# NumPy - Array From Numerical Ranges

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In this chapter, we will see how to create an array from numerical ranges.

## numpy.arange

This function returns an ndarray object containing evenly spaced values within a given range. The format of the function is as follows −

```numpy.arange(start, stop, step, dtype)
```

The constructor takes the following parameters.

Sr.No. Parameter & Description
1

start

The start of an interval. If omitted, defaults to 0

2

stop

The end of an interval (not including this number)

3

step

Spacing between values, default is 1

4

dtype

Data type of resulting ndarray. If not given, data type of input is used

The following examples show how you can use this function.

### Example 1

```import numpy as np
x = np.arange(5)
print x
```

Its output would be as follows −

```[0  1  2  3  4]
```

### Example 2

```import numpy as np
# dtype set
x = np.arange(5, dtype = float)
print x
```

Here, the output would be −

```[0.  1.  2.  3.  4.]
```

### Example 3

```# start and stop parameters set
import numpy as np
x = np.arange(10,20,2)
print x
```

Its output is as follows −

```[10  12  14  16  18]
```

## numpy.linspace

This function is similar to arange() function. In this function, instead of step size, the number of evenly spaced values between the interval is specified. The usage of this function is as follows −

```numpy.linspace(start, stop, num, endpoint, retstep, dtype)
```

The constructor takes the following parameters.

Sr.No. Parameter & Description
1

start

The starting value of the sequence

2

stop

The end value of the sequence, included in the sequence if endpoint set to true

3

num

The number of evenly spaced samples to be generated. Default is 50

4

endpoint

True by default, hence the stop value is included in the sequence. If false, it is not included

5

retstep

If true, returns samples and step between the consecutive numbers

6

dtype

Data type of output ndarray

The following examples demonstrate the use linspace function.

### Example 1

```import numpy as np
x = np.linspace(10,20,5)
print x
```

Its output would be −

```[10.   12.5   15.   17.5  20.]
```

### Example 2

```# endpoint set to false
import numpy as np
x = np.linspace(10,20, 5, endpoint = False)
print x
```

The output would be −

```[10.   12.   14.   16.   18.]
```

### Example 3

```# find retstep value
import numpy as np

x = np.linspace(1,2,5, retstep = True)
print x
# retstep here is 0.25
```

Now, the output would be −

```(array([ 1.  ,  1.25,  1.5 ,  1.75,  2.  ]), 0.25)
```

## numpy.logspace

This function returns an ndarray object that contains the numbers that are evenly spaced on a log scale. Start and stop endpoints of the scale are indices of the base, usually 10.

```numpy.logspace(start, stop, num, endpoint, base, dtype)
```

Following parameters determine the output of logspace function.

Sr.No. Parameter & Description
1

start

The starting point of the sequence is basestart

2

stop

The final value of sequence is basestop

3

num

The number of values between the range. Default is 50

4

endpoint

If true, stop is the last value in the range

5

base

Base of log space, default is 10

6

dtype

Data type of output array. If not given, it depends upon other input arguments

The following examples will help you understand the logspace function.

### Example 1

```import numpy as np
# default base is 10
a = np.logspace(1.0, 2.0, num = 10)
print a
```

Its output would be as follows −

```[ 10.           12.91549665     16.68100537      21.5443469  27.82559402
35.93813664   46.41588834     59.94842503      77.42636827    100.    ]
```

### Example 2

```# set base of log space to 2
import numpy as np
a = np.logspace(1,10,num = 10, base = 2)
print a
```

Now, the output would be −

```[ 2.     4.     8.    16.    32.    64.   128.   256.    512.   1024.]
```
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