Open In App

# Describe a NumPy Array in Python

NumPy is a Python library used for numerical computing. It offers robust multidimensional arrays as a Python object along with a variety of mathematical functions. In this article, we will go through all the essential NumPy functions used in the descriptive analysis of an array. Let’s start by initializing a sample array for our analysis.

The following code initializes a NumPy array:

## Python3

 `import` `numpy as np`  `# sample array``arr ``=` `np.array([``4``, ``5``, ``8``, ``5``, ``6``, ``4``,``                ``9``, ``2``, ``4``, ``3``, ``6``])``print``(arr)`

Output:

`[4 5 8 5 6 4 9 2 4 3 6]`

In order to describe our NumPy array, we need to find two types of statistics:

• Measures of central tendency.
• Measures of dispersion.

## Measures of central tendency

The following methods are used to find measures of central tendency in NumPy:

• mean()- takes a NumPy array as an argument and returns the arithmetic mean of the data.
`np.mean(arr)`
• median()- takes a NumPy array as an argument and returns the median of the data.
` np.median(arr)`

The following example illustrates the usage of the mean() and median() methods.

Example:

## Python3

 `import` `numpy as np`  `arr ``=` `np.array([``4``, ``5``, ``8``, ``5``, ``6``, ``4``,``                ``9``, ``2``, ``4``, ``3``, ``6``])   ` `# measures of central tendency``mean ``=` `np.mean(arr)``median ``=` `np.median(arr)` `print``(``"Array ="``, arr)``print``(``"Mean ="``, mean)``print``(``"Median ="``, median)`

Output:

```Array = [4 5 8 5 6 4 9 2 4 3 6]
Mean = 5.09090909091
Median = 5.0```

## Measures of dispersion

The following methods are used to find measures of dispersion in NumPy:

• amin()- it takes a NumPy array as an argument and returns the minimum.
`np.amin(arr)`
• amax()- it takes a NumPy array as an argument and returns maximum.
`np.amax(arr)`
• ptp()- it takes a NumPy array as an argument and returns the range of the data.
`np.ptp(arr)`
• var()- it takes a NumPy array as an argument and returns the variance of the data.
`np.var(arr)`
• std()- it takes a NumPy array as an argument and returns the standard variation of the data.
`np.std(arr)`

Example: The following code illustrates amin(), amax(), ptp(), var() and std() methods.

## Python3

 `import` `numpy as np`  `arr ``=` `np.array([``4``, ``5``, ``8``, ``5``, ``6``, ``4``,``                ``9``, ``2``, ``4``, ``3``, ``6``])` `# measures of dispersion``min` `=` `np.amin(arr)``max` `=` `np.amax(arr)``range` `=` `np.ptp(arr)``variance ``=` `np.var(arr)``sd ``=` `np.std(arr)` `print``(``"Array ="``, arr)``print``(``"Measures of Dispersion"``)``print``(``"Minimum ="``, ``min``)``print``(``"Maximum ="``, ``max``)``print``(``"Range ="``, ``range``)``print``(``"Variance ="``, variance)``print``(``"Standard Deviation ="``, sd)`

Output:

```Array = [4 5 8 5 6 4 9 2 4 3 6]
Measures of Dispersion
Minimum = 2
Maximum = 9
Range = 7
Variance = 3.90082644628
Standard Deviation = 1.9750509984```

Example: Now we can combine the above-mentioned examples to get a complete descriptive analysis of our array.

## Python3

 `import` `numpy as np `  `arr ``=` `np.array([``4``, ``5``, ``8``, ``5``, ``6``, ``4``,``                ``9``, ``2``, ``4``, ``3``, ``6``])   ` `# measures of central tendency``mean ``=` `np.mean(arr)``median ``=` `np.median(arr)` `# measures of dispersion``min` `=` `np.amin(arr)``max` `=` `np.amax(arr)``range` `=` `np.ptp(arr)``variance ``=` `np.var(arr)``sd ``=` `np.std(arr)` `print``(``"Descriptive analysis"``)``print``(``"Array ="``, arr)``print``(``"Measures of Central Tendency"``)``print``(``"Mean ="``, mean)``print``(``"Median ="``, median)``print``(``"Measures of Dispersion"``)``print``(``"Minimum ="``, ``min``)``print``(``"Maximum ="``, ``max``)``print``(``"Range ="``, ``range``)``print``(``"Variance ="``, variance)``print``(``"Standard Deviation ="``, sd)`

Output:

```Descriptive analysis
Array = [4 5 8 5 6 4 9 2 4 3 6]
Measurements of Central Tendency
Mean = 5.09090909091
Median = 5.0
Minimum = 2
Maximum = 9
Range = 7
Variance = 3.90082644628
Standard Deviation = 1.9750509984```