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Commit bdcd7018 authored by Bengt Giger's avatar Bengt Giger
Browse files

Initial commit

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Global Land and Ocean Temperature Anomalies, April-August
Units: Degrees Celsius
Base Period: 1901-2000
Missing: -999
Year,Value
1880,-0.09
1881,-0.03
1882,-0.13
1883,-0.12
1884,-0.27
1885,-0.29
1886,-0.22
1887,-0.27
1888,-0.13
1889,-0.06
1890,-0.33
1891,-0.20
1892,-0.33
1893,-0.29
1894,-0.34
1895,-0.24
1896,-0.11
1897,-0.03
1898,-0.26
1899,-0.18
1900,-0.07
1901,-0.11
1902,-0.28
1903,-0.40
1904,-0.47
1905,-0.26
1906,-0.18
1907,-0.37
1908,-0.41
1909,-0.47
1910,-0.34
1911,-0.49
1912,-0.28
1913,-0.35
1914,-0.20
1915,-0.07
1916,-0.32
1917,-0.36
1918,-0.37
1919,-0.22
1920,-0.22
1921,-0.19
1922,-0.25
1923,-0.30
1924,-0.22
1925,-0.20
1926,-0.13
1927,-0.19
1928,-0.22
1929,-0.32
1930,-0.14
1931,-0.06
1932,-0.14
1933,-0.22
1934,-0.10
1935,-0.20
1936,-0.11
1937,-0.01
1938,-0.04
1939,-0.02
1940,0.15
1941,0.27
1942,0.10
1943,0.07
1944,0.28
1945,0.22
1946,-0.04
1947,0.00
1948,-0.02
1949,-0.07
1950,-0.10
1951,0.05
1952,0.06
1953,0.16
1954,-0.12
1955,-0.12
1956,-0.19
1957,0.11
1958,0.09
1959,0.09
1960,0.02
1961,0.12
1962,0.08
1963,0.08
1964,-0.12
1965,-0.09
1966,0.02
1967,0.03
1968,-0.04
1969,0.13
1970,0.05
1971,-0.08
1972,0.07
1973,0.21
1974,-0.01
1975,0.04
1976,-0.07
1977,0.23
1978,0.07
1979,0.19
1980,0.28
1981,0.28
1982,0.17
1983,0.31
1984,0.18
1985,0.15
1986,0.23
1987,0.37
1988,0.40
1989,0.29
1990,0.44
1991,0.44
1992,0.23
1993,0.30
1994,0.36
1995,0.44
1996,0.34
1997,0.49
1998,0.73
1999,0.41
2000,0.46
2001,0.59
2002,0.59
2003,0.61
2004,0.50
2005,0.67
2006,0.60
2007,0.60
2008,0.54
2009,0.65
2010,0.76
2011,0.62
2012,0.72
2013,0.66
2014,0.79
2015,0.88
2016,0.98
2017,0.89
%% Cell type:markdown id:c90f2d96 tags:
### JupyterNotebook example: Load and plot global temperature anomaly data
Data is available for download at the NOAA (National Oceanic and Atmospheric Administration) National Centers for Environmental Information website https://www.ncdc.noaa.gov/cag/global/time-series
%% Cell type:code id:e33460e5-7bfb-41d4-96d9-1b48a73dd2f8 tags:
``` python
import matplotlib.pyplot as plt
import numpy as np
data = np.genfromtxt('../test_data/data.csv', delimiter=',', dtype=None, skip_header=5, names=('year', 'anomaly'))
```
%% Cell type:code id:e6312b7d-c583-4a16-8d41-bf6a2fc92dd8 tags:
``` python
plt.title('Global Land and Ocean Temperature Anomalies, April-August')
plt.xlabel('year')
plt.ylabel('degrees Celsius +/- from average')
plt.bar(data['year'], data['anomaly'], color='blue')
plt.show()
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:code id:1a46b559 tags:
``` python
```
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