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)
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
Content source: DaveBackus/Data_Bootcamp
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