Reading the data into a list You are surprised that the hottest year on record in Sacramento was more than a century ago You decide that you need to compute a moving average to observe the long term trend in the data Start a new program temp list py from your working version of read temp file py Comment out the line that printsout the year and temperature for each year in the file, and instead add the floating point temperatures one at a time to a growing Python list inside the for loop Test your code by printing the list of the loop that reads the file Below is an example of how your programs output should look Temperature anomaly filename SacramentoTemps csv 1 56, 0 08, 0 3, 1 44, , 0 06, 0 4, 0 48, 2 63, 0 18 Submit the completed program to Gradescope as temp list py This is SacramentoTemps csv Year Value 1880 1 56 1881 0 08 1882 0 3 1883 1 44 1884 2 29 1885 1 95 1886 0 63 1887 0 63 1888 0 72 1889 2 99 1890 0 5 1891 1 36 1892 1 03 1893 0 16 1894 0 01 1895 1 46 1896 1 23 1897 0 83 1898 0 91 1899 1 53 1900 1 08 1901 0 24 1902 1 08 1903 1 1 1904 0 83 1905 0 31 1906 0 22 1907 1 02 1908 1 05 1909 0 65 1910 0 65 1911 1 06 1912 0 65 1913 2 32 1914 1 12 1915 0 21 1916 0 57 1917 0 41 1918 0 13 1919 0 48 1920 0 67 1921 0 21 1922 0 65 1923 0 6 1924 1 1925 1 22 1926 1 1 1927 0 03 1928 0 61 1929 0 54 1930 0 66 1931 1 57 1932 0 15 1933 0 83 1934 0 35 1935 0 23 1936 0 66 1937 0 17 1938 0 56 1939 0 31 1940 0 09 1941 0 6 1942 0 51 1943 0 86 1944 0 09 1945 1 19 1946 0 77 1947 0 72 1948 0 33 1949 0 53 1950 0 08 1951 0 73 1952 0 31 1953 0 26 1954 1 9 1955 0 72 1956 0 95 1957 1 12 1958 1 28 1959 1 29 1960 0 28 1961 2 33 1962 0 32 1963 0 25 1964 0 13 1965 0 8 1966 0 1 1967 1 23 1968 0 06 1969 1 19 1970 1 51 1971 0 28 1972 0 15 1973 1 32 1974 0 72 1975 0 49 1976 1 43 1977 1 51 1978 1 61 1979 0 74 1980 1 41 1981 0 09 1982 0 33 1983 0 88 1984 0 06 1985 0 57 1986 0 83 1987 0 77 1988 0 21 1989 0 13 1990 0 54 1991 0 1 1992 0 21 1993 1 8 1994 1 47 1995 0 08 1996 1 28 1997 2 2 1998 0 49 1999 0 37 2000 0 73 2001 0 4 2002 0 15 2003 1 25 2004 0 03 2005 1 47 2006 0 39 2007 0 4 2008 0 59 2009 0 41 2010 0 34 2011 0 22 2012 1 03 2013 0 23 2014 0 06 2015 0 4 2016 0 48 2017 2 63 2018 0 18

Question: Reading the data into a list You are surprised that the hottest year on record in Sacramento was more than a century ago! You decide

Reading the data into a list

You are surprised that the hottest year on record in Sacramento was more than a century ago! You decide that you need to compute a moving average to observe the long-term trend in the data.

Start a new program temp_list.py from your working version of read_temp_file.py. Comment out the line that printsout the year and temperature for each year in the file, and instead add the floating point temperatures one at a time to a growing Python list inside the for loop. Test your code by printing the list of the loop that reads the file.

Below is an example of how your programs output should look:

Temperature anomaly filename:SacramentoTemps.csv [-1.56, -0.08, -0.3, -1.44, ..., -0.06, -0.4, 0.48, 2.63, 0.18]

Submit the completed program to Gradescope as temp_list.py

This is SacramentoTemps.csv

Year	Value
1880	-1.56
1881	-0.08
1882	-0.3
1883	-1.44
1884	-2.29
1885	-1.95
1886	-0.63
1887	0.63
1888	-0.72
1889	2.99
1890	-0.5
1891	-1.36
1892	1.03
1893	-0.16
1894	-0.01
1895	-1.46
1896	-1.23
1897	-0.83
1898	-0.91
1899	1.53
1900	1.08
1901	0.24
1902	-1.08
1903	-1.1
1904	-0.83
1905	-0.31
1906	-0.22
1907	-1.02
1908	-1.05
1909	-0.65
1910	-0.65
1911	-1.06
1912	-0.65
1913	-2.32
1914	1.12
1915	-0.21
1916	-0.57
1917	0.41
1918	-0.13
1919	0.48
1920	0.67
1921	0.21
1922	0.65
1923	-0.6
1924	-1
1925	-1.22
1926	-1.1
1927	-0.03
1928	0.61
1929	0.54
1930	0.66
1931	-1.57
1932	0.15
1933	0.83
1934	-0.35
1935	0.23
1936	-0.66
1937	0.17
1938	0.56
1939	0.31
1940	0.09
1941	-0.6
1942	-0.51
1943	-0.86
1944	-0.09
1945	-1.19
1946	0.77
1947	-0.72
1948	-0.33
1949	-0.53
1950	0.08
1951	0.73
1952	-0.31
1953	0.26
1954	-1.9
1955	-0.72
1956	-0.95
1957	-1.12
1958	1.28
1959	1.29
1960	0.28
1961	2.33
1962	0.32
1963	-0.25
1964	-0.13
1965	-0.8
1966	0.1
1967	1.23
1968	-0.06
1969	-1.19
1970	-1.51
1971	0.28
1972	-0.15
1973	1.32
1974	-0.72
1975	0.49
1976	-1.43
1977	-1.51
1978	1.61
1979	-0.74
1980	-1.41
1981	-0.09
1982	-0.33
1983	-0.88
1984	-0.06
1985	0.57
1986	-0.83
1987	-0.77
1988	-0.21
1989	-0.13
1990	-0.54
1991	-0.1
1992	-0.21
1993	-1.8
1994	1.47
1995	-0.08
1996	-1.28
1997	2.2
1998	0.49
1999	-0.37
2000	0.73
2001	0.4
2002	0.15
2003	-1.25
2004	0.03
2005	1.47
2006	-0.39
2007	0.4
2008	0.59
2009	0.41
2010	0.34
2011	-0.22
2012	1.03
2013	0.23
2014	-0.06
2015	-0.4
2016	0.48
2017	2.63
2018	0.18

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