Import data from Table of Input-Output from IBGE

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Project description

Imput Output Tables to Brazil IOTBR

Generate I O - T A B L E IOTBR.

Instructions

! pip install iotbr==0.0.3

from iotbr import tru as tru
from iotbr import tru_pb as tru_pb

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting iotbr==0.0.3
  Downloading iotbr-0.0.3-py3-none-any.whl (7.2 MB)
[2K     [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m7.2/7.2 MB[0m [31m38.7 MB/s[0m eta [36m0:00:00[0m
[?25hRequirement already satisfied: xlrd in /usr/local/lib/python3.10/dist-packages (from iotbr==0.0.3) (2.0.1)
Installing collected packages: iotbr
Successfully installed iotbr-0.0.3

#ler uma variável qualquer dentro da tru
tru.read_var()

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	PT
produtos
Arroz, trigo e outros cereais	12631
Milho em grão	43663
Algodão herbáceo, outras fibras da lav. temporária	18376
Cana-de-açúcar	57068
Soja em grão	153331
...	...
Serviços de artes, cultura, esporte e recreação	45636
Organizações patronais, sindicais e outros serviços associativos	93816
Manutenção de computadores, telefones e objetos domésticos	33107
Serviços pessoais	62283
Serviços domésticos	75158

128 rows × 1 columns

  <script>
    const buttonEl =
      document.querySelector('#df-804688f7-1f60-4742-b054-7c72d883e30b button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-804688f7-1f60-4742-b054-7c72d883e30b');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

#ler uma variável específica
tru.read_var('2019','68','MG_tra')

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	MG_tra
produtos
Arroz, trigo e outros cereais	1405
Milho em grão	2631
Algodão herbáceo, outras fibras da lav. temporária	370
Cana-de-açúcar	2996
Soja em grão	3433
...	...
Serviços de artes, cultura, esporte e recreação	0
Organizações patronais, sindicais e outros serviços associativos	0
Manutenção de computadores, telefones e objetos domésticos	0
Serviços pessoais	0
Serviços domésticos	0

128 rows × 1 columns

  <script>
    const buttonEl =
      document.querySelector('#df-a86d51f5-4ce0-4242-b2c3-a6892af845fc button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-a86d51f5-4ce0-4242-b2c3-a6892af845fc');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

#ler ler a variável com preços do ano anterior
tru.read_var('2019','68','MG_tra','t-1')

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	MG_tra
produtos
Arroz, trigo e outros cereais	1322
Milho em grão	2385
Algodão herbáceo, outras fibras da lav. temporária	365
Cana-de-açúcar	2875
Soja em grão	3089
...	...
Serviços de artes, cultura, esporte e recreação	0
Organizações patronais, sindicais e outros serviços associativos	0
Manutenção de computadores, telefones e objetos domésticos	0
Serviços pessoais	0
Serviços domésticos	0

128 rows × 1 columns

  <script>
    const buttonEl =
      document.querySelector('#df-d740900e-1799-4c36-923a-e761bfa570df button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-d740900e-1799-4c36-923a-e761bfa570df');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

#ler ler a variável com outro nível e ano
tru.read_var('2000','12')

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	PT
produtos
Agropecuária	87128.07773
Indústrias extrativas	35555.283895
Indústrias de transformação	648818.483278
Eletricidade e gás, água, esgoto, atividades de gestão de resíduos	64680.645095
Construção	146016.177837
Comércio	143766.942246
Transporte, armazenagem e correio	88682.618478
Informação e comunicação	86607.823103
Atividades financeiras, de seguros e serviços relacionados	105414.172763
Atividades imobiliárias	151048.781496
Outras atividades de serviços	320282.96256
Administração, defesa, saúde e educação públicas e seguridade social	209734.795687

  <script>
    const buttonEl =
      document.querySelector('#df-b985967a-f875-488f-96a9-618e391cc619 button.colab-df-convert');
    buttonEl.style.display =
      google.colab.kernel.accessAllowed ? 'block' : 'none';

    async function convertToInteractive(key) {
      const element = document.querySelector('#df-b985967a-f875-488f-96a9-618e391cc619');
      const dataTable =
        await google.colab.kernel.invokeFunction('convertToInteractive',
                                                 [key], {});
      if (!dataTable) return;

      const docLinkHtml = 'Like what you see? Visit the ' +
        '<a target="_blank" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'
        + ' to learn more about interactive tables.';
      element.innerHTML = '';
      dataTable['output_type'] = 'display_data';
      await google.colab.output.renderOutput(dataTable, element);
      const docLink = document.createElement('div');
      docLink.innerHTML = docLinkHtml;
      element.appendChild(docLink);
    }
  </script>
</div>

#encontrar matrizes de demanda final (mE) e demanda total (mU) a preços básicos
#usando o métrodo do Prof. Dr. Guilhoto
mU = tru_pb.D_total_pb('2019','68')[0]
mE = tru_pb.D_total_pb('2019','68')[1]
mE[1,:]

array([22855.033437625632, 0.0, 0.0, 3492.9691568353433, 0.0, -481.0],
      dtype=object)

#Estimar matriz a preços básicos para anos e níveis diferente
mU = tru_pb.D_total_pb('2010','20')[0]
mE = tru_pb.D_total_pb('2010','20')[1]
mE[1,:]

array([76824.77308806025, 0.0, 0.0, 0.0, 6221.934829327755, -875.0],
      dtype=object)

#Estimar matriz a preços básicos para anos, níveis e unidades diferentes
mU = tru_pb.D_total_pb('2010','51','t-1')[0]
mE = tru_pb.D_total_pb('2010','51','t-1')[1]
mU.shape

(107, 51)

#estimar a matrizes do sistema IO
import numpy as np
year = '2019'
level = '68'
unit = 't'
mU = tru_pb.D_total_pb(year,level,unit)[0]
mE = tru_pb.D_total_pb(year,level,unit)[1]


#Estimar matriz (D)
mV = tru.read_var(year,level,'P_matrix',unit).values.T
#Total produzido por produto
#vQ = np.sum(mV, axis=0)
vQ = tru.read_var(year,level,'PT',unit).values
mQChapeu = np.diagflat(1/vQ)
mD = np.dot(mV, mQChapeu)

#Estimar matriz (B)
#Total produzido por setor
vVBP = np.sum(mV, axis=1)
vX = np.copy(vVBP)
mXChapeu = np.diagflat(1/vX)
mB=np.dot(mU,mXChapeu)

mA = np.dot(mD,mB).astype(float)
mY = np.dot(mD,mE).astype(float)
mZ = np.dot(mD,mU).astype(float)
mI = np.eye(int(level))
 
mLeontief = np.linalg.inv(mI - mA)

mLeontief

array([[1.02795846e+00, 6.63434968e-02, 1.55683119e-02, ...,
        2.48657115e-03, 8.87445164e-03, 0.00000000e+00],
       [3.46679411e-03, 1.05066335e+00, 7.50231026e-03, ...,
        5.04707964e-04, 2.99970714e-03, 0.00000000e+00],
       [4.65358841e-03, 1.14566016e-02, 1.06662450e+00, ...,
        3.39112179e-04, 7.04559555e-04, 0.00000000e+00],
       ...,
       [2.57040133e-04, 4.90720175e-04, 2.96567252e-04, ...,
        1.02021266e+00, 6.72567669e-03, 0.00000000e+00],
       [1.72661929e-03, 1.85442303e-03, 1.52402071e-03, ...,
        3.19760462e-03, 1.00595709e+00, 0.00000000e+00],
       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,
        0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])

#Variáveis que o pkg iotbr reconhece dentro da tru

níveis 20 e 68 so tem dados a partir de 2010 -níveis 12 e 51 passaram a agregar exportações e importações a partir de 2010

Project details

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This version

0.0.19

Dec 5, 2023

0.0.18

Dec 4, 2023

0.0.17

Jul 26, 2023

0.0.16

Jul 26, 2023

0.0.15

Jul 26, 2023

0.0.14

Jul 25, 2023

0.0.13

Jul 25, 2023

0.0.12

Jul 25, 2023

0.0.11

Jun 5, 2023

0.0.10

Jun 2, 2023

0.0.9

Jun 2, 2023

0.0.8

May 31, 2023

0.0.7

May 30, 2023

0.0.6

May 30, 2023

0.0.5

May 29, 2023

0.0.4

May 24, 2023

0.0.3

May 10, 2023

0.0.1

May 6, 2023

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