Currency
About
The Federal Reserve Board is the issuing authority for Federal Reserve notes and ensures that there is enough cash in circulation to meet the public's demand. The Federal Reserve Banks distribute, receive, and process Federal Reserve notes and distribute and receive through depository institutions.
FedPy allows you to quickly and seamlessly pull data on their Currency Service.
in_circulation()
About
FedPy.Currency().in_circulation(_type)
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Returns a DataFrame of annual currency in circulation.
Parameters:
_type: string, by default is set to "val" if nothing is passed.- Valid _type's:
- "val", "Val", "value", or "Value" : Value of currency in circulation.
- "vol", "Vol", "volume", or "Volume" : Volume of currency in circulation.
- Valid _type's:
Example use
import FedPy
val_in_circulation = FedPy.Currency().in_circulation("val")
vol_in_circulation = FedPy.Currency().in_circulation("vol")
print(val_in_circulation, vol_in_circulation)
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Output:
val_in_circulation:
Year $1 ... $500 to $10,000 Total
2020 $13100000000 ... $300000000 $2040700000000
2019 $12700000000 ... $300000000 $1759800000000
2018 $12400000000 ... $300000000 $1671900000000
2017 $12100000000 ... $300000000 $1571100000000
2016 $11700000000 ... $300000000 $1463400000000
2015 $11400000000 ... $300000000 $1380000000000
... ... ... ... ...
2000 $7700000000 ... $300000000 $563900000000
vol_in_circulation:
Year $1 ... $500 to $10,000 Total
2020 13.1 ... 0.0004 50.3
2019 12.7 ... 0.0004 44.9
2018 12.4 ... 0.0004 43.4
2017 12.1 ... 0.0004 41.6
2016 11.7 ... 0.0004 39.8
2015 11.4 ... 0.0005 38.1
... ... ... ... ...
2001 7.8 ... 0.0005 22.1
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printing_cost()
About
FedPy.Currency().printing_cost()
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Returns a DataFrame of the annual cost of printing currency, and takes in no parameters.
Example use
import FedPy
printing_cost = FedPy.Currency().printing_cost()
print(printing_cost)
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Output:
Year Amount
2020 778000000
2019 641000000
2018 800000000
2017 674000000
2016 660000000
2015 689000000
2014 707000000
2013 717000000
... ...
2000 456000000
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operation_expenses()
About
FedPy.Currency().operation_expenses()
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Returns a DataFrame of cash operation expenses, including (processing, paying, recieving, verification, destruction, transportation, and non-standerd packaging), and takes in no parameters.
Example use
import FedPy
op_expense = FedPy.Currency().operation_expenses()
print(op_expense)
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Output:
Year Amount
2020 751000000
2019 700000000
2018 680000000
2017 628000000
2016 593000000
2015 578000000
... ...
2000 329000000
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payments()
About
FedPy.Currency().payments(_type)
1
Returns a DataFrame of payments of currency in either value or volume format.
Parameters:
_type: string of the format type, by default is "val" if nothing is passed.- Valid _type's :
- "val", "Val", "Value", "value" : Value format.
- "vol", "Vol", "Volume", "volume" : Volume format.
- Valid _type's :
Example use
import FedPy
payments_val = FedPy.Currency().payments("val")
payments_vol = FedPy.Currency().payments("vol")
print(payments_val, payments_vol)
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Output:
payments_val:
Year $1 $2 ... $50 $100 Total
2020 $7800000000 $100000000 ... $95900000000 $432800000000 $872800000000
2019 $10800000000 $200000000 ... $78900000000 $367700000000 $784100000000
2018 $11300000000 $200000000 ... $75400000000 $369200000000 $792600000000
2017 $11600000000 $200000000 ... $71900000000 $368800000000 $789700000000
2016 $11900000000 $200000000 ... $64500000000 $323800000000 $730000000000
2015 $12200000000 $200000000 ... $61000000000 $328600000000 $736000000000
... ... ... ... ... ... ...
2000 $11500000000 $100000000 ... $54100000000 $162500000000 $527300000000
payments_vol:
Year $1 $2 $5 $10 $20 $50 $100 Total
2020 7.8 0.1 2.5 1.5 15.4 1.9 4.3 33.5
2019 10.8 0.1 3.2 1.7 14.7 1.6 3.7 35.7
2018 11.3 0.1 3.3 1.7 15.1 1.5 3.7 36.8
2017 11.6 0.1 3.3 1.7 15.2 1.4 3.7 37.0
2016 11.9 0.1 3.2 1.7 14.8 1.3 3.2 36.3
2015 12.2 0.1 3.2 1.9 15.0 1.2 3.3 36.8
... ... ... ... ... ... ... ... ...
2000 11.5 0.0 2.4 2.3 13.2 1.1 1.6 32.1
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receipts()
TODO : spelt receipts wrong MUST fix you retard.
About
FedPy.Currency().reciepts(_type)
1
Returns a DataFrame of receipts of currency in either value or volume format.
Parameters:
_type: string of the format type, by default is "val" if nothing is passed.- Valid _type's :
- "val", "Val", "Value", "value" : Value format.
- "vol", "Vol", "Volume", "volume" : Volume format.
- Valid _type's :
Example use
import FedPy
receipts_val = FedPy.Currency().reciepts("val")
receipts_vol = FedPy.Currency().reciepts("vol")
print(receipts_val, receipts_vol)
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Output:
receipts_val:
Year $1 $2 ... $50 $100 Total
2020 $7400000000 $100000000 ... $74100000000 $220800000000 $592100000000
2019 $10500000000 $100000000 ... $75800000000 $286600000000 $696000000000
2018 $11000000000 $100000000 ... $72600000000 $277500000000 $692000000000
2017 $11300000000 $100000000 ... $69200000000 $272400000000 $682600000000
2016 $11600000000 $100000000 ... $60900000000 $251200000000 $646600000000
2015 $11800000000 $100000000 ... $58300000000 $260900000000 $655400000000
... ... ... ... ... ... ...
2000 $11400000000 $000000000 ... $63700000000 $170700000000 $564300000000
receipts_vol:
Year $1 $2 $5 $10 $20 $50 $100 Total
2020 7.4 0.0 2.4 1.4 13.2 1.5 2.2 28.1
2019 10.5 0.0 3.1 1.6 14.6 1.5 2.9 34.2
2018 11.0 0.0 3.2 1.7 14.9 1.5 2.8 35.0
2017 11.3 0.0 3.2 1.7 14.8 1.4 2.7 35.2
2016 11.6 0.0 3.1 1.7 14.5 1.2 2.5 34.7
2015 11.8 0.0 3.0 1.9 14.5 1.2 2.6 35.1
... ... ... ... ... ... ... ... ...
2000 11.4 0.0 2.4 2.5 14.1 1.3 1.7 33.3
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