def check_box_summary(psdf, pdf):
k = 1.5
stats, fences = BoxPlotBase.compute_stats(psdf["a"],
"a",
whis=k,
precision=0.01)
outliers = BoxPlotBase.outliers(psdf["a"], "a", *fences)
whiskers = BoxPlotBase.calc_whiskers("a", outliers)
fliers = BoxPlotBase.get_fliers("a", outliers, whiskers[0])
expected_mean = pdf["a"].mean()
expected_median = pdf["a"].median()
expected_q1 = np.percentile(pdf["a"], 25)
expected_q3 = np.percentile(pdf["a"], 75)
iqr = expected_q3 - expected_q1
expected_fences = (expected_q1 - k * iqr, expected_q3 + k * iqr)
pdf["outlier"] = ~pdf["a"].between(fences[0], fences[1])
expected_whiskers = (
pdf.query("not outlier")["a"].min(),
pdf.query("not outlier")["a"].max(),
)
expected_fliers = pdf.query("outlier")["a"].values
self.assertEqual(expected_mean, stats["mean"])
self.assertEqual(expected_median, stats["med"])
self.assertEqual(expected_q1, stats["q1"] + 0.5)
self.assertEqual(expected_q3, stats["q3"] - 0.5)
self.assertEqual(expected_fences[0], fences[0] + 2.0)
self.assertEqual(expected_fences[1], fences[1] - 2.0)
self.assertEqual(expected_whiskers[0], whiskers[0])
self.assertEqual(expected_whiskers[1], whiskers[1])
self.assertEqual(expected_fliers, fliers)
def check_box_multi_columns(psdf):
k = 1.5
multicol_stats = BoxPlotBase.compute_multicol_stats(
psdf, ["a", "b", "c"], whis=k, precision=0.01)
multicol_outliers = BoxPlotBase.multicol_outliers(
psdf, multicol_stats)
multicol_whiskers = BoxPlotBase.calc_multicol_whiskers(
["a", "b", "c"], multicol_outliers)
for col in ["a", "b", "c"]:
col_stats = multicol_stats[col]
col_whiskers = multicol_whiskers[col]
stats, fences = BoxPlotBase.compute_stats(psdf[col],
col,
whis=k,
precision=0.01)
outliers = BoxPlotBase.outliers(psdf[col], col, *fences)
whiskers = BoxPlotBase.calc_whiskers(col, outliers)
self.assertEqual(stats["mean"], col_stats["mean"])
self.assertEqual(stats["med"], col_stats["med"])
self.assertEqual(stats["q1"], col_stats["q1"])
self.assertEqual(stats["q3"], col_stats["q3"])
self.assertEqual(fences[0], col_stats["lfence"])
self.assertEqual(fences[1], col_stats["ufence"])
self.assertEqual(whiskers[0], col_whiskers["min"])
self.assertEqual(whiskers[1], col_whiskers["max"])
def _compute_plot_data(self):
colname = self.data.name
spark_column_name = self.data._internal.spark_column_name_for(
self.data._column_label)
data = self.data
# Updates all props with the rc defaults from matplotlib
self.kwds.update(PandasOnSparkBoxPlot.rc_defaults(**self.kwds))
# Gets some important kwds
showfliers = self.kwds.get("showfliers", False)
whis = self.kwds.get("whis", 1.5)
labels = self.kwds.get("labels", [colname])
# This one is pandas-on-Spark specific to control precision for approx_percentile
precision = self.kwds.get("precision", 0.01)
# # Computes mean, median, Q1 and Q3 with approx_percentile and precision
col_stats, col_fences = BoxPlotBase.compute_stats(
data, spark_column_name, whis, precision)
# # Creates a column to flag rows as outliers or not
outliers = BoxPlotBase.outliers(data, spark_column_name, *col_fences)
# # Computes min and max values of non-outliers - the whiskers
whiskers = BoxPlotBase.calc_whiskers(spark_column_name, outliers)
if showfliers:
fliers = BoxPlotBase.get_fliers(spark_column_name, outliers,
whiskers[0])
else:
fliers = []
# Builds bxpstats dict
stats = []
item = {
"mean": col_stats["mean"],
"med": col_stats["med"],
"q1": col_stats["q1"],
"q3": col_stats["q3"],
"whislo": whiskers[0],
"whishi": whiskers[1],
"fliers": fliers,
"label": labels[0],
}
stats.append(item)
self.data = {labels[0]: stats}
def plot_box(data: Union["ps.DataFrame", "ps.Series"], **kwargs):
import plotly.graph_objs as go
import pyspark.pandas as ps
if isinstance(data, ps.DataFrame):
raise RuntimeError(
"plotly does not support a box plot with Koalas DataFrame. Use Series instead."
)
# 'whis' isn't actually an argument in plotly (but in matplotlib). But seems like
# plotly doesn't expose the reach of the whiskers to the beyond the first and
# third quartiles (?). Looks they use default 1.5.
whis = kwargs.pop("whis", 1.5)
# 'precision' is Koalas specific to control precision for approx_percentile
precision = kwargs.pop("precision", 0.01)
# Plotly options
boxpoints = kwargs.pop("boxpoints", "suspectedoutliers")
notched = kwargs.pop("notched", False)
if boxpoints not in ["suspectedoutliers", False]:
raise ValueError(
"plotly plotting backend does not support 'boxpoints' set to '%s'. "
"Set to 'suspectedoutliers' or False." % boxpoints)
if notched:
raise ValueError(
"plotly plotting backend does not support 'notched' set to '%s'. "
"Set to False." % notched)
colname = name_like_string(data.name)
spark_column_name = data._internal.spark_column_name_for(
data._column_label)
# Computes mean, median, Q1 and Q3 with approx_percentile and precision
col_stats, col_fences = BoxPlotBase.compute_stats(data, spark_column_name,
whis, precision)
# Creates a column to flag rows as outliers or not
outliers = BoxPlotBase.outliers(data, spark_column_name, *col_fences)
# Computes min and max values of non-outliers - the whiskers
whiskers = BoxPlotBase.calc_whiskers(spark_column_name, outliers)
fliers = None
if boxpoints:
fliers = BoxPlotBase.get_fliers(spark_column_name, outliers,
whiskers[0])
fliers = [fliers] if len(fliers) > 0 else None
fig = go.Figure()
fig.add_trace(
go.Box(
name=colname,
q1=[col_stats["q1"]],
median=[col_stats["med"]],
q3=[col_stats["q3"]],
mean=[col_stats["mean"]],
lowerfence=[whiskers[0]],
upperfence=[whiskers[1]],
y=fliers,
boxpoints=boxpoints,
notched=notched,
**
kwargs, # this is for workarounds. Box takes different options from express.box.
))
fig["layout"]["xaxis"]["title"] = colname
fig["layout"]["yaxis"]["title"] = "value"
return fig