def build_histogram(data_id, col, query, point_filter):
data = run_query(
handle_predefined(data_id),
query,
global_state.get_context_variables(data_id),
)
query, _ = build_group_inputs_filter(data, [point_filter])
data = run_query(data, query)
s = data[~pd.isnull(data[col])][col]
hist_data, hist_labels = np.histogram(s, bins=10)
hist_labels = list(
map(lambda x: json_float(x, precision=3), hist_labels[1:]))
axes_builder = build_axes(
dict(
data=dict(all=dict(Frequency=hist_data, Bins=hist_labels)),
min=dict(Frequency=0),
max=dict(Frequency=max(hist_data)),
),
"Bins",
dict(type="single", data={}),
)
hist_data = dict(data={"all": dict(x=hist_labels, Frequency=hist_data)})
bars = bar_builder(
hist_data,
"Bins",
["Frequency"],
axes_builder,
chart_builder_passthru,
modal=True,
)
bars.figure["layout"]["xaxis"]["type"] = "category"
bars.figure["layout"]["title"]["text"] = "{} {} ({} {})".format(
text("Histogram of"), col, len(s), text("data points"))
return bars
def remove(self, df):
group = self.cfg.get("group")
duplicates = [g for _, g in df.groupby(group) if len(g) > 1]
if not duplicates:
raise NoDuplicatesToShowException("No duplicates to show!")
duplicates = pd.concat(duplicates)
group_filter = None
if self.cfg.get("filter"):
group_filter, _ = build_group_inputs_filter(
df, [{col: val
for col, val in zip(group, self.cfg["filter"])}])
duplicates = run_query(duplicates, group_filter)
code = self._build_code(group_filter)
return duplicates, code
def remove(self, df):
group = self.cfg.get("group")
duplicates = [g for _, g in df.groupby(group) if len(g) > 1]
if not duplicates:
raise NoDuplicatesToShowException("No duplicates to show!")
duplicates = pd.concat(duplicates)
group_filter = None
if self.cfg.get("filter"):
group_filter = build_group_inputs_filter(
df, [{col: val
for col, val in zip(group, self.cfg["filter"])}])
duplicates = duplicates.query(group_filter)
code = self._build_code(group_filter)
self.startup_kwargs["name"] = "{group}_duplicates".format(
group="_".join(group))
return duplicates, code
def load_drilldown_content(
_click_data_ts,
drilldown_type,
drilldown_x,
inputs,
chart_inputs,
yaxis_data,
map_data,
click_data,
drilldowns_on,
):
if not drilldowns_on:
raise PreventUpdate
data_id = inputs["data_id"]
all_inputs = combine_inputs(dash_app, inputs, chart_inputs, yaxis_data,
map_data)
agg = all_inputs.get("agg") or "raw"
chart_type = all_inputs.get("chart_type")
frame_col = all_inputs.get("animate_by")
all_inputs.pop("animate_by", None)
if agg == "raw":
raise PreventUpdate
if drilldown_x is None and chart_type != "maps":
raise PreventUpdate
if click_data:
click_point = next((p for p in click_data.get("points", [])), None)
if click_point:
curr_settings = global_state.get_settings(data_id) or {}
query = build_query(
data_id,
all_inputs.get("query") or curr_settings.get("query"))
x_col = all_inputs.get("x")
y_col = next((y2 for y2 in make_list(all_inputs.get("y"))),
None)
if chart_type in ZAXIS_CHARTS:
x, y, z, frame = (click_point.get(p)
for p in ["x", "y", "z", "customdata"])
if chart_type == "heatmap":
click_point_vals = {}
for dim in click_point["text"].split("<br>"):
prop, val = dim.split(": ")
click_point_vals[prop] = val
x, y, frame = (click_point_vals.get(p)
for p in [x_col, y_col, frame_col])
point_filter = {x_col: x, y_col: y}
if frame_col:
point_filter[frame_col] = frame
if drilldown_type == "histogram":
z_col = next(
(z2 for z2 in make_list(all_inputs.get("z"))),
None)
hist_chart = build_histogram(data_id, z_col, query,
point_filter)
return hist_chart, dict(display="none")
else:
xy_query, _ = build_group_inputs_filter(
global_state.get_data(data_id),
[point_filter],
)
if not query:
query = xy_query
else:
query = "({}) and ({})".format(query, xy_query)
all_inputs["query"] = query
all_inputs["chart_type"] = drilldown_type
all_inputs["agg"] = "raw"
all_inputs["modal"] = True
all_inputs["x"] = drilldown_x
all_inputs["y"] = [all_inputs["z"]]
chart, _, _ = build_chart(**all_inputs)
return chart, None
elif chart_type == "maps":
map_type = all_inputs.get("map_type")
point_filter = {}
if frame_col:
point_filter[frame_col] = click_point["customdata"]
if map_type == "choropleth":
point_filter[
all_inputs["loc"]] = click_point["location"]
elif map_type == "scattergeo":
lat, lon = (click_point.get(p) for p in ["lat", "lon"])
point_filter[all_inputs["lat"]] = lat
point_filter[all_inputs["lon"]] = lon
map_val = all_inputs["map_val"]
if drilldown_type == "histogram":
hist_chart = build_histogram(data_id, map_val, query,
point_filter)
return hist_chart, dict(display="none")
else:
map_query, _ = build_group_inputs_filter(
global_state.get_data(data_id),
[point_filter],
)
if not query:
query = map_query
else:
query = "({}) and ({})".format(query, map_query)
all_inputs["query"] = query
all_inputs["chart_type"] = drilldown_type
all_inputs["agg"] = "raw"
all_inputs["modal"] = True
data = global_state.get_data(data_id)
all_inputs["x"] = drilldown_x
x_style = None
if map_type != "choropleth":
all_inputs["x"] = "lat_lon"
lat, lon = (all_inputs.get(p)
for p in ["lat", "lon"])
data.loc[:,
"lat_lon"] = (data[lat].astype(str) +
"|" + data[lon].astype(str))
x_style = dict(display="none")
all_inputs["y"] = [map_val]
chart, _, _ = build_chart(data=data, **all_inputs)
return chart, x_style
else:
x_filter = click_point.get("x")
point_filter = {x_col: x_filter}
if frame_col:
point_filter[frame_col] = click_point.get("customdata")
if drilldown_type == "histogram":
hist_chart = build_histogram(data_id, y_col, query,
point_filter)
return hist_chart, dict(display="none")
else:
x_query, _ = build_group_inputs_filter(
global_state.get_data(data_id),
[point_filter],
)
if not query:
query = x_query
else:
query = "({}) and ({})".format(query, x_query)
all_inputs["query"] = query
all_inputs["chart_type"] = drilldown_type
all_inputs["agg"] = "raw"
all_inputs["modal"] = True
all_inputs["x"] = drilldown_x
chart, _, _ = build_chart(**all_inputs)
return chart, None
return None, dict(display="none")
