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# NumPy | set_printoptions method

schedule Aug 12, 2023
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Numpy's `set_printoptions(~)` method customizes how Numpy arrays are printed.

# Parameters

1. `precision` | `int` or `None` | `optional`

The number of decimal places to show. A precision of 3 would mean `3.1415` becomes `3.142`. If `None` is passed and `floatmode` is not `fixed`, then the precision will be such that they values are uniquely differentiated. By default, `precision=8`.

2. `threshold`link | `int` | `optional`

If the number of values in the array is larger than `threshold`, then instead of printing each and every values, the values will be truncated with `...`. By default, `threshold=1000`.

3. `edgeitems`link | `int` | `optional`

If truncation occurs, then the number of values to show in the front and back. By default, `edgeitems=3`.

4. `linewidth`link | `int` | `optional`

The number of characters per line. By default, `linewidth=75`.

5. `suppress`link | `boolean` | `optional`

Whether to show values in full decimals instead of using scientific notation. This is only applicable for floats whose absolute values are smaller than `1e-4`, or the ratio between the largest value and the smallest value in the array is larger than 1000. By default, `suppress=False`.

6. `nanstr`link | `string` | `optional`

The string to replace any `nan` in the array. By default, the `nanstr="nan"`.

7. `infstr`link | `string` | `optional`

The string to replace any `inf` in the array. By default, `infstr="inf"`.

8. `sign`link | `string` | `optional`

How to handle the sign of the values:

Value

Description

"-"

Omits the +.

"+"

Places a + in front of positive numbers.

" "

Places a single white space in front of positive numbers.

By default, `sign="-"`.

9. `formatter`link | `dict<string,function>` | `optional`

The mapping to apply to different data-types. The dictionary's key-value pair is as follows:

• key: the type you wish to apply a mapping on

• value: a function that takes as input the value with type `key`, and returns a new value.

Here are some of main data-types:

Type

Description

"bool"

Convert booleans.

"int"

Convert integers.

"float"

Convert floats.

"timedelta"

Convert timedeltas.

"datatime"

Convert datetimes.

Here are some special keys that you can provide:

Key

Description

"all"

Convert all data-types.

"float_kind"

Convert "float" and "longfloat"

"str_kind"

Convert "str" and "numpystr"

By default, `formatter=None`.

10. `floatmode`link | `string` | `optional`

How to handle precision for floats:

Value

Description

"fixed"

Always show the specified `precision`. This results in all floats having the same decimal place.

"unique"

Show minimum number of decimal places so as to uniquely identify the values. This ignores the specified `precision`.

"maxprec"

Prints at most the specified `precision`.

"maxprec_equal"

Prints at most the specified `precision`, and also ensures that all floats have the same decimal place.

By default, `floatmode="maxprec_equal"`.

# Return value

None, since this method just prints on the screen.

# Examples

## Basic usage

To show 3 fractional digits:

``` np.set_printoptions(precision=3)np.array([0.000005, 3.1416]) array([5.000e-06, 3.142e+00]) ```

To have unfixed `precision`, pass a `None`:

``` np.set_printoptions(precision=None)np.array([3.14, 3.1416]) array([3.14 , 3.142]) ```

## Specifying threshold

By default, `threshold=1000`, which means that printing arrays that have 1000 values or more will be summarised:

``` print(np.arange(1500)) [ 0 1 2 ... 1497 1498 1499] ```

This means that small arrays will not be summarised:

``` print(np.arange(7)) [0 1 2 3 4 5 6] ```

We can set a threshold so that even these small arrays will be summarised:

``` np.set_printoptions(threshold=3)np.arange(7) array([0, 1, 2, ..., 4, 5, 6]) ```

## Specifying edgeitems

By default, `edgeitems=3`, which means that when values are summarised, 3 values will be shown on the left, and 3 on the right:

``` print(np.arange(1500)) [ 0 1 2 ... 1497 1498 1499] ```

We can customise this by setting our own `edgeitems`:

``` np.set_printoptions(edgeitems=4)print(np.arange(1500)) [ 0 1 2 3 ... 1496 1497 1498 1499] ```

## Specifying linewidth

By default, `linewidth=75`, which means that each printed line can have at most 75 characters:

``` print(np.array([12, 34, 5])) [12 34 5] ```

To print at most only 7 characters per line:

``` np.set_printoptions(linewidth=7)print(np.array([12, 34, 5])) [12 34 5] ```

## Specifying suppress

To show all decimal places for numbers smaller than `1e-4`:

``` np.set_printoptions(suppress=True)print(np.array([1e-5])) [0.00001] ```

The default behaviour of `suppress=False` gives us the following:

``` np.set_printoptions(suppress=False)print(np.array([1e-5])) [1.e-05] ```

## Specifying nanstr

``` np.set_printoptions(nanstr="Missing!")print(np.array([np.NaN, 3.14])) [Missing! 3.14] ```

## Specifying infstr

``` np.set_printoptions(infstr="Infinity!")print(np.array([np.inf, 3.14])) [Infinity! 3.14] ```

## Specifying sign

To show the `+` sign for positive numbers:

``` np.set_printoptions(sign="+")print(np.array([np.inf, 3.14, -2])) [ +inf +3.14 -2. ] ```

To add a `" "` in front of positive numbers:

``` np.set_printoptions(sign=" ")print(np.array([np.inf, 3.14, -2])) [ inf 3.14 -2. ] ```

It's hard to see here, but a single whitespace has been added.

## Specifying formatter

To convert all boolean True to `1` and False to `"-1"`.

``` mapping = { "bool": lambda x: "1" if x else "-1"}np.set_printoptions(formatter=mapping)print(np.array([True, False, True])) [1 -1 1] ```

Here, make sure you return a string in the mapping, otherwise an error will be thrown.

## Specifying floatmode

### fixed

To print floats with the same decimal places (i.e. 8 by default):

``` np.set_printoptions(floatmode="fixed")print(np.array([5.05, 5.05001])) [5.05000000 5.05001000] ```

### unique

To print floats with the minimum number of decimal places so as to uniquely identify the values:

``` np.set_printoptions(floatmode="unique")print(np.array([5.05, 5.05001])) [5.05 5.05001] ```

Note that this ignores the `precision` parameter.

### maxprec

Same as unique, but the floats can have at most `precision`:

``` np.set_printoptions(floatmode="maxprec", precision=4)print(np.array([5.05, 5.052999])) [5.05 5.053] ```

### maxprec_equal

Same as `maxprec`, but the floats will all have the same `precision`:

``` np.set_printoptions(floatmode="maxprec_equal")print(np.array([5.05, 5.05001])) [5.05000 5.05001] ```
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