metric-forge 0.2.1

The Ultimate Metric Library

Metric Forge

Metric Forge is your ultimate toolkit for measuring and evaluating performance across various business domains.

Disclaimer

Metric Forge provides a collection of calculations across various domains, including finance, mortgage, marketing, and more. While every effort has been made to ensure the accuracy and reliability of the calculations and methods provided, Metric Forge is intended for informational and educational purposes only.

Important Notice:

No Warranty: The calculations and methods provided in this package are offered "as-is" without any guarantees or warranties of any kind, either express or implied. The package's creators do not assume any responsibility for errors or omissions or for any damages resulting from the use of the package.

Not Professional Advice: The results produced by the package should not be considered as professional financial, investment, legal, or any other type of advice. Users should consult with qualified professionals before making any decisions based on the outputs generated by this package.

Use at Your Own Risk: Users of Metric Forge assume full responsibility for the use of the package and its results. The creators of the package shall not be held liable for any decisions made based on the information provided or for any consequences arising from the use of the package.

By using this package, you acknowledge and agree to this disclaimer. If you do not agree, please refrain from using the Metric Forge package.

Getting Started

install the package via pip

pip install metric-forge

Ecommerce

You can use Ecommerce Metrics in two different ways:

• With Polars
• As a Single Value Function (SVF)

Metric Forge extends the Polars expression library; making it possible to perform row-wise-calculations for various metrics. In addition to mass calculations, you can also perform single value calculations that return just one value.

from metric_forge.ecommerce import * 
import polars as pl
data = pl.read_csv('datasets/ecommerce_metrics.csv')
data.head()

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (5, 15)

month	total_revenue	number_of_orders	cost_of_acquisition	new_customers	carts_created	completed_purchases	revenue_from_ads	advertising_spend	num_conversions	num_visitors	revenue_per_customer	average_customer_lifetime	num_customers_lost	total_customers_beginning
str	f64	i64	f64	i64	i64	i64	f64	f64	i64	i64	f64	f64	i64	i64
"2023-01"	87454.011885	991	22958.350559	406	1969	1425	81339.957696	5390.910169	300	13154	202.427329	3.159364	239	3306
"2023-02"	145071.430641	913	18736.874206	234	1506	1421	76875.083402	15585.037018	427	14762	116.173436	1.812245	124	4680
"2023-03"	123199.394181	1305	28355.586842	120	1497	965	116505.482191	14872.037954	367	10056	384.265156	4.771414	177	4675
"2023-04"	109865.84842	885	14184.81582	428	1963	1102	106008.046381	12337.204368	132	19948	144.356328	3.395462	299	2972
"2023-05"	65601.864044	691	18764.339456	266	1009	801	97258.809912	5351.995568	147	13110	275.734601	3.77914	296	3768

Customer Acquisition Cost

data.with_columns(pl.col('*').forge_ecommerce.customer_acquisition_cost('cost_of_acquisition', 'new_customers'))

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (12, 16)

month	total_revenue	number_of_orders	cost_of_acquisition	new_customers	carts_created	completed_purchases	revenue_from_ads	advertising_spend	num_conversions	num_visitors	revenue_per_customer	average_customer_lifetime	num_customers_lost	total_customers_beginning	customer_acquisition_cost
str	f64	i64	f64	i64	i64	i64	f64	f64	i64	i64	f64	f64	i64	i64	f64
"2023-01"	87454.011885	991	22958.350559	406	1969	1425	81339.957696	5390.910169	300	13154	202.427329	3.159364	239	3306	56.547661
"2023-02"	145071.430641	913	18736.874206	234	1506	1421	76875.083402	15585.037018	427	14762	116.173436	1.812245	124	4680	80.072112
"2023-03"	123199.394181	1305	28355.586842	120	1497	965	116505.482191	14872.037954	367	10056	384.265156	4.771414	177	4675	236.296557
"2023-04"	109865.84842	885	14184.81582	428	1963	1102	106008.046381	12337.204368	132	19948	144.356328	3.395462	299	2972	33.142093
"2023-05"	65601.864044	691	18764.339456	266	1009	801	97258.809912	5351.995568	147	13110	275.734601	3.77914	296	3768	70.54263
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
"2023-08"	136617.614577	660	33555.278842	188	1108	601	116872.977654	24754.388513	392	8840	290.148089	2.182535	280	3479	178.485526
"2023-09"	110111.501174	959	15990.213465	415	1975	1395	84633.082292	20148.99937	198	6028	325.310229	1.421977	239	3434	38.530635
"2023-10"	120807.25778	813	25427.033152	113	800	669	54839.926382	28157.521963	271	12385	378.206435	2.826138	274	2648	225.017992
"2023-11"	52058.44943	521	27772.437066	341	2005	1215	56664.615513	21276.925638	459	5502	155.732582	1.873762	178	2317	81.444097
"2023-12"	146990.985216	752	11393.512382	364	1704	855	44874.024516	27873.991889	313	11910	341.766952	2.66604	170	3257	31.300858

Average Order Value

data.with_columns(pl.col('*').forge_ecommerce.average_order_value('total_revenue', 'number_of_orders'))

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (12, 16)

month	total_revenue	number_of_orders	cost_of_acquisition	new_customers	carts_created	completed_purchases	revenue_from_ads	advertising_spend	num_conversions	num_visitors	revenue_per_customer	average_customer_lifetime	num_customers_lost	total_customers_beginning	average_order_value
str	f64	i64	f64	i64	i64	i64	f64	f64	i64	i64	f64	f64	i64	i64	f64
"2023-01"	87454.011885	991	22958.350559	406	1969	1425	81339.957696	5390.910169	300	13154	202.427329	3.159364	239	3306	88.248246
"2023-02"	145071.430641	913	18736.874206	234	1506	1421	76875.083402	15585.037018	427	14762	116.173436	1.812245	124	4680	158.895324
"2023-03"	123199.394181	1305	28355.586842	120	1497	965	116505.482191	14872.037954	367	10056	384.265156	4.771414	177	4675	94.405666
"2023-04"	109865.84842	885	14184.81582	428	1963	1102	106008.046381	12337.204368	132	19948	144.356328	3.395462	299	2972	124.142202
"2023-05"	65601.864044	691	18764.339456	266	1009	801	97258.809912	5351.995568	147	13110	275.734601	3.77914	296	3768	94.937575
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
"2023-08"	136617.614577	660	33555.278842	188	1108	601	116872.977654	24754.388513	392	8840	290.148089	2.182535	280	3479	206.996386
"2023-09"	110111.501174	959	15990.213465	415	1975	1395	84633.082292	20148.99937	198	6028	325.310229	1.421977	239	3434	114.819084
"2023-10"	120807.25778	813	25427.033152	113	800	669	54839.926382	28157.521963	271	12385	378.206435	2.826138	274	2648	148.594413
"2023-11"	52058.44943	521	27772.437066	341	2005	1215	56664.615513	21276.925638	459	5502	155.732582	1.873762	178	2317	99.920248
"2023-12"	146990.985216	752	11393.512382	364	1704	855	44874.024516	27873.991889	313	11910	341.766952	2.66604	170	3257	195.466736

Cart Abandonment Rate

data.with_columns(pl.col('*').forge_ecommerce.cart_abandonment_rate('carts_created', 'completed_purchases'))

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (12, 16)

month	total_revenue	number_of_orders	cost_of_acquisition	new_customers	carts_created	completed_purchases	revenue_from_ads	advertising_spend	num_conversions	num_visitors	revenue_per_customer	average_customer_lifetime	num_customers_lost	total_customers_beginning	cart_abandonment_rate
str	f64	i64	f64	i64	i64	i64	f64	f64	i64	i64	f64	f64	i64	i64	f64
"2023-01"	87454.011885	991	22958.350559	406	1969	1425	81339.957696	5390.910169	300	13154	202.427329	3.159364	239	3306	27.628238
"2023-02"	145071.430641	913	18736.874206	234	1506	1421	76875.083402	15585.037018	427	14762	116.173436	1.812245	124	4680	5.64409
"2023-03"	123199.394181	1305	28355.586842	120	1497	965	116505.482191	14872.037954	367	10056	384.265156	4.771414	177	4675	35.537742
"2023-04"	109865.84842	885	14184.81582	428	1963	1102	106008.046381	12337.204368	132	19948	144.356328	3.395462	299	2972	43.861437
"2023-05"	65601.864044	691	18764.339456	266	1009	801	97258.809912	5351.995568	147	13110	275.734601	3.77914	296	3768	20.61447
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
"2023-08"	136617.614577	660	33555.278842	188	1108	601	116872.977654	24754.388513	392	8840	290.148089	2.182535	280	3479	45.758123
"2023-09"	110111.501174	959	15990.213465	415	1975	1395	84633.082292	20148.99937	198	6028	325.310229	1.421977	239	3434	29.367089
"2023-10"	120807.25778	813	25427.033152	113	800	669	54839.926382	28157.521963	271	12385	378.206435	2.826138	274	2648	16.375
"2023-11"	52058.44943	521	27772.437066	341	2005	1215	56664.615513	21276.925638	459	5502	155.732582	1.873762	178	2317	39.401496
"2023-12"	146990.985216	752	11393.512382	364	1704	855	44874.024516	27873.991889	313	11910	341.766952	2.66604	170	3257	49.823944

Return on Advertising Spend

data.with_columns(pl.col('*').forge_ecommerce.return_on_advertising_spend('revenue_from_ads', 'advertising_spend'))

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (12, 16)

month	total_revenue	number_of_orders	cost_of_acquisition	new_customers	carts_created	completed_purchases	revenue_from_ads	advertising_spend	num_conversions	num_visitors	revenue_per_customer	average_customer_lifetime	num_customers_lost	total_customers_beginning	return_on_advertising_spend
str	f64	i64	f64	i64	i64	i64	f64	f64	i64	i64	f64	f64	i64	i64	f64
"2023-01"	87454.011885	991	22958.350559	406	1969	1425	81339.957696	5390.910169	300	13154	202.427329	3.159364	239	3306	15.088353
"2023-02"	145071.430641	913	18736.874206	234	1506	1421	76875.083402	15585.037018	427	14762	116.173436	1.812245	124	4680	4.932621
"2023-03"	123199.394181	1305	28355.586842	120	1497	965	116505.482191	14872.037954	367	10056	384.265156	4.771414	177	4675	7.833861
"2023-04"	109865.84842	885	14184.81582	428	1963	1102	106008.046381	12337.204368	132	19948	144.356328	3.395462	299	2972	8.59255
"2023-05"	65601.864044	691	18764.339456	266	1009	801	97258.809912	5351.995568	147	13110	275.734601	3.77914	296	3768	18.172438
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
"2023-08"	136617.614577	660	33555.278842	188	1108	601	116872.977654	24754.388513	392	8840	290.148089	2.182535	280	3479	4.721303
"2023-09"	110111.501174	959	15990.213465	415	1975	1395	84633.082292	20148.99937	198	6028	325.310229	1.421977	239	3434	4.200362
"2023-10"	120807.25778	813	25427.033152	113	800	669	54839.926382	28157.521963	271	12385	378.206435	2.826138	274	2648	1.947612
"2023-11"	52058.44943	521	27772.437066	341	2005	1215	56664.615513	21276.925638	459	5502	155.732582	1.873762	178	2317	2.663196
"2023-12"	146990.985216	752	11393.512382	364	1704	855	44874.024516	27873.991889	313	11910	341.766952	2.66604	170	3257	1.609889

Conversion Rate

data.with_columns(pl.col('*').forge_ecommerce.conversion_rate('num_conversions', 'num_visitors'))

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (12, 16)

month	total_revenue	number_of_orders	cost_of_acquisition	new_customers	carts_created	completed_purchases	revenue_from_ads	advertising_spend	num_conversions	num_visitors	revenue_per_customer	average_customer_lifetime	num_customers_lost	total_customers_beginning	conversion_rate
str	f64	i64	f64	i64	i64	i64	f64	f64	i64	i64	f64	f64	i64	i64	f64
"2023-01"	87454.011885	991	22958.350559	406	1969	1425	81339.957696	5390.910169	300	13154	202.427329	3.159364	239	3306	2.280675
"2023-02"	145071.430641	913	18736.874206	234	1506	1421	76875.083402	15585.037018	427	14762	116.173436	1.812245	124	4680	2.892562
"2023-03"	123199.394181	1305	28355.586842	120	1497	965	116505.482191	14872.037954	367	10056	384.265156	4.771414	177	4675	3.649562
"2023-04"	109865.84842	885	14184.81582	428	1963	1102	106008.046381	12337.204368	132	19948	144.356328	3.395462	299	2972	0.66172
"2023-05"	65601.864044	691	18764.339456	266	1009	801	97258.809912	5351.995568	147	13110	275.734601	3.77914	296	3768	1.121281
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
"2023-08"	136617.614577	660	33555.278842	188	1108	601	116872.977654	24754.388513	392	8840	290.148089	2.182535	280	3479	4.434389
"2023-09"	110111.501174	959	15990.213465	415	1975	1395	84633.082292	20148.99937	198	6028	325.310229	1.421977	239	3434	3.284672
"2023-10"	120807.25778	813	25427.033152	113	800	669	54839.926382	28157.521963	271	12385	378.206435	2.826138	274	2648	2.188131
"2023-11"	52058.44943	521	27772.437066	341	2005	1215	56664.615513	21276.925638	459	5502	155.732582	1.873762	178	2317	8.342421
"2023-12"	146990.985216	752	11393.512382	364	1704	855	44874.024516	27873.991889	313	11910	341.766952	2.66604	170	3257	2.628044

Customer Lifetime Value

data.with_columns(pl.col('*').forge_ecommerce.customer_lifetime_value('revenue_per_customer', 'average_customer_lifetime'))

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (12, 16)

month	total_revenue	number_of_orders	cost_of_acquisition	new_customers	carts_created	completed_purchases	revenue_from_ads	advertising_spend	num_conversions	num_visitors	revenue_per_customer	average_customer_lifetime	num_customers_lost	total_customers_beginning	customer_lifetime_value
str	f64	i64	f64	i64	i64	i64	f64	f64	i64	i64	f64	f64	i64	i64	f64
"2023-01"	87454.011885	991	22958.350559	406	1969	1425	81339.957696	5390.910169	300	13154	202.427329	3.159364	239	3306	639.54169
"2023-02"	145071.430641	913	18736.874206	234	1506	1421	76875.083402	15585.037018	427	14762	116.173436	1.812245	124	4680	210.534716
"2023-03"	123199.394181	1305	28355.586842	120	1497	965	116505.482191	14872.037954	367	10056	384.265156	4.771414	177	4675	1833.488253
"2023-04"	109865.84842	885	14184.81582	428	1963	1102	106008.046381	12337.204368	132	19948	144.356328	3.395462	299	2972	490.156408
"2023-05"	65601.864044	691	18764.339456	266	1009	801	97258.809912	5351.995568	147	13110	275.734601	3.77914	296	3768	1042.039585
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
"2023-08"	136617.614577	660	33555.278842	188	1108	601	116872.977654	24754.388513	392	8840	290.148089	2.182535	280	3479	633.258286
"2023-09"	110111.501174	959	15990.213465	415	1975	1395	84633.082292	20148.99937	198	6028	325.310229	1.421977	239	3434	462.583676
"2023-10"	120807.25778	813	25427.033152	113	800	669	54839.926382	28157.521963	271	12385	378.206435	2.826138	274	2648	1068.863683
"2023-11"	52058.44943	521	27772.437066	341	2005	1215	56664.615513	21276.925638	459	5502	155.732582	1.873762	178	2317	291.805755
"2023-12"	146990.985216	752	11393.512382	364	1704	855	44874.024516	27873.991889	313	11910	341.766952	2.66604	170	3257	911.164293

Product Development

You can use Product Development Metrics in two different ways:

• With Polars
• As a Single Value Function (SVF)

Metric Forge extends the Polars expression library; making it possible to perform row-wise-calculations for various metrics. In addition to mass calculations, you can also perform single value calculations that return just one value.

import polars as pl
data = pl.read_csv('datasets/customer-value.csv')
data

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (10, 3)

feature_name	importance_score	satisfaction_score
str	i64	i64
"Feature1"	8	7
"Feature2"	6	8
"Feature3"	7	9
"Feature4"	8	7
"Feature5"	6	6
"Feature6"	9	8
"Feature7"	7	9
"Feature8"	6	7
"Feature9"	9	8
"Feature10"	8	7

Underserved Customer Needs

from metric_forge.product_development import *


data.with_columns(pl.col('*').forge_product_dev.opportunity_score(importance_col='importance_score',satisfaction_col='satisfaction_score'))

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (10, 4)

feature_name	importance_score	satisfaction_score	opportunity_score
str	i64	i64	f64
"Feature1"	8	7	9.0
"Feature2"	6	8	4.0
"Feature3"	7	9	5.0
"Feature4"	8	7	9.0
"Feature5"	6	6	6.0
"Feature6"	9	8	10.0
"Feature7"	7	9	5.0
"Feature8"	6	7	5.0
"Feature9"	9	8	10.0
"Feature10"	8	7	9.0

data.with_columns(pl.col('*').forge_product_dev.customer_value_delivered(importance_col='importance_score',satisfaction_col='satisfaction_score'))

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (10, 4)

feature_name	importance_score	satisfaction_score	customer_value_delivered
str	i64	i64	f64
"Feature1"	8	7	0.56
"Feature2"	6	8	0.48
"Feature3"	7	9	0.63
"Feature4"	8	7	0.56
"Feature5"	6	6	0.36
"Feature6"	9	8	0.72
"Feature7"	7	9	0.63
"Feature8"	6	7	0.42
"Feature9"	9	8	0.72
"Feature10"	8	7	0.56

data.with_columns(pl.col('*').forge_product_dev.opportunity_to_add_value(importance_col='importance_score',satisfaction_col='satisfaction_score'))

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (10, 4)

feature_name	importance_score	satisfaction_score	opp_to_add_value
str	i64	i64	f64
"Feature1"	8	7	0.24
"Feature2"	6	8	0.12
"Feature3"	7	9	0.07
"Feature4"	8	7	0.24
"Feature5"	6	6	0.24
"Feature6"	9	8	0.18
"Feature7"	7	9	0.07
"Feature8"	6	7	0.18
"Feature9"	9	8	0.18
"Feature10"	8	7	0.24

import polars as pl
data = pl.read_csv('datasets/customer-value-added.csv')
data

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (10, 4)

feature_name	importance_score	satisfaction_score_before	satisfaction_score_after
str	i64	i64	i64
"Feature1"	8	7	9
"Feature2"	9	8	9
"Feature3"	7	9	8
"Feature4"	8	7	8
"Feature5"	6	6	8
"Feature6"	9	8	9
"Feature7"	7	9	8
"Feature8"	6	7	6
"Feature9"	9	8	8
"Feature10"	8	7	7

data.with_columns(pl.col('*').forge_product_dev.customer_value_created(importance_col='importance_score'
                       ,satisfaction_after_col='satisfaction_score_after'
                       ,satisfaction_before_col='satisfaction_score_before'))

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feature_name	importance_score	satisfaction_score_before	satisfaction_score_after	customer_value_created
str	i64	i64	i64	f64
"Feature1"	8	7	9	0.16
"Feature2"	9	8	9	0.09
"Feature3"	7	9	8	-0.07
"Feature4"	8	7	8	0.08
"Feature5"	6	6	8	0.12
"Feature6"	9	8	9	0.09
"Feature7"	7	9	8	-0.07
"Feature8"	6	7	6	-0.06
"Feature9"	9	8	8	0.0
"Feature10"	8	7	7	0.0

Finance

The Finance module contains financial statement objects that you can call certain methods off of to get key financial metrics. You need to define these objects prior to running calcuations.

Income Statement

from metric_forge import *

income = IncomeStatement(revenue=10000,cogs=2000,expenses=3000,)
income.ebitda_margin()

0.5

income.earnings_per_share()

'Missing parameters: revenue, cogs, expenses, shares_outstanding'

income.net_profit_margin()

0.5

Balance Sheet

BalanceSheet(assets=300000,total_liabilities=150000,equity=150000).check_balance()

'The balance equation is correct: Assets - Liabilities = Equity'

Cash Flow

CashFlowStatement(operating_cash_flow=3000,capital_expenditures=4000).free_cash_flow()

-1000

Mortgage

The Mortgage module contains mortgage objects that you enable you to assess mortgage metrics. You need to define these objects prior to running calcuations.

from metric_forge.mortgage import *

mortgage = Mortgage(principal=360000,annual_interest_rate=5.75,start_date='2023-09-01',term_years=30,extra_principal_payment=200)
mortgage.calculate_monthly_payment()

2100.8622831967773

mortgage.generate_amortization_schedule()

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payment_number	payment_date	payment	interest_payment	principal_payment	principal_remaining
i64	str	f64	f64	f64	f64
1	"2023-10-01"	2100.862283	1725.0	375.862283	359624.137717
2	"2023-10-31"	2100.862283	1723.198993	377.66329	359246.474427
3	"2023-11-30"	2100.862283	1721.389357	379.472927	358867.0015
4	"2023-12-30"	2100.862283	1719.571049	381.291234	358485.710266
5	"2024-01-29"	2100.862283	1717.744028	383.118255	358102.592011
…	…	…	…	…	…
356	"2052-11-27"	2100.862283	49.617632	2051.244651	8303.739506
357	"2052-12-27"	2100.862283	39.788752	2061.073531	6242.665975
358	"2053-01-26"	2100.862283	29.912774	2070.949509	4171.716466
359	"2053-02-25"	2100.862283	19.989475	2080.872808	2090.843657
360	"2053-03-27"	2100.862283	10.018626	2090.843657	1.4501e-8

mortgage.calculate_principal_only_payment()

{'new_term_years': 24.14759462810461, 'time_saved_years': 5.85240537189539}

mortgage.refinance_assessment(RefinanceOption(new_loan_amount=350000,new_annual_interest_rate=5.25,new_term_years=30,associated_costs=5000))

{'Old Monthly Payment': 2100.8622831967773,
 'New Monthly Payment': 1932.7129574966407,
 'Monthly Savings': 168.14932570013661,
 'Break-Even Point (months)': 29.735474579996712,
 'Total Savings (over the loan term)': 55533.75725204917,
 'Should Refinance': True}

Web Traffic

from metric_forge.web_traffic import *
import polars as pl

data = pl.read_csv('datasets/web_traffic.csv')
data

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total_duration	total_sessions	single_page_sessions	num_clicks	num_impressions	total_cost	exits	total_page_views
str	i64	i64	i64	i64	i64	i64	i64
"1200"	10	2	100	1000	50	5	50
"1500"	15	3	150	1500	75	7	70
"0"	0	1	0	0	0	2	20
"2500"	25	5	200	2000	100	10	100
null	null	null	null	null	null	null	null

data.with_columns(pl.col('*').forge_web_traffic.average_session_duration('total_duration', 'total_sessions'))

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

Cell In[25], line 1
----> 1 data.with_columns(pl.col('*').forge_web_traffic.average_session_duration('total_duration', 'total_sessions'))


TypeError: WebTraffic_Polars.average_session_duration() takes 2 positional arguments but 3 were given