home	teacher	...	4	3	4	1	1	3	6	5	6	6
1	GP	F	17	U	GT3	T	1	1	at_home	other	...	5	3	3	1	1	3	4	5	5	6
2	GP	F	15	U	LE3	T	1	1	at_home	other	...	4	3	2	2	3	3	10	7	8	10
3	GP	F	15	U	GT3	T	4	2	health	services	...	3	2	2	1	1	5	2	15	14	15
4	GP	F	16	U	GT3	T	3	3	other	other	...	4	3	2	1	2	5	4	6	10	10

5 rows × 33 columns

Step 4. For the purpose of this exercise slice the dataframe from 'school' until the 'guardian' column

In [7]:

# loc을 사용해서 col의 name으로 slicing
df2 = df.loc[:,'school':'guardian']
df2[:5]

Out[7]:

	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	Fjob	reason	guardian
0	GP	F	18	U	GT3	A	4	4	at_home	teacher	course	mother
1	GP	F	17	U	GT3	T	1	1	at_home	other	course	father
2	GP	F	15	U	LE3	T	1	1	at_home	other	other	mother
3	GP	F	15	U	GT3	T	4	2	health	services	home	mother
4	GP	F	16	U	GT3	T	3	3	other	other	home	father

Step 5. Create a lambda function that capitalize strings.

In [14]:

Cap = lambda x:x.capitalize()

Step 6. Capitalize both Mjob and Fjob

In [16]:

df2["Mjob"].apply(Cap)
df2['Fjob'].apply(Cap)

Out[16]:

0       Teacher
1         Other
2         Other
3      Services
4         Other
5         Other
6         Other
7       Teacher
8         Other
9         Other
10       Health
11        Other
12     Services
13        Other
14        Other
15        Other
16     Services
17        Other
18     Services
19        Other
20        Other
21       Health
22        Other
23        Other
24       Health
25     Services
26        Other
27     Services
28        Other
29      Teacher
         ...   
365       Other
366    Services
367    Services
368    Services
369     Teacher
370    Services
371    Services
372     At_home
373       Other
374       Other
375       Other
376       Other
377    Services
378       Other
379       Other
380     Teacher
381       Other
382    Services
383    Services
384       Other
385       Other
386     At_home
387       Other
388    Services
389       Other
390    Services
391    Services
392       Other
393       Other
394     At_home
Name: Fjob, Length: 395, dtype: object

Step 7. Print the last elements of the data set.

In [22]:

df2.tail()

Out[22]:

	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	Fjob	reason	guardian
390	MS	M	20	U	LE3	A	2	2	services	services	course	other
391	MS	M	17	U	LE3	T	3	1	services	services	course	mother
392	MS	M	21	R	GT3	T	1	1	other	other	course	other
393	MS	M	18	R	LE3	T	3	2	services	other	course	mother
394	MS	M	19	U	LE3	T	1	1	other	at_home	course	father

Step 8. Did you notice the original dataframe is still lowercase? Why is that? Fix it and capitalize Mjob and Fjob.

In [24]:

df2["Mjob"] = df2["Mjob"].apply(Cap)
df2['Fjob'] = df2['Fjob'].apply(Cap)
df2.tail()

Out[24]:

	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	Fjob	reason	guardian
390	MS	M	20	U	LE3	A	2	2	Services	Services	course	other
391	MS	M	17	U	LE3	T	3	1	Services	Services	course	mother
392	MS	M	21	R	GT3	T	1	1	Other	Other	course	other
393	MS	M	18	R	LE3	T	3	2	Services	Other	course	mother
394	MS	M	19	U	LE3	T	1	1	Other	At_home	course	father

Step 9. Create a function called majority that return a boolean value to a new column called legal_drinker (Consider majority as older than 17 years old)

In [25]:

def majority(x):
    if x > 17:
        return True
    else:
        return False
    

In [27]:

df2['legal_drinker'] = df2['age'].apply(majority)
df2[:5]

Out[27]:

	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	Fjob	reason	guardian	legal_drinker
0	GP	F	18	U	GT3	A	4	4	At_home	Teacher	course	mother	True
1	GP	F	17	U	GT3	T	1	1	At_home	Other	course	father	False
2	GP	F	15	U	LE3	T	1	1	At_home	Other	other	mother	False
3	GP	F	15	U	GT3	T	4	2	Health	Services	home	mother	False
4	GP	F	16	U	GT3	T	3	3	Other	Other	home	father	False

Step 10. Multiply every number of the dataset by 10.

I know this makes no sense, don't forget it is just an exercise

In [33]:

def multiple10(x):
    if type(x) == int:
        return x * 10
    return x

In [36]:

# applymap : Apply a function to a Dataframe elementwise.
# 각각 연산해주기 위해서 applymap 이용
df2.applymap(multiple10).head()

Out[36]:

	school	sex	age	address	famsize	Pstatus	Medu	Fedu	Mjob	Fjob	reason	guardian	legal_drinker
0	GP	F	180	U	GT3	A	40	40	At_home	Teacher	course	mother	True
1	GP	F	170	U	GT3	T	10	10	At_home	Other	course	father	False
2	GP	F	150	U	LE3	T	10	10	At_home	Other	other	mother	False
3	GP	F	150	U	GT3	T	40	20	Health	Services	home	mother	False
4	GP	F	160	U	GT3	T	30	30	Other	Other	home	father	False