世界の年平均気温偏差

pandasを用いてネット上に公開されているcsvファイルを読み込む例。

code:p.py

import pandas as pd

code:an_wld.csv

年,世界全体,北半球,南半球

1891,-0.78,-0.88,-0.68

1892,-0.89,-1.00,-0.74

1893,-0.94,-1.06,-0.79

1894,-0.86,-0.93,-0.77

1895,-0.82,-0.95,-0.67

1896,-0.61,-0.69,-0.52

1897,-0.62,-0.69,-0.55

1898,-0.79,-0.82,-0.76

1899,-0.70,-0.74,-0.67

1900,-0.62,-0.66,-0.58

1901,-0.70,-0.72,-0.68

1902,-0.83,-0.93,-0.73

1903,-0.89,-0.94,-0.85

1904,-0.94,-1.01,-0.86

1905,-0.82,-0.89,-0.74

1906,-0.74,-0.77,-0.69

1907,-0.91,-1.01,-0.79

1908,-0.94,-1.00,-0.88

1909,-0.94,-0.99,-0.87

1910,-0.92,-0.96,-0.87

1911,-0.93,-0.95,-0.92

1912,-0.86,-1.00,-0.68

1913,-0.83,-0.94,-0.69

1914,-0.66,-0.72,-0.57

1915,-0.61,-0.67,-0.50

1916,-0.85,-0.90,-0.76

1917,-0.91,-0.97,-0.79

1918,-0.78,-0.83,-0.67

1919,-0.73,-0.82,-0.55

1920,-0.69,-0.74,-0.61

1921,-0.62,-0.61,-0.62

1922,-0.71,-0.75,-0.67

1923,-0.70,-0.73,-0.66

1924,-0.71,-0.72,-0.70

1925,-0.63,-0.62,-0.62

1926,-0.51,-0.52,-0.51

1927,-0.62,-0.61,-0.63

1928,-0.65,-0.67,-0.62

1929,-0.75,-0.81,-0.66

1930,-0.56,-0.55,-0.57

1931,-0.49,-0.49,-0.50

1932,-0.56,-0.55,-0.57

1933,-0.66,-0.71,-0.60

1934,-0.55,-0.56,-0.54

1935,-0.59,-0.61,-0.57

1936,-0.53,-0.53,-0.53

1937,-0.44,-0.45,-0.44

1938,-0.44,-0.43,-0.45

1939,-0.52,-0.53,-0.52

1940,-0.55,-0.62,-0.38

1941,-0.45,-0.54,-0.28

1942,-0.48,-0.55,-0.36

1943,-0.49,-0.50,-0.47

1944,-0.36,-0.39,-0.29

1945,-0.49,-0.57,-0.33

1946,-0.58,-0.61,-0.53

1947,-0.58,-0.65,-0.48

1948,-0.51,-0.54,-0.47

1949,-0.51,-0.55,-0.45

1950,-0.63,-0.71,-0.53

1951,-0.50,-0.51,-0.47

1952,-0.48,-0.55,-0.39

1953,-0.41,-0.41,-0.40

1954,-0.64,-0.67,-0.61

1955,-0.67,-0.68,-0.67

1956,-0.76,-0.85,-0.64

1957,-0.49,-0.59,-0.36

1958,-0.44,-0.50,-0.36

1959,-0.50,-0.54,-0.44

1960,-0.57,-0.60,-0.52

1961,-0.49,-0.54,-0.42

1962,-0.49,-0.52,-0.47

1963,-0.45,-0.45,-0.43

1964,-0.70,-0.75,-0.63

1965,-0.63,-0.74,-0.50

1966,-0.55,-0.58,-0.52

1967,-0.55,-0.55,-0.56

1968,-0.60,-0.65,-0.53

1969,-0.48,-0.65,-0.27

1970,-0.53,-0.64,-0.40

1971,-0.66,-0.77,-0.54

1972,-0.54,-0.77,-0.26

1973,-0.41,-0.55,-0.26

1974,-0.68,-0.82,-0.51

1975,-0.63,-0.70,-0.53

1976,-0.74,-0.89,-0.55

1977,-0.45,-0.56,-0.31

1978,-0.52,-0.63,-0.38

1979,-0.39,-0.56,-0.19

1980,-0.38,-0.53,-0.21

1981,-0.34,-0.36,-0.31

1982,-0.47,-0.61,-0.31

1983,-0.31,-0.44,-0.16

1984,-0.51,-0.67,-0.31

1985,-0.51,-0.72,-0.28

1986,-0.42,-0.57,-0.25

1987,-0.27,-0.46,-0.06

1988,-0.28,-0.37,-0.17

1989,-0.35,-0.43,-0.26

1990,-0.19,-0.22,-0.17

1991,-0.24,-0.30,-0.16

1992,-0.39,-0.52,-0.23

1993,-0.35,-0.48,-0.20

1994,-0.27,-0.31,-0.22

1995,-0.15,-0.14,-0.17

1996,-0.28,-0.40,-0.15

1997,-0.09,-0.14,-0.02

1998,+0.06,+0.02,+0.11

1999,-0.17,-0.18,-0.15

2000,-0.19,-0.20,-0.17

2001,-0.05,-0.07,-0.04

2002,0.00,-0.04,+0.03

2003,+0.01,+0.01,+0.01

2004,-0.05,-0.05,-0.05

2005,+0.06,+0.09,+0.02

2006,+0.02,+0.05,0.00

2007,0.00,+0.06,-0.06

2008,-0.08,-0.07,-0.08

2009,+0.03,-0.02,+0.08

2010,+0.11,+0.14,+0.07

2011,-0.05,-0.06,-0.04

2012,+0.01,0.00,+0.03

2013,+0.07,+0.06,+0.08

2014,+0.13,+0.15,+0.10

2015,+0.30,+0.38,+0.20

2016,+0.35,+0.43,+0.26

2017,+0.26,+0.34,+0.17

2018,+0.16,+0.19,+0.13

2019,+0.31,+0.38,+0.23

2020,+0.34,+0.51,+0.16

2021,+0.22,+0.35,+0.09

2022,+0.24,+0.35,+0.11

2023,+0.54,+0.68,+0.38

2024,+0.62,+0.79,+0.42

系列「年」をインデックスとし、更にdatetime型とする。

code:p.py

dataset = pd.read_csv(

encoding='shift_jis',

index_col='年',

)

'''

世界全体 北半球 南半球

年

1891-01-01 -0.78 -0.88 -0.68

1892-01-01 -0.89 -1.00 -0.74

1893-01-01 -0.94 -1.06 -0.79

1894-01-01 -0.86 -0.93 -0.77

1895-01-01 -0.82 -0.95 -0.67

... ... ... ...

2020-01-01 0.34 0.51 0.16

2021-01-01 0.22 0.35 0.09

2022-01-01 0.24 0.35 0.11

2023-01-01 0.54 0.68 0.38

2024-01-01 0.62 0.79 0.42

'''