Metadata¶
Every Ipeadata’s time series is accompanied by a set of metadata. Metadata are data about data. Some examples of the elements of this set of metadata are country, big theme, theme, source and unit of measure. Some specific kinds of metadata have their own function on Ipeadata API. Let’s see some of them:
Countries¶
You can have a look at the available Ipeadata’s countries by running the countries() function:
>>> ipeadatapy.countries()
ID COUNTRY
0 ZAF ?frica do Sul
1 DEU Alemanha
2 LATI Am?rica Latina
3 AGO Angola
4 SAU Ar?bia Saudita
5 DZA Arg?lia
6 ARG Argentina
7 AUS Austr?lia
8 AUT ?ustria
9 BEL B?lgica
10 BOL Bol?via
.. ... ...
Themes¶
You can also have a look on the available themes for Ipeadata using the function themes():
>>> ipeadatapy.themes()
ID NAME MACRO REGIONAL SOCIAL
0 28 Agropecu?ria NaN 1.0 NaN
1 23 Assist?ncia social NaN NaN 1.0
2 25 Avalia??o do governo NaN NaN NaN
3 10 Balan?o de pagamentos 1.0 NaN NaN
4 7 C?mbio 1.0 NaN NaN
5 5 Com?rcio exterior 1.0 1.0 NaN
6 2 Consumo e vendas 1.0 1.0 NaN
7 8 Contas nacionais 1.0 NaN NaN
8 81 Contas Regionais NaN 1.0 NaN
9 24 Corre??o monet?ria 1.0 NaN NaN
10 37 Demografia NaN NaN 1.0
.. .. ... ... ... ...
Let’s suppose you have the interest to know which of the themes of Ipeadata are related to the Macroeconomics big theme. The parameter macro will solve this problem:
>>> ipeadatapy.themes(macro=1)
ID NAME MACRO REGIONAL SOCIAL
3 10 Balan?o de pagamentos 1.0 NaN NaN
4 7 C?mbio 1.0 NaN NaN
5 5 Com?rcio exterior 1.0 1.0 NaN
6 2 Consumo e vendas 1.0 1.0 NaN
7 8 Contas nacionais 1.0 NaN NaN
9 24 Corre??o monet?ria 1.0 NaN NaN
.. .. ... ... ... ...
Let’s now suppose that you just want the function to return themes that are related both to the macroeconomics and regional themes. For this, use macro and regional parameters together:
>>> ipeadatapy.themes(macro=1, regional=1)
ID NAME MACRO REGIONAL SOCIAL
5 5 Com?rcio exterior 1.0 1.0 NaN
6 2 Consumo e vendas 1.0 1.0 NaN
18 12 Emprego 1.0 1.0 NaN
19 19 Estoque de capital 1.0 1.0 NaN
20 6 Finan?as p?blicas 1.0 1.0 NaN
31 3 Moeda e cr?dito 1.0 1.0 NaN
33 14 Popula??o 1.0 1.0 NaN
34 9 Pre?os 1.0 1.0 NaN
37 1 Produ??o 1.0 1.0 NaN
45 33 Transporte 1.0 1.0 NaN
.. .. ... ... ... ...
The parameter social is also available and works in the same way of macro and regional. For more parameters available for the function themes() run help(idpy.themes).
Sources¶
Other important metadata is the source. This metadata have his own functions, sources(). Let’s have a look:
>>> ipeadatapy.sources()
0 Abia
1 Abinee
2 ABPO
3 Abracal
4 Abras
5 ACSP/IEGV
6 Anac
7 Anatel
8 Anbima
9 Anbima
10 Anda
.. ...
Territories¶
For regional time series we also have some information about Brazilian territories through the function territories():
>>> ipeadatapy.territories()
NAME ID ... AREA CAPITAL
0 (n?o definido) ... NaN None
1 Brasil 0 ... 8531507.6 False
2 Regi?o Norte 1 ... 3869637.9 False
3 Rond?nia 11 ... 238512.8 False
4 Alta Floresta D'Oeste 1100015 ... 7111.8 False
5 Ariquemes 1100023 ... 4995.3 False
6 Cabixi 1100031 ... 1530.7 False
7 Cacoal 1100049 ... 3808.4 False
8 Cerejeiras 1100056 ... 2645.0 False
9 Colorado do Oeste 1100064 ... 1442.4 False
10 Corumbiara 1100072 ... 3079.7 False
... ... ... ... ... ...
Two interesting parameters of territories() function are areaGreaterThan and areaSmallerThan. With these parameters, it is possible to filter the return of the function for just territories greater than, smaller than or between the specified parameters. For example, let’s check which of the Brazilian territories have the area greater than 1000000:
>>> ipeadatapy.territories(areaGreaterThan=1000000)
NAME ID LEVEL AREA CAPITAL
1 Brasil 0 Brasil 8531507.6 False
2 Regi?o Norte 1 Regi?es 3869637.9 False
138 Amazonas 13 Estados 1577820.2 False
386 Par? 15 Estados 1253164.5 False
1161 Regi?o Nordeste 2 Regi?es 1558200.4 False
17960 Regi?o Centro-oeste 5 Regi?es 1612077.2 False
18452 AMC1872_1997 001 513AMC1872_1997001 AMC 1872-00 1947986.1 None
18454 AMC2097 001 51AMC2097001 AMC 20-00 1061175.7 None
Let’s now check the territories which the area is between 1000000 and 1100000:
>>> ipeadatapy.territories(areaGreaterThan=1000000, areaSmallerThan=1500000)
NAME ID LEVEL AREA CAPITAL
386 Par? 15 Estados 1253164.5 False
18454 AMC2097 001 51AMC2097001 AMC 20-00 1061175.7 None
Other metadata¶
Although only 4 metadata from Ipeadata have their own function, there are a lot more metadata available for the data base time series. The function metadata() returns all Ipeadata time series in a data frame with all of his metadata. Each of the collumns of the data frame represents a metadata.
>>> ipeadatapy.metadata()
BIG THEME SOURCE SOURCE ACRONYM ... SERIES STATUS THEME CODE MEASURE
0 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Tonelada
1 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Tonelada
2 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Tonelada
3 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Cabe?a
4 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Cabe?a
5 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Cabe?a
6 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... I 1 Tonelada
7 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Tonelada
8 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Tonelada
9 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Tonelada
10 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Tonelada
11 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Tonelada
12 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Tonelada
13 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... I 1 Tonelada
14 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... I 1 Cabe?a
15 Macroecon?mico Instituto Brasileiro de Geografia e Estat?stic... IBGE/Coagro ... A 1 Cabe?a
... ... ... ... ... ... ... ...
[8549 rows x 15 columns]
As you can see, this data frame is too big to be represented here. His dimension is 8549 rows by 15 columns. Each of these columns represents one metadata. The columns are BIG THEME, SOURCE, SOURCE ACRONYM, SOURCE URL, UNIT, COUNTRY, FREQUENCY, LAST UPDATE, CODE, COMMENT, NAME, NUMERICA, SERIES STATUS, THEME CODE, and MEASURE. In the next section, we will learn how to use these metadata as filtering options to improve our research.