GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars. Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.

GDP Per Capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current U.S. dollars.

Quarterly GDP data for Germany, France, Italy, Spain, Belgium and Poland, which I used in the Tabular Playground Series - Sep 2022. Data was downloaded from the ECB Statistical Datawarehouse: Query: https://sdw.ecb.europa.eu/browseSelection.do?org.apache.struts.taglib.html.TOKEN=6ce29fda93ae6f7ca8b8934e2a3239ca&df=true&ec=&dc=&oc=&pb=&rc=&DATASET=0&removeItem=&removedItemList=&mergeFilter=&activeTab=MNA&showHide=&FREQ.249=Q&ADJUSTMENT.249=N&REF_AREA.249=BE&REF_AREA.249=DE&REF_AREA.249=ES&REF_AREA.249=FR&REF_AREA.249=IT&REF_AREA.249=PL&ACTIVITY.249=_Z&PRICES.249=V&MAX_DOWNLOAD_SERIES=500&SERIES_MAX_NUM=50&node=9683074&legendRef=reference&ajaxTab=true

To add it to your dataframe the following code might help (ensure that you have year and quarter in the starting df) ``` ## GDP reformatting df_gdp.date = pd.to_datetime(df_gdp.date)+ pd.offsets.QuarterEnd(0) df_gdp["quarter"] = df_gdp.date.dt.quarter df_gdp["year"] = df_gdp.date.dt.year

## Merge dataframes
df = df.merge(df_gdp[['country', 'year', 'quarter', 'gdp']], left_on=['country', 'year', 'quarter'], right_on=['country', 'year', 'quarter'],how='left')

Source: UNECE Statistical Database, compiled from national and international (CIS, EUROSTAT, IMF, OECD, World Bank) official sources.General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked and rescaled to build long consistent time series.The national accounts estimates are compiled according to 2008 SNA (System of National Accounts 2008) or 1993 SNA (System of National Accounts 1993).Constant price estimates are based on data compiled by the National Statistical Offices (NSOs), which reflect various national practices (different base years, fixed base, chain, etc.). To facilitate international comparisons, the data reported by the NSOs have been scaled to the current price value of of the common reference year. The resulting chain constant price data are not additive.Common currency (US$) estimates are computed by the secretariat using purchasing power parities (PPPs), which are the rates of currency conversion that equalise the purchasing power of different currencies. PPPs, and not exchange rates, should be used in international comparisons of GDP and its components.Growth rates (per cent) are over the preceding period, unless otherwise specified.Contributions to per cent growth in GDP (in percentage points) are over the preceding period, unless otherwise specified.Regional aggregates are computed by the secretariat. For national accounts all current price aggregates are sums of national series converted into US$ at current PPPs of GDP; all constant price aggregates are calculated by summing up national series scaled to the price level of the common reference year and then converted into US$ using PPPs of GDP of the common reference year. Due to conversion and rounding the resulting aggregates and components could be non-additive.Aggregates are computed for the following regions:UNECE-52:Albania; Armenia; Austria; Azerbaijan; Belarus; Belgium; Bosnia and Herzegovina; Bulgaria; Canada; Croatia; Cyprus; Czech Republic; Denmark; Estonia; Finland; France; Georgia; Germany; Greece; Hungary; Iceland; Ireland; Israel; Italy; Kazakhstan; Kyrgyzstan; Latvia; Lithuania; Luxembourg; Malta; Montenegro; Netherlands; North Macedonia; Norway; Poland; Portugal; Republic of Moldova; Romania; Russian Federation; Serbia; Slovakia; Slovenia; Spain; Sweden; Switzerland; Tajikistan; Turkey; Turkmenistan; Ukraine; United Kingdom; United States; Uzbekistan.North America-2:Canada; United States.European Union-27 (31/12/2020):Austria; Belgium; Bulgaria; Cyprus; Croatia; Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Hungary; Ireland; Italy; Latvia; Lithuania; Luxembourg; Malta; Netherlands; Poland; Portugal; Romania; Slovakia; Slovenia; Spain; Sweden.Euro area-20:Austria; Belgium; Croatia; Cyprus; Estonia; Finland; France; Germany; Greece; Ireland; Italy; Latvia; Lithuania; Luxembourg; Malta; Netherlands; Portugal; Slovakia; Slovenia; Spain.Eastern Europe, Caucasus and Central Asia (EECCA):Armenia; Azerbaijan; Belarus; Georgia; Kazakhstan; Kyrgyzstan; Republic of Moldova; Russian Federation; Tajikistan; Turkmenistan; Ukraine; Uzbekistan.CIS-11:Armenia; Azerbaijan; Belarus; Kazakhstan; Kyrgyzstan; Republic of Moldova; Russian Federation; Tajikistan; Turkmenistan; Ukraine; Uzbekistan.Western Balkans-6:Albania; Bosnia and Herzegovina; Croatia; Montenegro; North Macedonia; Serbia... - data not availableThe Coronavirus (COVID-19) pandemic impacts the production of statistics and may limit available resources and data sources. This may impact the quality of statistics for 2020, and could lead to later revisions.

results_direct(marine) and indirect(else)_mPLN.csv- The text presents direct, indirect and induced impact of the marine sector on the wider economy in terms of global output. The attached tables present these categories.- The file “results_direct(marine) and indirect(else)_mPLN.csv” presents direct effects in columns B-Q (marine sectors) and indirect effects in the remaining columns (other sectors, i.e. standard sectors net of the marine economy). Individual rows break down the effects into 16 voivodships.- The file “results_induced.csv” presents induced effects for non-marine sectors, in an analogous layout.- The analogous files with “Gx and Zx” in the filenames present the results in which only the part of the marine sector prevalent in two voivodships: Pomorskie and Zachodniopomorskie is taken into account as an impulse (direct effect). The indirect and induced effects are presented in the same fashion as before, but correspond to a different impulse.### Shift-share_outputs.xlsxThe XLS file "Shift-share_outputs" presents the shift-share decomposition of the blue growth in the BSR countries (Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Poland, Sweden) for the period 2009-2018 and two subperiods, i.e. 2009-2013 and 2013-2018.### INPUT-OUTPUT matrix for the maritime sectors in Polish economy in 2015The INPUT-OUTPUT matrix for the maritime sectors is presented as a section (a fragment) of the fullmatrix for the entire national economy estimated by the Polish Central Statistical Office ("Input -output table at basic prices in 2015": https://stat.gov.pl/en/topics/national-accounts /annual-national-accounts/input-output-table-at-basic-prices-in-2015,5,3.html.).The presented values were determined for 2015 and are expressed in thousands of PLN at baseprices (current prices).The 16 maritime sectors were separated from 77 economic sectors included in the INPUT-OUTPUTmatrix for the entire economy.The INPUTS sheet contains data on the maritime sectors' use of products produced in other sectors.The OUTPUTS sheet contains data on the production of maritime sectors consumed by other sectors.### Marine_Spatial_Rent_Offshore_Wind_Farms.xlsxSpatial rent as a tool for effective management of marine space planning for offshore wind energy development on the example of Polish offshore wind farmsFunding: by the Polish National Science Centre, grant: 2018/31/B/HS4/03890, "Multiplier effects of maritime space".

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.