def plot_histogram(data: Union["ps.DataFrame", "ps.Series"], **kwargs):
import plotly.graph_objs as go
bins = kwargs.get("bins", 10)
kdf, bins = HistogramPlotBase.prepare_hist_data(data, bins)
assert len(bins) > 2, "the number of buckets must be higher than 2."
output_series = HistogramPlotBase.compute_hist(kdf, bins)
prev = float("%.9f" % bins[0]) # to make it prettier, truncate.
text_bins = []
for b in bins[1:]:
norm_b = float("%.9f" % b)
text_bins.append("[%s, %s)" % (prev, norm_b))
prev = norm_b
text_bins[
-1] = text_bins[-1][:-1] + "]" # replace ) to ] for the last bucket.
bins = 0.5 * (bins[:-1] + bins[1:])
output_series = list(output_series)
bars = []
for series in output_series:
bars.append(
go.Bar(
x=bins,
y=series,
name=name_like_string(series.name),
text=text_bins,
hovertemplate=("variable=" + name_like_string(series.name) +
"<br>value=%{text}<br>count=%{y}"),
))
fig = go.Figure(data=bars, layout=go.Layout(barmode="stack"))
fig["layout"]["xaxis"]["title"] = "value"
fig["layout"]["yaxis"]["title"] = "count"
return fig
def plot_kde(data: Union["ps.DataFrame", "ps.Series"], **kwargs):
from plotly import express
import pyspark.pandas as ps
if isinstance(data, ps.DataFrame) and "color" not in kwargs:
kwargs["color"] = "names"
kdf = KdePlotBase.prepare_kde_data(data)
sdf = kdf._internal.spark_frame
data_columns = kdf._internal.data_spark_columns
ind = KdePlotBase.get_ind(sdf.select(*data_columns),
kwargs.pop("ind", None))
bw_method = kwargs.pop("bw_method", None)
pdfs = []
for label in kdf._internal.column_labels:
pdfs.append(
pd.DataFrame({
"Density":
KdePlotBase.compute_kde(
sdf.select(kdf._internal.spark_column_for(label)),
ind=ind,
bw_method=bw_method,
),
"names":
name_like_string(label),
"index":
ind,
}))
pdf = pd.concat(pdfs)
fig = express.line(pdf, x="index", y="Density", **kwargs)
fig["layout"]["xaxis"]["title"] = None
return fig
def plot_histogram(data: Union["ps.DataFrame", "ps.Series"], **kwargs):
import plotly.graph_objs as go
import pyspark.pandas as ps
bins = kwargs.get("bins", 10)
y = kwargs.get("y")
if y and isinstance(data, ps.DataFrame):
# Note that the results here are matched with matplotlib. x and y
# handling is different from pandas' plotly output.
data = data[y]
psdf, bins = HistogramPlotBase.prepare_hist_data(data, bins)
assert len(bins) > 2, "the number of buckets must be higher than 2."
output_series = HistogramPlotBase.compute_hist(psdf, bins)
prev = float("%.9f" % bins[0]) # to make it prettier, truncate.
text_bins = []
for b in bins[1:]:
norm_b = float("%.9f" % b)
text_bins.append("[%s, %s)" % (prev, norm_b))
prev = norm_b
text_bins[
-1] = text_bins[-1][:-1] + "]" # replace ) to ] for the last bucket.
bins = 0.5 * (bins[:-1] + bins[1:])
output_series = list(output_series)
bars = []
for series in output_series:
bars.append(
go.Bar(
x=bins,
y=series,
name=name_like_string(series.name),
text=text_bins,
hovertemplate=("variable=" + name_like_string(series.name) +
"<br>value=%{text}<br>count=%{y}"),
))
layout_keys = inspect.signature(go.Layout).parameters.keys()
layout_kwargs = {k: v for k, v in kwargs.items() if k in layout_keys}
fig = go.Figure(data=bars, layout=go.Layout(**layout_kwargs))
fig["layout"]["barmode"] = "stack"
fig["layout"]["xaxis"]["title"] = "value"
fig["layout"]["yaxis"]["title"] = "count"
return fig
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