NumPy | array2string method
Start your free 7-days trial now!
Numpy's array2string(~) method returns a string representation of your Numpy array formatted as specified.
This method defaults to using the configuration specified via Numpy's get_printoptions(~). You can change the default configuration using set_printoptions(~).
Parameters
1. a | array-like
The input array. Unlike most Numpy methods, a has to be a Numpy array.
2. max_line_widthlink | int | optional
The maximum number of characters per line. By default, max_line_width=75, unless overridden via set_printoptions(~).
3. precisionlink | 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 the values are uniquely differentiated. By default, precision=8 unless overridden.
4. suppress_smalllink | 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_small=False unless overridden.
5. separatorlink | string | optional
The string to separate the values. By default, separator=" " (i.e. a single whitespace).
6. prefixlink | string | optional
See the explanation of the suffix parameter below.
7. suffixlink | string | optional
Parameters prefix and suffix are used to add padding to each line. Each line can only hold a maximum of max_line_width-len(prefix)-len(suffix) characters. Note that the length of prefix and suffix matters, not their actual value since they are not printed out.
8. formatterlink | 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 unless overridden.
9. thresholdlink | int | optional
If the number of values in the array is larger than threshold, then instead of obtaining each and every values, the values will be truncated with .... By default, threshold=1000 unless overridden.
10. edgeitemslink | int | optional
If truncation occurs, then the number of values to show in the front and back. By default, edgeitems=3 unless overridden.
11. signlink | 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="-" unless overridden.
10. floatmodelink | string | optional
How to handle precision for floats:
Value | Description |
|---|---|
"fixed" | Always show the specified |
"unique" | Show minimum number of decimal places so as to uniquely identify the values. This ignores the specified |
"maxprec" | Prints at most the specified |
"maxprec_equal" | Prints at most the specified |
By default, floatmode="maxprec_equal" unless overridden.
Return Value
None, since this method just prints on the screen.
Examples
Basic usage
To show 3 fractional digits:
a = np.array([0.000005, 3.1416])np.arraystring(a, precision=3)
'[5.000e-06 3.142e+00]'
To have unfixed precision, pass a None:
a = np.array([3.14, 3.1416])np.arraystring(a, precision=None)
'[3.14 3.1416]'
Specifying max_line_width
By default, max_line_width=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:
a = np.array([12, 34, 5])np.arraystring(a, max_line_width=7)
'[12 34\n 5]'
This is what you see when you actually print the returned string:
print(np.arraystring(a, max_line_width=7))
[12 34 5]
Specifying suppress_small
To show all decimal places for numbers smaller than 1e-4:
a = np.array([1e-5])np.arraystring(a, suppress_small=True)
'[0.00001]'
The default behavior of suppress=False gives us the following:
a = np.array([1e-5])np.arraystring(a)
'[1.e-05]'
Specifying separator
To override the default delimiter of a single whitespace, pass in the separator parameter:
a = np.array([3,4,5])np.arraystring(a, separator="A")
'[3A4A5]'
Specifying suppress
To show all decimal places for numbers smaller than 1e-4:
a = np.array([1e-5])np.arraystring(a, suppress_small=True)
'[0.00001]'
The default behaviour of suppress=False gives us the following:
a = np.array([1e-5])np.arraystring(a)
'[1.e-05]'
Specifying prefix and suffix
The length of prefix and suffix is used to add padding to each line:
a = np.arange(5)np.arraystring(a, max_line_width=15, prefix="BB", suffix="AAA")
'[0 1 2 3\n 4]'
Here, each line is formatted as follows:
prefix + array2string(a) + suffix
Each line can only hold a maximum of 15-len(prefix)-len(suffix)=10 characters. The actual content of prefix and suffix does not matter at all since they are not printed out - only their lengths matter.
Specifying formatter
To convert all boolean True to 1 and False to "-1".
mapping = { "bool": lambda x: "1" if x else "-1"}
a = np.array([True, False, True])np.arraystring(a, formatter=mapping)
[1 -1 1]
Here, make sure you return a string in the mapping, otherwise an error will be thrown.
Specifying threshold
By default, threshold=1000, which means that arrays that have 1000 values or more will be summarised:
a = np.arange(1500)np.arraystring(a)
[ 0 1 2 ... 1497 1498 1499]
This means that small arrays will not be summarised:
a = np.arange(7)np.arraystring(a)
[0 1 2 3 4 5 6]
We can set a threshold so that even these small arrays will be summarised:
a = np.arange(7)np.arraystring(a,threshold=3)
'[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:
a = np.arange(1500)np.arraystring(a)
'[ 0 1 2 ... 1497 1498 1499]'
We can customise this by setting our own edgeitems:
a = np.arange(1500)np.arraystring(a, edgeitems=4)
'[ 0 1 2 3 ... 1496 1497 1498 1499]'
Specifying sign
To show the + sign for positive numbers:
a = np.array([np.inf, 3.14, -2])np.arraystring(a, sign="+")
'[ +inf +3.14 -2. ]'
To add a " " in front of positive numbers:
a = np.array([np.inf, 3.14, -2])np.arraystring(a, sign=" ")
'[ inf 3.14 -2. ]'
It's hard to see here, but a single whitespace has been added.
Specifying floatmode
fixed
To obtain floats with the same decimal places (i.e. 8 by default):
a = np.array([5.05, 5.05001])np.arraystring(a, floatmode="fixed")
'[5.05000000 5.05001000]'
unique
To obtain floats with the minimum number of decimal places so as to uniquely identify the values:
a = np.array([5.05, 5.05001])np.arraystring(a, floatmode="unique")
'[5.05 5.05001]'
Note that this ignores the precision parameter.
maxprec
Same as unique, but the floats can have at most precision:
a = np.array([5.05, 5.052999])np.arraystring(a, floatmode="maxprec", precision=4)
'[5.05 5.053]'
maxprec_equal
Same as maxprec, but the floats will all have the same precision:
a = np.array([5.05, 5.05001])np.arraystring(a, floatmode="maxprec_equal")
'[5.05000 5.05001]'
