데이터프레임 분석(.value_counts(), .shape(), .info(), .sort_values())

퀴즈 해설

 

df['City / Urban area'].value_counts()

df['City / Urban area'].value_counts().shape

(249,)

df['Country'].value_counts().shape

(61,)

퀴즈 해설

 

먼저 인구 밀도를 계산

df["Density"] = df["Population"] / df["Land area (in sqKm)"]

계산한 인구 밀도에 불린 연산을 적용하여 인덱싱

df_high_density = df[df["Density"] > 10000]

info() 메소드를 적용

df_high_density.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 19 entries, 32 to 129
Data columns (total 5 columns):
City / Urban area      19 non-null object
Country                19 non-null object
Population             19 non-null int64
Land area (in sqKm)    19 non-null int64
Density                19 non-null float64
dtypes: float64(1), int64(2), object(2)
memory usage: 912.0+ bytes

퀴즈 해설

 

df["Density"] = df["Population"] / df["Land area (in sqKm)"]

 

density_ranks = df.sort_values(by="Density", ascending = False)

 

density_ranks['City / Urban area']

75                      Mumbai
74                     Kolkata
101                    Karachi
                ...           
57                         Pau
220                    Hickory
196            Barnstable Town
Name: City / Urban area, Length: 249, dtype: object

 

퀴즈 해설

 

먼저, 각 도시의 국가 정보를 가져옵니다.

import pandas as pd

world_cities = pd.read_csv("data/world_cities.csv")

world_cities['Country']

0       Argentina
1       Australia
2       Australia
3       Australia
4       Australia
5       Australia
6       Australia
7         Austria
8      Azerbaijan
9         Belgium
10        Belgium
          ...    
220           USA
221           USA

countries = world_cities['Country'].value_counts()

USA             106
France           15
Brazil           10
Canada            9
               ... 
Vietnam           1
Sudan             1

countries[countries == 4]

Italy    4
Name: Country, dtype: int64