nq

fcp.nq(**kwargs)

Plot the normal quantiles of a data set.

Parameters

df (DataFrame | numpy array) – DataFrame containing a column of values data or a DataFrame or numpy array containing a set of 2D values that can be used to calculate quantiles at various “sigma” intervals

Keyword Arguments

    BASIC:

  • x (str) – x-axis column name (if using a 1D dataset). Defaults to None. More details

    • CALCULATION:

  • sigma (float) – Maximum sigma value to use for the calculation; range will be +/- this value. Defaults to Auto- calculated based on the dataset using “fcp.utilities.sigma”. More details

  • step_inner (float) – Delta between sigma values outside of the tail (around sigma=0). Defaults to 0.5. More details

  • step_tail (float) – Delta between sigma values in the tails (all value >= and <= to keyword “tail”). Defaults to 0.2. More details

  • tail (float) – Sigma value that represents the start of the tail of the distribution. Defaults to 3. More details

Examples

“Normal” distribution:

>>> import fivecentplots as fcp
>>> from pathlib import Path
>>> import pandas as pd
>>> import numpy as np
>>> # Make a normal distribution from noise
>>> img = np.ones([1000, 1000]) * 2**12 / 2
>>> img += np.random.normal(-0.025*img.mean(), 0.025*img.mean(), img.shape)
>>> fcp.nq(img, marker_size=4, line_width=2)
../_images/example_nq.png