Projected Life Expectancy By Age (reached During The Year), Sex, Type Of Projection And NUTS 3 Region

Eurostat code: proj_19ralexp3 · (2019 - 2100)

@eurostat.proj_19ralexp3

Table List

2 tables(53.19 MB)

Projected Life Expectancy By Age (reached During The Year), Sex, Type Of Projection And NUTS 3 Region - Raw

Projected Life Expectancy By Age (reached During The Year), Sex, Type Of Projection And NUTS 3 Region - Unpivoted

Projected Life Expectancy By Age (reached During The Year), Sex, Type Of Projection And NUTS 3 Region - Raw

@eurostat.proj_19ralexp3.raw

This table contains the original data as downloaded from the Eurostat API, with labels mapped where available.

23.81 MB
245632 rows
88 columns

freq Freq	projection Projection	age Age	sex Sex	unit Unit	geo Geo	n_2019 2019	n_2020 2020	n_2021 2021	n_2022 2022	n_2023 2023	n_2024 2024	n_2025 2025	n_2026 2026	n_2027 2027	n_2028 2028	n_2029 2029	n_2030 2030	n_2031 2031	n_2032 2032	n_2033 2033	n_2034 2034	n_2035 2035	n_2036 2036	n_2037 2037	n_2038 2038	n_2039 2039	n_2040 2040	n_2041 2041	n_2042 2042	n_2043 2043	n_2044 2044	n_2045 2045	n_2046 2046	n_2047 2047	n_2048 2048	n_2049 2049	n_2050 2050	n_2051 2051	n_2052 2052	n_2053 2053	n_2054 2054	n_2055 2055	n_2056 2056	n_2057 2057	n_2058 2058	n_2059 2059	n_2060 2060	n_2061 2061	n_2062 2062	n_2063 2063	n_2064 2064	n_2065 2065	n_2066 2066	n_2067 2067	n_2068 2068	n_2069 2069	n_2070 2070	n_2071 2071	n_2072 2072	n_2073 2073	n_2074 2074	n_2075 2075	n_2076 2076	n_2077 2077	n_2078 2078	n_2079 2079	n_2080 2080	n_2081 2081	n_2082 2082	n_2083 2083	n_2084 2084	n_2085 2085	n_2086 2086	n_2087 2087	n_2088 2088	n_2089 2089	n_2090 2090	n_2091 2091	n_2092 2092	n_2093 2093	n_2094 2094	n_2095 2095	n_2096 2096	n_2097 2097	n_2098 2098	n_2099 2099	n_2100 2100
Annual	Baseline projections	1 year	Females	Year	Mittelburgenland	83.3	83.4	83.5	83.7	83.8	83.9	84.1	84.2	84.4	84.5	84.6	84.8	84.9	85	85.2	85.3	85.4	85.6	85.7	85.8	85.9	86.1	86.2	86.3	86.4	86.6	86.7	86.8	86.9	87.1	87.2	87.3	87.4	87.5	87.6	87.8	87.9	88	88.1	88.2	88.3	88.4	88.5	88.6	88.8	88.9	89	89.1	89.2	89.3	89.4	89.5	89.6	89.7	89.8	89.9	90	90.1	90.2	90.3	90.4	90.5	90.6	90.7	90.7	90.8	90.9	91	91.1	91.2	91.3	91.4	91.5	91.5	91.6	91.7	91.8	91.9	92	92.1	92.1	92.2
Annual	Baseline projections	1 year	Females	Year	Nordburgenland	83.8	83.8	84	84.1	84.2	84.4	84.5	84.6	84.8	84.9	85	85.2	85.3	85.4	85.5	85.7	85.8	85.9	86	86.2	86.3	86.4	86.5	86.6	86.8	86.9	87	87.1	87.2	87.3	87.5	87.6	87.7	87.8	87.9	88	88.1	88.2	88.3	88.4	88.5	88.7	88.8	88.9	89	89.1	89.2	89.3	89.4	89.5	89.6	89.7	89.8	89.9	90	90.1	90.1	90.2	90.3	90.4	90.5	90.6	90.7	90.8	90.9	91	91.1	91.1	91.2	91.3	91.4	91.5	91.6	91.7	91.7	91.8	91.9	92	92.1	92.1	92.2	92.3
Annual	Baseline projections	1 year	Females	Year	Südburgenland	82.6	82.6	82.8	82.9	83.1	83.2	83.4	83.5	83.7	83.8	83.9	84.1	84.2	84.4	84.5	84.7	84.8	84.9	85.1	85.2	85.4	85.5	85.6	85.8	85.9	86	86.2	86.3	86.4	86.5	86.7	86.8	86.9	87	87.2	87.3	87.4	87.5	87.7	87.8	87.9	88	88.1	88.2	88.4	88.5	88.6	88.7	88.8	88.9	89	89.1	89.2	89.3	89.5	89.6	89.7	89.8	89.9	90	90.1	90.2	90.3	90.4	90.5	90.6	90.7	90.8	90.9	91	91	91.1	91.2	91.3	91.4	91.5	91.6	91.7	91.8	91.9	91.9	92
Annual	Baseline projections	1 year	Females	Year	Mostviertel-Eisenwurzen	83.8	83.8	83.9	84.1	84.2	84.3	84.5	84.6	84.7	84.9	85	85.1	85.3	85.4	85.5	85.6	85.8	85.9	86	86.1	86.2	86.4	86.5	86.6	86.7	86.8	87	87.1	87.2	87.3	87.4	87.5	87.6	87.8	87.9	88	88.1	88.2	88.3	88.4	88.5	88.6	88.7	88.8	88.9	89	89.1	89.2	89.3	89.4	89.5	89.6	89.7	89.8	89.9	90	90.1	90.2	90.3	90.4	90.5	90.6	90.7	90.8	90.9	90.9	91	91.1	91.2	91.3	91.4	91.5	91.5	91.6	91.7	91.8	91.9	92	92	92.1	92.2	92.3
Annual	Baseline projections	1 year	Females	Year	Niederösterreich-Süd	82.8	82.9	83	83.2	83.3	83.5	83.6	83.7	83.9	84	84.2	84.3	84.5	84.6	84.7	84.9	85	85.2	85.3	85.4	85.6	85.7	85.8	86	86.1	86.2	86.3	86.5	86.6	86.7	86.8	87	87.1	87.2	87.3	87.5	87.6	87.7	87.8	87.9	88.1	88.2	88.3	88.4	88.5	88.6	88.7	88.8	89	89.1	89.2	89.3	89.4	89.5	89.6	89.7	89.8	89.9	90	90.1	90.2	90.3	90.4	90.5	90.6	90.7	90.8	90.9	91	91.1	91.2	91.3	91.3	91.4	91.5	91.6	91.7	91.8	91.9	92	92	92.1
Annual	Baseline projections	1 year	Females	Year	Sankt Pölten	83.4	83.4	83.6	83.7	83.8	84	84.1	84.3	84.4	84.5	84.7	84.8	84.9	85.1	85.2	85.3	85.5	85.6	85.7	85.8	86	86.1	86.2	86.4	86.5	86.6	86.7	86.8	87	87.1	87.2	87.3	87.4	87.6	87.7	87.8	87.9	88	88.1	88.2	88.3	88.5	88.6	88.7	88.8	88.9	89	89.1	89.2	89.3	89.4	89.5	89.6	89.7	89.8	89.9	90	90.1	90.2	90.3	90.4	90.5	90.6	90.7	90.8	90.9	91	91	91.1	91.2	91.3	91.4	91.5	91.6	91.7	91.7	91.8	91.9	92	92.1	92.2	92.2
Annual	Baseline projections	1 year	Females	Year	Waldviertel	84.2	84.2	84.4	84.5	84.6	84.7	84.9	85	85.1	85.2	85.4	85.5	85.6	85.7	85.9	86	86.1	86.2	86.3	86.4	86.6	86.7	86.8	86.9	87	87.1	87.2	87.4	87.5	87.6	87.7	87.8	87.9	88	88.1	88.2	88.3	88.4	88.5	88.6	88.7	88.8	88.9	89	89.1	89.2	89.3	89.4	89.5	89.6	89.7	89.8	89.9	90	90.1	90.2	90.3	90.4	90.5	90.5	90.6	90.7	90.8	90.9	91	91.1	91.2	91.2	91.3	91.4	91.5	91.6	91.7	91.7	91.8	91.9	92	92.1	92.1	92.2	92.3	92.4
Annual	Baseline projections	1 year	Females	Year	Weinviertel	81.7	81.8	81.9	82.1	82.3	82.4	82.6	82.7	82.9	83.1	83.2	83.4	83.5	83.7	83.8	84	84.1	84.3	84.4	84.6	84.7	84.9	85	85.2	85.3	85.5	85.6	85.7	85.9	86	86.2	86.3	86.4	86.6	86.7	86.8	87	87.1	87.2	87.3	87.5	87.6	87.7	87.9	88	88.1	88.2	88.3	88.5	88.6	88.7	88.8	88.9	89	89.2	89.3	89.4	89.5	89.6	89.7	89.8	89.9	90	90.1	90.3	90.4	90.5	90.6	90.7	90.8	90.9	91	91.1	91.2	91.3	91.4	91.5	91.5	91.6	91.7	91.8	91.9
Annual	Baseline projections	1 year	Females	Year	Wiener Umland/Nordteil	83.4	83.4	83.6	83.7	83.9	84	84.1	84.3	84.4	84.5	84.7	84.8	84.9	85.1	85.2	85.3	85.4	85.6	85.7	85.8	86	86.1	86.2	86.3	86.4	86.6	86.7	86.8	86.9	87	87.2	87.3	87.4	87.5	87.6	87.7	87.8	88	88.1	88.2	88.3	88.4	88.5	88.6	88.7	88.8	88.9	89	89.1	89.2	89.4	89.5	89.6	89.7	89.8	89.9	90	90	90.1	90.2	90.3	90.4	90.5	90.6	90.7	90.8	90.9	91	91.1	91.2	91.3	91.3	91.4	91.5	91.6	91.7	91.8	91.9	91.9	92	92.1	92.2
Annual	Baseline projections	1 year	Females	Year	Wiener Umland/Südteil	82.9	83	83.1	83.3	83.4	83.5	83.7	83.8	84	84.1	84.2	84.4	84.5	84.7	84.8	84.9	85.1	85.2	85.3	85.5	85.6	85.7	85.9	86	86.1	86.3	86.4	86.5	86.6	86.8	86.9	87	87.1	87.2	87.4	87.5	87.6	87.7	87.8	87.9	88.1	88.2	88.3	88.4	88.5	88.6	88.7	88.8	89	89.1	89.2	89.3	89.4	89.5	89.6	89.7	89.8	89.9	90	90.1	90.2	90.3	90.4	90.5	90.6	90.7	90.8	90.9	91	91.1	91.1	91.2	91.3	91.4	91.5	91.6	91.7	91.8	91.9	91.9	92	92.1


CREATE TABLE raw (
  "freq" VARCHAR,
  "projection" VARCHAR,
  "age" VARCHAR,
  "sex" VARCHAR,
  "unit" VARCHAR,
  "geo" VARCHAR,
  "n_2019" DOUBLE,
  "n_2020" DOUBLE,
  "n_2021" DOUBLE,
  "n_2022" DOUBLE,
  "n_2023" DOUBLE,
  "n_2024" DOUBLE,
  "n_2025" DOUBLE,
  "n_2026" DOUBLE,
  "n_2027" DOUBLE,
  "n_2028" DOUBLE,
  "n_2029" DOUBLE,
  "n_2030" DOUBLE,
  "n_2031" DOUBLE,
  "n_2032" DOUBLE,
  "n_2033" DOUBLE,
  "n_2034" DOUBLE,
  "n_2035" DOUBLE,
  "n_2036" DOUBLE,
  "n_2037" DOUBLE,
  "n_2038" DOUBLE,
  "n_2039" DOUBLE,
  "n_2040" DOUBLE,
  "n_2041" DOUBLE,
  "n_2042" DOUBLE,
  "n_2043" DOUBLE,
  "n_2044" DOUBLE,
  "n_2045" DOUBLE,
  "n_2046" DOUBLE,
  "n_2047" DOUBLE,
  "n_2048" DOUBLE,
  "n_2049" DOUBLE,
  "n_2050" DOUBLE,
  "n_2051" DOUBLE,
  "n_2052" DOUBLE,
  "n_2053" DOUBLE,
  "n_2054" DOUBLE,
  "n_2055" DOUBLE,
  "n_2056" DOUBLE,
  "n_2057" DOUBLE,
  "n_2058" DOUBLE,
  "n_2059" DOUBLE,
  "n_2060" DOUBLE,
  "n_2061" DOUBLE,
  "n_2062" DOUBLE,
  "n_2063" DOUBLE,
  "n_2064" DOUBLE,
  "n_2065" DOUBLE,
  "n_2066" DOUBLE,
  "n_2067" DOUBLE,
  "n_2068" DOUBLE,
  "n_2069" DOUBLE,
  "n_2070" DOUBLE,
  "n_2071" DOUBLE,
  "n_2072" DOUBLE,
  "n_2073" DOUBLE,
  "n_2074" DOUBLE,
  "n_2075" DOUBLE,
  "n_2076" DOUBLE,
  "n_2077" DOUBLE,
  "n_2078" DOUBLE,
  "n_2079" DOUBLE,
  "n_2080" DOUBLE,
  "n_2081" DOUBLE,
  "n_2082" DOUBLE,
  "n_2083" DOUBLE,
  "n_2084" DOUBLE,
  "n_2085" DOUBLE,
  "n_2086" DOUBLE,
  "n_2087" DOUBLE,
  "n_2088" DOUBLE,
  "n_2089" DOUBLE,
  "n_2090" DOUBLE,
  "n_2091" DOUBLE,
  "n_2092" DOUBLE,
  "n_2093" DOUBLE,
  "n_2094" DOUBLE,
  "n_2095" DOUBLE,
  "n_2096" DOUBLE,
  "n_2097" DOUBLE,
  "n_2098" DOUBLE,
  "n_2099" DOUBLE,
  "n_2100" DOUBLE
);