Updated On : Aug-03,2020 Time Investment : ~10 mins

# Ultimate Guide For Numpy Beginners¶

#### Part 1: "NumPy - Introduction To Numpy For Beginners"

• Introduction To Numpy
• Who Can Benefit From The Numpy Tutorial
• What Is Numpy?
• What Are The Operations Used In NumPy?
• Environment For Numpy
• #### Part 2: "NumPy - Ndarray Object For Beginners"

The most important object defined in NumPy is an N-dimensional array type called ndarray. It describes the collection of items of the same type. Items in the collection can be accessed using a zero-based index. Every item in an ndarray takes the same size of block in the memory. Each element in ndarray is an object of data-type object (called dtype). #### Part 3: "NumPy - Data Types For Beginners"

NumPy numerical types are instances of dtype (data-type) objects, each having unique characteristics. The dtypes are available as np.bool_, np.float32, etc. #### Part 4: "NumPy - Array Attributes For Beginners"

• ndarray.shape
• ndarray.ndim
• numpy.itemsize
• numpy.flags
• #### Part 5: "NumPy - Array Creation Routines For Beginners"

A new ndarray object can be constructed by any of the following array creation routines or using a low-level ndarray constructor. #### Part 6: "NumPy - Array From Existing Data For Beginners"

NumPy provides us the way to create an array by using the existing data. #### Part 7: "NumPy - Array From Numerical Ranges For Beginners"

NumPy offers a lot of array creation routines for different circumstances. arange() is one such function based on numerical ranges. It’s often referred to as np.arange() because np is a widely used abbreviation for NumPy. #### Part 8: "NumPy - Indexing And Slicing For Beginners"

Contents of ndarray object can be accessed and modified by indexing or slicing, just like Python's in-built container objects. Basic slicing is an extension of Python's basic concept of slicing to n dimensions. A Python slice object is constructed by giving start, stop, and step parameters to the built-in slice function. This slice object is passed to the array to extract a part of array. #### Part 9: "Numpy Advance Indexing For Beginners"

There are two types of Advanced Indexing − Integer and Boolean.

• Integer Indexing This mechanism helps in selecting any arbitrary item in an array based on its Ndimensional index. Each integer array represents the number of indexes into that dimension. When the index consists of as many integer arrays as the dimensions of the target ndarray, it becomes straightforward.
• Boolean Array Indexing: advanced indexing is used when the resultant object is meant to be the result of Boolean operations, such as comparison operators.
• #### Part 10: "Numpy Broadcasting For Beginners"

Broadcasting refers to the ability of NumPy to treat arrays of different shapes during arithmetic operations. Arithmetic operations on arrays are usually done on corresponding elements. #### Part 11: "Numpy Learning Iterating Over Array For Beginners"

NumPy package contains an iterator object numpy.nditer. It is an efficient multidimensional iterator object using which it is possible to iterate over an array. Each element of an array is visited using Python’s standard Iterator interface. #### Part 12: "NumPy - Array Manipulation For Beginners"

Several routines are available in NumPy package for manipulation of elements in ndarray object. You would be learning about - Changing Shape, Transpose Operation, Changing Dimensions, Joining Arrays, Splitting Arrays, Adding/Removing Elements. #### Part 13: "Numpy Binary Operators For Beginners"

Learn about the binary operators or the functions for bitwise operations available in NumPy package. #### Part 14: "Mathematical Functions For Beginners"

NumPy contains a large number of various mathematical operations. NumPy provides standard trigonometric functions, functions for arithmetic operations, handling complex numbers, etc. Trigonometric Functions NumPy has standard trigonometric functions which return trigonometric ratios for a given angle in radians. #### Part 15: "NumPy - Arithmetic Operations For Beginners"

Input arrays for performing arithmetic operations such as add(), subtract(), multiply(), and divide() must be either of the same shape or should conform to array broadcasting rules. #### Part 16: "NumPy - Statistical Functions"

NumPy has quite a few useful statistical functions for finding minimum, maximum, percentile standard deviation and variance, etc. from the given elements in the array. #### Part 17: "NumPy - Sort, Search And Counting Functions"

These sorting functions implement different sorting algorithms, each of them characterized by the speed of execution, worst case performance, the workspace required and the stability of algorithms. #### Part 18: "NumPy Byte Swapping For Beginners"

Swap the bytes of the array elements. Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually. #### Part 19: "Numpy Copies And Views For Beginners"

While executing the functions, some of them return a copy of the input array, while some return the view. When the contents are physically stored in another location, it is called Copy. If on the other hand, a different view of the same memory content is provided, we call it as View. #### Part 20: "NumPy - I/O with Numpy For Beginners"

The ndarray objects can be saved to and loaded from the disk files. The IO functions available are −load() and save() functions handle /numPy binary files (with npy extension) loadtxt() and savetxt() functions handle normal text files #### Part 21: "Numpy Matplotlib For Beginners"

Matplotlib is a plotting library for Python. It is used along with NumPy to provide an environment that is an effective open source alternative for MatLab. It can also be used with graphics toolkits like PyQt and wxPython. Dolly Solanki

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