The first task is to read a database with HS6 code which is based on INTTRA database. This data is stored in Excel file with multiple sheets. 

import pandas as pd

url = 'https://drive.google.com/uc?export=download&id=1iR7-yUYoaT4jfvFwK4uhriJJj0yFQyjv'

xls = pd.ExcelFile(url)

The next step is to obtain all sheets names.

sheet_names = xls.sheet_names

sheet_names

This will produce the following output:

['bookings', 'shipping_instructions', 'track&trace'] 

Finally, let's select data from the first sheet.

df1 = pd.read_excel(xls, sheet_names[0])

df1

This will return the following data frame:

Let's read the names of all columns of the first sheet.

df1.columns

This will return the following column names:

Index(['bk_transaction_id', 'si_transaction_id', 'revision_date', 'carrier', 'requester', 'requester_street', 'requester_city', 'requester_state', 'requester_postal_code', 'requester_country', 'shipper', 'shipper_street', 'shipper_city', 'shipper_state', 'shipper_postal_code', 'shipper_country', 'forwarder', 'forwarder_street', 'forwarder_city', 'forwarder_state', 'forwarder_postal_code', 'forwarder_country', 'consignee', 'consignee_street', 'consignee_city', 'consignee_state', 'consignee_postal_code', 'consignee_country', 'contract_party', 'contract_party_street', 'contract_party_city', 'contract_party_state', 'contract_party_postal_code', 'contract_party_country', 'notify_party', 'notify_party_street', 'notify_party_city', 'notify_party_state', 'notify_party_postal_code', 'notify_party_country', 'second_notify_party', 'second_notify_party_street', 'second_notify_party_city', 'second_notify_party_state', 'second_notify_party_postal_code', 'second_notify_party_country', 'third_notify_party', 'third_notify_party_street', 'third_notify_party_city', 'third_notify_party_state', 'third_notify_party_postal_code', 'third_notify_party_country', 'move_type', 'pol_vessel_name', 'pol_conveyance_number', 'pol_city_unlocode', 'pol_city_name', 'pol_country_name', 'pol_etd', 'pod_vessel_name', 'pod_conveyance_number', 'pod_city_unlocode', 'pod_city_name', 'pod_country_name', 'pod_eta', 'plor_city_unlocode', 'plor_city_name', 'plor_country_name', 'plor_etd', 'plod_city_unlocode', 'plod_city_name', 'plod_country_name', 'plod_eta', 'line_item_id', 'package_count', 'cargo_description', 'harmonized_system_code', 'ncm_code', 'schedule_b', 'hazmat_undg_number', 'hazmat_imo_class', 'weight_kg', 'allocated_container_count', 'allocated_teu_count'], dtype='object') 

Now, it is possible to filter rows according to certain values in column 'weight_kg' and showing the remaining rows.

filter = df1['weight_kg'] < 100000

df1.loc[filter]

This will return a smaller data frame in terms of number of rows.

 Another possibility, it is to filter rows according to certain values in column 'weight_kg' and show the distribution for column 'weight_kg'.

weight = df1.loc[filter]['weight_kg']

weight.hist()

This will produce the following graph: