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Slicing | Indexing and Slicing
Ultimate NumPy

SlicingSlicing

Slicing in Python refers to retrieving elements from one index to another within a sequence. In this chapter, however, we will focus on slicing in NumPy arrays.

Slicing in 1D Arrays

The general syntax for slicing in 1D arrays is as follows: array[start:end:step]. Let’s take a more detailed look at it:

  • start is the index at which to start slicing;
  • end is the index at which slicing ends (the index itself is not included);
  • step specifies the increments between the indices (default is 1).

Here is an example to clarify everything (purple squares represent the elements retrieved from slicing):

1D slicing example

Note

Since we did not explicitly specify step, it defaults to a value of 1.

Here is the code for this example:

Omitting Start, End, and Step

As you can see, we can often omit the start, end, step, or even all of them at the same time. For example, step can be omitted when we want it to be equal to 1. start and end can be omitted in the following scenarios:

  1. Omitting start:
    • Slicing from the first element (step is positive);
    • Slicing from the last element (step is negative).
  2. Omitting end:
    • Slicing to the last element inclusive (step is positive);
    • Slicing to the first element inclusive (step is negative).

Let's take a look at a few more examples (the black arrow indicates that the elements are taken in reverse order):

1D slicing example

Here is the corresponding code:

Завдання

You are analyzing the daily sales data of a small retail store. The sales for the past week are stored in a NumPy array, with each element representing the sales for a specific day. Create a slice of weekly_sales containing the sales data for every second day, starting from the second day (Tuesday), and store it in alternate_day_sales (use a positive index for start and do not specify end).

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Ultimate NumPy

SlicingSlicing

Slicing in Python refers to retrieving elements from one index to another within a sequence. In this chapter, however, we will focus on slicing in NumPy arrays.

Slicing in 1D Arrays

The general syntax for slicing in 1D arrays is as follows: array[start:end:step]. Let’s take a more detailed look at it:

  • start is the index at which to start slicing;
  • end is the index at which slicing ends (the index itself is not included);
  • step specifies the increments between the indices (default is 1).

Here is an example to clarify everything (purple squares represent the elements retrieved from slicing):

1D slicing example

Note

Since we did not explicitly specify step, it defaults to a value of 1.

Here is the code for this example:

Omitting Start, End, and Step

As you can see, we can often omit the start, end, step, or even all of them at the same time. For example, step can be omitted when we want it to be equal to 1. start and end can be omitted in the following scenarios:

  1. Omitting start:
    • Slicing from the first element (step is positive);
    • Slicing from the last element (step is negative).
  2. Omitting end:
    • Slicing to the last element inclusive (step is positive);
    • Slicing to the first element inclusive (step is negative).

Let's take a look at a few more examples (the black arrow indicates that the elements are taken in reverse order):

1D slicing example

Here is the corresponding code:

Завдання

You are analyzing the daily sales data of a small retail store. The sales for the past week are stored in a NumPy array, with each element representing the sales for a specific day. Create a slice of weekly_sales containing the sales data for every second day, starting from the second day (Tuesday), and store it in alternate_day_sales (use a positive index for start and do not specify end).

Все було зрозуміло?

Секція 2. Розділ 3
toggle bottom row
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