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Fixed axis labels being cut off in interactive histograms #617

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Fixed axis labels being cut off in interactive histograms #617

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107 changes: 52 additions & 55 deletions datascience/tables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5349,15 +5349,14 @@ def hist(self, *columns, overlay=True, bins=None, bin_column=None, unit=None, co
>>> t.hist('value', group='category') # doctest: +SKIP
<two overlaid histograms of the data [1, 2, 3] and [2, 5]>
"""

# Matplotlib has deprecated the normed keyword.
# TODO consider changing this function to use density= instead too
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What is the justification for removing this TODO?

if 'normed' not in vargs and 'density' not in vargs:
vargs['density'] = True
elif 'normed' in vargs and 'density' not in vargs:
vargs['density'] = vargs.pop('normed')
elif 'normed' in vargs and 'density' in vargs:
raise ValueError("You can't specify both normed and density. "
"Use one or the other.")
raise ValueError("You can't specify both normed and density. Use one or the other.")

global _INTERACTIVE_PLOTS
if _INTERACTIVE_PLOTS:
Expand All @@ -5366,18 +5365,18 @@ def hist(self, *columns, overlay=True, bins=None, bin_column=None, unit=None, co

return self.ihist(
*columns,
overlay = overlay,
bins = bins,
bin_column = bin_column,
unit = unit,
counts = counts,
group = group,
side_by_side = side_by_side,
left_end = left_end,
right_end = right_end,
width = width,
height = height,
rug = rug,
overlay=overlay,
bins=bins,
bin_column=bin_column,
unit=unit,
counts=counts,
group=group,
side_by_side=side_by_side,
left_end=left_end,
right_end=right_end,
width=width,
height=height,
rug=rug,
**vargs
)

Expand All @@ -5389,7 +5388,7 @@ def hist(self, *columns, overlay=True, bins=None, bin_column=None, unit=None, co

if counts is not None and bin_column is None:
warnings.warn("counts arg of hist is deprecated; use bin_column")
bin_column=counts
bin_column = counts
if columns:
columns_included = list(columns)
if bin_column is not None:
Expand All @@ -5399,19 +5398,15 @@ def hist(self, *columns, overlay=True, bins=None, bin_column=None, unit=None, co
self = self.select(*columns_included)
if group is not None:
if bin_column is not None:
raise ValueError("Using bin_column and group together is "
"currently unsupported.")
raise ValueError("Using bin_column and group together is currently unsupported.")
if len(columns) > 1:
raise ValueError("Using group with multiple histogram value "
"columns is currently unsupported.")
raise ValueError("Using group with multiple histogram value columns is currently unsupported.")

# Check for non-numerical values and raise a ValueError if any found
for col in self:
if col != group and any(isinstance(cell, np.flexible) for cell in self[col]):
raise ValueError("The column '{0}' contains non-numerical "
"values. A histogram cannot be drawn for this table."
.format(col))

raise ValueError("The column '{0}' contains non-numerical values. A histogram cannot be drawn for this table."
.format(col))

if bin_column is not None and bins is None:
bins = np.unique(self.column(bin_column))
Expand All @@ -5431,20 +5426,19 @@ def prepare_hist_with_group(group):
grouped = self.group(group, np.array)
if grouped.num_rows > 20:
warnings.warn("It looks like you're making a grouped histogram with "
"a lot of groups ({:d}), which is probably incorrect."
.format(grouped.num_rows))
"a lot of groups ({:d}), which is probably incorrect."
.format(grouped.num_rows))
return [("{}={}".format(group, k), (v[0][1],)) for k, v in grouped.index_by(group).items()]

# Populate values_dict: An ordered dict from column name to singleton
# tuple of array of values or a (values, weights) pair of arrays. If
# any values have weights, they all must have weights.
# Populate values_dict: An ordered dict from column name to data arrays
if bin_column is not None:
values_dict = prepare_hist_with_bin_column(bin_column)
elif group is not None:
values_dict = prepare_hist_with_group(group)
else:
values_dict = [(k, (self.column(k),)) for k in self.labels]
values_dict = collections.OrderedDict(values_dict)

if left_end is not None or right_end is not None:
if left_end is None:
if bins is not None and bins[0]:
Expand All @@ -5458,26 +5452,23 @@ def prepare_hist_with_group(group):
right_end = max([max(self.column(k)) for k in self.labels if np.issubdtype(self.column(k).dtype, np.number)])

def draw_hist(values_dict):
# Check if np.printoptions is set to legacy. Throw UserWarning if not
if np.get_printoptions()['legacy'] != '1.13':
warnings.warn("We've detected you're not using the '1.13' legacy setting for `np.printoptions`. "
"This may cause excessive error terms in your plots. We recommend solving this by running the "
"following code: `np.set_printoptions(legacy='1.13')`", UserWarning)
# This code is factored as a function for clarity only.
warnings.warn("We've detected you're not using the '1.13' legacy setting for `np.printoptions`. "
"This may cause excessive error terms in your plots. We recommend solving this by running the "
"following code: `np.set_printoptions(legacy='1.13')`", UserWarning)
n = len(values_dict)
colors = [rgb_color + (self.default_alpha,) for rgb_color in
itertools.islice(itertools.cycle(self.chart_colors), n)]
itertools.islice(itertools.cycle(self.chart_colors), n)]
hist_names = list(values_dict.keys())
values = [v[0] for v in values_dict.values()]
weights = [v[1] for v in values_dict.values() if len(v) > 1]
if n > len(weights) > 0:
raise ValueError("Weights were provided for some columns, but not "
" all, and that's not supported.")
raise ValueError("Weights were provided for some columns, but not all, and that's not supported.")
if rug and overlay and n > 1:
warnings.warn("Cannot plot overlaid rug plots; rug=True ignored", UserWarning)
if vargs['density']:
y_label = 'Percent per ' + (unit if unit else 'unit')
percentage = plt.FuncFormatter(lambda x, _: "{:g}".format(100*x))
percentage = plt.FuncFormatter(lambda x, _: "{:g}".format(100 * x))
else:
y_label = 'Count'

Expand All @@ -5490,32 +5481,31 @@ def draw_hist(values_dict):
vargs['weights'] = weights
if not side_by_side:
vargs.setdefault('histtype', 'stepfilled')
figure = plt.figure(figsize=(width, height))
plt.hist(values, color=colors, **vargs)
# if rug:
# plt.scatter(values, np.zeros_like(values), marker="|", color=colors)
axis = figure.get_axes()[0]
_vertical_x(axis)
axis.set_ylabel(y_label)
# Added to ensure proper default dimensions
plt.figure(figsize=(width, height))
plt.hist(values, color=colors, label=hist_names, **vargs)
plt.legend(loc=2, bbox_to_anchor=(1.05, 1))
_vertical_x(plt.gca())
plt.ylabel(y_label)
if vargs['density']:
axis.yaxis.set_major_formatter(percentage)
plt.gca().yaxis.set_major_formatter(percentage)
x_unit = ' (' + unit + ')' if unit else ''
if group is not None and len(self.labels) == 2:
#There's a grouping in place but we're only plotting one column's values
# There's a grouping in place but we're only plotting one column's values
label_not_grouped = [l for l in self.labels if l != group][0]
axis.set_xlabel(label_not_grouped + x_unit, fontsize=16)
plt.xlabel(label_not_grouped + x_unit, fontsize=16)
else:
axis.set_xlabel(x_unit, fontsize=16)
plt.legend(hist_names, loc=2, bbox_to_anchor=(1.05, 1))
type(self).plots.append(axis)
plt.xlabel(x_unit, fontsize=16)
type(self).plots.append(plt.gca())
else:
_, axes = plt.subplots(n, 1, figsize=(width, height * n))
num_plots = n
fig, axes = plt.subplots(num_plots, 1, figsize=(width, height * num_plots))
if 'bins' in vargs:
bins = vargs['bins']
if isinstance(bins, numbers.Integral) and bins > 76 or hasattr(bins, '__len__') and len(bins) > 76:
# Use stepfilled when there are too many bins
vargs.setdefault('histtype', 'stepfilled')
if n == 1:
if num_plots == 1:
axes = [axes]
for i, (axis, hist_name, values_for_hist, color) in enumerate(zip(axes, hist_names, values, colors)):
axis.set_ylabel(y_label)
Expand All @@ -5529,14 +5519,21 @@ def draw_hist(values_dict):
if left_end is not None and right_end is not None:
x_shade, height_shade, width_shade = _compute_shading(heights, bins.copy(), left_end, right_end)
axis.bar(x_shade, height_shade, width=width_shade,
color=self.chart_colors[1], align="edge")
color=self.chart_colors[1], align="edge")
_vertical_x(axis)
if rug:
axis.scatter(values_for_hist, np.zeros_like(values_for_hist), marker="|",
color="black", s=100, zorder=10)
color="black", s=100, zorder=10)
type(self).plots.append(axis)

# Added to ensure proaper axis label dimensions
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proaper -> proper

plt.tight_layout()

draw_hist(values_dict)
# Added to make sure graph displays
plt.show()



def hist_of_counts(self, *columns, overlay=True, bins=None, bin_column=None,
group=None, side_by_side=False, width=None, height=None, **vargs):
Expand Down