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Python- dictionary with list of list elements
How to merge two dictionaries in a single expression?Calling an external command in PythonWhat are metaclasses in Python?Finding the index of an item given a list containing it in PythonDifference between append vs. extend list methods in PythonDoes Python have a ternary conditional operator?How do I sort a dictionary by value?Getting the last element of a list in PythonHow do I get the number of elements in a list in Python?How do I list all files of a directory?
1
Edit: Sorry - I am very inexperienced at basically all of this. I will try again.
It appears that my dictionaries are messed up somehow because when I call the function, most of the results are nan
Please let me know if you need anything in addition to the below, I barely know how to write code and I certainly don't know how to fix other peoples code so just let me know what other info you may need.
N_SAMPLES = 10000
IEA_LV = fin_data['Yield_IEA'][0]
IEA_STD = fin_data['Yield_IEA'].std()
IBL_LV = fin_data['Cost_IBL'][0]
IBL_STD = fin_data['Cost_IBL'].std()
# Proxy for Default Rate- See Notes
fin_data['NPL'] = fin_data['NPL_to_Loan']*fin_data['Total_Loans']
NPL_LV = fin_data['NPL'][0]
# default rate info (based on past due loans)
default_mean = fin_data['Default_Rate'].mean()
default_std = fin_data['Default_Rate'].std()
LGD = fin_data['LGD'][0]
prior_LGD = fin_data['LGD'][1]
fin_data['non_int_exp_toAssets'] = fin_data['Non_Int_Exp']/fin_data['Total_Loans']
# non-interest expense info
nonint_exp_LV = fin_data['non_int_exp_toAssets'][0]
nonint_exp_STD = fin_data['non_int_exp_toAssets'].std()
#deposit info
dep_LV = fin_data['Deposits'][0]
dep_STD = fin_data['Deposits'].std()
dep_min = fin_data['Deposits'].min()
# Loan growth information
fin_data['loan_g'] = fin_data['Total_Loans']/fin_data['Total_Loans'].shift(-1) - 1
loan_g_min = fin_data['loan_g'].min()
loan_g_avg = fin_data['loan_g'].mean()
loan_g_std = fin_data['loan_g'].std()
w_minus_r = fin_data['Net_Chg_Off'].mean()
def_credit_flow = default_mean*fin_data['Total_Loans'][0]
# Projected NPLs
next_NPL = NPL_LV + def_credit_flow + NPL_LV * (1 - w_minus_r)
#Net adjustment for impaired loans needs LGD updated
adj_imp_loans = def_credit_flow * LGD + NPL_LV * (1-w_minus_r) * (LGD - prior_LGD)
# Reserve for Loan Loss
ALLL = fin_data['ALLL'][0] * fin_data['Total_Loans'][0] + adj_imp_loans - NPL_LV * w_minus_r
# variable: [most recent value or average, standard deviation]
var_stats_dict = 'IEA': [IEA_LV, IEA_STD], 'IBL': [IBL_LV, IBL_STD], 'Default Rate': [default_mean, default_std],
'Non Int Exp': [nonint_exp_LV, nonint_exp_STD],
'GPL': [loan_g_avg, loan_g_std], 'Deposits': [dep_LV, dep_STD]
# var: [[min, max for severe], [min, max for moderate], [min, max for low]]
# severe = 0, moderate = 1, low = 2
severity_dict = 'IEA': [[IEA_LV - 3 * IEA_STD, IEA_LV + 1 * IEA_STD],
[IEA_LV - 2 * IEA_STD, IEA_LV + 2 * IEA_STD],
[IEA_LV - 1 * IEA_STD, IEA_LV + 3 * IEA_STD]],
'IBL': [[IBL_LV - 1 * IBL_STD, IBL_LV + 3 * IBL_STD],
[IBL_LV - 2 * IBL_STD, IBL_LV + 2 * IBL_STD],
[IBL_LV - 3 * IBL_STD, IBL_LV + 1 * IBL_STD]],
'Default Rate': [[default_mean - 1 * default_std,
default_mean + 3 * default_std],
[default_mean - 2 * default_std,
default_mean + 2 * default_std],
[default_mean - 3 * default_std,
default_mean + 1 * default_std]],
'Non Int Exp': [nonint_exp_LV - 1 * nonint_exp_STD,
nonint_exp_LV + 1 * nonint_exp_STD],
'GPL': [[loan_g_min, loan_g_avg],
[loan_g_avg - 1 * loan_g_std, loan_g_avg + 1 * loan_g_std],
[loan_g_avg, loan_g_avg + 2 * loan_g_std]],
'Deposits': [[dep_min, dep_LV],
[dep_LV - 1 * dep_STD, dep_LV + 1 * dep_STD],
[dep_LV, dep_LV + 2 * dep_STD]]
#vars = IEA, IBL, Default Rate, Non Int Exp, GPL, Deposits
def mc_generator(var, severity):
low_cap = severity_dict[var][severity][0]
up_cap = severity_dict[var][severity][1]
mu = var_stats_dict[var][0]
sigma = var_stats_dict[var][1]
container = []
for i in range(0, N_SAMPLES):
x = np.random.normal(loc = mu, scale = sigma)
result = stats.beta.pdf(x, low_cap, up_cap)
container.append(result)
avg_val = sum(container)/N_SAMPLES
return round(avg_val, 10)
print(mc_generator('Non Int Exp', 0))
python
asked Mar 8 at 1:46
Frank PaolantonioFrank Paolantonio
62
add a comment |
1
Edit: Sorry - I am very inexperienced at basically all of this. I will try again.
It appears that my dictionaries are messed up somehow because when I call the function, most of the results are nan
Please let me know if you need anything in addition to the below, I barely know how to write code and I certainly don't know how to fix other peoples code so just let me know what other info you may need.
N_SAMPLES = 10000
IEA_LV = fin_data['Yield_IEA'][0]
IEA_STD = fin_data['Yield_IEA'].std()
IBL_LV = fin_data['Cost_IBL'][0]
IBL_STD = fin_data['Cost_IBL'].std()
# Proxy for Default Rate- See Notes
fin_data['NPL'] = fin_data['NPL_to_Loan']*fin_data['Total_Loans']
NPL_LV = fin_data['NPL'][0]
# default rate info (based on past due loans)
default_mean = fin_data['Default_Rate'].mean()
default_std = fin_data['Default_Rate'].std()
LGD = fin_data['LGD'][0]
prior_LGD = fin_data['LGD'][1]
fin_data['non_int_exp_toAssets'] = fin_data['Non_Int_Exp']/fin_data['Total_Loans']
# non-interest expense info
nonint_exp_LV = fin_data['non_int_exp_toAssets'][0]
nonint_exp_STD = fin_data['non_int_exp_toAssets'].std()
#deposit info
dep_LV = fin_data['Deposits'][0]
dep_STD = fin_data['Deposits'].std()
dep_min = fin_data['Deposits'].min()
# Loan growth information
fin_data['loan_g'] = fin_data['Total_Loans']/fin_data['Total_Loans'].shift(-1) - 1
loan_g_min = fin_data['loan_g'].min()
loan_g_avg = fin_data['loan_g'].mean()
loan_g_std = fin_data['loan_g'].std()
w_minus_r = fin_data['Net_Chg_Off'].mean()
def_credit_flow = default_mean*fin_data['Total_Loans'][0]
# Projected NPLs
next_NPL = NPL_LV + def_credit_flow + NPL_LV * (1 - w_minus_r)
#Net adjustment for impaired loans needs LGD updated
adj_imp_loans = def_credit_flow * LGD + NPL_LV * (1-w_minus_r) * (LGD - prior_LGD)
# Reserve for Loan Loss
ALLL = fin_data['ALLL'][0] * fin_data['Total_Loans'][0] + adj_imp_loans - NPL_LV * w_minus_r
# variable: [most recent value or average, standard deviation]
var_stats_dict = 'IEA': [IEA_LV, IEA_STD], 'IBL': [IBL_LV, IBL_STD], 'Default Rate': [default_mean, default_std],
'Non Int Exp': [nonint_exp_LV, nonint_exp_STD],
'GPL': [loan_g_avg, loan_g_std], 'Deposits': [dep_LV, dep_STD]
# var: [[min, max for severe], [min, max for moderate], [min, max for low]]
# severe = 0, moderate = 1, low = 2
severity_dict = 'IEA': [[IEA_LV - 3 * IEA_STD, IEA_LV + 1 * IEA_STD],
[IEA_LV - 2 * IEA_STD, IEA_LV + 2 * IEA_STD],
[IEA_LV - 1 * IEA_STD, IEA_LV + 3 * IEA_STD]],
'IBL': [[IBL_LV - 1 * IBL_STD, IBL_LV + 3 * IBL_STD],
[IBL_LV - 2 * IBL_STD, IBL_LV + 2 * IBL_STD],
[IBL_LV - 3 * IBL_STD, IBL_LV + 1 * IBL_STD]],
'Default Rate': [[default_mean - 1 * default_std,
default_mean + 3 * default_std],
[default_mean - 2 * default_std,
default_mean + 2 * default_std],
[default_mean - 3 * default_std,
default_mean + 1 * default_std]],
'Non Int Exp': [nonint_exp_LV - 1 * nonint_exp_STD,
nonint_exp_LV + 1 * nonint_exp_STD],
'GPL': [[loan_g_min, loan_g_avg],
[loan_g_avg - 1 * loan_g_std, loan_g_avg + 1 * loan_g_std],
[loan_g_avg, loan_g_avg + 2 * loan_g_std]],
'Deposits': [[dep_min, dep_LV],
[dep_LV - 1 * dep_STD, dep_LV + 1 * dep_STD],
[dep_LV, dep_LV + 2 * dep_STD]]
#vars = IEA, IBL, Default Rate, Non Int Exp, GPL, Deposits
def mc_generator(var, severity):
low_cap = severity_dict[var][severity][0]
up_cap = severity_dict[var][severity][1]
mu = var_stats_dict[var][0]
sigma = var_stats_dict[var][1]
container = []
for i in range(0, N_SAMPLES):
x = np.random.normal(loc = mu, scale = sigma)
result = stats.beta.pdf(x, low_cap, up_cap)
container.append(result)
avg_val = sum(container)/N_SAMPLES
return round(avg_val, 10)
print(mc_generator('Non Int Exp', 0))
python
asked Mar 8 at 1:46
Frank PaolantonioFrank Paolantonio
62
Function is returning nan Show us the calling code. All I see here is a function definition that is never actually called.
– John Gordon
Mar 8 at 1:53
1
Could you add an Minimal, Complete, and Verifiable example showing a complete minimal working version of your code? For example, are you simply callingmc_generator()? Answering your question is difficult without knowing the full context of the problem.
– Chris Larson
Mar 8 at 1:58
1
As it stands, the indentation of the first line needs to be corrected, your dictionaries are populated with variables that are not defined in your code, and we have no idea what you are doing to call your function. Without those, it's not really practical for anyone to try to answer.
– Chris Larson
Mar 8 at 2:02
add a comment |
1
1
1
Edit: Sorry - I am very inexperienced at basically all of this. I will try again.
It appears that my dictionaries are messed up somehow because when I call the function, most of the results are nan
Please let me know if you need anything in addition to the below, I barely know how to write code and I certainly don't know how to fix other peoples code so just let me know what other info you may need.
N_SAMPLES = 10000
IEA_LV = fin_data['Yield_IEA'][0]
IEA_STD = fin_data['Yield_IEA'].std()
IBL_LV = fin_data['Cost_IBL'][0]
IBL_STD = fin_data['Cost_IBL'].std()
# Proxy for Default Rate- See Notes
fin_data['NPL'] = fin_data['NPL_to_Loan']*fin_data['Total_Loans']
NPL_LV = fin_data['NPL'][0]
# default rate info (based on past due loans)
default_mean = fin_data['Default_Rate'].mean()
default_std = fin_data['Default_Rate'].std()
LGD = fin_data['LGD'][0]
prior_LGD = fin_data['LGD'][1]
fin_data['non_int_exp_toAssets'] = fin_data['Non_Int_Exp']/fin_data['Total_Loans']
# non-interest expense info
nonint_exp_LV = fin_data['non_int_exp_toAssets'][0]
nonint_exp_STD = fin_data['non_int_exp_toAssets'].std()
#deposit info
dep_LV = fin_data['Deposits'][0]
dep_STD = fin_data['Deposits'].std()
dep_min = fin_data['Deposits'].min()
# Loan growth information
fin_data['loan_g'] = fin_data['Total_Loans']/fin_data['Total_Loans'].shift(-1) - 1
loan_g_min = fin_data['loan_g'].min()
loan_g_avg = fin_data['loan_g'].mean()
loan_g_std = fin_data['loan_g'].std()
w_minus_r = fin_data['Net_Chg_Off'].mean()
def_credit_flow = default_mean*fin_data['Total_Loans'][0]
# Projected NPLs
next_NPL = NPL_LV + def_credit_flow + NPL_LV * (1 - w_minus_r)
#Net adjustment for impaired loans needs LGD updated
adj_imp_loans = def_credit_flow * LGD + NPL_LV * (1-w_minus_r) * (LGD - prior_LGD)
# Reserve for Loan Loss
ALLL = fin_data['ALLL'][0] * fin_data['Total_Loans'][0] + adj_imp_loans - NPL_LV * w_minus_r
# variable: [most recent value or average, standard deviation]
var_stats_dict = 'IEA': [IEA_LV, IEA_STD], 'IBL': [IBL_LV, IBL_STD], 'Default Rate': [default_mean, default_std],
'Non Int Exp': [nonint_exp_LV, nonint_exp_STD],
'GPL': [loan_g_avg, loan_g_std], 'Deposits': [dep_LV, dep_STD]
# var: [[min, max for severe], [min, max for moderate], [min, max for low]]
# severe = 0, moderate = 1, low = 2
severity_dict = 'IEA': [[IEA_LV - 3 * IEA_STD, IEA_LV + 1 * IEA_STD],
[IEA_LV - 2 * IEA_STD, IEA_LV + 2 * IEA_STD],
[IEA_LV - 1 * IEA_STD, IEA_LV + 3 * IEA_STD]],
'IBL': [[IBL_LV - 1 * IBL_STD, IBL_LV + 3 * IBL_STD],
[IBL_LV - 2 * IBL_STD, IBL_LV + 2 * IBL_STD],
[IBL_LV - 3 * IBL_STD, IBL_LV + 1 * IBL_STD]],
'Default Rate': [[default_mean - 1 * default_std,
default_mean + 3 * default_std],
[default_mean - 2 * default_std,
default_mean + 2 * default_std],
[default_mean - 3 * default_std,
default_mean + 1 * default_std]],
'Non Int Exp': [nonint_exp_LV - 1 * nonint_exp_STD,
nonint_exp_LV + 1 * nonint_exp_STD],
'GPL': [[loan_g_min, loan_g_avg],
[loan_g_avg - 1 * loan_g_std, loan_g_avg + 1 * loan_g_std],
[loan_g_avg, loan_g_avg + 2 * loan_g_std]],
'Deposits': [[dep_min, dep_LV],
[dep_LV - 1 * dep_STD, dep_LV + 1 * dep_STD],
[dep_LV, dep_LV + 2 * dep_STD]]
#vars = IEA, IBL, Default Rate, Non Int Exp, GPL, Deposits
def mc_generator(var, severity):
low_cap = severity_dict[var][severity][0]
up_cap = severity_dict[var][severity][1]
mu = var_stats_dict[var][0]
sigma = var_stats_dict[var][1]
container = []
for i in range(0, N_SAMPLES):
x = np.random.normal(loc = mu, scale = sigma)
result = stats.beta.pdf(x, low_cap, up_cap)
container.append(result)
avg_val = sum(container)/N_SAMPLES
return round(avg_val, 10)
print(mc_generator('Non Int Exp', 0))
python
asked Mar 8 at 1:46
Frank PaolantonioFrank Paolantonio
62
Edit: Sorry - I am very inexperienced at basically all of this. I will try again.
It appears that my dictionaries are messed up somehow because when I call the function, most of the results are nan
Please let me know if you need anything in addition to the below, I barely know how to write code and I certainly don't know how to fix other peoples code so just let me know what other info you may need.
N_SAMPLES = 10000
IEA_LV = fin_data['Yield_IEA'][0]
IEA_STD = fin_data['Yield_IEA'].std()
IBL_LV = fin_data['Cost_IBL'][0]
IBL_STD = fin_data['Cost_IBL'].std()
# Proxy for Default Rate- See Notes
fin_data['NPL'] = fin_data['NPL_to_Loan']*fin_data['Total_Loans']
NPL_LV = fin_data['NPL'][0]
# default rate info (based on past due loans)
default_mean = fin_data['Default_Rate'].mean()
default_std = fin_data['Default_Rate'].std()
LGD = fin_data['LGD'][0]
prior_LGD = fin_data['LGD'][1]
fin_data['non_int_exp_toAssets'] = fin_data['Non_Int_Exp']/fin_data['Total_Loans']
# non-interest expense info
nonint_exp_LV = fin_data['non_int_exp_toAssets'][0]
nonint_exp_STD = fin_data['non_int_exp_toAssets'].std()
#deposit info
dep_LV = fin_data['Deposits'][0]
dep_STD = fin_data['Deposits'].std()
dep_min = fin_data['Deposits'].min()
# Loan growth information
fin_data['loan_g'] = fin_data['Total_Loans']/fin_data['Total_Loans'].shift(-1) - 1
loan_g_min = fin_data['loan_g'].min()
loan_g_avg = fin_data['loan_g'].mean()
loan_g_std = fin_data['loan_g'].std()
w_minus_r = fin_data['Net_Chg_Off'].mean()
def_credit_flow = default_mean*fin_data['Total_Loans'][0]
# Projected NPLs
next_NPL = NPL_LV + def_credit_flow + NPL_LV * (1 - w_minus_r)
#Net adjustment for impaired loans needs LGD updated
adj_imp_loans = def_credit_flow * LGD + NPL_LV * (1-w_minus_r) * (LGD - prior_LGD)
# Reserve for Loan Loss
ALLL = fin_data['ALLL'][0] * fin_data['Total_Loans'][0] + adj_imp_loans - NPL_LV * w_minus_r
# variable: [most recent value or average, standard deviation]
var_stats_dict = 'IEA': [IEA_LV, IEA_STD], 'IBL': [IBL_LV, IBL_STD], 'Default Rate': [default_mean, default_std],
'Non Int Exp': [nonint_exp_LV, nonint_exp_STD],
'GPL': [loan_g_avg, loan_g_std], 'Deposits': [dep_LV, dep_STD]
# var: [[min, max for severe], [min, max for moderate], [min, max for low]]
# severe = 0, moderate = 1, low = 2
severity_dict = 'IEA': [[IEA_LV - 3 * IEA_STD, IEA_LV + 1 * IEA_STD],
[IEA_LV - 2 * IEA_STD, IEA_LV + 2 * IEA_STD],
[IEA_LV - 1 * IEA_STD, IEA_LV + 3 * IEA_STD]],
'IBL': [[IBL_LV - 1 * IBL_STD, IBL_LV + 3 * IBL_STD],
[IBL_LV - 2 * IBL_STD, IBL_LV + 2 * IBL_STD],
[IBL_LV - 3 * IBL_STD, IBL_LV + 1 * IBL_STD]],
'Default Rate': [[default_mean - 1 * default_std,
default_mean + 3 * default_std],
[default_mean - 2 * default_std,
default_mean + 2 * default_std],
[default_mean - 3 * default_std,
default_mean + 1 * default_std]],
'Non Int Exp': [nonint_exp_LV - 1 * nonint_exp_STD,
nonint_exp_LV + 1 * nonint_exp_STD],
'GPL': [[loan_g_min, loan_g_avg],
[loan_g_avg - 1 * loan_g_std, loan_g_avg + 1 * loan_g_std],
[loan_g_avg, loan_g_avg + 2 * loan_g_std]],
'Deposits': [[dep_min, dep_LV],
[dep_LV - 1 * dep_STD, dep_LV + 1 * dep_STD],
[dep_LV, dep_LV + 2 * dep_STD]]
#vars = IEA, IBL, Default Rate, Non Int Exp, GPL, Deposits
def mc_generator(var, severity):
low_cap = severity_dict[var][severity][0]
up_cap = severity_dict[var][severity][1]
mu = var_stats_dict[var][0]
sigma = var_stats_dict[var][1]
container = []
for i in range(0, N_SAMPLES):
x = np.random.normal(loc = mu, scale = sigma)
result = stats.beta.pdf(x, low_cap, up_cap)
container.append(result)
avg_val = sum(container)/N_SAMPLES
return round(avg_val, 10)
print(mc_generator('Non Int Exp', 0))
python
python
asked Mar 8 at 1:46
Frank PaolantonioFrank Paolantonio
62
asked Mar 8 at 1:46
Frank PaolantonioFrank Paolantonio
62
edited Mar 8 at 16:51
Frank Paolantonio
asked Mar 8 at 1:46
Frank PaolantonioFrank Paolantonio
62
asked Mar 8 at 1:46
Frank PaolantonioFrank Paolantonio
62
asked Mar 8 at 1:46
Frank PaolantonioFrank Paolantonio
62
62
Function is returning nan Show us the calling code. All I see here is a function definition that is never actually called.
– John Gordon
Mar 8 at 1:53
1
Could you add an Minimal, Complete, and Verifiable example showing a complete minimal working version of your code? For example, are you simply callingmc_generator()? Answering your question is difficult without knowing the full context of the problem.
– Chris Larson
Mar 8 at 1:58
1
As it stands, the indentation of the first line needs to be corrected, your dictionaries are populated with variables that are not defined in your code, and we have no idea what you are doing to call your function. Without those, it's not really practical for anyone to try to answer.
– Chris Larson
Mar 8 at 2:02
add a comment |
Function is returning nan Show us the calling code. All I see here is a function definition that is never actually called.
– John Gordon
Mar 8 at 1:53
1
Could you add an Minimal, Complete, and Verifiable example showing a complete minimal working version of your code? For example, are you simply callingmc_generator()? Answering your question is difficult without knowing the full context of the problem.
– Chris Larson
Mar 8 at 1:58
1
As it stands, the indentation of the first line needs to be corrected, your dictionaries are populated with variables that are not defined in your code, and we have no idea what you are doing to call your function. Without those, it's not really practical for anyone to try to answer.
– Chris Larson
Mar 8 at 2:02
Function is returning nan Show us the calling code. All I see here is a function definition that is never actually called.
– John Gordon
Mar 8 at 1:53
Function is returning nan Show us the calling code. All I see here is a function definition that is never actually called.
– John Gordon
Mar 8 at 1:53
1
1
Could you add an Minimal, Complete, and Verifiable example showing a complete minimal working version of your code? For example, are you simply calling
mc_generator()? Answering your question is difficult without knowing the full context of the problem.– Chris Larson
Mar 8 at 1:58
Could you add an Minimal, Complete, and Verifiable example showing a complete minimal working version of your code? For example, are you simply calling
mc_generator()? Answering your question is difficult without knowing the full context of the problem.– Chris Larson
Mar 8 at 1:58
1
1
As it stands, the indentation of the first line needs to be corrected, your dictionaries are populated with variables that are not defined in your code, and we have no idea what you are doing to call your function. Without those, it's not really practical for anyone to try to answer.
– Chris Larson
Mar 8 at 2:02
As it stands, the indentation of the first line needs to be corrected, your dictionaries are populated with variables that are not defined in your code, and we have no idea what you are doing to call your function. Without those, it's not really practical for anyone to try to answer.
– Chris Larson
Mar 8 at 2:02
add a comment |
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Function is returning nan Show us the calling code. All I see here is a function definition that is never actually called.
– John Gordon
Mar 8 at 1:53
1
Could you add an Minimal, Complete, and Verifiable example showing a complete minimal working version of your code? For example, are you simply calling
mc_generator()? Answering your question is difficult without knowing the full context of the problem.– Chris Larson
Mar 8 at 1:58
1
As it stands, the indentation of the first line needs to be corrected, your dictionaries are populated with variables that are not defined in your code, and we have no idea what you are doing to call your function. Without those, it's not really practical for anyone to try to answer.
– Chris Larson
Mar 8 at 2:02