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Brazil Soy v2.6.1: OLAM's supply chain
OLAM become a major trader of soy from Brazil in 2019. What do our results say about them?
- SEI-PCS results from S3, with 2017-18 coming from v2.6.0 and 2019-22 coming from v2.6.1
- MDIC from S3
- Comtrade from S3
The results of this notebook along with a discussion and some extra research can be found here:
https://docs.google.com/document/d/16XxH0Yv6wKl3QbbuplQkiRYgK7H8P2nOv-v3UDQio-M/edit#heading=h.32xl9armn1ki
from trase.tools import sps
import plotly.express as px
import pandas as pd
########################################################################################
# Read SEI-PCS results
########################################################################################
dfs = [
sps.get_pandas_df_once(
f"brazil/soy/sei_pcs/v{version}/SEIPCS_BRAZIL_SOY_{year}.csv",
dtype=str,
na_filter=False,
print_version_id=True,
)
for version, year in [
("2.6.0", 2017),
("2.6.0", 2018),
("2.6.1", 2019),
("2.6.1", 2020),
("2.6.1", 2021),
("2.6.1", 2022),
]
]
common_columns = set.intersection(*[set(df.columns) for df in dfs])
df = sps.concat(df[list(common_columns)] for df in dfs)
df = df.astype({"VOLUME_RAW": float, "YEAR": int, "FOB": float})
# fetch company groups from the database
from trase.tools.pandasdb.find import find_traders_and_groups_by_trase_id
from psycopg2 import sql
df_exporter_trase_ids = df[["EXPORTER_TRASE_ID", "YEAR"]].drop_duplicates()
df_exporter_trase_ids[["EXPORTER_GROUP"]] = find_traders_and_groups_by_trase_id(
df_exporter_trase_ids,
returning=["group_name"],
trase_id=sql.Identifier("EXPORTER_TRASE_ID"),
year=sql.Identifier("YEAR"),
)
df_with_groups = pd.merge(
df,
df_exporter_trase_ids,
on=["EXPORTER_TRASE_ID", "YEAR"],
how="left",
validate="many_to_one",
indicator=True,
)
assert all(df_with_groups.pop("_merge") == "both")
# we say the trade is OLAM if the exporter or exporter group contains "OLAM"
is_olam = df_with_groups["EXPORTER"].str.contains("OLAM") | df_with_groups[
"EXPORTER_GROUP"
].str.contains("OLAM")
# we make a new column where any of these rows has the exporter group "OLAM", regardless of what the real exporter group in the database was
df_with_groups["MODIFIED_EXPORTER_GROUP"] = (
df_with_groups["EXPORTER_GROUP"]
.mask(is_olam, "OLAM")
.combine_first(df_with_groups["EXPORTER"])
)
# new dataframe with just OLAM trade
df_olam = df_with_groups[is_olam].copy()
df_olam["EXPORTER_AND_GROUP"] = df_olam["EXPORTER"] + " - " + df_olam["EXPORTER_GROUP"]
df_olam["YEARLY_VOLUME"] = df_olam.groupby("YEAR")["VOLUME_RAW"].transform("sum")
df_olam["PERCENTAGE"] = 100 * df_olam["VOLUME_RAW"] / df_olam["YEARLY_VOLUME"]
########################################################################################
# read CNAE
########################################################################################
df_cnae = sps.get_pandas_df_once(
"brazil/logistics/cnpj/receita_federal_do_brasil/cnaes-2022-11-24.csv",
dtype=str,
na_filter=False,
usecols=["cnae_code", "description"],
print_version_id=True,
)
/Users/harrybiddle/dev/TRASE/trase/tools/sei_pcs/pandas_utilities.py:18: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
from pandas.util.testing import assert_frame_equal
Accessing s3://trase-storage/brazil/soy/sei_pcs/v2.6.0/SEIPCS_BRAZIL_SOY_2017.csv version_id=qKtDR2xK7EEjeI_6eRsVmJa.9A1qfH_b
Accessing s3://trase-storage/brazil/soy/sei_pcs/v2.6.0/SEIPCS_BRAZIL_SOY_2018.csv version_id=j4zTGNLsjTIts0AAg8J.LGP4ycc6KEt9
Accessing s3://trase-storage/brazil/soy/sei_pcs/v2.6.1/SEIPCS_BRAZIL_SOY_2019.csv version_id=a1fTkYc0FghpLMTtpD3_wAsHFE1U6FBi
Accessing s3://trase-storage/brazil/soy/sei_pcs/v2.6.1/SEIPCS_BRAZIL_SOY_2020.csv version_id=nuArlOqWRXyEzXZHeIcaHEs0QNEH8i_c
Accessing s3://trase-storage/brazil/soy/sei_pcs/v2.6.1/SEIPCS_BRAZIL_SOY_2021.csv version_id=ClAQ9M4tm7u4teAyw3ChcpEdjxOFxgef
Accessing s3://trase-storage/brazil/soy/sei_pcs/v2.6.1/SEIPCS_BRAZIL_SOY_2022.csv version_id=Pr5.DDGjAGVkZo8pVrz0qO3kkLhqsX_I
Accessing s3://trase-storage/brazil/logistics/cnpj/receita_federal_do_brasil/cnaes-2022-11-24.csv version_id=WJnPRXxQVHeCJ4xynlunGd1UC_Zfy2ru
data = sps.consolidate(df_olam, ["VOLUME_RAW"], ["YEAR", "EXPORTER_AND_GROUP"])
fig = px.bar(
data,
x="YEAR",
y="VOLUME_RAW",
color="EXPORTER_AND_GROUP",
barmode="stack",
title="OLAM export volume<br />Broken down by <i>exporter - exporter group</i>",
)
fig.show("png")
data = sps.consolidate(df_olam, ["VOLUME_RAW"], ["YEAR", "PRODUCT"])
fig = px.bar(
data,
x="YEAR",
y="VOLUME_RAW",
color="PRODUCT",
barmode="stack",
title="OLAM export volume<br />Broken down by product",
)
fig.show("png")
To put this in perspective, let's plot the top ten exporters per year.
The smallest of these has around 2.5 - 3B kg per year. OLAM is just outside the top ten.
df_with_groups = df_with_groups[df_with_groups["BRANCH"] != "DOMESTIC"]
df_exporters = sps.consolidate(
df_with_groups, ["VOLUME_RAW"], ["YEAR", "MODIFIED_EXPORTER_GROUP"]
)
df_exporters["YEARLY_VOLUME"] = df_exporters.groupby("YEAR")["VOLUME_RAW"].transform(
"sum"
)
df_exporters["PROPORTION"] = df_exporters["VOLUME_RAW"] / df_exporters["YEARLY_VOLUME"]
# find the top ten exporters per year
# there must be a nicer way to do this using group by ...? ah well
top_ten_cutoff_per_year = (
df_exporters.groupby("YEAR")["PROPORTION"]
.nlargest(10)
.reset_index()
.groupby("YEAR")["PROPORTION"]
.min()
.rename("CUTOFF")
.reset_index()
)
df_exporters = pd.merge(df_exporters, top_ten_cutoff_per_year, on=["YEAR"], how="left")
is_top_ten = df_exporters["PROPORTION"] >= df_exporters["CUTOFF"]
# graph of top ten exporter groups per year
fig = px.bar(
df_exporters[is_top_ten].sort_values("PROPORTION", ascending=False),
x="YEAR",
y="VOLUME_RAW",
color="MODIFIED_EXPORTER_GROUP",
barmode="group",
title="Top ten exporter groups per year",
)
fig.update_layout(
legend=dict(yanchor="top", y=-0.1, xanchor="left", x=0.01),
height=800,
)
fig.show("png")
# ranking
print(
"Ranking of OLAM as an exporter, where 1 represents the biggest exporter in that year"
)
df_exporters["RANKING"] = (
df_exporters.groupby("YEAR")["PROPORTION"].rank(ascending=False).astype(int)
)
display(
df_exporters[df_exporters["MODIFIED_EXPORTER_GROUP"] == "OLAM"][["YEAR", "RANKING"]]
)
Ranking of OLAM as an exporter, where 1 represents the biggest exporter in that year
| YEAR | RANKING | |
|---|---|---|
| 231 | 2017 | 238 |
| 539 | 2018 | 19 |
| 887 | 2019 | 11 |
| 1275 | 2020 | 12 |
| 1541 | 2021 | 10 |
| 1746 | 2022 | 11 |
data = sps.consolidate(
df_olam, ["PERCENTAGE"], ["YEAR", "EXPORTER_AND_GROUP", "BRANCH"]
)
fig = px.bar(
data,
x="YEAR",
y="PERCENTAGE",
color="BRANCH",
barmode="stack",
title="OLAM export volume<br />Broken down by <i>exporter - exporter group</i>",
)
fig.show("png")
Let's look at OLAM's CNPJs. There are about eight which are important
data = sps.consolidate(df_olam, ["PERCENTAGE"], ["CNPJ", "YEAR"])
data["CNPJ_VOLUME"] = data.groupby("CNPJ")["PERCENTAGE"].transform("sum")
is_top_three_cnpj_per_year = (
data.groupby("YEAR")["CNPJ_VOLUME"].rank(ascending=False).astype(int) <= 3
)
data["LABEL"] = data["CNPJ"].mask(data["PERCENTAGE"] < 6, "OTHER")
fig = px.bar(
data.sort_values("CNPJ_VOLUME", ascending=False),
x="YEAR",
y="PERCENTAGE",
color="LABEL",
title="OLAM CNPJs by export volume",
)
fig.show("png")
Let's focus now on 2022 and look at how each CNPJ is solved:
data = df_olam[df_olam["YEAR"] == 2022]
data = sps.consolidate(data, ["PERCENTAGE"], ["CNPJ", "BRANCH"])
fig = px.bar(
data,
y="CNPJ",
x="PERCENTAGE",
color="BRANCH",
labels={
"PERCENTAGE": "Percentage of 2022 export volume",
"CNPJ": "OLAM CNPJ",
},
)
fig.show("png")
Let's look at this on a map. Most sourcing is from Minas Gerais in the east.
from urllib.request import urlopen
import json
with urlopen(
# 'https://raw.githubusercontent.com/eupimenta/geoJSON/master/BRMUE250GC_SIR.geojson',
"https://github.com/luizpedone/municipal-brazilian-geodata/raw/master/data/Brasil.json",
) as response:
geometry = json.load(response)
data = df_olam[df_olam["YEAR"] == 2022].copy()
STATE_NUMBER_TO_LETTER = {
"11": "RO",
"12": "AC",
"13": "AM",
"14": "RR",
"15": "PA",
"16": "AP",
"17": "TO",
"21": "MA",
"22": "PI",
"23": "CE",
"24": "RN",
"25": "PB",
"26": "PE",
"27": "AL",
"28": "SE",
"29": "BA",
"31": "MG",
"32": "ES",
"33": "RJ",
"35": "SP",
"41": "PR",
"42": "SC",
"43": "RS",
"50": "MS",
"51": "MT",
"52": "GO",
"53": "DF",
"XX": "XX",
}
data["STATE_GEOCODE"] = (
data["LVL6_TRASE_ID_LH"].str.slice(3, 5).apply(lambda x: STATE_NUMBER_TO_LETTER[x])
)
data = data[data["STATE_GEOCODE"] != "XX"]
data = sps.consolidate(data, ["VOLUME_RAW"], ["STATE_GEOCODE"])
fig = px.choropleth(
data,
geojson=geometry,
color_continuous_scale="Blues",
locations="STATE_GEOCODE",
color="VOLUME_RAW",
featureidkey="properties.UF",
title="OLAM 2022 Logistics Hubs<br />Aggregated by state",
)
fig.update_geos(fitbounds="locations")
fig.add_scattergeo(
geojson=geometry,
locations=data["STATE_GEOCODE"],
text=data["STATE_GEOCODE"],
featureidkey="properties.UF",
mode="text",
)
fig.show("png")
data = df_olam[df_olam["YEAR"] == 2022].copy()
data = sps.consolidate(data, ["PERCENTAGE"], ["LVL6_TRASE_ID_LH"])
fig = px.bar(
data.sort_values("LVL6_TRASE_ID_LH"),
y="PERCENTAGE",
x="LVL6_TRASE_ID_LH",
title="OLAM 2022 logistics hubs",
)
fig.show("png")
Now let's look at the top four 2022 OLAM CNPJs in more detail
# get top four OLAM CNPJs by export volume
df_olam_2022 = df_olam[df_olam["YEAR"] == 2022]
data = sps.consolidate(df_olam_2022, ["PERCENTAGE", "VOLUME_RAW"], ["CNPJ"])
data = data.sort_values("PERCENTAGE", ascending=False)
data = data.head(4)
print("Most important OLAM CNPJs in 2022:\n")
display(data)
# get data from database
from trase.tools.pandasdb.query import query_with_dataframe
from trase.tools.pcs.db import connect_to_db
from trase.tools.pcs.connect import loggingConnection
cnx = connect_to_db()
df_db = query_with_dataframe(
data,
"select * from cnpj.cnpj right join df using (cnpj)",
cnx=loggingConnection(cnx),
cur=cnx.cursor(),
)
from trase.tools.pcs.reports import print_header
for _, row in df_db.iterrows():
cnpj = row["cnpj"]
percentage = row["percentage"]
volume = row["volume_raw"]
print_header(f"Focus on CNPJ {cnpj}")
df_cnpj = df_db[df_db["cnpj"] == cnpj]
df_results = sps.consolidate(
df_olam_2022[df_olam_2022["CNPJ"] == cnpj],
["VOLUME_RAW"],
["LVL6_TRASE_ID_LH", "LVL6_NAME_LH", "BRANCH"],
)
pretty_volume_tns = sps.format_float(volume / 100, significant_digits=2)
print(
f"Responsible for {percentage:.0f}% of 2022 OLAM export volume, or {pretty_volume_tns} tn\n"
)
print("Here is how SEI-PCS locates the logistics hub for this CNPJ:\n")
fig = px.bar(
df_results,
x="VOLUME_RAW",
y="LVL6_TRASE_ID_LH",
color="BRANCH",
title=f"2022 logistics hubs for {cnpj}",
)
fig.show("png")
print("Here is what information we have in the database:")
display(df_cnpj.T)
print("Primary CNAE:\n")
primary_cnae = df_cnpj["cnae"].astype(str).str.rjust(7, "0")
df_primary_cnae = pd.merge(
pd.DataFrame({"cnae_code": primary_cnae}),
df_cnae,
)
print(df_primary_cnae.to_string())
print("\nSecondary CNAEs:\n")
secondary_cnaes = df_cnpj["cnae_secondary"].str.split(",").explode()
df_secondary_cnaes = pd.merge(
pd.DataFrame({"cnae_code": secondary_cnaes}),
df_cnae,
)
print(df_secondary_cnaes.to_string())
[1mFocus on CNPJ 07028528000118[0m
────────────────────────────────────────────────────────────────────────────────
Responsible for 27% of 2022 OLAM export volume, or 6,600,000 tn
Here is how SEI-PCS locates the logistics hub for this CNPJ:
Here is what information we have in the database:
| 0 | |
|---|---|
| cnpj | 07028528000118 |
| id | 40605536 |
| company_name | OLAM AGRICOLA LTDA. |
| juridic_nature | 2062 |
| nature_name | Sociedade Empresária Limitada |
| company_social_capital | 56737205.0 |
| company_size | 05 |
| company_size_name | DEMAIS |
| national_responsible_entity | None |
| id_head_branch | 1 |
| head_branch_name | MATRIZ |
| trade_name | None |
| id_registration_status | 2 |
| registration_status_name | ATIVA |
| registration_status_date | None |
| id_registration_status_reason | 0 |
| reason_name | SEM MOTIVO |
| foreign_city_name | None |
| id_country | None |
| country_name | None |
| start_activity_date | 2004-08-13 |
| cnae | 4621400 |
| cnae_name | Comércio atacadista de café em grão |
| cnae_secondary | 0121101,0134200,1081301,1081302,1082100,109370... |
| address_type | RODOVIA |
| address_street | BR-491 |
| address_number | 2005 |
| address_complement | LETRA C |
| address_neighbourhood | VILA TEIXEIRA |
| postal_code | 37132411 |
| id_city | 4031 |
| city_name | ALFENAS |
| municipality | 3101607 |
| id_state | 31 |
| state | MG |
| phone_1_area_code | 13 |
| phone_1 | 32135200 |
| phone_2_area_code | None |
| phone_2 | None |
| fax_area_code | 13 |
| fax | 32135204 |
| tax.br@olamnet.com | |
| special_status | None |
| special_status_date | None |
| created_at | 2023-11-10 02:32:32.206897 |
| percentage | 27.269491 |
| volume_raw | 659742355.474752 |
Primary CNAE:
cnae_code description
0 4621400 Wholesale of coffee beans
Secondary CNAEs:
cnae_code description
0 0121101 Horticulture, except strawberry
1 0134200 Coffee cultivation
2 1081301 Coffee processing
3 1081302 Coffee roasting and grinding
4 1082100 Manufacture of coffee-based products
5 1093701 Manufacture of cocoa and chocolate products
6 4611700 Commercial representatives and agents for the sale of agricultural raw materials and live animals
7 4622200 Soybean wholesaler
8 4623103 Cotton wholesaler
9 4623105 Cacao wholesaler
10 4623106 Wholesale of seeds, flowers, plants and grasses
11 4623199 Wholesale of agricultural raw materials not specified above
12 4631100 Wholesale of milk and dairy products
13 4632001 Wholesale of processed grains and legumes
14 4637102 Sugar wholesaler
15 4637199 Wholesaler specializing in other food products not specified above
16 4683400 Wholesale of pesticides, fertilizers and soil conditioners
17 4684299 Wholesale of other chemicals and petrochemicals not specified above
18 6463800 Other holding companies, except holding companies
19 7490103 Agronomy and consultancy services for agricultural and livestock activities
[1mFocus on CNPJ 03902252000102[0m
────────────────────────────────────────────────────────────────────────────────
Responsible for 22% of 2022 OLAM export volume, or 5,300,000 tn
Here is how SEI-PCS locates the logistics hub for this CNPJ:
Here is what information we have in the database:
| 1 | |
|---|---|
| cnpj | 03902252000102 |
| id | 35360654 |
| company_name | OLAM BRASIL LTDA |
| juridic_nature | 2062 |
| nature_name | Sociedade Empresária Limitada |
| company_social_capital | 79183473.0 |
| company_size | 05 |
| company_size_name | DEMAIS |
| national_responsible_entity | None |
| id_head_branch | 1 |
| head_branch_name | MATRIZ |
| trade_name | None |
| id_registration_status | 2 |
| registration_status_name | ATIVA |
| registration_status_date | 2004-12-24 |
| id_registration_status_reason | 0 |
| reason_name | SEM MOTIVO |
| foreign_city_name | None |
| id_country | None |
| country_name | None |
| start_activity_date | 2000-06-21 |
| cnae | 4689399 |
| cnae_name | Comércio atacadista especializado em outros pr... |
| cnae_secondary | 4622200,4623103,4623199,4631100,4649499,466999... |
| address_type | RUA |
| address_street | RUA PROJETADA - ROD ES 137 |
| address_number | 350 |
| address_complement | SALA 01 |
| address_neighbourhood | COQUEIRAL |
| postal_code | 29830000 |
| id_city | 5677 |
| city_name | NOVA VENECIA |
| municipality | 3203908 |
| id_state | 32 |
| state | ES |
| phone_1_area_code | 35 |
| phone_1 | 32992550 |
| phone_2_area_code | 35 |
| phone_2 | 92730000 |
| fax_area_code | 11 |
| fax | 38496413 |
| taxcoe.br@olamagri.com | |
| special_status | None |
| special_status_date | None |
| created_at | 2023-11-10 02:32:32.206897 |
| percentage | 21.738788 |
| volume_raw | 525935713.522766 |
Primary CNAE:
cnae_code description
0 4689399 Wholesaler specializing in other intermediate products not specified above
Secondary CNAEs:
cnae_code description
0 4622200 Soybean wholesaler
1 4623103 Cotton wholesaler
2 4623199 Wholesale of agricultural raw materials not specified above
3 4631100 Wholesale of milk and dairy products
4 4649499 Wholesale of other equipment and articles for personal and household use not specified above
5 4669999 Wholesale of other machinery and equipment not specified above; parts and pieces
6 4671100 Wholesale of wood and derived products
7 4681801 Wholesale of alcohol, fuel, biodiesel, gasoline and other petroleum products, except lubricants, not carried out by a conveyor of
8 5250805 Multimodal Transport Operator - OTM
[1mFocus on CNPJ 07028528004539[0m
────────────────────────────────────────────────────────────────────────────────
Responsible for 19% of 2022 OLAM export volume, or 4,700,000 tn
Here is how SEI-PCS locates the logistics hub for this CNPJ:
Here is what information we have in the database:
| 2 | |
|---|---|
| cnpj | 07028528004539 |
| id | 40612302 |
| company_name | OLAM AGRICOLA LTDA. |
| juridic_nature | 2062 |
| nature_name | Sociedade Empresária Limitada |
| company_social_capital | 56737205.0 |
| company_size | 05 |
| company_size_name | DEMAIS |
| national_responsible_entity | None |
| id_head_branch | 2 |
| head_branch_name | FILIAL |
| trade_name | None |
| id_registration_status | 8 |
| registration_status_name | BAIXADA |
| registration_status_date | 2020-09-30 |
| id_registration_status_reason | 1 |
| reason_name | EXTINCAO POR ENCERRAMENTO LIQUIDACAO VOLUNTARIA |
| foreign_city_name | None |
| id_country | None |
| country_name | None |
| start_activity_date | 2017-06-13 |
| cnae | 4621400 |
| cnae_name | Comércio atacadista de café em grão |
| cnae_secondary | 4622200,4623103,4623199,4637102 |
| address_type | RUA |
| address_street | ENGENHEIRO CARLOS FIRMO SCHMIDT ROVER (DIRG) |
| address_number | 5015 |
| address_complement | SALA 4 |
| address_neighbourhood | ZONA PORTUARIA |
| postal_code | 96204470 |
| id_city | 8815 |
| city_name | RIO GRANDE |
| municipality | 4315602 |
| id_state | 43 |
| state | RS |
| phone_1_area_code | 13 |
| phone_1 | 32135200 |
| phone_2_area_code | None |
| phone_2 | None |
| fax_area_code | None |
| fax | None |
| fiscal.santos@olamnet.com | |
| special_status | None |
| special_status_date | None |
| created_at | 2023-11-10 02:32:32.206897 |
| percentage | 19.44504 |
| volume_raw | 470442090.640714 |
Primary CNAE:
cnae_code description
0 4621400 Wholesale of coffee beans
Secondary CNAEs:
cnae_code description
0 4622200 Soybean wholesaler
1 4623103 Cotton wholesaler
2 4623199 Wholesale of agricultural raw materials not specified above
3 4637102 Sugar wholesaler
[1mFocus on CNPJ 03902252001095[0m
────────────────────────────────────────────────────────────────────────────────
Responsible for 12% of 2022 OLAM export volume, or 3,000,000 tn
Here is how SEI-PCS locates the logistics hub for this CNPJ:
Here is what information we have in the database:
| 3 | |
|---|---|
| cnpj | 03902252001095 |
| id | 35354050 |
| company_name | OLAM BRASIL LTDA |
| juridic_nature | 2062 |
| nature_name | Sociedade Empresária Limitada |
| company_social_capital | 79183473.0 |
| company_size | 05 |
| company_size_name | DEMAIS |
| national_responsible_entity | None |
| id_head_branch | 2 |
| head_branch_name | FILIAL |
| trade_name | None |
| id_registration_status | 2 |
| registration_status_name | ATIVA |
| registration_status_date | 2004-10-15 |
| id_registration_status_reason | 0 |
| reason_name | SEM MOTIVO |
| foreign_city_name | None |
| id_country | None |
| country_name | None |
| start_activity_date | 2004-10-15 |
| cnae | 4623199 |
| cnae_name | Comércio atacadista de matérias-primas agrícol... |
| cnae_secondary | 4649499,5250805,4622200,4671100,4623103 |
| address_type | RUA |
| address_street | FUNCHAL |
| address_number | 418 |
| address_complement | CONJ 901 |
| address_neighbourhood | VILA OLIMPIA |
| postal_code | 04551060 |
| id_city | 7107 |
| city_name | SAO PAULO |
| municipality | 3550308 |
| id_state | 35 |
| state | SP |
| phone_1_area_code | 11 |
| phone_1 | 30409060 |
| phone_2_area_code | None |
| phone_2 | None |
| fax_area_code | 11 |
| fax | 38496888 |
| taxcoe.br@olamagri.com | |
| special_status | None |
| special_status_date | None |
| created_at | 2023-11-10 02:32:32.206897 |
| percentage | 12.487018 |
| volume_raw | 302103727.511581 |
Primary CNAE:
cnae_code description
0 4623199 Wholesale of agricultural raw materials not specified above
Secondary CNAEs:
cnae_code description
0 4649499 Wholesale of other equipment and articles for personal and household use not specified above
1 5250805 Multimodal Transport Operator - OTM
2 4622200 Soybean wholesaler
3 4671100 Wholesale of wood and derived products
4 4623103 Cotton wholesaler