# numpy.arange() in Python

arange([start,] stop[, step,][, dtype]) : Returns an array with evenly spaced elements as per the interval. The interval mentioned is half opened i.e. [Start, Stop)
Parameters :

```start : [optional] start of interval range. By default start = 0
stop  : end of interval range
step  : [optional] step size of interval. By default step size = 1,
For any output out, this is the distance between two adjacent values, out[i+1] - out[i].
dtype : type of output array
```

Return:

```Array of evenly spaced values.
Length of array being generated  = Ceil((Stop - Start) / Step)```

 `# Python Programming illustrating ` `# numpy.arange method ` ` `  `import` `numpy as geek ` ` `  `print``(``"A\n"``, geek.arange(``4``).reshape(``2``, ``2``), ``"\n"``) ` ` `  `print``(``"A\n"``, geek.arange(``4``, ``10``), ``"\n"``) ` ` `  `print``(``"A\n"``, geek.arange(``4``, ``20``, ``3``), ``"\n"``) `

Output :

```A
[[0 1]
[2 3]]

A
[4 5 6 7 8 9]

A
[ 4  7 10 13 16 19]

```

Note 1:
These NumPy-Python programs won’t run on onlineID, so run them on your systems to explore them.

Note 2:
The advantage of numpy.arange() over the normal in-built range() function is that it allows us to generate sequences of numbers that are not integers. For example

 `# Python Programming illustrating ` `# numpy.arange method ` ` `  `import` `numpy as np ` ` `  `# Printing all numbers from 1 to 2 in steps of 0.1 ` `print``(np.arange(``1``, ``2``, ``0.1``)) `

Output:

```[1.  1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9]
```

If you try it with the range() function, you get a TypeError.

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