In [1]:

```
# HIDDEN
from datascience import *
path_data = '../../../../data/'
```

While there are many kinds of collections in Python, we will work primarily with arrays in this class. We've already seen that the `make_array`

function can be used to create arrays of numbers.

Arrays can also contain strings or other types of values, but a single array can only contain a single kind of data. (It usually doesn't make sense to group together unlike data anyway.) For example:

In [2]:

```
english_parts_of_speech = make_array("noun", "pronoun", "verb", "adverb", "adjective", "conjunction", "preposition", "interjection")
english_parts_of_speech
```

Out[2]:

Returning to the temperature data, we create arrays of average daily high temperatures for the decades surrounding 1850, 1900, 1950, and 2000.

In [3]:

```
baseline_high = 14.48
highs = make_array(baseline_high - 0.880,
baseline_high - 0.093,
baseline_high + 0.105,
baseline_high + 0.684)
highs
```

Out[3]:

Arrays can be used in arithmetic expressions to compute over their contents. When an array is combined with a single number, that number is combined with each element of the array. Therefore, we can convert all of these temperatures to Fahrenheit by writing the familiar conversion formula.

In [4]:

```
(9/5) * highs + 32
```

Out[4]:

Arrays also have *methods*, which are functions that operate on the array values. The `mean`

of a collection of numbers is its average value: the sum divided by the length. Each pair of parentheses in the examples below is part of a call expression; it's calling a function with no arguments to perform a computation on the array called `highs`

.

In [5]:

```
highs.size
```

Out[5]:

In [6]:

```
highs.sum()
```

Out[6]:

In [7]:

```
highs.mean()
```

Out[7]:

The `numpy`

package, abbreviated `np`

in programs, provides Python programmers with convenient and powerful functions for creating and manipulating arrays.

In [8]:

```
import numpy as np
```

For example, the `diff`

function computes the difference between each adjacent pair of elements in an array. The first element of the `diff`

is the second element minus the first.

In [9]:

```
np.diff(highs)
```

Out[9]:

The full Numpy reference lists these functions exhaustively, but only a small subset are used commonly for data processing applications. These are grouped into different packages within `np`

. Learning this vocabulary is an important part of learning the Python language, so refer back to this list often as you work through examples and problems.

However, you **don't need to memorize these**. Use this as a reference.

Each of these functions takes an array as an argument and returns a single value.

Function |
Description |
---|---|

`np.prod` |
Multiply all elements together |

`np.sum` |
Add all elements together |

`np.all` |
Test whether all elements are true values (non-zero numbers are true) |

`np.any` |
Test whether any elements are true values (non-zero numbers are true) |

`np.count_nonzero` |
Count the number of non-zero elements |

Each of these functions takes an array as an argument and returns an array of values.

Function |
Description |
---|---|

`np.diff` |
Difference between adjacent elements |

`np.round` |
Round each number to the nearest integer (whole number) |

`np.cumprod` |
A cumulative product: for each element, multiply all elements so far |

`np.cumsum` |
A cumulative sum: for each element, add all elements so far |

`np.exp` |
Exponentiate each element |

`np.log` |
Take the natural logarithm of each element |

`np.sqrt` |
Take the square root of each element |

`np.sort` |
Sort the elements |

Each of these functions takes an array of strings and returns an array.

Function |
Description |
---|---|

`np.char.lower` |
Lowercase each element |

`np.char.upper` |
Uppercase each element |

`np.char.strip` |
Remove spaces at the beginning or end of each element |

`np.char.isalpha` |
Whether each element is only letters (no numbers or symbols) |

`np.char.isnumeric` |
Whether each element is only numeric (no letters) |

Each of these functions takes both an array of strings and a *search string*; each returns an array.

Function |
Description |
---|---|

`np.char.count` |
Count the number of times a search string appears among the elements of an array |

`np.char.find` |
The position within each element that a search string is found first |

`np.char.rfind` |
The position within each element that a search string is found last |

`np.char.startswith` |
Whether each element starts with the search string |