Histogram. Args: data_frame (pandas.DataFrame): Data source for the plot. values_column (str): Column of numeric values. color_column (str, optional): Column name to group by on the color dimension. color_order (list, optional)
(
self,
data_frame,
values_column,
color_column=None,
color_order=None,
method="count",
bins="auto",
)
| 672 | # set(inherited_public_methods)) |
| 673 | |
| 674 | def histogram( |
| 675 | self, |
| 676 | data_frame, |
| 677 | values_column, |
| 678 | color_column=None, |
| 679 | color_order=None, |
| 680 | method="count", |
| 681 | bins="auto", |
| 682 | ): |
| 683 | """Histogram. |
| 684 | |
| 685 | Args: |
| 686 | data_frame (pandas.DataFrame): Data source for the plot. |
| 687 | values_column (str): Column of numeric values. |
| 688 | color_column (str, optional): Column name to group by on |
| 689 | the color dimension. |
| 690 | color_order (list, optional): List of values within the |
| 691 | 'color_column' for specific sorting of the colors. |
| 692 | method (str, optional): |
| 693 | - 'count': Result will contain the number of samples at each bin. |
| 694 | - 'density': Result is the value of the probability density |
| 695 | function at each bin. |
| 696 | The PDF is normalized so that the integral over the range is 1. |
| 697 | - 'mass': Result is the value of the probability mass |
| 698 | function at each bin. |
| 699 | The PMF is normalized so that the value is equivalent to |
| 700 | the sample count at each bin divided by the total count. |
| 701 | bins (int or sequence of scalars or str, optional): |
| 702 | If bins is an int, it defines the number of equal-width |
| 703 | bins in the given range. |
| 704 | If bins is a sequence, it defines the bin edges, |
| 705 | including the rightmost edge, allowing for non-uniform |
| 706 | bin widths. See numpy.histogram documentation for more details. |
| 707 | - ‘auto’: |
| 708 | Maximum of the ‘sturges’ and ‘fd’ estimators. |
| 709 | Provides good all around performance. |
| 710 | - ‘fd’ (Freedman Diaconis Estimator) |
| 711 | Robust (resilient to outliers) estimator that takes into |
| 712 | account data variability and data size. |
| 713 | - ‘doane’ |
| 714 | An improved version of Sturges’ estimator that works |
| 715 | better with non-normal datasets. |
| 716 | - ‘scott’ |
| 717 | Less robust estimator that that takes into account data |
| 718 | variability and data size. |
| 719 | - ‘rice’ |
| 720 | Estimator does not take variability into account, only |
| 721 | data size. Commonly overestimates number of bins required. |
| 722 | - ‘sturges’ |
| 723 | R’s default method, only accounts for data size. |
| 724 | Only optimal for gaussian data and underestimates number |
| 725 | of bins for large non-gaussian datasets. |
| 726 | - ‘sqrt’ |
| 727 | Square root (of data size) estimator, used by Excel and |
| 728 | other programs for its speed and simplicity. |
| 729 | """ |
| 730 | vertical = self._chart.axes._vertical |
| 731 |