def get_store_contents():
"""
Return an ordered tuple of attributes representing the store contents.
Useful for ensuring key properties stay the same when switching between systems.
"""
_get_one = [
serialized_dataframe(global_state.get_data('1')),
global_state.get_dtypes('1'),
global_state.get_settings('1'),
global_state.get_metadata('1'),
global_state.get_context_variables('1'),
global_state.get_history('1'),
]
_get_all = [
{
k: serialized_dataframe(v)
for k, v in global_state.get_data().items()
},
global_state.get_dtypes(),
global_state.get_settings(),
global_state.get_metadata(),
global_state.get_context_variables(),
global_state.get_history(),
]
_lengths = [
len(global_state.DATA),
len(global_state.DTYPES),
len(global_state.SETTINGS),
len(global_state.METADATA),
len(global_state.CONTEXT_VARIABLES),
len(global_state.HISTORY),
]
return (_get_one, _get_all, _lengths)
def get_store_contents():
"""
Return an ordered tuple of attributes representing the store contents.
Useful for ensuring key properties stay the same when switching between systems.
"""
_get_one = [
serialized_dataframe(global_state.get_data("1")),
global_state.get_dtypes("1"),
global_state.get_settings("1"),
global_state.get_metadata("1"),
global_state.get_context_variables("1"),
global_state.get_history("1"),
]
_get_all = [
{
int(k): serialized_dataframe(v.data)
for k, v in global_state.items()
},
{int(k): v.dtypes
for k, v in global_state.items()},
{int(k): v.settings
for k, v in global_state.items()},
{int(k): v.metadata
for k, v in global_state.items()},
{int(k): v.context_variables
for k, v in global_state.items()},
{int(k): v.history
for k, v in global_state.items()},
]
_lengths = [
global_state.size(),
]
return (_get_one, _get_all, _lengths)
def reshape(self):
data = run_query(
global_state.get_data(self.data_id),
(global_state.get_settings(self.data_id) or {}).get("query"),
global_state.get_context_variables(self.data_id),
)
return self.builder.reshape(data)
def run(self):
data = run_query(
global_state.get_data(self.data_id),
(global_state.get_settings(self.data_id) or {}).get("query"),
global_state.get_context_variables(self.data_id),
)
return self.report.run(data)
def test_transpose(custom_data, unittest):
from dtale.views import build_dtypes_state
global_state.clear_store()
with app.test_client() as c:
data = {c.port: custom_data}
dtypes = {c.port: build_dtypes_state(custom_data)}
settings = {c.port: {}}
build_data_inst(data)
build_dtypes(dtypes)
build_settings(settings)
reshape_cfg = dict(index=["security_id"], columns=["Col0"])
resp = c.get(
"/dtale/reshape/{}".format(c.port),
query_string=dict(output="new",
type="transpose",
cfg=json.dumps(reshape_cfg)),
)
response_data = json.loads(resp.data)
new_key = int(c.port) + 1
assert "error" in response_data
min_date = custom_data["date"].min().strftime("%Y-%m-%d")
global_state.set_settings(c.port,
dict(query="date == '{}'".format(min_date)))
reshape_cfg = dict(index=["date", "security_id"], columns=["Col0"])
resp = c.get(
"/dtale/reshape/{}".format(c.port),
query_string=dict(output="new",
type="transpose",
cfg=json.dumps(reshape_cfg)),
)
response_data = json.loads(resp.data)
assert response_data["data_id"] == new_key
assert len(global_state.keys()) == 2
unittest.assertEqual(
[d["name"] for d in global_state.get_dtypes(new_key)],
[
"index",
"{} 00:00:00 100000".format(min_date),
"{} 00:00:00 100001".format(min_date),
],
)
assert len(global_state.get_data(new_key)) == 1
assert global_state.get_settings(new_key).get(
"startup_code") is not None
c.get("/dtale/cleanup-datasets", query_string=dict(dataIds=new_key))
reshape_cfg = dict(index=["date", "security_id"])
resp = c.get(
"/dtale/reshape/{}".format(c.port),
query_string=dict(output="override",
type="transpose",
cfg=json.dumps(reshape_cfg)),
)
response_data = json.loads(resp.data)
assert response_data["data_id"] == c.port
def build_code_export(data_id, imports='import pandas as pd\n\n', query=None):
"""
Helper function for building a string representing the code that was run to get the data you are viewing to that
point.
:param data_id: integer string identifier for a D-Tale process's data
:type data_id: str
:param imports: string representing the imports at the top of the code string
:type imports: string, optional
:param query: pandas dataframe query string
:type query: str, optional
:return: python code string
"""
history = global_state.get_history(data_id) or []
settings = global_state.get_settings(data_id) or {}
ctxt_vars = global_state.get_context_variables(data_id)
startup_code = settings.get('startup_code')
startup_code = '# Data Re-shaping\n{}\n\n'.format(
startup_code) if startup_code else ''
startup_str = (
"# DISCLAIMER: 'df' refers to the data you passed in when calling 'dtale.show'\n\n"
'{imports}'
'{startup}'
'if isinstance(df, (pd.DatetimeIndex, pd.MultiIndex)):\n'
'\tdf = df.to_frame(index=False)\n\n'
'# remove any pre-existing indices for ease of use in the D-Tale code, but this is not required\n'
"df = df.reset_index().drop('index', axis=1, errors='ignore')\n"
'df.columns = [str(c) for c in df.columns] # update columns to strings in case they are numbers\n'
).format(imports=imports, startup=startup_code)
final_history = [startup_str] + history
final_query = query
if final_query is None:
final_query = settings.get('query')
if final_query is not None:
if len(ctxt_vars or {}):
final_history.append((
"\n# this is injecting any context variables you may have passed into 'dtale.show'\n"
"import dtale.global_state as dtale_global_state\n"
"\n# DISCLAIMER: running this line in a different process than the one it originated will produce\n"
"# differing results\n"
"ctxt_vars = dtale_global_state.get_context_variables('{data_id}')\n\n"
"df = df.query('{query}', local_dict=ctxt_vars)\n").format(
query=final_query, data_id=data_id))
else:
final_history.append("df = df.query('{}')\n".format(final_query))
elif 'query' in settings:
final_history.append("df = df.query('{}')\n".format(settings['query']))
if 'sort' in settings:
cols, dirs = [], []
for col, dir in settings['sort']:
cols.append(col)
dirs.append('True' if dir == 'ASC' else 'False')
final_history.append(
"df = df.sort_values(['{cols}'], ascending=[{dirs}])\n".format(
cols=', '.join(cols), dirs="', '".join(dirs)))
return final_history
def display_page(_ts, pathname, search):
"""
dash callback which gets called on initial load of each dash page (main & popup)
"""
dash_app.config.suppress_callback_exceptions = False
params = chart_url_params(search)
data_id = get_data_id(pathname)
df = global_state.get_data(data_id)
settings = global_state.get_settings(data_id) or {}
return charts_layout(df, settings, **params)
def save_filter(self):
curr_settings = global_state.get_settings(self.data_id)
curr_filters = curr_settings.get('columnFilters') or {}
fltr = self.builder.build_filter()
if fltr is None:
curr_filters.pop(self.column, None)
else:
curr_filters[self.column] = fltr
curr_settings['columnFilters'] = curr_filters
global_state.set_settings(self.data_id, curr_settings)
def load_filterable_data(data_id, req, query=None):
filtered = get_bool_arg(req, "filtered")
curr_settings = global_state.get_settings(data_id) or {}
if filtered:
final_query = query or build_query(data_id, curr_settings.get("query"))
return run_query(
handle_predefined(data_id),
final_query,
global_state.get_context_variables(data_id),
ignore_empty=True,
)
return global_state.get_data(data_id)
def save_filter(self):
curr_settings = global_state.get_settings(self.data_id)
filters_key = '{}Filters'.format('outlier' if self.cfg['type'] ==
'outliers' else 'column')
curr_filters = curr_settings.get(filters_key) or {}
fltr = self.builder.build_filter()
if fltr is None:
curr_filters.pop(self.column, None)
else:
curr_filters[self.column] = fltr
curr_settings[filters_key] = curr_filters
global_state.set_settings(self.data_id, curr_settings)
return curr_filters
def save_filter(self):
curr_settings = global_state.get_settings(self.data_id)
filters_key = "{}Filters".format("outlier" if self.cfg["type"] ==
"outliers" else "column")
curr_filters = curr_settings.get(filters_key) or {}
fltr = self.builder.build_filter()
if fltr is None:
curr_filters.pop(self.column, None)
else:
curr_filters[self.column] = fltr
curr_settings[filters_key] = curr_filters
global_state.set_settings(self.data_id, curr_settings)
return curr_filters
def execute(self):
from dtale.views import startup
data = global_state.get_data(self.data_id)
try:
df, code = self.checker.remove(data)
instance = startup(data=df, **self.checker.startup_kwargs)
curr_settings = global_state.get_settings(instance._data_id)
global_state.set_settings(
instance._data_id,
dict_merge(curr_settings, dict(startup_code=code)),
)
return instance._data_id
except NoDuplicatesException:
return self.data_id
def display_page(pathname, search):
"""
dash callback which gets called on initial load of each dash page (main & popup)
"""
dash_app.config.suppress_callback_exceptions = False
if pathname is None:
raise PreventUpdate
params = chart_url_params(search)
params["data_id"] = params.get("data_id") or get_data_id(pathname)
df = global_state.get_data(params["data_id"])
settings = global_state.get_settings(params["data_id"]) or {}
return html.Div(
charts_layout(df, settings, **params) + saved_charts.build_layout(),
className="charts-body",
)
def handle_predefined(data_id, df=None):
import dtale.predefined_filters as predefined_filters
df = global_state.get_data(data_id) if df is None else df
filters = predefined_filters.get_filters()
if not filters:
return df
curr_settings = global_state.get_settings(data_id) or {}
filter_values = curr_settings.get("predefinedFilters")
if not filter_values:
return df
for f in filters:
if f.name in filter_values:
df = f.handler(df, filter_values[f.name])
return df
def test_convert():
from dtale.views import startup
from tests.dtale.test_replacements import replacements_data
import dtale.global_state as global_state
global_state.clear_store()
with app.test_client() as c:
global_state.new_data_inst(c.port)
startup(URL, data=replacements_data(), data_id=c.port)
resp = c.get(
"/dtale/to-xarray/{}".format(c.port),
query_string=dict(index=json.dumps(["a"])),
)
assert resp.status_code == 200
assert global_state.get_dataset(c.port) is not None
assert global_state.get_settings(c.port)["locked"] == ["a"]
def test_resample(unittest):
from dtale.views import build_dtypes_state, format_data
start, end = "2000-10-01 23:30:00", "2000-10-03 00:30:00"
rng = pd.date_range(start, end, freq="7min")
ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
ts2 = pd.Series(np.arange(len(rng)) * 0.32, index=rng)
df = pd.DataFrame(data={"col1": ts, "col2": ts2})
df, _ = format_data(df)
global_state.clear_store()
with app.test_client() as c:
data = {c.port: df}
dtypes = {c.port: build_dtypes_state(df)}
settings = {c.port: {}}
build_data_inst(data)
build_dtypes(dtypes)
build_settings(settings)
reshape_cfg = dict(index="index",
columns=["col1"],
freq="17min",
agg="mean")
resp = c.get(
"/dtale/reshape/{}".format(c.port),
query_string=dict(output="new",
type="resample",
cfg=json.dumps(reshape_cfg)),
)
response_data = json.loads(resp.data)
new_key = int(c.port) + 1
assert response_data["data_id"] == new_key
assert len(global_state.keys()) == 2
unittest.assertEqual(
[d["name"] for d in global_state.get_dtypes(new_key)],
["index_17min", "col1"],
)
assert len(global_state.get_data(new_key)) == 90
assert global_state.get_settings(new_key).get(
"startup_code") is not None
c.get("/dtale/cleanup-datasets", query_string=dict(dataIds=new_key))
def __init__(self, data_id, req):
self.data_id = data_id
self.analysis_type = get_str_arg(req, "type")
curr_settings = global_state.get_settings(data_id) or {}
self.query = build_query(data_id, curr_settings.get("query"))
data = load_filterable_data(data_id, req, query=self.query)
self.selected_col = find_selected_column(
data, get_str_arg(req, "col", "values")
)
self.data = data[~pd.isnull(data[self.selected_col])]
self.dtype = find_dtype(self.data[self.selected_col])
self.classifier = classify_type(self.dtype)
self.code = build_code_export(
data_id,
imports="{}\n".format(
"\n".join(
[
"import numpy as np",
"import pandas as pd",
"import plotly.graph_objs as go",
]
)
),
)
if self.analysis_type is None:
self.analysis_type = (
"histogram" if self.classifier in ["F", "I", "D"] else "value_counts"
)
if self.analysis_type == "geolocation":
self.analysis = GeolocationAnalysis(req)
elif self.analysis_type == "histogram":
self.analysis = HistogramAnalysis(req)
elif self.analysis_type == "categories":
self.analysis = CategoryAnalysis(req)
elif self.analysis_type == "value_counts":
self.analysis = ValueCountAnalysis(req)
elif self.analysis_type == "word_value_counts":
self.analysis = WordValueCountAnalysis(req)
elif self.analysis_type == "qq":
self.analysis = QQAnalysis()
def build_query(data_id, query=None):
curr_settings = global_state.get_settings(data_id) or {}
return inner_build_query(curr_settings, query)
def test_pivot(custom_data, unittest):
from dtale.views import build_dtypes_state
global_state.clear_store()
with app.test_client() as c:
data = {c.port: custom_data}
dtypes = {c.port: build_dtypes_state(custom_data)}
settings = {c.port: {}}
build_data_inst(data)
build_dtypes(dtypes)
build_settings(settings)
reshape_cfg = dict(index=["date"],
columns=["security_id"],
values=["Col0"],
aggfunc="mean")
resp = c.get(
"/dtale/reshape/{}".format(c.port),
query_string=dict(output="new",
type="pivot",
cfg=json.dumps(reshape_cfg)),
)
response_data = json.loads(resp.data)
new_key = int(c.port) + 1
assert response_data["data_id"] == new_key
assert len(global_state.keys()) == 2
unittest.assertEqual(
[d["name"] for d in global_state.get_dtypes(new_key)],
["date", "100000", "100001"],
)
assert len(global_state.get_data(new_key)) == 365
assert global_state.get_settings(new_key).get(
"startup_code") is not None
resp = c.get("/dtale/cleanup-datasets",
query_string=dict(dataIds=new_key))
assert json.loads(resp.data)["success"]
assert len(global_state.keys()) == 1
reshape_cfg["columnNameHeaders"] = True
reshape_cfg["aggfunc"] = "sum"
resp = c.get(
"/dtale/reshape/{}".format(c.port),
query_string=dict(output="new",
type="pivot",
cfg=json.dumps(reshape_cfg)),
)
response_data = json.loads(resp.data)
assert response_data["data_id"] == new_key
assert len(global_state.keys()) == 2
unittest.assertEqual(
[d["name"] for d in global_state.get_dtypes(new_key)],
["date", "security_id-100000", "security_id-100001"],
)
assert len(global_state.get_data(new_key)) == 365
assert global_state.get_settings(new_key).get(
"startup_code") is not None
c.get("/dtale/cleanup-datasets", query_string=dict(dataIds=new_key))
reshape_cfg["columnNameHeaders"] = False
reshape_cfg["values"] = ["Col0", "Col1"]
resp = c.get(
"/dtale/reshape/{}".format(c.port),
query_string=dict(output="new",
type="pivot",
cfg=json.dumps(reshape_cfg)),
)
response_data = json.loads(resp.data)
assert response_data["data_id"] == new_key
assert len(global_state.keys()) == 2
unittest.assertEqual(
[d["name"] for d in global_state.get_dtypes(new_key)],
[
"date", "Col0 100000", "Col0 100001", "Col1 100000",
"Col1 100001"
],
)
assert len(global_state.get_data(new_key)) == 365
assert global_state.get_settings(new_key).get(
"startup_code") is not None
c.get("/dtale/cleanup-datasets", query_string=dict(dataIds=new_key))
def test_aggregate(custom_data, unittest):
from dtale.views import build_dtypes_state
global_state.clear_store()
with app.test_client() as c:
data = {c.port: custom_data}
dtypes = {c.port: build_dtypes_state(custom_data)}
settings = {c.port: {}}
build_data_inst(data)
build_dtypes(dtypes)
build_settings(settings)
reshape_cfg = dict(
index="date",
agg=dict(type="col",
cols={
"Col0": ["sum", "mean"],
"Col1": ["count"]
}),
)
resp = c.get(
"/dtale/reshape/{}".format(c.port),
query_string=dict(output="new",
type="aggregate",
cfg=json.dumps(reshape_cfg)),
)
response_data = json.loads(resp.data)
new_key = int(c.port) + 1
assert response_data["data_id"] == new_key
assert len(global_state.keys()) == 2
unittest.assertEqual(
[d["name"] for d in global_state.get_dtypes(new_key)],
["date", "Col0 sum", "Col0 mean", "Col1 count"],
)
assert len(global_state.get_data(new_key)) == 365
assert global_state.get_settings(new_key).get(
"startup_code") is not None
c.get("/dtale/cleanup-datasets", query_string=dict(dataIds=new_key))
reshape_cfg = dict(index="date",
agg=dict(type="func",
func="mean",
cols=["Col0", "Col1"]))
resp = c.get(
"/dtale/reshape/{}".format(c.port),
query_string=dict(output="new",
type="aggregate",
cfg=json.dumps(reshape_cfg)),
)
response_data = json.loads(resp.data)
assert response_data["data_id"] == new_key
assert len(global_state.keys()) == 2
unittest.assertEqual(
[d["name"] for d in global_state.get_dtypes(new_key)],
["date", "Col0", "Col1"],
)
assert len(global_state.get_data(new_key)) == 365
assert global_state.get_settings(new_key).get(
"startup_code") is not None
c.get("/dtale/cleanup-datasets", query_string=dict(dataIds=new_key))
reshape_cfg = dict(index="date", agg=dict(type="func", func="mean"))
resp = c.get(
"/dtale/reshape/{}".format(c.port),
query_string=dict(output="new",
type="aggregate",
cfg=json.dumps(reshape_cfg)),
)
response_data = json.loads(resp.data)
assert response_data["data_id"] == new_key
assert len(global_state.keys()) == 2
unittest.assertEqual(
[d["name"] for d in global_state.get_dtypes(new_key)],
[
"date", "security_id", "int_val", "Col0", "Col1", "Col2",
"bool_val"
],
)
assert len(global_state.get_data(new_key)) == 365
assert global_state.get_settings(new_key).get(
"startup_code") is not None
c.get("/dtale/cleanup-datasets", query_string=dict(dataIds=new_key))
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")
def build_code_export(data_id, imports="import pandas as pd\n\n", query=None):
"""
Helper function for building a string representing the code that was run to get the data you are viewing to that
point.
:param data_id: integer string identifier for a D-Tale process's data
:type data_id: str
:param imports: string representing the imports at the top of the code string
:type imports: string, optional
:param query: pandas dataframe query string
:type query: str, optional
:return: python code string
"""
history = global_state.get_history(data_id) or []
settings = global_state.get_settings(data_id) or {}
ctxt_vars = global_state.get_context_variables(data_id)
startup_code = settings.get("startup_code") or ""
if startup_code and not startup_code.endswith("\n"):
startup_code += "\n"
xarray_setup = ""
if data_id in global_state.DATASETS:
xarray_dims = global_state.get_dataset_dim(data_id)
if len(xarray_dims):
xarray_setup = (
"df = ds.sel({selectors}).to_dataframe()\n"
"df = df.reset_index().drop('index', axis=1, errors='ignore')\n"
"df = df.set_index(list(ds.dims.keys()))\n"
).format(
selectors=", ".join(
"{}='{}'".format(k, v) for k, v in xarray_dims.items()
)
)
else:
xarray_setup = (
"df = ds.to_dataframe()\n"
"df = df.reset_index().drop('index', axis=1, errors='ignore')\n"
"df = df.set_index(list(ds.dims.keys()))\n"
)
startup_str = (
"# DISCLAIMER: 'df' refers to the data you passed in when calling 'dtale.show'\n\n"
"{imports}"
"{xarray_setup}"
"{startup}"
"if isinstance(df, (pd.DatetimeIndex, pd.MultiIndex)):\n"
"\tdf = df.to_frame(index=False)\n\n"
"# remove any pre-existing indices for ease of use in the D-Tale code, but this is not required\n"
"df = df.reset_index().drop('index', axis=1, errors='ignore')\n"
"df.columns = [str(c) for c in df.columns] # update columns to strings in case they are numbers\n"
).format(imports=imports, xarray_setup=xarray_setup, startup=startup_code)
final_history = [startup_str] + history
final_query = query
if final_query is None:
final_query = settings.get("query")
if final_query is not None and final_query != "":
if len(ctxt_vars or {}):
final_history.append(
(
"\n# this is injecting any context variables you may have passed into 'dtale.show'\n"
"import dtale.global_state as dtale_global_state\n"
"\n# DISCLAIMER: running this line in a different process than the one it originated will produce\n"
"# differing results\n"
"ctxt_vars = dtale_global_state.get_context_variables('{data_id}')\n\n"
"df = df.query({query}, local_dict=ctxt_vars)\n"
).format(query=triple_quote(final_query), data_id=data_id)
)
else:
final_history.append(
"df = df.query({})\n".format(triple_quote(final_query))
)
elif settings.get("query"):
final_history.append(
"df = df.query({})\n".format(triple_quote(settings["query"]))
)
if "sort" in settings:
cols, dirs = [], []
for col, dir in settings["sort"]:
cols.append(col)
dirs.append("True" if dir == "ASC" else "False")
final_history.append(
"df = df.sort_values(['{cols}'], ascending=[{dirs}])\n".format(
cols=", ".join(cols), dirs="', '".join(dirs)
)
)
return final_history
