#### Overview of this book

Learning SciPy for Numerical and Scientific Computing Second Edition
Credits
www.PacktPub.com
Preface
Free Chapter
Introduction to SciPy
Working with the NumPy Array As a First Step to SciPy
SciPy for Linear Algebra
SciPy for Numerical Analysis
SciPy for Signal Processing
SciPy for Data Mining
SciPy for Computational Geometry
Interaction with Other Languages
Index

## Object essentials

We have been introduced to NumPy's main object—the homogeneous multidimensional array, also referred to as `ndarray`. All elements of the array are casted to the same datatype (homogeneous). We obtain the datatype by the `dtype` attribute, its dimension by the `shape` attribute, the total number of elements in the array by the `size` attribute, and elements by referring to their positions:

```>>> img.dtype, img.shape, img.size
```

The output is shown as follows:

```(dtype('int64'), (512, 512), 262144)
```

Let's compute the grayscale values now:

```>>> img[32,67]
```

The output is shown as follows:

```87
```

Let's interpret the outputs. The elements of `img` are 64-bit integer values ('int64'). This may vary depending on the system, the Python installation, and the computer specifications. The shape of the array (note it comes as a Python tuple) is 512 x 512, and the number of elements 262144. The grayscale value of the image in the 33rd column and 68th row is `87` (note that in NumPy, as in Python...