Climate data

There's a lot of climate data available online. This notebook follows Radford Neal's blog post and his links to climate data from NASA. See also his links to followup posts and R code. This is not an area in which I have even a little expertise, so I make no claims any of this makes any sense.

Data links

This notebook written by Dave Backus for the NYU Stern course Data Bootcamp.

Warning: This doesn't work yet.


In [22]:
import pandas as pd
import numpy as np

In [23]:
url = 'http://data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt'
giss = pd.read_csv(url, na_values=['***', '****'], sep=' +', skiprows=7, engine='python')
giss


Out[23]:
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year.1
0 1880 -29 -20 -18 -27 -14 -28 -22 -7 -16 -15 -18 -20 -19 NaN NaN -19 -19 -16 1880
1 1881 -9 -13 2 -2 -4 -28 -5 -2 -8 -19 -25 -14 -11 -11 -14 -1 -12 -17 1881
2 1882 10 9 2 -19 -17 -24 -9 4 0 -22 -20 -24 -9 -8 1 -11 -10 -14 1882
3 1883 -33 -41 -17 -24 -25 -11 -7 -12 -18 -11 -19 -17 -20 -20 -33 -22 -10 -16 1883
4 1884 -18 -11 -33 -35 -31 -37 -33 -25 -22 -21 -29 -28 -27 -26 -15 -33 -32 -24 1884
5 1885 -64 -29 -23 -43 -41 -50 -28 -27 -19 -18 -21 -5 -31 -33 -40 -36 -35 -19 1885
6 1886 -41 -45 -41 -29 -27 -38 -16 -31 -19 -24 -25 -24 -30 -28 -30 -32 -28 -23 1886
7 1887 -65 -48 -31 -37 -33 -20 -18 -26 -19 -31 -25 -37 -32 -31 -46 -33 -21 -25 1887
8 1888 -43 -42 -47 -28 -22 -20 -8 -10 -7 2 0 -11 -20 -22 -40 -32 -13 -2 1888
9 1889 -19 15 5 5 -2 -11 -4 -17 -18 -21 -30 -29 -10 -9 -5 3 -11 -23 1889
10 1890 -46 -46 -40 -37 -47 -27 -28 -35 -35 -22 -36 -29 -36 -36 -41 -42 -30 -31 1890
11 1891 -45 -48 -14 -25 -16 -21 -21 -20 -13 -22 -36 0 -23 -26 -40 -18 -21 -24 1891
12 1892 -25 -13 -35 -34 -24 -19 -26 -19 -24 -15 -49 -27 -26 -24 -13 -31 -21 -29 1892
13 1893 -67 -50 -23 -32 -34 -22 -12 -22 -17 -15 -16 -36 -29 -28 -48 -29 -19 -16 1893
14 1894 -54 -31 -20 -41 -29 -43 -31 -27 -21 -16 -24 -21 -30 -31 -40 -30 -33 -20 1894
15 1895 -43 -42 -29 -22 -22 -24 -16 -15 -1 -10 -15 -11 -21 -22 -35 -24 -18 -9 1895
16 1896 -22 -14 -29 -32 -19 -13 -5 -9 -4 5 -15 -11 -14 -14 -16 -27 -9 -5 1896
17 1897 -22 -19 -11 -1 0 -12 -4 -3 -4 -8 -17 -25 -10 -9 -17 -4 -6 -10 1897
18 1898 -6 -33 -55 -33 -36 -20 -22 -23 -19 -32 -35 -21 -28 -28 -21 -41 -22 -29 1898
19 1899 -18 -39 -35 -21 -20 -26 -12 -3 0 1 12 -26 -16 -15 -26 -25 -14 4 1899
20 1900 -39 -7 2 -14 -5 -14 -8 -3 2 9 -12 -13 -9 -10 -24 -6 -8 0 1900
21 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year
22 1901 -29 -5 5 -6 -18 -10 -8 -12 -16 -29 -17 -29 -14 -13 -15 -6 -10 -20 1901
23 1902 -20 -4 -29 -28 -31 -35 -25 -28 -21 -28 -36 -46 -28 -26 -17 -29 -30 -28 1902
24 1903 -28 -7 -22 -40 -42 -44 -30 -43 -42 -41 -38 -47 -35 -35 -27 -35 -39 -40 1903
25 1904 -64 -54 -47 -49 -50 -49 -48 -43 -46 -34 -16 -28 -44 -46 -55 -48 -47 -32 1904
26 1905 -37 -58 -25 -36 -33 -31 -24 -20 -14 -22 -8 -20 -27 -28 -41 -31 -25 -15 1905
27 1906 -30 -33 -14 -2 -20 -21 -25 -18 -25 -20 -38 -17 -22 -22 -27 -12 -21 -27 1906
28 1907 -43 -52 -24 -40 -46 -43 -34 -36 -31 -24 -51 -49 -39 -37 -37 -37 -38 -35 1907
29 1908 -45 -35 -57 -46 -40 -38 -34 -45 -32 -43 -50 -49 -43 -43 -43 -48 -39 -42 1908
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
121 1996 27 49 34 36 29 26 36 50 26 21 41 40 34 34 35 33 37 29 1996
122 1997 32 37 51 37 38 54 36 41 56 64 65 59 48 46 37 42 44 62 1997
123 1998 61 88 62 64 70 77 70 69 46 46 49 57 63 63 70 65 72 47 1998
124 1999 48 67 33 34 32 37 41 35 43 42 40 47 42 43 57 33 38 42 1999
125 2000 26 59 59 59 39 43 42 43 43 29 33 30 42 44 44 53 42 35 2000
126 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year
127 2001 44 46 57 52 59 54 60 49 55 51 70 54 54 52 40 56 54 59 2001
128 2002 74 76 90 58 65 55 61 54 64 56 59 42 63 64 68 71 57 60 2002
129 2003 72 55 57 54 63 48 55 66 67 75 54 73 62 59 56 58 56 65 2003
130 2004 58 69 65 62 42 42 27 45 53 66 72 51 54 56 67 56 38 63 2004
131 2005 72 58 69 69 64 65 65 62 76 79 75 67 68 67 60 67 64 77 2005
132 2006 56 71 63 49 47 64 55 71 64 70 73 77 63 62 65 53 63 69 2006
133 2007 96 69 70 75 68 58 62 61 64 60 57 49 66 68 81 71 60 61 2007
134 2008 26 35 74 53 51 47 60 44 64 67 67 54 54 53 37 59 51 66 2008
135 2009 61 53 53 60 64 66 72 67 70 65 78 65 64 64 56 59 68 71 2009
136 2010 73 79 92 87 75 64 61 65 62 71 79 49 72 73 72 85 63 71 2010
137 2011 50 51 63 65 53 58 74 73 57 66 55 53 60 60 50 61 68 59 2011
138 2012 45 48 57 68 76 62 56 63 74 79 74 51 63 63 49 67 60 76 2012
139 2013 66 56 64 52 61 65 59 66 77 69 80 66 65 64 58 59 63 75 2013
140 2014 73 50 77 78 86 66 58 82 90 85 68 78 74 73 63 80 68 81 2014
141 2015 81 87 90 73 78 77 73 77 82 106 105 NaN NaN 84 82 80 76 98 2015
142 Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year
143 Divide by 100 to get changes in degrees Celsius (deg-C). None None None None None None None None None None
144 Multiply that result by 1.8(=9/5) to get changes in degrees Fahrenheit (deg-F). None None None None None None None None
145 Best estimate for absolute global mean for 1951-1980 is 14.0 deg-C or 57.2 deg-F, None None None None None None
146 so add that to the temperature change if you want to use an absolute scale None None None None None
147 (this note applies to global annual means only, J-D and D-N !) None None None None None None None None
148 Example -- Table Value : 40 None None None None None None None None None None None None None None
149 change : 0.40 deg-C or 0.72 deg-F None None None None None None None None None None None None None
150 abs. scale if global annual mean : 14.40 deg-C or 57.92 deg-F None None None None None None None None

151 rows × 20 columns


In [17]:
giss.shape


Out[17]:
(10, 1)

In [ ]:
lis(giss)

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