# Testing for near equality with “allclose”#

When the computer calculates a floating point value, there will often be some degree of error in the calculation, because the computer floating point format cannot represent every floating point number exactly. See:

When we check the results of a floating point calculation, we often want to
avoid checking if the returned value is exactly equal to a desired value.
Rather, we want to check whether the returned value is close enough, given the
usual floating point error. A common idiom in NumPy is to use the
`np.allclose`

function, which checks whether two values or two arrays are equal, within a
small amount of error:

```
import numpy as np
```

```
np.pi == 3.1415926
```

```
False
```

```
# pi to 7 decimal places not exactly equal to pi
np.allclose(np.pi, 3.1415926)
```

```
True
```

```
# pi to 7 dp is "close" to pi
np.allclose([np.pi, 2 * np.pi], [3.1415926, 6.2831852])
```

```
True
```

See the docstring for
`np.allclose`

for details of what “close” means.