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Return a string representation of an array.
Input array.
Inserts newlines if text is longer than max_line_width.
Defaults to numpy.get_printoptions()['linewidth'].
Floating point precision.
Defaults to numpy.get_printoptions()['precision'].
Represent numbers very close to zero as zero; default is False.
Very close is defined by precision: if the precision is 8, e.g.,
numbers smaller (in absolute value) than 5e-9 are represented as
zero.
Defaults to numpy.get_printoptions()['suppress'].
Inserted between elements.
The length of the prefix and suffix strings are used to respectively align and wrap the output. An array is typically printed as:
prefix + array2string(a) + suffix
The output is left-padded by the length of the prefix string, and
wrapping is forced at the column max_line_width - len(suffix).
It should be noted that the content of prefix and suffix strings are
not included in the output.
If not None, the keys should indicate the type(s) that the respective formatting function applies to. Callables should return a string. Types that are not specified (by their corresponding keys) are handled by the default formatters. Individual types for which a formatter can be set are:
bool
int
timedelta : a numpy.timedelta64
datetime : a numpy.datetime64
float
longfloat : 128-bit floats
complexfloat
longcomplexfloat : composed of two 128-bit floats
void : type numpy.void
numpystr : types numpy.bytes_ and numpy.str_
Other keys that can be used to set a group of types at once are:
all : sets all types
int_kind : sets int
float_kind : sets float and longfloat
complex_kind : sets complexfloat and longcomplexfloat
str_kind : sets numpystr
Total number of array elements which trigger summarization
rather than full repr.
Defaults to numpy.get_printoptions()['threshold'].
Number of array items in summary at beginning and end of
each dimension.
Defaults to numpy.get_printoptions()['edgeitems'].
Controls printing of the sign of floating-point types. If +, always
print the sign of positive values. If , always prints a space
(whitespace character) in the sign position of positive values. If
-, omit the sign character of positive values.
Defaults to numpy.get_printoptions()['sign'].
Changed in version 2.0: The sign parameter can now be an integer type, previously types were floating-point types.
Controls the interpretation of the precision option for
floating-point types.
Defaults to numpy.get_printoptions()['floatmode'].
Can take the following values:
fixed: Always print exactly precision fractional digits, even if this would print more or fewer digits than necessary to specify the value uniquely.
unique: Print the minimum number of fractional digits necessary to represent each value uniquely. Different elements may have a different number of digits. The value of the precision option is ignored.
maxprec: Print at most precision fractional digits, but if an element can be uniquely represented with fewer digits only print it with that many.
maxprec_equal: Print at most precision fractional digits, but if every element in the array can be uniquely represented with an equal number of fewer digits, use that many digits for all elements.
If set to the string '1.13' enables 1.13 legacy printing mode. This
approximates numpy 1.13 print output by including a space in the sign
position of floats and different behavior for 0d arrays. If set to
False, disables legacy mode. Unrecognized strings will be ignored
with a warning for forward compatibility.
String representation of the array.
if a callable in formatter does not return a string.
See also
Notes
If a formatter is specified for a certain type, the precision keyword is ignored for that type.
This is a very flexible function; array_repr and array_str are using
array2string internally so keywords with the same name should work
identically in all three functions.
Examples
>>> import numpy as np
>>> x = np.array([1e-16,1,2,3])
>>> np.array2string(x, precision=2, separator=',',
... suppress_small=True)
'[0.,1.,2.,3.]'
>>> x = np.arange(3.)
>>> np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x})
'[0.00 1.00 2.00]'
>>> x = np.arange(3)
>>> np.array2string(x, formatter={'int':lambda x: hex(x)})
'[0x0 0x1 0x2]'
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