오답노트. Introduction to Pandas (2877~2891)

 

2877. Create a DataFrame from List

Write a solution to create a DataFrame from a 2D list called student_data. This 2D list contains the IDs and ages of some students.

The DataFrame should have two columns, student_id and age, and be in the same order as the original 2D list.

The result format is in the following example.

student_data:
[ 
[1, 15],
[2, 11],
[3, 11],
[4, 20]
]

 

정답은,

import pandas as pd
def createDataframe(student_data: List[List[int]] ->pd.DataFrame:
	df= pd.DataFrame(student_data, columns=['student_id', 'age'])
    return df

 

2878. Get the Size of a DataFrame

DataFrame players:

Column Name	Type
player_id	int
name	object
age	int
position	object
...	...

Write a solution to calculate and display the number of rows and columns of players.

Return the result as an array:

[number of rows, number of columns]

The result format is in the following example.

 

정답은,

import pandas as pd
def getDataframeSize(players: pd.DataFrame) -> List[int]:
	return [players.shape[0], players.shape[1]]

 

2880. Select Data

DataFrame students

Column Name	Type
student_id	int
name	object
age	int

Write a solution to select the name and age of the student with student_id = 101.

The result format is in the following example.

 

정답은

import pandas as pd
def selectData(students: pd.DataFrame) -> pd.DataFrame:
	students[students['student_id'==101]][['name','age']]

 

2882. Drop Duplicate Rows

DataFrame customers

Column Name	Type
customer_id	int
name	object
email	object

There are some duplicate rows in the DataFrame based on the email column.

Write a solution to remove these duplicate rows and keep only the first occurrence.

The result format is in the following example.

Example 1:
Input:

customer_id	name	email
1	Ella	emily@example.com
2	David	michael@example.com
3	Zachary	sarah@example.com
4	Alice	john@example.com
5	Finn	john@example.com
6	Violet	alice@example.com

Output:

customer_id	name	email
1	Ella	emily@example.com
2	David	michael@example.com
3	Zachary	sarah@example.com
4	Alice	john@example.com
6	Violet	alice@example.com

Explanation:
Alic (customer_id = 4) and Finn (customer_id = 5) both use john@example.com, so only the first occurrence of this email is retained.

 

정답은,

import pandas as pd
def dropDuplicateEmails(customers: pd.DataFrame) -> pd.DataFrame:
	df=pd.Dataframe(customers)
    df.drop_duplicates(subset='email', keep='first', inplace=True)
    return df

 

2883. Drop Missing Data

DataFrame students

Column Name	Type
student_id	int
name	object
age	int

There are some rows having missing values in the name column.

Write a solution to remove the rows with missing values.

The result format is in the following example.

Example 1:

Input:

student_id	name	age
32	Piper	5
217	None	19
779	Georgia	20
849	Willow	14

Output:

student_id	name	age
32	Piper	5
779	Georgia	20
849	Willow	14

Explanation:
Student with id 217 havs empty value in the name column, so it will be removed.

 

정답은,

import pandas as pd
def dropMissingData(students: pd.DataFrame) -> pd.DataFrame:
    df=pd.DataFrame(students)
    df.dropna(subset=['name'], inplace=True)
    return df

 

2885. Rename Columns

DataFrame students

Column Name	Type
id	int
first	object
last	object
age	int

Write a solution to rename the columns as follows:

id to student_id
first to first_name
last to last_name
age to age_in_years
The result format is in the following example.

Example 1:
Input:

id	first	last	age
1	Mason	King	6
2	Ava	Wright	7
3	Taylor	Hall	16
4	Georgia	Thompson	18
5	Thomas	Moore	10

Output:

student_id	first_name	last_name	age_in_years
1	Mason	King	6
2	Ava	Wright	7
3	Taylor	Hall	16
4	Georgia	Thompson	18
5	Thomas	Moore	10

Explanation:
The column names are changed accordingly.

 

정답은,

import pandas as pd
def renameColumns(students: pd.DataFrame) -> pd.DataFrame:
    df = pd.DataFrame(students)
    df.rename(columns={'id':'student_id', 'first':'first_name', 'last':'last_name', 'age':'age_in_years'}, inplace=True)
    return df

 

2886. Change Data Type

DataFrame students

Column Name	Type
student_id	int
name	object
age	int
grade	float

Write a solution to correct the errors:

The grade column is stored as floats, convert it to integers.

The result format is in the following example.

Example 1:
Input:
DataFrame students:

student_id	name	age	grade
1	Ava	6	73.0
2	Kate	15	87.0

Output:

student_id	name	age	grade
1	Ava	6	73
2	Kate	15	87

Explanation:
The data types of the column grade is converted to int.

 

정답은,

import pandas as pd
def changeDatatype(students: pd.DataFrame) -> pd.DataFrame:
    students['grade']=students['grade'].astype(int)
    return students

 

2887. Fill Missing Data

DataFrame products

Column Name	Type
name	object
quantity	int
price	int

Write a solution to fill in the missing value as 0 in the quantity column.

The result format is in the following example.

Example 1:
Input:

name	quantity	price
Wristwatch	None	135
WirelessEarbuds	None	821
GolfClubs	779	9319
Printer	849	3051

Output:

name	quantity	price
Wristwatch	0	135
WirelessEarbuds	0	821
GolfClubs	779	9319
Printer	849	3051

Explanation:
The quantity for Wristwatch and WirelessEarbuds are filled by 0.

 

정답은,

import pandas as pd
def fillMissingValues(products: pd.DataFrame) -> pd.DataFrame:
    products['quantity'].fillna(0, inplace=True)
    return products

 

2889. Reshape Data: Pivot

DataFrame weather

Column Name	Type
city	object
month	object
temperature	int

Write a solution to pivot the data so that each row represents temperatures for a specific month, and each city is a separate column.

The result format is in the following example.

Example 1:
Input:

city	month	temperature
Jacksonville	January	13
Jacksonville	February	23
Jacksonville	March	38
Jacksonville	April	5
Jacksonville	May	34
ElPaso	January	20
ElPaso	February	6
ElPaso	March	26
ElPaso	April	2
ElPaso	May	43

Output:

month	ElPaso	Jacksonville
April	2	5
February	6	23
January	20	13
March	26	38
May	43	34

Explanation:
The table is pivoted, each column represents a city, and each row represents a specific month.

 

정답은,

import pandas as pd
def pivotTable(weather: pd.DataFrame) -> pd.DataFrame:
    return weather.pivot(index='month', columns='city', values='temperature')

 

2890. Reshape Data: Melt

DataFrame report

Column Name	Type
product	object
quarter_1	int
quarter_2	int
quarter_3	int
quarter_4	int

Write a solution to reshape the data so that each row represents sales data for a product in a specific quarter.

The result format is in the following example.

Example 1:

Input:

product	quarter_1	quarter_2	quarter_3	quarter_4
Umbrella	417	224	379	611
SleepingBag	800	936	93	875

Output:

product	quarter	sales
Umbrella	quarter_1	417
SleepingBag	quarter_1	800
Umbrella	quarter_2	224
SleepingBag	quarter_2	936
Umbrella	quarter_3	379
SleepingBag	quarter_3	93
Umbrella	quarter_4	611
SleepingBag	quarter_4	875

Explanation:
The DataFrame is reshaped from wide to long format. Each row represents the sales of a product in a quarter.

 

정답은,

import pandas as pd
def meltTable(report: pd.DataFrame) -> pd.DataFrame:
    return pd.melt(report, id_vars=['product'], var_name='quarter', value_name='sales')

 

2891. Method Chaining

DataFrame animals

Column Name	Type
name	object
species	object
age	int
weight	int

Write a solution to list the names of animals that weigh strictly more than 100 kilograms.

Return the animals sorted by weight in descending order.

The result format is in the following example.

Example 1:

Input:
DataFrame animals:

name	species	age	weight
Tatiana	Snake	98	464
Khaled	Giraffe	50	41
Alex	Leopard	6	328
Jonathan	Monkey	45	463
Stefan	Bear	100	50
Tommy	Panda	26	349

Output:

name
Tatiana
Jonathan
Tommy
Alex

Explanation:
All animals weighing more than 100 should be included in the results table.
Tatiana's weight is 464, Jonathan's weight is 463, Tommy's weight is 349, and Alex's weight is 328.
The results should be sorted in descending order of weight.

In Pandas, method chaining enables us to perform operations on a DataFrame without breaking up each operation into a separate line or creating multiple temporary variables.

Can you complete this task in just one line of code using method chaining?

 

정답은,

import pandas as pd
def findHeavyAnimals(animals: pd.DataFrame) -> pd.DataFrame:
    return animals[animals['weight']>100].sort_values('weight', ascending=False)[['name']]