The basics¶
Firt of all, you need to import the package:
>>> import ipeadatapy
Finding and analyzing your desired time series¶
Running this function will show all Ipeadata’s time series:
>>> ipeadatapy.list_series()
If you are looking for series of a specific subject, you can filter the function output for only series containing some keyword. For example, let’s filter the function return for only time series containing the word ‘BPM6’ in their names. This functionality can be used as a searching mechanism.
>>> ipeadatapy.list_series('BPM6')
CODE NAME
6721 BPAG_AR Ativos de reserva (Nova metod. - BPM6)
6722 BPAG_BC Balanca comercial - Saldo (Nova metod. - BPM6)
6723 BPAG_BCM Balanca comercial - Importacoes (Nova metod. -...
6724 BPAG_BCX Balanca comercial - Exportacoes (Nova metod. -...
6725 BPAG_CF Conta Financeira - Saldo (Captacoes - Concesso...
6726 BPAG_CK Conta capital - Saldo (Nova metod. - BPM6)
6727 BPAG_CKD Conta capital - Desp. (Nova metod. - BPM6)
6728 BPAG_CKR Conta capital - Rec. (Nova metod. - BPM6)
6729 BPAG_CRCO Outros invest. - Cr?d. comerciais e adiantamen...
6730 BPAG_CRCOA Outros invest. - Cr?d. comerciais e adiantamen...
... ... ...
You can also use any other keyword you want. Be aware of the case sensitiveness of the keyword.
Getting more details¶
Let’s suppose that we found our desired time series. Its code is BPAG_AR. We can use a handy command called describe() to confirm the details about this time series.
>>> ipeadatapy.describe('BPAG_AR')
Ativos de reserva (Nova metod. - BPM6)
Name Ativos de reserva (Nova metod. - BPM6)
Code BPAG_AR
Big Theme Macroeconomico
Theme Balanco de pagamentos
Source Banco Central do Brasil, Balanco de Pagamentos...
Source acronym Bacen/BP (BPM6)
Comment Metodologia do Manual de Balanco de Pagamentos...
Last update 2019-03-14T13:48:00.803-03:00
Frequency Anual
Measure US$
Unit milhoes
Status A
As you can see, this function returns some details about the specified time series: the name of the series, his code, the big theme and theme which this series correspond, his source, the source acronym, the comment, his last update date and time, his frequency, measure, unit and status. Thus, this function is a good way to have an overview of a specific time series.
If you are not satisfied with these information, you can check a more complete metadata data frame about the series by running:
>>> ipeadatapy.metadata('BPAG_AR')
BIG THEME FNTEXTURL FNTID SOURCE SOURCE ACRONYM ... SERTEMMUN THEME CODE TEMCODIGOPAI THEME MEASURE
0 Macroecon?mico None 1333080354 Banco Central do Brasil, Balan?o de Pagamentos... Bacen/BP (BPM6) ... None 10 None Balan?o de pagamentos US$
We will be digging deeper into the metadata() function in future sections.
Observing the data¶
Now that you are sure about your selected time series, you might be wondering how to observe what really matters: the data. For this purpose, use the function timeseries():
>>> ipeadatapy.timeseries('BPAG_AR')
YEAR DAY MONTH CODE DATE VALUE (US$)
0 1995 1 1 BPAG_AR 1995-01-01T00:00:00-02:00 12918.900000
1 1996 1 1 BPAG_AR 1996-01-01T00:00:00-02:00 8666.100000
2 1997 1 1 BPAG_AR 1997-01-01T00:00:00-02:00 -7907.159127
3 1998 1 1 BPAG_AR 1998-01-01T00:00:00-02:00 -7970.207388
4 1999 1 1 BPAG_AR 1999-01-01T00:00:00-02:00 -7822.039996
5 2000 1 1 BPAG_AR 2000-01-01T00:00:00-02:00 -2261.654351
6 2001 1 1 BPAG_AR 2001-01-01T00:00:00-02:00 3306.600484
7 2002 1 1 BPAG_AR 2002-01-01T00:00:00-02:00 302.087225
8 2003 1 1 BPAG_AR 2003-01-01T00:00:00-02:00 8495.650494
9 2004 1 1 BPAG_AR 2004-01-01T00:00:00-02:00 2244.029835
10 2005 1 1 BPAG_AR 2005-01-01T00:00:00-02:00 4319.463872
11 2006 1 1 BPAG_AR 2006-01-01T00:00:00-02:00 30569.117416
12 2007 1 1 BPAG_AR 2007-01-01T00:00:00-02:00 87484.245682
13 2008 1 1 BPAG_AR 2008-01-01T00:00:00-02:00 2969.072068
14 2009 1 1 BPAG_AR 2009-01-01T00:00:00-02:00 46650.987800
15 2010 1 1 BPAG_AR 2010-01-01T00:00:00-02:00 49100.503587
16 2011 1 1 BPAG_AR 2011-01-01T00:00:00-02:00 58636.807211
17 2012 1 1 BPAG_AR 2012-01-01T00:00:00-02:00 18899.552358
18 2013 1 1 BPAG_AR 2013-01-01T00:00:00-02:00 -5926.487151
19 2014 1 1 BPAG_AR 2014-01-01T00:00:00-02:00 10832.657276
20 2015 1 1 BPAG_AR 2015-01-01T00:00:00-02:00 1568.772099
21 2016 1 1 BPAG_AR 2016-01-01T00:00:00-02:00 9237.436064
22 2017 1 1 BPAG_AR 2017-01-01T00:00:00-02:00 5092.868662
23 2018 1 1 BPAG_AR 2018-01-01T00:00:00-02:00 2927.674626
24 2019 1 1 BPAG_AR 2019-01-01T00:00:00-02:00 813.549854
Observing intervals of the data¶
If you just want the data for a specific year you can use the parameter year:
>>> ipeadatapy.timeseries("GM366_ERC366", year=2019)
YEAR DAY MONTH CODE DATE VALUE (R$)
12078 2019 2 1 GM366_ERC366 2019-01-02T00:00:00-02:00 3.8589
12079 2019 3 1 GM366_ERC366 2019-01-03T00:00:00-02:00 3.7677
12080 2019 4 1 GM366_ERC366 2019-01-04T00:00:00-02:00 3.7621
12081 2019 7 1 GM366_ERC366 2019-01-07T00:00:00-02:00 3.7056
12082 2019 8 1 GM366_ERC366 2019-01-08T00:00:00-02:00 3.7202
12083 2019 9 1 GM366_ERC366 2019-01-09T00:00:00-02:00 3.6925
12084 2019 10 1 GM366_ERC366 2019-01-10T00:00:00-02:00 3.6863
12085 2019 11 1 GM366_ERC366 2019-01-11T00:00:00-02:00 3.7135
12086 2019 14 1 GM366_ERC366 2019-01-14T00:00:00-02:00 3.7255
12087 2019 15 1 GM366_ERC366 2019-01-15T00:00:00-02:00 3.7043
... ... ... ... ... ... ...
[89 rows x 6 columns]
If you just want the data for a specific month of a specific year, use both the parameters year and month:
>>> ipeadatapy.timeseries("GM366_ERC366", year=2019, month=4)
YEAR DAY MONTH CODE DATE VALUE (R$)
12139 2019 1 4 GM366_ERC366 2019-04-01T00:00:00-03:00 3.8676
12140 2019 2 4 GM366_ERC366 2019-04-02T00:00:00-03:00 3.8655
12141 2019 3 4 GM366_ERC366 2019-04-03T00:00:00-03:00 3.8430
12142 2019 4 4 GM366_ERC366 2019-04-04T00:00:00-03:00 3.8707
12143 2019 5 4 GM366_ERC366 2019-04-05T00:00:00-03:00 3.8616
12144 2019 8 4 GM366_ERC366 2019-04-08T00:00:00-03:00 3.8652
12145 2019 9 4 GM366_ERC366 2019-04-09T00:00:00-03:00 3.8557
12146 2019 10 4 GM366_ERC366 2019-04-10T00:00:00-03:00 3.8339
... ... ... ... ... ... ...
Similarly, if you just want the data for a specific day of a specific month of a specific year use together the parameters year, month and day:
>>> ipeadatapy.timeseries("GM366_ERC366", year=2019, month=4, day=1)
YEAR DAY MONTH CODE DATE VALUE (R$)
12139 2019 1 4 GM366_ERC366 2019-04-01T00:00:00-03:00 3.8676
Another option is to return only data relative to years greater than some year, say, 2017. For this, use the parameter yearGreaterThan:
>>> ipeadatapy.timeseries("GM366_ERC366", yearGreaterThan=2017)
YEAR DAY MONTH CODE DATE VALUE (R$)
11828 2018 2 1 GM366_ERC366 2018-01-02T00:00:00-02:00 3.2691
11829 2018 3 1 GM366_ERC366 2018-01-03T00:00:00-02:00 3.2529
11830 2018 4 1 GM366_ERC366 2018-01-04T00:00:00-02:00 3.2312
11831 2018 5 1 GM366_ERC366 2018-01-05T00:00:00-02:00 3.2403
11832 2018 8 1 GM366_ERC366 2018-01-08T00:00:00-02:00 3.2351
11833 2018 9 1 GM366_ERC366 2018-01-09T00:00:00-02:00 3.2391
11834 2018 10 1 GM366_ERC366 2018-01-10T00:00:00-02:00 3.2461
11835 2018 11 1 GM366_ERC366 2018-01-11T00:00:00-02:00 3.2295
11836 2018 12 1 GM366_ERC366 2018-01-12T00:00:00-02:00 3.2192
11837 2018 15 1 GM366_ERC366 2018-01-15T00:00:00-02:00 3.1957
... ... ... ... ... ... ...
[340 rows x 6 columns]
You can also select an interval of years, say, from 2017 to 2018 using together with yearGreaterThan the parameter yearSmallerThan:
>>> ipeadatapy.timeseries("GM366_ERC366", yearGreaterThan=2016, yearSmallerThan=2019)
YEAR DAY MONTH CODE DATE VALUE (R$)
11579 2017 2 1 GM366_ERC366 2017-01-02T00:00:00-02:00 3.2723
11580 2017 3 1 GM366_ERC366 2017-01-03T00:00:00-02:00 3.2626
11581 2017 4 1 GM366_ERC366 2017-01-04T00:00:00-02:00 3.2327
11582 2017 5 1 GM366_ERC366 2017-01-05T00:00:00-02:00 3.2123
11583 2017 6 1 GM366_ERC366 2017-01-06T00:00:00-02:00 3.2051
11584 2017 9 1 GM366_ERC366 2017-01-09T00:00:00-02:00 3.2091
11585 2017 10 1 GM366_ERC366 2017-01-10T00:00:00-02:00 3.1912
11586 2017 11 1 GM366_ERC366 2017-01-11T00:00:00-02:00 3.2148
11587 2017 12 1 GM366_ERC366 2017-01-12T00:00:00-02:00 3.1655
11588 2017 13 1 GM366_ERC366 2017-01-13T00:00:00-02:00 3.2028
11589 2017 16 1 GM366_ERC366 2017-01-16T00:00:00-02:00 3.2228
11590 2017 17 1 GM366_ERC366 2017-01-17T00:00:00-02:00 3.2094
11591 2017 18 1 GM366_ERC366 2017-01-18T00:00:00-02:00 3.2205
11592 2017 19 1 GM366_ERC366 2017-01-19T00:00:00-02:00 3.2107
11593 2017 20 1 GM366_ERC366 2017-01-20T00:00:00-02:00 3.1912
... ... ... ... ... ... ...
[499 rows x 6 columns]
The same logic applies to the parameters monthGreaterThan and monthSmallerThan. For example, let’s restrict the function output to an interval of months (e.g.: from june to december) for a specifc year, say, 2018:
>>> ipeadatapy.timeseries("GM366_ERC366", year=2018, monthGreaterThan=5, monthSmallerThan=13)
YEAR DAY MONTH CODE DATE VALUE (R$)
11931 2018 1 6 GM366_ERC366 2018-06-01T00:00:00-03:00 3.7407
11932 2018 4 6 GM366_ERC366 2018-06-04T00:00:00-03:00 3.7418
11933 2018 5 6 GM366_ERC366 2018-06-05T00:00:00-03:00 3.7746
11934 2018 6 6 GM366_ERC366 2018-06-06T00:00:00-03:00 3.8187
11935 2018 7 6 GM366_ERC366 2018-06-07T00:00:00-03:00 3.8994
11936 2018 8 6 GM366_ERC366 2018-06-08T00:00:00-03:00 3.7853
11937 2018 11 6 GM366_ERC366 2018-06-11T00:00:00-03:00 3.6907
11938 2018 12 6 GM366_ERC366 2018-06-12T00:00:00-03:00 3.7038
11939 2018 13 6 GM366_ERC366 2018-06-13T00:00:00-03:00 3.7048
11940 2018 14 6 GM366_ERC366 2018-06-14T00:00:00-03:00 3.7051
11941 2018 15 6 GM366_ERC366 2018-06-15T00:00:00-03:00 3.7732
11942 2018 18 6 GM366_ERC366 2018-06-18T00:00:00-03:00 3.7537
11943 2018 19 6 GM366_ERC366 2018-06-19T00:00:00-03:00 3.7560
11944 2018 20 6 GM366_ERC366 2018-06-20T00:00:00-03:00 3.7329
... ... ... ... ... ... ...
[147 rows x 6 columns]
From now on, use your creativity. There are a lot of possibilities with these parameter combinations. The available parameters for the function timeseries() can be found using the function help(): help(ipeadatapy.timeseries).