HICP (2015 = 100) - quarterly data (index) - prc_hicp_midx_Q

Data - Eurostat

Author

François Geerolf

Last compile: 04 sept. 2026, 00:16

source dataset Title Updated
eurostat prc_hicp_manr HICP (2015 = 100) - monthly data (annual rate of change) - prc_hicp_manr 2026-08-07
eurostat prc_hicp_midx HICP (2015 = 100) - monthly data (index) - prc_hicp_midx 2026-08-07
eurostat prc_hicp_aind HICP (2015 = 100) - annual data (average index and rate of change) - prc_hicp_aind 2026-08-12
eurostat prc_hicp_cow HICP - country weights - prc_hicp_cow 2026-07-15

Structure

LAST_COMPILE

LAST_COMPILE
2026-09-04

Last

Code 
prc_hicp_midx |>
  group_by(time) |>
  summarise(Nobs = n()) |>
  arrange(desc(time)) |>
  head(1) |>
  print_table_conditional()
time Nobs
2025M12 27318

coicop

All

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

2-digit (12 categories)

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  filter(nchar(coicop) == 4 & substr(coicop, 1, 2) == "CP") |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
coicop Coicop Nobs
CP00 All-items HICP 40745
CP01 Food and non-alcoholic beverages 40745
CP02 Alcoholic beverages, tobacco and narcotics 40745
CP03 Clothing and footwear 40745
CP04 Housing, water, electricity, gas and other fuels 40745
CP05 Furnishings, household equipment and routine household maintenance 40745
CP06 Health 40745
CP07 Transport 40745
CP08 Communications 40745
CP09 Recreation and culture 40745
CP11 Restaurants and hotels 40745
CP12 Miscellaneous goods and services 40745
CP10 Education 40291

3-digit (42 categories)

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  filter(nchar(coicop) == 5) |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  print_table_conditional()

4-digit (95 categories)

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  filter(nchar(coicop) == 6 & substr(coicop, 1, 2) == "CP") |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  print_table_conditional()

5-digit (264 categories)

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  filter(nchar(coicop) == 7 & substr(coicop, 1, 2) == "CP") |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  print_table_conditional()

Non-Coicop

Code 
prc_hicp_midx |>
  left_join(coicop, by = "coicop") |>
  filter(substr(coicop, 1, 2) != "CP") |>
  group_by(coicop, Coicop) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

geo

Code 
prc_hicp_midx |>
  left_join(geo, by = "geo") |>
  group_by(geo, Geo) |>
  summarise(Nobs = n()) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Last

Code 
prc_hicp_midx |>
  filter(time == last_time) |>
  select_if(function(col) length(unique(col)) > 1) |>
  left_join(geo, by = "geo") |>
  left_join(coicop, by = "coicop") |>
  select(geo, Geo, coicop, Coicop, values) |>
  print_table_conditional()

2017T1-

Monthly

Code 
prc_hicp_midx |>
  filter(unit == "I15",
         coicop %in% c("CP00"),
         geo %in% c("DE", "FR", "IT", "EA20", "ES")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  month_to_date() |>
  filter(date >= as.Date("2017-01-01")) |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  add_flag_color("Geo") |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Augmentation vs. Octobre 2021") +
  scale_x_date(breaks = seq.Date(as.Date("2017-01-01"), Sys.Date(), "6 months"),
               labels = date_format("%b %Y")) +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Quarterly

Code 
prc_hicp_midx |>
  filter(unit == "I15",
         coicop %in% c("CP00"),
         geo %in% c("DE", "FR", "IT", "EA20", "ES")) |>
  left_join(geo, by = "geo") |>
  select(Geo, time, values) |>
  month_to_date() |>
  filter(date >= as.Date("2017-01-01")) |>
  mutate(date = as.yearqtr(date)) |>
  group_by(Geo, date) |>
  filter(n() == 3) |>
  summarise(values = mean(values)) |>
  ungroup() |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  add_flag_color("Geo") |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_y_log10(breaks = seq(0, 200, 2),
                labels = percent(seq(0, 200, 2)/100-1)) +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(1, 3)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date == max(date)),
                  aes(x = date, y = values, color = color, label = percent(values/100-1, acc = 0.01)))

2017T2-2024T2

Monthly

Code 
prc_hicp_midx |>
  filter(unit == "I15",
         coicop %in% c("CP00"),
         geo %in% c("DE", "FR", "IT", "NL", "ES")) |>
  left_join(geo, by = "geo") |>
  select(geo, Geo, coicop, time, values) |>
  month_to_date() |>
  filter(date >= as.Date("2017-04-01")) |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  add_flag_color("Geo") |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Augmentation vs. Octobre 2021") +
  scale_x_date(breaks = "6 months",
               labels = date_format("%b %Y")) +
  scale_y_log10(breaks = seq(0, 200, 2),
                labels = percent(seq(0, 200, 2)/100-1)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Quarterly

Code 
prc_hicp_midx |>
  filter(unit == "I15",
         coicop %in% c("CP00"),
         geo %in% c("DE", "FR", "IT", "NL", "ES")) |>
  left_join(geo, by = "geo") |>
  select(Geo, time, values) |>
  month_to_date() |>
  mutate(date = as.yearqtr(date)) |>
  filter(date >= as.yearqtr("2017 Q2"),
         date <= as.yearqtr("2024 Q2")) |>
  group_by(Geo, date) |>
  filter(n() == 3) |>
  summarise(values = mean(values)) |>
  ungroup() |>
  group_by(Geo) |>
  arrange(date) |>
  mutate(values = 100*values/values[1]) |>
  add_flag_color("Geo") |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Augmentation vs. Octobre 2021") +
  scale_y_log10(breaks = seq(0, 200, 2),
                labels = percent(seq(0, 200, 2)/100-1)) +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_color_identity() + add_flags +
  theme(legend.position = "none",
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>% filter(date == max(date)),
                  aes(x = date, y = values, color = color, label = percent(values/100-1, acc = 0.01)))