# [Solved] OverflowError: long int too large to convert to float in python

Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error OverflowError: long int too large to convert to float in python in python. So Here I am Explain to you all the possible solutions here.

## How OverflowError: long int too large to convert to float in python Error Occurs?

Today I get the following error OverflowError: long int too large to convert to float in python in python.

## How To SolveOverflowError: long int too large to convert to float in python Error ?

1. How To SolveOverflowError: long int too large to convert to float in python Error ?

To SolveOverflowError: long int too large to convert to float in python Error Another approach when dealing with very large numbers is to work in the log scale.

2. OverflowError: long int too large to convert to float in python

To SolveOverflowError: long int too large to convert to float in python Error Another approach when dealing with very large numbers is to work in the log scale.

## Solution 1

Factorials get large real fast:

```>>> math.factorial(170)
7257415615307998967396728211129263114716991681296451376543577798900561843401706157852350749242617459511490991237838520776666022565442753025328900773207510902400430280058295603966612599658257104398558294257568966313439612262571094946806711205568880457193340212661452800000000000000000000000000000000000000000L
```

Note the `L`; the factorial of 170 is still convertable to a float:

```>>> float(math.factorial(170))
7.257415615307999e+306
```

but the next factorial is too large:

```>>> float(math.factorial(171))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
OverflowError: long int too large to convert to float
```

You could use the `decimal` module; calculations will be slower, but the `Decimal()` class can handle factorials this size:

```>>> from decimal import Decimal
>>> Decimal(math.factorial(171))
Decimal('1241018070217667823424840524103103992616605577501693185388951803611996075221691752992751978120487585576464959501670387052809889858690710767331242032218484364310473577889968548278290754541561964852153468318044293239598173696899657235903947616152278558180061176365108428800000000000000000000000000000000000000000')
```

You’ll have to use `Decimal()` values throughout:

```from decimal import *

with localcontext() as ctx:
ctx.prec = 32  # desired precision
p = ctx.power(3, idx)
depart = ctx.exp(-3) * p
depart /= math.factorial(idx)```

## Solution 2

When `idx` gets large either the `math.pow` and/or the `math.factorial` will become insanely large and be unable to convert to a floating value (`idx=1000` triggers the error on my 64 bit machine). You’ll want to not use the math.pow function as it overflows earlier than the built in `**` operator because it tries to keep higher precision by float converting earlier. Additionally, you can wrap each function call in a `Decimal` object for higher precision.

Another approach when dealing with very large numbers is to work in the log scale. Take the log of every value (or calculate the log version of each value) and perform all required operations before taking the exponentiation of the results. This allows for your values to temporary leave the floating domain space while still accurately computing a final answer that lies within floating domain.

```3 ** idx  =>  math.log(3) * idx
math.exp(-3) * p  =>  -3 + math.log(p)
math.factorial(idx)  =>  sum(math.log(ii) for ii in range(1, idx + 1))
...
math.exp(result)
```

This stays in the log domain until the very end so your numbers can get very, very large before you’ll hit overflow problems.

## Summery

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.