Usage¶

The simplest way to use KnowYourData is to pass it a numpy array:

import numpy as np
from knowyourdata import kyd

# setting x as a numpy array
x = np.random.randn(200)
kyd(x)

| Basic Statistics                                          | Array Structure                   |
|                                                           |                                   |
|    Mean:         Min:  -2.313       -99 CI:  -2.189       | Number of Dimensions:   1         |
|   0.04288         1Q:  -0.6402      -95 CI:  -1.969       | Shape of Dimensions:    (200,)    |
|               Median:   0.009476    -68 CI:  -0.8657      | Array Data Type:        float64   |
|   Std Dev:        3Q:   0.633       +68 CI:   1.041       | Memory Size:            1.7KiB    |
|    0.9815        Max:   3.276       +95 CI:   2.075       |                                   |
|                                     +99 CI:   3.195       | Number of NaN:  0                 |
|                                                           | Number of Inf:  0                 |

or if you are in a jupyter notebook, an HTML version.

The kyd function returns a structure that contains the information extracted from the data array. You can access this information through:

info_x = kyd(x)

# The Third Quartile of x:
print(info_x.thirdquartile)

Returned Parameters¶

Basic Statistics¶

All statistics are calculated on a data array filtered for all non-finite elements.

Mean: The arithmetic mean as determined by numpy.mean:

\[\bar{x} = \frac{1}{N} \sum x\]

Std Dev: The standard deviation as determined by numpy.std. As is customary in numpy:

\[\sigma = \sqrt{\frac{1}{N} \sum (x - \bar{x})^2}\]

Min: The minimum of the data array as determined by numpy.min.
1Q: The first quartile of the data.
Median: The median of the data as determined by numpy.median.
3Q: The third quartile of the data.
Max: The maximum of the data as determined by numpy.max.
-99 CI, +99 CI: The location of the 99% confidence interval.
-95 CI, +95 CI: The location of the 95% confidence interval.
-68 CI, +68 CI: The location of the 68% confidence interval.

Array Structure¶

Number of Dimensions: The number of dimensions of the data array numpy.ndim(x)
Shape of Dimensions: The length of each of dimension as determined by numpy.shape(x)
Array Data Type: The Data Type populating the array.
Memory Size: The total size of the array in memory.
Number of NaN: The number of Not a Number values in the data array
Number of Inf: The number of Infinity values in the data array