metric-forge 0.3.0

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.

Financial Statement Objects

from metric_forge import *

fs = import_financials(ticker='MAR',engine='yfinance')
fs

Income Statement

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date	Tax Effect Of Unusual Items	Tax Rate For Calcs	Normalized EBITDA	Total Unusual Items	Total Unusual Items Excluding Goodwill	Net Income From Continuing Operation Net Minority Interest	Reconciled Depreciation	Reconciled Cost Of Revenue	EBITDA	EBIT	Net Interest Income	Interest Expense	Interest Income	Normalized Income	Net Income From Continuing And Discontinued Operation	Total Expenses	Total Operating Income As Reported	Diluted Average Shares	Basic Average Shares	Diluted EPS	Basic EPS	Diluted NI Availto Com Stockholders	Net Income Common Stockholders	Net Income	Net Income Including Noncontrolling Interests	Net Income Continuous Operations	Tax Provision	Pretax Income	Other Income Expense	Other Non Operating Income Expenses	Special Income Charges	Other Special Charges	Restructuring And Mergern Acquisition	Earnings From Equity Interest	Net Non Operating Interest Income Expense	Interest Expense Non Operating	Interest Income Non Operating	Operating Income	Operating Expense	Depreciation Amortization Depletion Income Statement	Depreciation And Amortization In Income Statement	Selling General And Administration	General And Administrative Expense	Other Gand A	Gross Profit	Cost Of Revenue	Total Revenue	Operating Revenue
str	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
"2023-12-31 00:00:00"	-5.22e6	0.087	4.2800e9	-6e7	-6e7	3.0830e9	2.77e8	1.8501e10	4.2200e9	3.9430e9	-5.3500e8	5.65e8	3e7	3.1378e9	3.0830e9	1.9789e10	3.8640e9	3.029e8	3.015e8	10.18	10.23	3.0830e9	3.0830e9	3.0830e9	3.0830e9	3.0830e9	2.95e8	3.3780e9	-1.1e7	4e7	-6e7	null	6e7	9e6	-5.3500e8	5.65e8	3e7	3.9240e9	1.2000e9	1.89e8	1.89e8	1.0110e9	1.0110e9	1.0110e9	5.1240e9	1.8589e10	2.3713e10	6.3880e9
"2022-12-31 00:00:00"	-2.916e6	0.243	3.8110e9	-1.2e7	-1.2e7	2.3580e9	2.82e8	1.6126e10	3.7990e9	3.5170e9	-3.7700e8	4.03e8	2.6e7	2.3671e9	2.3580e9	1.7299e10	3.4620e9	3.258e8	3.244e8	7.24	7.27	2.3580e9	2.3580e9	2.3580e9	2.3580e9	2.3580e9	7.56e8	3.1140e9	1.7e7	1.1e7	-1.2e7	null	1.2e7	1.8e7	-3.7700e8	4.03e8	2.6e7	3.4740e9	1.0840e9	1.93e8	1.93e8	8.91e8	8.91e8	8.91e8	4.5580e9	1.6215e10	2.0773e10	5.4450e9
"2021-12-31 00:00:00"	-1.1696e7	0.068	2.0670e9	-1.7200e8	-1.7200e8	1.0990e9	2.95e8	1.0981e10	1.8950e9	1.6000e9	-3.9200e8	4.2e8	2.8e7	1.2593e9	1.0990e9	1.2099e10	1.7500e9	3.293e8	3.272e8	3.34	3.36	1.0990e9	1.0990e9	1.0990e9	1.0990e9	1.0990e9	8.1e7	1.1800e9	-1.8600e8	1e7	-1.7200e8	1.64e8	8e6	-2.4e7	-3.9200e8	4.2e8	2.8e7	1.7580e9	1.0430e9	2.2e8	2.2e8	8.23e8	8.23e8	8.23e8	2.8010e9	1.1056e10	1.3857e10	3.4900e9
"2020-12-31 00:00:00"	-7.209e7	0.27	7.24e8	-2.6700e8	-2.6700e8	-2.6700e8	4.78e8	8.9800e9	4.57e8	-2.1e7	-4.1800e8	4.45e8	2.7e7	-7.209e7	-2.6700e8	1.0220e10	8.4e7	3.258e8	3.258e8	-0.82	-0.82	-2.6700e8	-2.6700e8	-2.6700e8	-2.6700e8	-2.6700e8	-1.9900e8	-4.6600e8	-3.9900e8	9e6	-2.6700e8	null	2.67e8	-1.4100e8	-4.1800e8	4.45e8	2.7e7	3.51e8	1.1080e9	3.46e8	3.46e8	7.62e8	7.62e8	7.62e8	1.4590e9	9.1120e9	1.0571e10	2.1190e9
"2019-12-31 00:00:00"	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	-1.5400e8	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null

Balance Sheet

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (5, 69)

date	Ordinary Shares Number	Share Issued	Net Debt	Total Debt	Tangible Book Value	Invested Capital	Working Capital	Net Tangible Assets	Capital Lease Obligations	Common Stock Equity	Total Capitalization	Total Equity Gross Minority Interest	Stockholders Equity	Gains Losses Not Affecting Retained Earnings	Other Equity Adjustments	Treasury Stock	Retained Earnings	Additional Paid In Capital	Capital Stock	Common Stock	Total Liabilities Net Minority Interest	Total Non Current Liabilities Net Minority Interest	Other Non Current Liabilities	Non Current Deferred Liabilities	Non Current Deferred Revenue	Non Current Deferred Taxes Liabilities	Long Term Debt And Capital Lease Obligation	Long Term Capital Lease Obligation	Long Term Debt	Current Liabilities	Other Current Liabilities	Current Debt And Capital Lease Obligation	Current Debt	Payables And Accrued Expenses	Current Accrued Expenses	Payables	Accounts Payable	Total Assets	Total Non Current Assets	Other Non Current Assets	Non Current Deferred Assets	Non Current Deferred Taxes Assets	Non Current Note Receivables	Investments And Advances	Long Term Equity Investment	Goodwill And Other Intangible Assets	Other Intangible Assets	Goodwill	Net PPE	Accumulated Depreciation	Gross PPE	Construction In Progress	Other Properties	Machinery Furniture Equipment	Buildings And Improvements	Land And Improvements	Properties	Current Assets	Other Current Assets	Assets Held For Sale Current	Prepaid Assets	Receivables	Accounts Receivable	Allowance For Doubtful Accounts Receivable	Gross Accounts Receivable	Cash Cash Equivalents And Short Term Investments	Cash And Cash Equivalents	Cash Financial
str	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
"2023-12-31 00:00:00"	2.90539975e8	2.90539975e8	1.1535e10	1.2760e10	-1.8758e10	1.1191e10	-4.4510e9	-1.8758e10	8.87e8	-6.8200e8	1.0638e10	-6.8200e8	-6.8200e8	-6.4700e8	-6.4700e8	2.0929e10	1.4838e10	6.0510e9	5e6	5e6	2.6356e10	1.8594e10	5.1600e9	1.2270e9	1.0180e9	2.09e8	1.2207e10	8.87e8	1.1320e10	7.7620e9	3.3280e9	5.53e8	5.53e8	3.8810e9	3.1430e9	7.38e8	7.38e8	2.5674e10	2.2363e10	6.58e8	6.73e8	6.73e8	1.38e8	3.08e8	3.08e8	1.8076e10	9.1900e9	8.8860e9	2.5100e9	-8.9000e8	3.4000e9	7.2e7	9.29e8	6.22e8	1.1080e9	6.69e8	0.0	3.3110e9	2.61e8	null	null	2.7120e9	2.7120e9	-1.9700e8	2.9090e9	3.38e8	3.38e8	null
"2022-12-31 00:00:00"	3.106e8	3.106e8	9.5570e9	1.1098e10	-1.7051e10	1.0632e10	-4.0260e9	-1.7051e10	1.0340e9	5.68e8	9.9480e9	5.68e8	5.68e8	-7.2900e8	-7.2900e8	1.7015e10	1.2342e10	5.9650e9	5e6	5e6	2.4247e10	1.6908e10	5.1220e9	1.3720e9	1.0590e9	3.13e8	1.0414e10	1.0340e9	9.3800e9	7.3390e9	3.3140e9	6.84e8	6.84e8	3.3410e9	2.5950e9	7.46e8	7.46e8	2.4815e10	2.1502e10	5.84e8	2.4e8	2.4e8	1.52e8	3.35e8	3.35e8	1.7619e10	8.7470e9	8.8720e9	2.5720e9	-8.7400e8	3.4460e9	3.6e7	9.87e8	6.49e8	1.0860e9	6.88e8	0.0	3.3130e9	2.35e8	null	null	2.5710e9	2.5710e9	-1.9100e8	2.7620e9	5.07e8	5.07e8	null
"2021-12-31 00:00:00"	3.273e8	3.273e8	8.7450e9	1.1236e10	-1.6585e10	1.1552e10	-2.7810e9	-1.6585e10	1.0980e9	1.4140e9	1.0747e10	1.4140e9	1.4140e9	-3.4200e8	-3.4200e8	1.4446e10	1.0305e10	5.8920e9	5e6	5e6	2.4139e10	1.7732e10	5.9510e9	1.3500e9	1.1810e9	1.69e8	1.0431e10	1.0980e9	9.3330e9	6.4070e9	2.5220e9	8.05e8	8.05e8	3.0800e9	2.3540e9	7.26e8	7.26e8	2.5553e10	2.1927e10	6.04e8	2.28e8	2.28e8	1.44e8	3.87e8	3.87e8	1.7999e10	8.9260e9	9.0730e9	2.5650e9	-8.5000e8	3.4150e9	1.37e8	1.0620e9	5.45e8	9.85e8	6.86e8	0.0	3.6260e9	2.51e8	null	2.51e8	1.9820e9	1.9820e9	-1.8700e8	2.1690e9	1.3930e9	1.3930e9	null
"2020-12-31 00:00:00"	3.244e8	3.244e8	9.4990e9	1.1199e10	-1.7734e10	1.0806e10	-2.9270e9	-1.7734e10	8.23e8	4.3e8	9.6330e9	4.3e8	4.3e8	-1.3500e8	-1.3500e8	1.4497e10	9.2060e9	5.8510e9	5e6	5e6	2.4271e10	1.8519e10	6.8680e9	1.6250e9	1.5420e9	8.3e7	1.0026e10	8.23e8	9.2030e9	5.7520e9	1.7690e9	1.1730e9	1.1730e9	2.8100e9	2.2830e9	5.27e8	5.27e8	2.4701e10	2.1876e10	6.16e8	2.49e8	2.49e8	1.59e8	4.22e8	null	1.8164e10	8.9890e9	9.1750e9	2.2660e9	-8.8800e8	3.1540e9	2.9e7	7.52e8	6.4e8	1.0450e9	6.88e8	0.0	2.8250e9	1.8e8	8e6	1.8e8	1.7680e9	1.7680e9	null	null	8.77e8	8.77e8	null
"2019-12-31 00:00:00"	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	2.55e8	2.52e8	null	null	null	null	null	null	2.25e8

Cash Flow

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (5, 44)

date	Free Cash Flow	Repurchase Of Capital Stock	Repayment Of Debt	Issuance Of Debt	Issuance Of Capital Stock	Capital Expenditure	End Cash Position	Beginning Cash Position	Changes In Cash	Financing Cash Flow	Cash Flow From Continuing Financing Activities	Net Other Financing Charges	Cash Dividends Paid	Common Stock Dividend Paid	Net Common Stock Issuance	Common Stock Payments	Common Stock Issuance	Net Issuance Payments Of Debt	Net Short Term Debt Issuance	Net Long Term Debt Issuance	Long Term Debt Payments	Long Term Debt Issuance	Investing Cash Flow	Cash Flow From Continuing Investing Activities	Net Other Investing Changes	Net Business Purchase And Sale	Sale Of Business	Purchase Of Business	Net PPE Purchase And Sale	Sale Of PPE	Capital Expenditure Reported	Operating Cash Flow	Cash Flow From Continuing Operating Activities	Change In Working Capital	Other Non Cash Items	Stock Based Compensation	Deferred Tax	Deferred Income Tax	Depreciation Amortization Depletion	Depreciation And Amortization	Depreciation	Operating Gains Losses	Net Income From Continuing Operations
str	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
"2023-12-31 00:00:00"	2.7180e9	-3.9530e9	-6.8400e8	1.9180e9	2.9e7	-4.5200e8	3.66e8	5.25e8	-1.5900e8	-2.8640e9	-2.8640e9	-1.3300e8	-5.8700e8	-5.8700e8	-3.9240e9	-3.9530e9	2.9e7	1.7800e9	5.46e8	1.2340e9	-6.8400e8	1.9180e9	-4.6500e8	-4.6500e8	1.7e7	-1.0100e8	null	-1.0100e8	7.1e7	7.1e7	-4.5200e8	3.1700e9	3.1700e9	6.9e7	1.48e8	2.05e8	-6.1200e8	-6.1200e8	2.77e8	2.77e8	2.77e8	null	3.0830e9
"2022-12-31 00:00:00"	2.0310e9	-2.5660e9	-8.0400e8	9.83e8	0.0	-3.3200e8	5.25e8	1.4210e9	-8.9600e8	-2.9620e9	-2.9620e9	-7.2e7	-3.2100e8	-3.2100e8	-2.5660e9	-2.5660e9	0.0	-3e6	-1.8200e8	1.79e8	-8.0400e8	9.83e8	-2.9700e8	-2.9700e8	3.4e7	0.0	null	0.0	1e6	1e6	-3.3200e8	2.3630e9	2.3630e9	-5.4200e8	-2.0700e8	1.92e8	2.8e8	2.8e8	2.82e8	2.82e8	2.82e8	null	2.3580e9
"2021-12-31 00:00:00"	9.94e8	0.0	-2.1740e9	1.7930e9	2e6	-1.8300e8	1.4210e9	8.94e8	5.27e8	-4.6300e8	-4.6300e8	-2.3400e8	0.0	0.0	2e6	0.0	2e6	-2.3100e8	1.5e8	-3.8100e8	-2.1740e9	1.7930e9	-1.8700e8	-1.8700e8	-1.6e7	0.0	null	0.0	1.2e7	1.2e7	-1.8300e8	1.1770e9	1.1770e9	1.1e8	-3.9200e8	1.82e8	-2.8100e8	-2.8100e8	2.95e8	2.95e8	2.95e8	1.64e8	1.0990e9
"2020-12-31 00:00:00"	1.5040e9	-1.5000e8	-1.8870e9	3.5610e9	0.0	-1.3500e8	8.94e8	2.53e8	6.41e8	-1.0330e9	-1.0330e9	-1.1100e8	-1.5600e8	-1.5600e8	-1.5000e8	-1.5000e8	0.0	-6.1600e8	-2.2900e9	1.6740e9	-1.8870e9	3.5610e9	3.5e7	3.5e7	-9e7	2.6e8	2.6e8	null	2.6e8	2.6e8	-1.3500e8	1.6390e9	1.6390e9	-2.8e7	1.7300e9	2.01e8	-4.7800e8	-4.7800e8	4.78e8	4.78e8	4.78e8	3e6	-2.6700e8
"2019-12-31 00:00:00"	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	3.95e8	null	null	null	null	null	null	null	null	null	null	null	null	null	null	-1.4700e8	null

FinancialStatements(ticker=MAR)

Income Statement

import polars as pl

fs.income_statement.with_columns(pl.col('*').forge_income_statement.gross_profit_margin(revenue_column='Total Revenue',cogs_column='Cost Of Revenue'))

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (5, 50)

date	Tax Effect Of Unusual Items	Tax Rate For Calcs	Normalized EBITDA	Total Unusual Items	Total Unusual Items Excluding Goodwill	Net Income From Continuing Operation Net Minority Interest	Reconciled Depreciation	Reconciled Cost Of Revenue	EBITDA	EBIT	Net Interest Income	Interest Expense	Interest Income	Normalized Income	Net Income From Continuing And Discontinued Operation	Total Expenses	Total Operating Income As Reported	Diluted Average Shares	Basic Average Shares	Diluted EPS	Basic EPS	Diluted NI Availto Com Stockholders	Net Income Common Stockholders	Net Income	Net Income Including Noncontrolling Interests	Net Income Continuous Operations	Tax Provision	Pretax Income	Other Income Expense	Other Non Operating Income Expenses	Special Income Charges	Other Special Charges	Restructuring And Mergern Acquisition	Earnings From Equity Interest	Net Non Operating Interest Income Expense	Interest Expense Non Operating	Interest Income Non Operating	Operating Income	Operating Expense	Depreciation Amortization Depletion Income Statement	Depreciation And Amortization In Income Statement	Selling General And Administration	General And Administrative Expense	Other Gand A	Gross Profit	Cost Of Revenue	Total Revenue	Operating Revenue	gross_profit_margin
str	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
"2023-12-31 00:00:00"	-5.22e6	0.087	4.2800e9	-6e7	-6e7	3.0830e9	2.77e8	1.8501e10	4.2200e9	3.9430e9	-5.3500e8	5.65e8	3e7	3.1378e9	3.0830e9	1.9789e10	3.8640e9	3.029e8	3.015e8	10.18	10.23	3.0830e9	3.0830e9	3.0830e9	3.0830e9	3.0830e9	2.95e8	3.3780e9	-1.1e7	4e7	-6e7	null	6e7	9e6	-5.3500e8	5.65e8	3e7	3.9240e9	1.2000e9	1.89e8	1.89e8	1.0110e9	1.0110e9	1.0110e9	5.1240e9	1.8589e10	2.3713e10	6.3880e9	0.216084
"2022-12-31 00:00:00"	-2.916e6	0.243	3.8110e9	-1.2e7	-1.2e7	2.3580e9	2.82e8	1.6126e10	3.7990e9	3.5170e9	-3.7700e8	4.03e8	2.6e7	2.3671e9	2.3580e9	1.7299e10	3.4620e9	3.258e8	3.244e8	7.24	7.27	2.3580e9	2.3580e9	2.3580e9	2.3580e9	2.3580e9	7.56e8	3.1140e9	1.7e7	1.1e7	-1.2e7	null	1.2e7	1.8e7	-3.7700e8	4.03e8	2.6e7	3.4740e9	1.0840e9	1.93e8	1.93e8	8.91e8	8.91e8	8.91e8	4.5580e9	1.6215e10	2.0773e10	5.4450e9	0.219419
"2021-12-31 00:00:00"	-1.1696e7	0.068	2.0670e9	-1.7200e8	-1.7200e8	1.0990e9	2.95e8	1.0981e10	1.8950e9	1.6000e9	-3.9200e8	4.2e8	2.8e7	1.2593e9	1.0990e9	1.2099e10	1.7500e9	3.293e8	3.272e8	3.34	3.36	1.0990e9	1.0990e9	1.0990e9	1.0990e9	1.0990e9	8.1e7	1.1800e9	-1.8600e8	1e7	-1.7200e8	1.64e8	8e6	-2.4e7	-3.9200e8	4.2e8	2.8e7	1.7580e9	1.0430e9	2.2e8	2.2e8	8.23e8	8.23e8	8.23e8	2.8010e9	1.1056e10	1.3857e10	3.4900e9	0.202136
"2020-12-31 00:00:00"	-7.209e7	0.27	7.24e8	-2.6700e8	-2.6700e8	-2.6700e8	4.78e8	8.9800e9	4.57e8	-2.1e7	-4.1800e8	4.45e8	2.7e7	-7.209e7	-2.6700e8	1.0220e10	8.4e7	3.258e8	3.258e8	-0.82	-0.82	-2.6700e8	-2.6700e8	-2.6700e8	-2.6700e8	-2.6700e8	-1.9900e8	-4.6600e8	-3.9900e8	9e6	-2.6700e8	null	2.67e8	-1.4100e8	-4.1800e8	4.45e8	2.7e7	3.51e8	1.1080e9	3.46e8	3.46e8	7.62e8	7.62e8	7.62e8	1.4590e9	9.1120e9	1.0571e10	2.1190e9	0.138019
"2019-12-31 00:00:00"	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	-1.5400e8	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null

fs.income_statement.with_columns(pl.col('*').forge_income_statement.net_profit_margin(revenue_column='Total Revenue',cogs_column='Cost Of Revenue',expenses_column='Total Expenses'))

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date	Tax Effect Of Unusual Items	Tax Rate For Calcs	Normalized EBITDA	Total Unusual Items	Total Unusual Items Excluding Goodwill	Net Income From Continuing Operation Net Minority Interest	Reconciled Depreciation	Reconciled Cost Of Revenue	EBITDA	EBIT	Net Interest Income	Interest Expense	Interest Income	Normalized Income	Net Income From Continuing And Discontinued Operation	Total Expenses	Total Operating Income As Reported	Diluted Average Shares	Basic Average Shares	Diluted EPS	Basic EPS	Diluted NI Availto Com Stockholders	Net Income Common Stockholders	Net Income	Net Income Including Noncontrolling Interests	Net Income Continuous Operations	Tax Provision	Pretax Income	Other Income Expense	Other Non Operating Income Expenses	Special Income Charges	Other Special Charges	Restructuring And Mergern Acquisition	Earnings From Equity Interest	Net Non Operating Interest Income Expense	Interest Expense Non Operating	Interest Income Non Operating	Operating Income	Operating Expense	Depreciation Amortization Depletion Income Statement	Depreciation And Amortization In Income Statement	Selling General And Administration	General And Administrative Expense	Other Gand A	Gross Profit	Cost Of Revenue	Total Revenue	Operating Revenue	net_profit_margin
str	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
"2023-12-31 00:00:00"	-5.22e6	0.087	4.2800e9	-6e7	-6e7	3.0830e9	2.77e8	1.8501e10	4.2200e9	3.9430e9	-5.3500e8	5.65e8	3e7	3.1378e9	3.0830e9	1.9789e10	3.8640e9	3.029e8	3.015e8	10.18	10.23	3.0830e9	3.0830e9	3.0830e9	3.0830e9	3.0830e9	2.95e8	3.3780e9	-1.1e7	4e7	-6e7	null	6e7	9e6	-5.3500e8	5.65e8	3e7	3.9240e9	1.2000e9	1.89e8	1.89e8	1.0110e9	1.0110e9	1.0110e9	5.1240e9	1.8589e10	2.3713e10	6.3880e9	-0.618437
"2022-12-31 00:00:00"	-2.916e6	0.243	3.8110e9	-1.2e7	-1.2e7	2.3580e9	2.82e8	1.6126e10	3.7990e9	3.5170e9	-3.7700e8	4.03e8	2.6e7	2.3671e9	2.3580e9	1.7299e10	3.4620e9	3.258e8	3.244e8	7.24	7.27	2.3580e9	2.3580e9	2.3580e9	2.3580e9	2.3580e9	7.56e8	3.1140e9	1.7e7	1.1e7	-1.2e7	null	1.2e7	1.8e7	-3.7700e8	4.03e8	2.6e7	3.4740e9	1.0840e9	1.93e8	1.93e8	8.91e8	8.91e8	8.91e8	4.5580e9	1.6215e10	2.0773e10	5.4450e9	-0.613344
"2021-12-31 00:00:00"	-1.1696e7	0.068	2.0670e9	-1.7200e8	-1.7200e8	1.0990e9	2.95e8	1.0981e10	1.8950e9	1.6000e9	-3.9200e8	4.2e8	2.8e7	1.2593e9	1.0990e9	1.2099e10	1.7500e9	3.293e8	3.272e8	3.34	3.36	1.0990e9	1.0990e9	1.0990e9	1.0990e9	1.0990e9	8.1e7	1.1800e9	-1.8600e8	1e7	-1.7200e8	1.64e8	8e6	-2.4e7	-3.9200e8	4.2e8	2.8e7	1.7580e9	1.0430e9	2.2e8	2.2e8	8.23e8	8.23e8	8.23e8	2.8010e9	1.1056e10	1.3857e10	3.4900e9	-0.670997
"2020-12-31 00:00:00"	-7.209e7	0.27	7.24e8	-2.6700e8	-2.6700e8	-2.6700e8	4.78e8	8.9800e9	4.57e8	-2.1e7	-4.1800e8	4.45e8	2.7e7	-7.209e7	-2.6700e8	1.0220e10	8.4e7	3.258e8	3.258e8	-0.82	-0.82	-2.6700e8	-2.6700e8	-2.6700e8	-2.6700e8	-2.6700e8	-1.9900e8	-4.6600e8	-3.9900e8	9e6	-2.6700e8	null	2.67e8	-1.4100e8	-4.1800e8	4.45e8	2.7e7	3.51e8	1.1080e9	3.46e8	3.46e8	7.62e8	7.62e8	7.62e8	1.4590e9	9.1120e9	1.0571e10	2.1190e9	-0.828777
"2019-12-31 00:00:00"	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	-1.5400e8	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null

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=390000,annual_interest_rate=5.75,start_date='2023-09-01',term_years=30,extra_principal_payment=200)
mortgage.calculate_monthly_payment()

2275.934140129842

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"	2275.93414	1868.75	407.18414	389592.81586
2	"2023-10-31"	2275.93414	1866.798909	409.135231	389183.680629
3	"2023-11-30"	2275.93414	1864.83847	411.09567	388772.584959
4	"2023-12-30"	2275.93414	1862.868636	413.065504	388359.519455
5	"2024-01-29"	2275.93414	1860.889364	415.044776	387944.474679
…	…	…	…	…	…
356	"2052-11-27"	2275.93414	53.752435	2222.181705	8995.717798
357	"2052-12-27"	2275.93414	43.104481	2232.829659	6762.888139
358	"2053-01-26"	2275.93414	32.405506	2243.528634	4519.359505
359	"2053-02-25"	2275.93414	21.655264	2254.278876	2265.080629
360	"2053-03-27"	2275.93414	10.853511	2265.080629	1.2655e-8

mortgage.calculate_principal_only_payment()

{'new_term_years': 24.502707600761994, 'time_saved_years': 5.497292399238006}

mortgage.refinance_assessment(RefinanceOption(new_loan_amount=340000,new_annual_interest_rate=5.25,new_term_years=30,associated_costs=5000))

{'Old Monthly Payment': 2275.934140129842,
 'New Monthly Payment': 1877.492587282451,
 'Monthly Savings': 398.441552847391,
 'Break-Even Point (months)': 12.548891962367875,
 'Total Savings (over the loan term)': 138438.95902506076,
 '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
i64	i64	i64	i64	i64	i64	i64	i64
1200	10	2	100	1000	50	5	50
1500	15	3	150	1500	75	7	70
60	2	1	0	0	0	2	20
2500	25	5	200	2000	100	10	100

Average Session Duration

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

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total_duration	total_sessions	single_page_sessions	num_clicks	num_impressions	total_cost	exits	total_page_views	average_session_duration
i64	i64	i64	i64	i64	i64	i64	i64	f64
1200	10	2	100	1000	50	5	50	120.0
1500	15	3	150	1500	75	7	70	100.0
60	2	1	0	0	0	2	20	30.0
2500	25	5	200	2000	100	10	100	100.0

Bounce Rate

data.with_columns(pl.col('*').forge_web_traffic.bounce_rate(single_page_sessions_col='single_page_sessions', total_sessions_col='total_sessions'))

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total_duration	total_sessions	single_page_sessions	num_clicks	num_impressions	total_cost	exits	total_page_views	bounce_rate
i64	i64	i64	i64	i64	i64	i64	i64	f64
1200	10	2	100	1000	50	5	50	20.0
1500	15	3	150	1500	75	7	70	20.0
60	2	1	0	0	0	2	20	50.0
2500	25	5	200	2000	100	10	100	20.0

Click Through Rate

data.with_columns(pl.col('*').forge_web_traffic.click_through_rate(num_clicks_col='num_clicks', num_impressions_col='num_impressions'))

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total_duration	total_sessions	single_page_sessions	num_clicks	num_impressions	total_cost	exits	total_page_views	click_through_rate
i64	i64	i64	i64	i64	i64	i64	i64	f64
1200	10	2	100	1000	50	5	50	10.0
1500	15	3	150	1500	75	7	70	10.0
60	2	1	0	0	0	2	20	NaN
2500	25	5	200	2000	100	10	100	10.0

Cost Per Click

data.with_columns(pl.col('*').forge_web_traffic.cost_per_click(total_cost_col='total_cost',num_clicks_col='num_clicks'))

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total_duration	total_sessions	single_page_sessions	num_clicks	num_impressions	total_cost	exits	total_page_views	cost_per_click
i64	i64	i64	i64	i64	i64	i64	i64	f64
1200	10	2	100	1000	50	5	50	0.5
1500	15	3	150	1500	75	7	70	0.5
60	2	1	0	0	0	2	20	0.0
2500	25	5	200	2000	100	10	100	0.5

Exit Rate

data.with_columns(pl.col('*').forge_web_traffic.exit_rate(exits_col='exits',total_page_views_col='total_page_views'))

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total_duration	total_sessions	single_page_sessions	num_clicks	num_impressions	total_cost	exits	total_page_views	exit_rate
i64	i64	i64	i64	i64	i64	i64	i64	f64
1200	10	2	100	1000	50	5	50	10.0
1500	15	3	150	1500	75	7	70	10.0
60	2	1	0	0	0	2	20	10.0
2500	25	5	200	2000	100	10	100	10.0

Pages Per Session

data.with_columns(pl.col('*').forge_web_traffic.pages_per_session(total_page_views_col='total_page_views',total_sessions_col='total_sessions'))

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total_duration	total_sessions	single_page_sessions	num_clicks	num_impressions	total_cost	exits	total_page_views	pages_per_session
i64	i64	i64	i64	i64	i64	i64	i64	f64
1200	10	2	100	1000	50	5	50	5.0
1500	15	3	150	1500	75	7	70	4.666667
60	2	1	0	0	0	2	20	10.0
2500	25	5	200	2000	100	10	100	4.0