Pandas - Gapminder Data 분석(TSV File) 2

1. Import Module

import pandas as pd
from print_df import print_df
import matplotlib.pyplot as plt

2. Data 분석

- DataFrame 여러 행을 추출 1(df.loc[인덱스 번호])

print_df(df.loc[[0, 1, 2]])

+---+-------------+-----------+------+--------------------+----------+-------------------+

+---+-------------+-----------+------+--------------------+----------+-------------------+

| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.4453145 |

| 1 | Afghanistan | Asia | 1957 | 30.331999999999997 | 9240934 | 820.8530296 |

| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.1007099999999 |

+---+-------------+-----------+------+--------------------+----------+-------------------+

Process finished with exit code 0

- DataFrame 여러 행을 추출 2(df.loc[인덱스 배열])

print_df(df.loc[840:851])

+-----+-------------+-----------+------+--------------------+----------+--------------------+

+-----+-------------+-----------+------+--------------------+----------+--------------------+

| 840 | Korea, Rep. | Asia | 1952 | 47.453 | 20947571 | 1030.592226 |

| 841 | Korea, Rep. | Asia | 1957 | 52.681000000000004 | 22611552 | 1487.593537 |

| 842 | Korea, Rep. | Asia | 1962 | 55.292 | 26420307 | 1536.3443869999999 |

| 843 | Korea, Rep. | Asia | 1967 | 57.716 | 30131000 | 2029.2281420000002 |

| 844 | Korea, Rep. | Asia | 1972 | 62.611999999999995 | 33505000 | 3030.87665 |

| 845 | Korea, Rep. | Asia | 1977 | 64.766 | 36436000 | 4657.22102 |

| 846 | Korea, Rep. | Asia | 1982 | 67.123 | 39326000 | 5622.942464 |

| 847 | Korea, Rep. | Asia | 1987 | 69.81 | 41622000 | 8533.088805 |

| 848 | Korea, Rep. | Asia | 1992 | 72.244 | 43805450 | 12104.27872 |

| 849 | Korea, Rep. | Asia | 1997 | 74.64699999999999 | 46173816 | 15993.52796 |

| 850 | Korea, Rep. | Asia | 2002 | 77.045 | 47969150 | 19233.98818 |

| 851 | Korea, Rep. | Asia | 2007 | 78.623 | 49044790 | 23348.139730000003 |

+-----+-------------+-----------+------+--------------------+----------+--------------------+

Process finished with exit code 0

- 특정 데이터 추출(df.loc[행 이름, 열이름])

print(df.loc[851, 'pop'])

49044790

Process finished with exit code 0

- 행 번호 840 ~ 851까지의 pop 열 의 Data만 추출

korea_pop = df.loc[840:851, 'pop']
print(korea_pop)

840 20947571

841 22611552

842 26420307

843 30131000

844 33505000

845 36436000

846 39326000

847 41622000

848 43805450

849 46173816

850 47969150

851 49044790

Name: pop, dtype: int64

Process finished with exit code 0

- 행 번호 840 ~ 851 까지의 'country', 'year', 'pop' 열들의 데이터 추출

korea_year_pop = df.loc[840:851, ['country', 'year', 'pop']]
print_df(korea_year_pop)

+-----+-------------+------+----------+

+-----+-------------+------+----------+

| 840 | Korea, Rep. | 1952 | 20947571 |

| 841 | Korea, Rep. | 1957 | 22611552 |

| 842 | Korea, Rep. | 1962 | 26420307 |

| 843 | Korea, Rep. | 1967 | 30131000 |

| 844 | Korea, Rep. | 1972 | 33505000 |

| 845 | Korea, Rep. | 1977 | 36436000 |

| 846 | Korea, Rep. | 1982 | 39326000 |

| 847 | Korea, Rep. | 1987 | 41622000 |

| 848 | Korea, Rep. | 1992 | 43805450 |

| 849 | Korea, Rep. | 1997 | 46173816 |

| 850 | Korea, Rep. | 2002 | 47969150 |

| 851 | Korea, Rep. | 2007 | 49044790 |

+-----+-------------+------+----------+

Process finished with exit code 0

- 연도별로 그룹화 및 그룹 타입 확인

grouped_year = df.groupby('year')
print(type(grouped_year))
print(grouped_year)

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000185FFC0B5F8>

Process finished with exit code 0

- 연도 그룹 평균값을 계산할 컬럼 추출 / 타입 확인

grouped_year = df.groupby('year')
grouped_year_lifeExp = grouped_year['lifeExp']
print(type(grouped_year_lifeExp))
print(grouped_year_lifeExp)

<pandas.core.groupby.generic.SeriesGroupBy object at 0x00000210C9BCF6D8>

Process finished with exit code 0

- 추출된 컬럼의 평균 값 계산

grouped_year = df.groupby('year')
grouped_year_lifeExp = grouped_year['lifeExp']
mean = grouped_year_lifeExp.mean()
print(mean)

year

1952 49.057620

1957 51.507401

1962 53.609249

1967 55.678290

1972 57.647386

1977 59.570157

1982 61.533197

1987 63.212613

1992 64.160338

1997 65.014676

2002 65.694923

2007 67.007423

Name: lifeExp, dtype: float64

Process finished with exit code 0

- 년도별 기대 수명의 평균 값 추출(위에 3단계 한줄로)

print(df.groupby('year')['lifeExp'].mean())

year

1952 49.057620

1957 51.507401

1962 53.609249

1967 55.678290

1972 57.647386

1977 59.570157

1982 61.533197

1987 63.212613

1992 64.160338

1997 65.014676

2002 65.694923

2007 67.007423

Name: lifeExp, dtype: float64

Process finished with exit code 0

- 전체 인구의 기대 수명 평균값

print(df['lifeExp'].mean())

59.474439366197174

Process finished with exit code 0

- 연도별 인구수의 평균

print(df.groupby('year')['pop'].mean())

year

1952 1.695040e+07

1957 1.876341e+07

1962 2.042101e+07

1967 2.265830e+07

1972 2.518998e+07

1977 2.767638e+07

1982 3.020730e+07

1987 3.303857e+07

1992 3.599092e+07

1997 3.883947e+07

2002 4.145759e+07

2007 4.402122e+07

Name: pop, dtype: float64

Process finished with exit code 0

- 연도별 > 국가별 그룹 기준 기대 수명 평균값

print(df.groupby(['year', 'continent'])['lifeExp'].mean())

year continent

1952 Africa 39.135500

Americas 53.279840

Asia 46.314394

Europe 64.408500

Oceania 69.255000

1957 Africa 41.266346

Americas 55.960280

Asia 49.318544

Europe 66.703067

Oceania 70.295000

1962 Africa 43.319442

Americas 58.398760

Asia 51.563223

Europe 68.539233

Oceania 71.085000

1967 Africa 45.334538

Americas 60.410920

Asia 54.663640

Europe 69.737600

Oceania 71.310000

1972 Africa 47.450942

Americas 62.394920

Asia 57.319269

Europe 70.775033

Oceania 71.910000

1977 Africa 49.580423

Americas 64.391560

Asia 59.610556

Europe 71.937767

Oceania 72.855000

1982 Africa 51.592865

Americas 66.228840

Asia 62.617939

Europe 72.806400

Oceania 74.290000

1987 Africa 53.344788

Americas 68.090720

Asia 64.851182

Europe 73.642167

Oceania 75.320000

1992 Africa 53.629577

Americas 69.568360

Asia 66.537212

Europe 74.440100

Oceania 76.945000

1997 Africa 53.598269

Americas 71.150480

Asia 68.020515

Europe 75.505167

Oceania 78.190000

2002 Africa 53.325231

Americas 72.422040

Asia 69.233879

Europe 76.700600

Oceania 79.740000

2007 Africa 54.806038

Americas 73.608120

Asia 70.728485

Europe 77.648600

Oceania 80.719500

Name: lifeExp, dtype: float64

Process finished with exit code 0

- 국가별 > 연도별 그룹 기준 기대 수명 평균값

print(df.groupby(['continent', 'year'])['lifeExp'].mean())

continent year

Africa 1952 39.135500

1957 41.266346

1962 43.319442

1967 45.334538

1972 47.450942

1977 49.580423

1982 51.592865

1987 53.344788

1992 53.629577

1997 53.598269

2002 53.325231

2007 54.806038

Americas 1952 53.279840

1957 55.960280

1962 58.398760

1967 60.410920

1972 62.394920

1977 64.391560

1982 66.228840

1987 68.090720

1992 69.568360

1997 71.150480

2002 72.422040

2007 73.608120

Asia 1952 46.314394

1957 49.318544

1962 51.563223

1967 54.663640

1972 57.319269

1977 59.610556

1982 62.617939

1987 64.851182

1992 66.537212

1997 68.020515

2002 69.233879

2007 70.728485

Europe 1952 64.408500

1957 66.703067

1962 68.539233

1967 69.737600

1972 70.775033

1977 71.937767

1982 72.806400

1987 73.642167

1992 74.440100

1997 75.505167

2002 76.700600

2007 77.648600

Oceania 1952 69.255000

1957 70.295000

1962 71.085000

1967 71.310000

1972 71.910000

1977 72.855000

1982 74.290000

1987 75.320000

1992 76.945000

1997 78.190000

2002 79.740000

2007 80.719500

Name: lifeExp, dtype: float64

Process finished with exit code 0

- gapminder 데이터 프레임에서 나라이름이 'Korea, Rep'만 추출

print_df(df[df['country'] == 'Korea, Rep.'])