numpy-financial | Standalone package of the NumPy financial functions | Cloud Functions library
kandi X-RAY | numpy-financial Summary
kandi X-RAY | numpy-financial Summary
The numpy-financial package contains a collection of elementary financial functions. The financial functions in NumPy are deprecated and eventually will be removed from NumPy; see NEP-32 for more information. This package is the replacement for the original NumPy financial functions. The source code for this package is available at The importable name of the package is numpy_financial. The recommended alias is npf. For example,.
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Top functions reviewed by kandi - BETA
- Calculate PMT
- Computes the discounted pmt
- Calculate the fv for a given frequency
- Compute the PMT
- Evaluate rbl function
- Return the value of a given value
- Calculate pv
- Convert to numeric
- Calculate the rate rate for a given function
- Evaluate the g_divide function
- Compute the mirr correlation coefficient
- Calculate an numpy array of values
- Calculate the total number of payments per day
- Get the version number
- Generate RST files
numpy-financial Key Features
numpy-financial Examples and Code Snippets
Lines of Code : 7License : Strong Copyleft (CC BY-SA 4.0)
Copy>>> from numpy_financial import irr
>>> r = irr([-5000,-10000,0,0,-6000, 50000])
>>> 5000*(1+r)**5 + 10000*(1+r)**4 + 6000*(1+r)
49999.99999999977
>>> r
0.2689942559381253
>>> npf.pmt(0.04/12, 12*5, 0, 1000000, when='end')
-15083.18872193264
df = pd.DataFrame.from_dict({'nper': {0: 56}, 'pmt': {0: 281}, 'pv': {0: -22057}, 'fv': {0: 9365}})
np.rate(df.nper, df.pmt, df.pv, df.fv, when=1, guess=0.01, tol=1e-06, maxiter=100)
df['rate'] = np.rate(df.nper, dnpf.npv(0.06, [0] + [4000 for i in range(8)])
interest_rate = 4.84 / 100 / 12
loan_duration_months = 60
payment_delayed_months = 6
loan_amount = 10000
delayed_period_interest_amount = interest_rate * (payment_delayed_months - 1) * loan_amount
print(delayed_period_interest_amount)
pip uninstall tb-nightly tensorboard tensorflow-estimator tensorflow-gpu tf-estimator-nightly
pip install tensorflow # or `tensorflow-gpu`, or `tf-nightly`, ...
import pkg_resources
for entry_point in pkg_resourprint(df)
contribution monthly_return
2020-01-01 91.91 NaN
2020-02-01 102.18 0.037026
2020-03-01 95.90 -0.012792
2020-04-01 117.89 -0.009188
2020-05-01 100.44Community Discussions
Trending Discussions on numpy-financial
QUESTION
I am running into a roadblock and would appreciate some help on this.
Problem Statement:
I am trying to calculate XIRR for a cash flow over 30 years in Python.
What have I tried so far:
However, none of the established libraries(like numpy and pandas) seem to have support for this. After doing some research, I learned through this source (https://vindeep.com/Corporate/XIRRCalculation.aspx) that with some simple manipulation, XIRR can be calculated from IRR.
So, all I need is an IRR function that is implemented well. The functionality used to exist in numpy but has moved to this other package (https://github.com/numpy/numpy-financial). While, this package works, it is very very slow. Here is a small test:
...ANSWER
Answered 2021-Jun-18 at 03:48Taking a look at the implementation on their GitHub, it is pretty evident to me that the npf.irr() function is implemented pretty well. Your alternative seems to be to implement the function yourself using NumPy operations but I am doubtful that a) that is easy to accomplish or b) possible to accomplish in pure Python.
NumPy Financial seems to be doing their implementation using eigenvalues which means they are performing complex mathematic operations. Perhaps, if you are not bounded to Python, consider Microsoft's C# implementation of IRR and see if that works faster. I suspect that they are using regression to calculate the IRR. Therefore, based on your guess, it may indeed be quicker than NumPy Financial.
Your final alternative is to continue with what you have at the moment and just run on a more powerful machine. On my machine, this operation took about 71 seconds and it is does not even have a GPU. I am sure more powerful computers, with parallelization, should be able to compute this much much faster than that.
QUESTION
There are many ways to compute the mortgage monthly payment, this can be done using numpy-financial package as follow:
with an interest rate of 4.84, and an amount of 5000 over 60 months duration
...ANSWER
Answered 2020-Sep-23 at 01:54The solution to the mentioned problem is simple
First we need to compute the amount of interest of our loan that adds up to the loan amount, let's say we borrowed 10000$ with an interest rate of 5%, the accumulated interest of our loan over 6 months (meaning we didn't make a payment during this 6 months)
QUESTION
I am using cloud storage with App Engine Flex. Out of the blue i start getting this error message after deploy succeeds
The error is happening from these lines in my flask app.
...ANSWER
Answered 2020-Aug-06 at 16:17This is due to https://github.com/googleapis/google-cloud-python/issues/10471.
I'd recommend upgrading google-cloud-core and google-api-core to the latest versions with the bugfix.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install numpy-financial
You can use numpy-financial like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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