Dry Bulk Flows¶

Run this example in Colab.¶

Setup¶

Install the Signal Ocean SDK:

pip install signal-ocean

Set your subscription key acquired here: https://apis.signalocean.com/profile

In [ ]:

!pip install signal-ocean

!pip install signal-ocean

In [ ]:

signal_ocean_api_key = '' #replace with your subscription key

signal_ocean_api_key = '' #replace with your subscription key

Call the Voyages API¶

The Voyages API retrieves information about vessel voyages.

In [2]:

from signal_ocean import Connection
from signal_ocean.voyages import VoyagesAPI
import pandas as pd
from datetime import date, datetime
import plotly.express as px

from signal_ocean import Connection from signal_ocean.voyages import VoyagesAPI import pandas as pd from datetime import date, datetime import plotly.express as px

In [3]:

connection = Connection(signal_ocean_api_key)
api = VoyagesAPI(connection)

connection = Connection(signal_ocean_api_key) api = VoyagesAPI(connection)

Get vessel class ids for Dry vessel classes

In [4]:

vessel_classes = api.get_vessel_classes()
vessel_classes_df = pd.DataFrame(v.__dict__ for v in vessel_classes)
vessel_classes_df[vessel_classes_df['vessel_type']=='Dry'].head(10)

vessel_classes = api.get_vessel_classes() vessel_classes_df = pd.DataFrame(v.__dict__ for v in vessel_classes) vessel_classes_df[vessel_classes_df['vessel_type']=='Dry'].head(10)

Out[4]:

	vessel_class_id	vessel_class_name	vessel_type_id	vessel_type
4	69	VLOC	3	Dry
5	70	Capesize	3	Dry
6	72	Post Panamax	3	Dry
7	74	Panamax	3	Dry
8	75	Supramax	3	Dry
9	76	Handymax	3	Dry
10	77	Handysize	3	Dry
19	92	Small	3	Dry

Get voyages for Capesize¶

For Capesize we will use (vessel_class_id = 70) when calling the API

In [5]:

voyages = api.get_voyages_condensed(vessel_class_id=70, date_from=datetime.strptime("2021-09-01", "%Y-%m-%d"))
voyages = pd.DataFrame([v.__dict__ for v in voyages])
voyages.tail(5)

voyages = api.get_voyages_condensed(vessel_class_id=70, date_from=datetime.strptime("2021-09-01", "%Y-%m-%d")) voyages = pd.DataFrame([v.__dict__ for v in voyages]) voyages.tail(5)

Out[5]:

	imo	voyage_number	vessel_type_id	vessel_class_id	vessel_status_id	commercial_operator_id	deleted	events	id	horizon_id	...	last_discharge_sailing_date	last_discharge_country_id	last_discharge_country_name	last_discharge_area_id_level0	last_discharge_area_name_level0	repairs_ind	storage_ind	sts_load_ind	sts_discharge_ind	local_trade_ind
22314	9974084	3	3	70	1	1072.0	False	None	I98314446VEDD103100	2	...	2024-03-29 01:31:24.704000+00:00	55.0	China	None	North China	False	False	False	False	False
22315	9974096	1	3	70	1	NaN	False	None	I98315046VEDC71FD00	2	...	2024-02-14 19:45:07.773000+00:00	121.0	Japan	None	Japan Island	True	False	False	False	False
22316	9974096	2	3	70	1	NaN	False	None	I98315046VEDD37BE00	3	...	2024-03-19 14:25:10.371000+00:00	55.0	China	None	North China	False	False	False	False	False
22317	9978573	1	3	70	1	NaN	False	None	I9842CD46VEDD37BE00	2	...	NaT	NaN	None	None	None	True	False	False	False	True
22318	9980174	1	3	70	1	NaN	False	None	I98490E46VEDD37BE00	2	...	NaT	NaN	None	None	None	True	False	False	False	True

5 rows × 97 columns

Convert Timestamp to datetime, drop voyages with first_load_sailing_date in the future (ongoing or future)

In [6]:

voyages['first_load_sailing_date'] = voyages['first_load_sailing_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None))
voyages=voyages[voyages['first_load_sailing_date']<datetime.now()]

voyages['first_load_sailing_date'] = voyages['first_load_sailing_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None)) voyages=voyages[voyages['first_load_sailing_date']

Examine Capesize Australia Capesize Coal Exports¶

Group by origin and destination (first load, last discharge), and use first_load_sailing_date as date of export

In [7]:

coal_exports_country_level = voyages[(voyages['first_load_sailing_date']>"2022-01-01")&(voyages['cargo_sub_group']=='Coal')] \
    .set_index('first_load_sailing_date') \
    .groupby(['first_load_country_name', 'last_discharge_country_name'])['quantity'] \
    .resample('MS') \
    .sum() \
    .reset_index() \
    .rename(columns={'first_load_sailing_date': 'Date'})

coal_exports_country_level = voyages[(voyages['first_load_sailing_date']>"2022-01-01")&(voyages['cargo_sub_group']=='Coal')] \ .set_index('first_load_sailing_date') \ .groupby(['first_load_country_name', 'last_discharge_country_name'])['quantity'] \ .resample('MS') \ .sum() \ .reset_index() \ .rename(columns={'first_load_sailing_date': 'Date'})

Drop intra country flows

In [8]:

coal_exports_country_level = coal_exports_country_level[coal_exports_country_level['first_load_country_name']!=coal_exports_country_level['last_discharge_country_name']]
coal_exports_country_level.head()

coal_exports_country_level = coal_exports_country_level[coal_exports_country_level['first_load_country_name']!=coal_exports_country_level['last_discharge_country_name']] coal_exports_country_level.head()

Out[8]:

	first_load_country_name	last_discharge_country_name	Date	quantity
0	Australia	Belgium	2022-04-01	131000.0
1	Australia	Brazil	2022-01-01	315000.0
2	Australia	Brazil	2022-02-01	0.0
3	Australia	Brazil	2022-03-01	0.0
4	Australia	Brazil	2022-04-01	0.0

• Keep only Australia exports
• Aggregate all exports monthly
• Drop unnecessary columns

In [9]:

australia_coal_exports = coal_exports_country_level[(coal_exports_country_level['first_load_country_name']=='Australia')].copy()

australia_coal_exports['TotalExports'] = australia_coal_exports['quantity'].groupby(australia_coal_exports['Date']).transform('sum')
australia_coal_exports.drop_duplicates(subset=['Date'], keep='last', inplace = True)
australia_coal_exports.drop(['last_discharge_country_name', 'quantity'], axis=1, inplace = True)
australia_coal_exports.head()

australia_coal_exports = coal_exports_country_level[(coal_exports_country_level['first_load_country_name']=='Australia')].copy() australia_coal_exports['TotalExports'] = australia_coal_exports['quantity'].groupby(australia_coal_exports['Date']).transform('sum') australia_coal_exports.drop_duplicates(subset=['Date'], keep='last', inplace = True) australia_coal_exports.drop(['last_discharge_country_name', 'quantity'], axis=1, inplace = True) australia_coal_exports.head()

Out[9]:

	first_load_country_name	Date	TotalExports
383	Australia	2022-01-01	11198000.0
384	Australia	2022-02-01	10673000.0
385	Australia	2022-03-01	10345000.0
386	Australia	2022-04-01	9261000.0
387	Australia	2022-05-01	12362000.0

Plot Australia Capesize Coal exports¶

In [10]:

australia_coal_exports['Year'] = australia_coal_exports['Date'].apply(lambda date : date.year)
australia_coal_exports['Month'] = australia_coal_exports['Date'].apply(lambda date : date.month)
australia_coal_exports.sort_values(['Month', 'Year'], inplace = True)
australia_coal_exports['Month'] = australia_coal_exports['Date'].apply(lambda date : date.strftime("%b"))

fig = px.histogram(australia_coal_exports, x="Month", y="TotalExports", color='Year',
             barmode='group', height=500, width=1000,
             title='Australia Capesize Coal Exports',
             color_discrete_sequence=["lightgray", "gray", "lightblue"])
fig.show()

australia_coal_exports['Year'] = australia_coal_exports['Date'].apply(lambda date : date.year) australia_coal_exports['Month'] = australia_coal_exports['Date'].apply(lambda date : date.month) australia_coal_exports.sort_values(['Month', 'Year'], inplace = True) australia_coal_exports['Month'] = australia_coal_exports['Date'].apply(lambda date : date.strftime("%b")) fig = px.histogram(australia_coal_exports, x="Month", y="TotalExports", color='Year', barmode='group', height=500, width=1000, title='Australia Capesize Coal Exports', color_discrete_sequence=["lightgray", "gray", "lightblue"]) fig.show()

Examine Australia to China Capesize Iron Ore Flows - Imports to China¶

Convert Timestamp to datetime, drop voyages with last_discharge_arrival_date in the future (ongoing or future voyages)

In [11]:

voyages['last_discharge_arrival_date'] = voyages['last_discharge_arrival_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None))
voyages=voyages[voyages['last_discharge_arrival_date']<datetime.now()]
voyages.head()

voyages['last_discharge_arrival_date'] = voyages['last_discharge_arrival_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None)) voyages=voyages[voyages['last_discharge_arrival_date']

Out[11]:

	imo	voyage_number	vessel_type_id	vessel_class_id	vessel_status_id	commercial_operator_id	deleted	events	id	horizon_id	...	last_discharge_sailing_date	last_discharge_country_id	last_discharge_country_name	last_discharge_area_id_level0	last_discharge_area_name_level0	repairs_ind	storage_ind	sts_load_ind	sts_discharge_ind	local_trade_ind
0	9123180	69	3	70	1	832.0	False	None	I8B356CVED8BCC500	1	...	2021-10-04 19:54:56+00:00	112.0	India	None	East Coast India	False	False	False	False	False
1	9123180	70	3	70	1	832.0	False	None	I8B356CVED8E45200	1	...	2021-11-27 23:58:17+00:00	112.0	India	None	East Coast India	False	False	False	False	False
2	9123180	71	3	70	1	832.0	False	None	I8B356CVED9336C00	1	...	2022-01-20 23:56:47+00:00	112.0	India	None	East Coast India	False	False	False	False	False
3	9123180	72	3	70	1	832.0	False	None	I8B356CVED95AF900	1	...	2022-01-31 19:57:54+00:00	112.0	India	None	East Coast India	False	False	False	False	True
4	9123180	73	3	70	1	832.0	False	None	I8B356CVED9828600	1	...	2022-02-11 19:57:47+00:00	112.0	India	None	East Coast India	False	False	False	False	True

5 rows × 97 columns

• Keep only voyages with 'Iron Ore' as cargo group
• Group by origin and destination (first load, last discharge), and use last_discharge_arrival_date as date of flow

In [12]:

iron_ore_flows_country_level = voyages[(voyages['last_discharge_arrival_date']>"2022-01-01")&(voyages['cargo_sub_group']=='Iron Ore')] \
    .set_index('last_discharge_arrival_date') \
    .groupby(['first_load_country_name', 'last_discharge_country_name'])['quantity'] \
    .resample('MS') \
    .sum() \
    .reset_index() \
    .rename(columns={'last_discharge_arrival_date': 'Date'})
iron_ore_flows_country_level.head()

iron_ore_flows_country_level = voyages[(voyages['last_discharge_arrival_date']>"2022-01-01")&(voyages['cargo_sub_group']=='Iron Ore')] \ .set_index('last_discharge_arrival_date') \ .groupby(['first_load_country_name', 'last_discharge_country_name'])['quantity'] \ .resample('MS') \ .sum() \ .reset_index() \ .rename(columns={'last_discharge_arrival_date': 'Date'}) iron_ore_flows_country_level.head()

Out[12]:

	first_load_country_name	last_discharge_country_name	Date	quantity
0	Australia	Australia	2022-01-01	310000.0
1	Australia	Australia	2022-02-01	164000.0
2	Australia	Australia	2022-03-01	139000.0
3	Australia	Australia	2022-04-01	420000.0
4	Australia	Australia	2022-05-01	145000.0

Keep only flows between Australia and China

In [13]:

china_australia_iron_ore_flows = iron_ore_flows_country_level[(iron_ore_flows_country_level['first_load_country_name']=='Australia')&(iron_ore_flows_country_level['last_discharge_country_name']=='China')].copy()
china_australia_iron_ore_flows.head()

china_australia_iron_ore_flows = iron_ore_flows_country_level[(iron_ore_flows_country_level['first_load_country_name']=='Australia')&(iron_ore_flows_country_level['last_discharge_country_name']=='China')].copy() china_australia_iron_ore_flows.head()

Out[13]:

	first_load_country_name	last_discharge_country_name	Date	quantity
26	Australia	China	2022-01-01	55255000.0
27	Australia	China	2022-02-01	41381000.0
28	Australia	China	2022-03-01	48940000.0
29	Australia	China	2022-04-01	47581000.0
30	Australia	China	2022-05-01	53061000.0

Plot Australia to China Capesize Iron Ore Flows - Imports to China¶

In [14]:

china_australia_iron_ore_flows['Year'] = china_australia_iron_ore_flows['Date'].apply(lambda date : date.year)
china_australia_iron_ore_flows['Month'] = china_australia_iron_ore_flows['Date'].apply(lambda date : date.month)
china_australia_iron_ore_flows.sort_values(['Month', 'Year'], inplace = True)
china_australia_iron_ore_flows['Month'] = china_australia_iron_ore_flows['Date'].apply(lambda date : date.strftime("%b"))

fig = px.histogram(china_australia_iron_ore_flows, x="Month", y="quantity", color='Year',
             barmode='group', height=500, width=1000,
             title='Australia to China Capesize Iron Ore Flows - Imports to China',
             color_discrete_sequence=["lightgray", "gray", "lightblue"])
fig.show()

china_australia_iron_ore_flows['Year'] = china_australia_iron_ore_flows['Date'].apply(lambda date : date.year) china_australia_iron_ore_flows['Month'] = china_australia_iron_ore_flows['Date'].apply(lambda date : date.month) china_australia_iron_ore_flows.sort_values(['Month', 'Year'], inplace = True) china_australia_iron_ore_flows['Month'] = china_australia_iron_ore_flows['Date'].apply(lambda date : date.strftime("%b")) fig = px.histogram(china_australia_iron_ore_flows, x="Month", y="quantity", color='Year', barmode='group', height=500, width=1000, title='Australia to China Capesize Iron Ore Flows - Imports to China', color_discrete_sequence=["lightgray", "gray", "lightblue"]) fig.show()

Build your custom Flows - Brazil Grain Exports to specific countries by Panamax and Supramax¶

Get vessel class ids for Dry vessel classes

In [15]:

vessel_classes = api.get_vessel_classes()
vessel_classes_df = pd.DataFrame(v.__dict__ for v in vessel_classes)
vessel_classes_df[vessel_classes_df['vessel_type']=='Dry'].head(10)

vessel_classes = api.get_vessel_classes() vessel_classes_df = pd.DataFrame(v.__dict__ for v in vessel_classes) vessel_classes_df[vessel_classes_df['vessel_type']=='Dry'].head(10)

Out[15]:

	vessel_class_id	vessel_class_name	vessel_type_id	vessel_type
4	69	VLOC	3	Dry
5	70	Capesize	3	Dry
6	72	Post Panamax	3	Dry
7	74	Panamax	3	Dry
8	75	Supramax	3	Dry
9	76	Handymax	3	Dry
10	77	Handysize	3	Dry
19	92	Small	3	Dry

Select Vessel Classes of interest¶

Get voyages for selected vessel classes (Panamax and Supramax) starting from a given date

In [16]:

vessel_class_ids = (74, 75) # Get only voyages for Panamax and Supramax
date_from = datetime.strptime("2022-09-01", "%Y-%m-%d")

voyages = []
for vessel_class_id in vessel_class_ids:    
    voyages.append(api.get_voyages_condensed(vessel_class_id=vessel_class_id, date_from=date_from))
    
voyages = pd.DataFrame([v.__dict__ for vc in voyages for v in vc])
voyages.tail(5)

vessel_class_ids = (74, 75) # Get only voyages for Panamax and Supramax date_from = datetime.strptime("2022-09-01", "%Y-%m-%d") voyages = [] for vessel_class_id in vessel_class_ids: voyages.append(api.get_voyages_condensed(vessel_class_id=vessel_class_id, date_from=date_from)) voyages = pd.DataFrame([v.__dict__ for vc in voyages for v in vc]) voyages.tail(5)

Out[16]:

	imo	voyage_number	vessel_type_id	vessel_class_id	vessel_status_id	commercial_operator_id	deleted	events	id	horizon_id	...	last_discharge_sailing_date	last_discharge_country_id	last_discharge_country_name	last_discharge_area_id_level0	last_discharge_area_name_level0	repairs_ind	storage_ind	sts_load_ind	sts_discharge_ind	local_trade_ind
68442	9984900	50	3	75	1	NaN	False	None	I985B844BVEDD44ED00	1	...	2024-02-07 05:20:21.777000+00:00	55.0	China	None	Central China	False	False	False	False	True
68443	9984900	51	3	75	1	NaN	False	None	I985B844BVEDD521C00	2	...	2024-02-15 21:53:37.775000+00:00	55.0	China	None	Central China	False	False	False	False	True
68444	9990818	1	3	75	1	NaN	False	None	I9872A24BVEDCE8A400	2	...	2024-03-14 18:15:56.194000+00:00	184.0	Philippines	None	Philippines	True	False	False	False	False
68445	9990832	1	3	75	1	NaN	False	None	I9872B04BVEDD37BE00	2	...	NaT	NaN	None	None	None	True	False	False	False	True
68446	9991513	1	3	75	1	NaN	False	None	I9875594BVEDCE8A400	2	...	2024-02-15 20:55:55.178000+00:00	55.0	China	None	Central China	True	False	False	False	False

5 rows × 97 columns

Date of flows will be based on the export date

• Convert Timestamp to datetime, drop voyages with first_load_sailing_date in the future (ongoing or future)

In [17]:

voyages['first_load_sailing_date'] = voyages['first_load_sailing_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None))
voyages=voyages[voyages['first_load_sailing_date']<datetime.now()]
voyages.head()

voyages['first_load_sailing_date'] = voyages['first_load_sailing_date'].apply(lambda d : pd.to_datetime(d).tz_localize(None)) voyages=voyages[voyages['first_load_sailing_date']

Out[17]:

	imo	voyage_number	vessel_type_id	vessel_class_id	vessel_status_id	commercial_operator_id	deleted	events	id	horizon_id	...	last_discharge_sailing_date	last_discharge_country_id	last_discharge_country_name	last_discharge_area_id_level0	last_discharge_area_name_level0	repairs_ind	storage_ind	sts_load_ind	sts_discharge_ind	local_trade_ind
0	7514684	339	3	74	1	3968.0	False	None	I72AA3CVEDAA49000	1	...	2022-09-14 11:54:21+00:00	240.0	United States	None	Great Lakes	False	False	False	False	True
1	7514684	340	3	74	1	3968.0	False	None	I72AA3CVEDAB1BF00	1	...	2022-09-21 23:54:51+00:00	240.0	United States	None	Great Lakes	False	False	False	False	True
2	7514684	341	3	74	1	3968.0	False	None	I72AA3CVEDAB85680	1	...	2022-10-19 23:54:55+00:00	240.0	United States	None	Great Lakes	False	False	False	False	True
3	7514684	342	3	74	1	3968.0	False	None	I72AA3CVEDABEEE00	1	...	2022-10-27 19:54:27+00:00	240.0	United States	None	Great Lakes	False	False	False	False	True
4	7514684	343	3	74	1	3968.0	False	None	I72AA3CVEDAE67B00	1	...	2022-11-06 11:57:15+00:00	240.0	United States	None	Great Lakes	False	False	False	False	True

5 rows × 97 columns

Select cargo types of interest¶

Examine different cargo types under the "Grains" cargo group

In [18]:

voyages[(voyages['cargo_sub_group']=='Grains')&(voyages['cargo_type']!='Grains')].drop_duplicates(subset=['cargo_group', 'cargo_type'], keep='last', inplace = False)[['cargo_sub_group', 'cargo_type']].head(10)

voyages[(voyages['cargo_sub_group']=='Grains')&(voyages['cargo_type']!='Grains')].drop_duplicates(subset=['cargo_group', 'cargo_type'], keep='last', inplace = False)[['cargo_sub_group', 'cargo_type']].head(10)

Out[18]:

	cargo_sub_group	cargo_type
68025	Grains	Barley
68296	Grains	Corn
68352	Grains	Rice
68360	Grains	Soybeans
68383	Grains	Sorghum
68384	Grains	Wheat

In [19]:

voyages[(voyages['first_load_country_name']=='Brazil')&(voyages['cargo_sub_group']=='Grains')]['cargo_type'].value_counts().head()

voyages[(voyages['first_load_country_name']=='Brazil')&(voyages['cargo_sub_group']=='Grains')]['cargo_type'].value_counts().head()

Out[19]:

Soybeans    1650
Corn        1007
Wheat         38
Rice          14
Name: cargo_type, dtype: int64

• Keep only voyages with 'Soybeans' and 'Corn' as cargo type
• Group by origin and destination (first load, last discharge) and cargo type
• Use last_discharge_arrival_date as date of flow

In [20]:

cargo_types_of_interest = ('Soybeans', 'Corn')
custom_flows_country_level = voyages[(voyages['first_load_sailing_date']>"2023-01-01")&(voyages['cargo_type'].isin(cargo_types_of_interest))] \
    .set_index('first_load_sailing_date') \
    .groupby(['first_load_country_name', 'last_discharge_country_name', 'cargo_type'])['quantity'] \
    .resample('MS') \
    .sum() \
    .reset_index() \
    .rename(columns={'first_load_sailing_date': 'Date'})
custom_flows_country_level.head()

cargo_types_of_interest = ('Soybeans', 'Corn') custom_flows_country_level = voyages[(voyages['first_load_sailing_date']>"2023-01-01")&(voyages['cargo_type'].isin(cargo_types_of_interest))] \ .set_index('first_load_sailing_date') \ .groupby(['first_load_country_name', 'last_discharge_country_name', 'cargo_type'])['quantity'] \ .resample('MS') \ .sum() \ .reset_index() \ .rename(columns={'first_load_sailing_date': 'Date'}) custom_flows_country_level.head()

Out[20]:

	first_load_country_name	last_discharge_country_name	cargo_type	Date	quantity
0	Algeria	Colombia	Corn	2023-01-01	40000.0
1	Algeria	Colombia	Corn	2023-02-01	0.0
2	Algeria	Colombia	Corn	2023-03-01	38000.0
3	Algeria	Colombia	Corn	2023-04-01	0.0
4	Algeria	Colombia	Corn	2023-05-01	0.0

Select flows of interest¶

Keep only flows between Brazil and (China, South Korea, Japan)

In [21]:

import_countries = ('China', 'Korea, Republic of', 'Japan')
custom_flows = custom_flows_country_level[(custom_flows_country_level['first_load_country_name']=='Brazil')&(custom_flows_country_level['last_discharge_country_name'].isin(import_countries))].copy()
custom_flows.head()

import_countries = ('China', 'Korea, Republic of', 'Japan') custom_flows = custom_flows_country_level[(custom_flows_country_level['first_load_country_name']=='Brazil')&(custom_flows_country_level['last_discharge_country_name'].isin(import_countries))].copy() custom_flows.head()

Out[21]:

	first_load_country_name	last_discharge_country_name	cargo_type	Date	quantity
493	Brazil	China	Corn	2023-01-01	559000.0
494	Brazil	China	Corn	2023-02-01	0.0
495	Brazil	China	Corn	2023-03-01	0.0
496	Brazil	China	Corn	2023-04-01	114000.0
497	Brazil	China	Corn	2023-05-01	187000.0

Plot custom flows¶

In [22]:

custom_flows['Year'] = custom_flows['Date'].apply(lambda date : date.year)
custom_flows['Month'] = custom_flows['Date'].apply(lambda date : date.month)
custom_flows.sort_values(['Year', 'Month'], inplace = True)

custom_flows['Month'] = custom_flows['Date'].apply(lambda date : date.strftime("%b"))
custom_flows['Year'] = custom_flows['Date'].apply(lambda date : date.strftime("%Y"))
custom_flows['MonthYear'] = custom_flows['Month'].astype(str) + ' ' + custom_flows['Year'].astype(str)

fig = px.histogram(custom_flows, x="MonthYear", y="quantity", color='cargo_type',
            height=500, width=1000, title='custom Flows - Brazil Grain Exports to (China, South Korea, Japan)',
             color_discrete_sequence=px.colors.qualitative.Pastel)
fig.update_layout(bargap=0.2)
fig.show()

custom_flows['Year'] = custom_flows['Date'].apply(lambda date : date.year) custom_flows['Month'] = custom_flows['Date'].apply(lambda date : date.month) custom_flows.sort_values(['Year', 'Month'], inplace = True) custom_flows['Month'] = custom_flows['Date'].apply(lambda date : date.strftime("%b")) custom_flows['Year'] = custom_flows['Date'].apply(lambda date : date.strftime("%Y")) custom_flows['MonthYear'] = custom_flows['Month'].astype(str) + ' ' + custom_flows['Year'].astype(str) fig = px.histogram(custom_flows, x="MonthYear", y="quantity", color='cargo_type', height=500, width=1000, title='custom Flows - Brazil Grain Exports to (China, South Korea, Japan)', color_discrete_sequence=px.colors.qualitative.Pastel) fig.update_layout(bargap=0.2) fig.show()