def check_exceptions(
df,
allow_duplicates,
unlimited_data=False,
data_limit=15000,
limit_msg=LIMIT_MSG,
dupes_msg=DUPES_MSG,
):
"""
Checker function to test the output of any chart aggregations to see if it is one of the following:
- too large to be rendered by web client
- contains duplicate data points which can't be rendered (ex: multiple points for a single point on the x-axis
of a bar chart within the same series)
:param df: dataframe whose data needs to be checked
:type df: :class:`pandas:pandas.DataFrame`
:param allow_duplicates: flag to allow duplicates to be ignored (usually for scatter plots)
:type allow_duplicates: bool
:param data_limit: maximum rows allowed for chart rendering (default: 15,000)
:type data_limit: int, optional
:param limit_msg: error message template
:type limit_msg: str, optional
:raises Exception: if any failure condition is met
"""
if not allow_duplicates and any(df.duplicated()):
raise ChartBuildingError(dupes_msg.format(", ".join(df.columns)))
if not unlimited_data and len(df) > data_limit:
raise ChartBuildingError(limit_msg.format(data_limit))
def build_base_chart(raw_data,
x,
y,
group_col=None,
group_val=None,
agg=None,
allow_duplicates=False,
return_raw=False,
unlimited_data=False,
animate_by=None,
**kwargs):
"""
Helper function to return data for 'chart-data' & 'correlations-ts' endpoints. Will return a dictionary of
dictionaries (one for each series) which contain the data for the x & y axes of the chart as well as the minimum &
maximum of all the series for the y-axis. If there is only one series (no group_col specified) the only key in the
dictionary of series data will be 'all' otherwise the keys will be the values of the groups.
:param raw_data: dataframe to be used for chart
:type raw_data: :class:`pandas:pandas.DataFrame`
:param x: column to be used as x-axis of chart
:type x: str
:param y: column to be used as y-axis of chart
:type y: list of strings
:param group: comma-separated string of columns to group chart data by
:type group: str, optional
:param agg: points to a specific function that can be applied to
:func: pandas.core.groupby.DataFrameGroupBy. Possible values are: count, first, last mean,
median, min, max, std, var, mad, prod, sum
:type agg: str, optional
:param allow_duplicates: flag to allow duplicates to be ignored (usually for scatter plots)
:type allow_duplicates: bool, optional
:return: dict
"""
group_fmt_overrides = {
"I": lambda v, as_string: json_int(v, as_string=as_string, fmt="{}")
}
data, code = retrieve_chart_data(raw_data,
x,
y,
kwargs.get("z"),
group_col,
animate_by,
group_val=group_val)
x_col = str("x")
if x is None:
x = x_col
data.loc[:, x_col] = range(1,
len(data) +
1) # sequential integers: 1, 2, ..., N
y_cols = make_list(y)
z_col = kwargs.get("z")
z_cols = make_list(z_col)
sort_cols = y_cols if len(z_cols) else []
if group_col is not None and len(group_col):
main_group = group_col
if animate_by is not None:
main_group = [animate_by] + main_group
sort_cols = main_group + [x] + sort_cols
data = data.sort_values(sort_cols)
code.append("chart_data = chart_data.sort_values(['{cols}'])".format(
cols="', '".join(sort_cols)))
check_all_nan(data)
data = data.rename(columns={x: x_col})
code.append("chart_data = chart_data.rename(columns={'" + x + "': '" +
x_col + "'})")
if agg is not None:
data, agg_code = build_agg_data(
data,
x_col,
y_cols,
kwargs,
agg,
z=z_col,
group_col=group_col,
animate_by=animate_by,
)
code += agg_code
MAX_GROUPS = 30
group_vals = data[group_col].drop_duplicates()
if len(group_vals) > MAX_GROUPS:
dtypes = get_dtypes(group_vals)
group_fmts = {
c: find_dtype_formatter(dtypes[c],
overrides=group_fmt_overrides)
for c in group_col
}
group_f, _ = build_formatters(group_vals)
group_vals = group_f.format_lists(group_vals)
group_vals = pd.DataFrame(group_vals, columns=group_col)
msg = (
"Group ({}) contains more than {} unique values, more groups than that will make the chart unreadable. "
'You can choose specific groups to display from then "Group(s)" dropdown above. The available group(s) '
"are listed below:\n\n{}").format(
", ".join(group_col), MAX_GROUPS,
group_vals.to_string(index=False))
raise ChartBuildingError(msg, group_vals.to_string(index=False))
data = data.dropna()
if return_raw:
return data.rename(columns={x_col: x})
code.append("chart_data = chart_data.dropna()")
data_f, range_f = build_formatters(data)
ret_data = dict(
data={},
min={
col: fmt(data[col].min(), None)
for _, col, fmt in range_f.fmts
if col in [x_col] + y_cols + z_cols
},
max={
col: fmt(data[col].max(), None)
for _, col, fmt in range_f.fmts
if col in [x_col] + y_cols + z_cols
},
)
dtypes = get_dtypes(data)
group_fmts = {
c: find_dtype_formatter(dtypes[c], overrides=group_fmt_overrides)
for c in group_col
}
def _load_groups(df):
for group_val, grp in df.groupby(group_col):
def _group_filter():
for gv, gc in zip(make_list(group_val), group_col):
classifier = classify_type(dtypes[gc])
yield group_filter_handler(
gc, group_fmts[gc](gv, as_string=True), classifier)
group_filter = " and ".join(list(_group_filter()))
yield group_filter, data_f.format_lists(grp)
if animate_by is not None:
frame_fmt = find_dtype_formatter(dtypes[animate_by],
overrides=group_fmt_overrides)
ret_data["frames"] = []
for frame_key, frame in data.sort_values(animate_by).groupby(
animate_by):
ret_data["frames"].append(
dict(
data=dict(_load_groups(frame)),
name=frame_fmt(frame_key, as_string=True),
))
ret_data["data"] = copy.deepcopy(ret_data["frames"][-1]["data"])
else:
ret_data["data"] = dict(_load_groups(data))
return ret_data, code
main_group = [x]
if animate_by is not None:
main_group = [animate_by] + main_group
sort_cols = main_group + sort_cols
data = data.sort_values(sort_cols)
code.append("chart_data = chart_data.sort_values(['{cols}'])".format(
cols="', '".join(sort_cols)))
check_all_nan(data)
y_cols = [str(y_col) for y_col in y_cols]
data = data[main_group + y_cols + z_cols]
data = data.rename(columns={x: x_col})
main_group = [x_col if c == x else c for c in main_group]
code.append("chart_data = chart_data.rename(columns={'" + x + "': '" +
x_col + "'})")
if agg is not None:
data, agg_code = build_agg_data(data,
x_col,
y_cols,
kwargs,
agg,
z=z_col,
animate_by=animate_by)
code += agg_code
data = data.dropna()
if return_raw:
return data.rename(columns={x_col: x})
code.append("chart_data = chart_data.dropna()")
dupe_cols = main_group + (y_cols if len(z_cols) else [])
check_exceptions(
data[dupe_cols].rename(columns={x_col: x}),
allow_duplicates or agg in ["raw", "drop_duplicates"],
unlimited_data=unlimited_data,
data_limit=40000 if len(z_cols) or animate_by is not None else 15000,
)
data_f, range_f = build_formatters(data)
ret_data = dict(
min={
col: fmt(data[col].min(), None)
for _, col, fmt in range_f.fmts if col in [x_col] + y_cols + z_cols
},
max={
col: fmt(data[col].max(), None)
for _, col, fmt in range_f.fmts if col in [x_col] + y_cols + z_cols
},
)
if animate_by is not None:
frame_fmt = find_dtype_formatter(find_dtype(data[animate_by]),
overrides=group_fmt_overrides)
ret_data["frames"] = []
for frame_key, frame in data.sort_values(animate_by).groupby(
animate_by):
ret_data["frames"].append(
dict(
data={str("all"): data_f.format_lists(frame)},
name=frame_fmt(frame_key, as_string=True),
))
ret_data["data"] = copy.deepcopy(ret_data["frames"][-1]["data"])
else:
ret_data["data"] = {str("all"): data_f.format_lists(data)}
return ret_data, code
def build_base_chart(raw_data,
x,
y,
group_col=None,
group_type=None,
group_val=None,
bins_val=None,
bin_type=None,
agg=None,
extended_aggregation=[],
allow_duplicates=False,
return_raw=False,
unlimited_data=False,
animate_by=None,
cleaners=[],
**kwargs):
"""
Helper function to return data for 'chart-data' & 'correlations-ts' endpoints. Will return a dictionary of
dictionaries (one for each series) which contain the data for the x & y axes of the chart as well as the minimum &
maximum of all the series for the y-axis. If there is only one series (no group_col specified) the only key in the
dictionary of series data will be 'all' otherwise the keys will be the values of the groups.
:param raw_data: dataframe to be used for chart
:type raw_data: :class:`pandas:pandas.DataFrame`
:param x: column to be used as x-axis of chart
:type x: str
:param y: column to be used as y-axis of chart
:type y: list of strings
:param group: comma-separated string of columns to group chart data by
:type group: str, optional
:param agg: points to a specific function that can be applied to
:func: pandas.core.groupby.DataFrameGroupBy. Possible values are: count, first, last mean,
median, min, max, std, var, mad, prod, sum
:type agg: str, optional
:param extended_aggregation: list of configurations that point to a specific function that can be applied to
:func: pandas.core.groupby.DataFrameGroupBy. Possible values are: count, first, last mean,
median, min, max, std, var, mad, prod, sum
:type agg: list, optional
:param allow_duplicates: flag to allow duplicates to be ignored (usually for scatter plots)
:type allow_duplicates: bool, optional
:return: dict
"""
group_fmt_overrides = {
"I": lambda v, as_string: json_int(v, as_string=as_string, fmt="{}")
}
data, code = retrieve_chart_data(raw_data,
x,
y,
kwargs.get("z"),
group_col,
animate_by,
group_val=group_val)
cleaners = cleaners or []
if len(cleaners):
for col in data.columns:
if classify_type(find_dtype(data[col])) == "S":
code.append("s = chart_data['{}']".format(col))
cleaned_col, cleaned_code = handle_cleaners(
data[col], ",".join(cleaners))
data.loc[:, col] = cleaned_col
code += cleaned_code
code.append("chart_data.loc[:, '{}'] = s".format(col))
x_col = str("x")
if x is None:
x = x_col
data.loc[:, x_col] = range(1,
len(data) +
1) # sequential integers: 1, 2, ..., N
y_cols = make_list(y)
z_col = kwargs.get("z")
z_cols = make_list(z_col)
y_cols = [str(col) for col in y_cols]
is_z = len(z_cols) > 0
y_group_cols = y_cols if is_z else []
sort_cols = y_group_cols
final_cols = y_cols + z_cols
if group_col is not None and len(group_col):
for col in make_list(group_col):
classifier = classify_type(find_dtype(data[col]))
if classifier == "F" or (classifier == "I"
and group_type == "bins"):
if bin_type == "width":
data.loc[:, col] = pd.qcut(data[col],
q=bins_val,
duplicates="drop").astype("str")
code.append((
"chart_data.loc[:, '{col}'] = pd.qcut(chart_data['{col}'], q={bins}, duplicates=\"drop\")"
).format(col=col, bins=bins_val))
else:
bins_data = data[col].dropna()
npt = len(bins_data)
equal_freq_bins = np.interp(
np.linspace(0, npt, bins_val + 1),
np.arange(npt),
np.sort(bins_data),
)
data.loc[:, col] = pd.cut(data[col],
bins=equal_freq_bins,
duplicates="drop").astype("str")
code.append((
"bins_data = data['{col}'].dropna()\n"
"npt = len(bins_data)\n"
"equal_freq_bins = np.interp(np.linspace(0, npt, {bins}), np.arange(npt), "
"np.sort(bins_data))\n"
"chart_data.loc[:, '{col}'] = pd.cut(chart_data['{col}'], bins=equal_freq_bins, "
'duplicates="drop")').format(col=col,
bins=bins_val + 1))
main_group = group_col
if animate_by is not None:
main_group = [animate_by] + main_group
sort_cols = main_group + [x] + sort_cols
data = data.sort_values(sort_cols)
code.append("chart_data = chart_data.sort_values(['{cols}'])".format(
cols="', '".join(sort_cols)))
check_all_nan(data)
data = data.rename(columns={x: x_col})
code.append("chart_data = chart_data.rename(columns={'" + x + "': '" +
x_col + "'})")
if agg is not None or len(extended_aggregation):
data, agg_code, final_cols = build_agg_data(
data,
x_col,
y_cols,
kwargs,
agg,
z=z_col,
group_col=group_col,
animate_by=animate_by,
extended_aggregation=extended_aggregation,
)
code += agg_code
group_vals = data[group_col].drop_duplicates()
if len(group_vals) > MAX_GROUPS:
dtypes = get_dtypes(group_vals)
group_fmts = {
c: find_dtype_formatter(dtypes[c],
overrides=group_fmt_overrides)
for c in group_col
}
group_f, _ = build_formatters(group_vals)
group_vals = group_f.format_lists(group_vals)
group_vals = pd.DataFrame(group_vals, columns=group_col)
msg = (
"Group ({}) contains more than {} unique values, more groups than that will make the chart unreadable. "
'You can choose specific groups to display from then "Group(s)" dropdown above. The available group(s) '
"are listed below:\n\n{}").format(
", ".join(group_col), MAX_GROUPS,
group_vals.to_string(index=False))
raise ChartBuildingError(msg, group_vals.to_string(index=False))
data = data.dropna()
if return_raw:
return data.rename(columns={x_col: x})
code.append("chart_data = chart_data.dropna()")
data_f, range_f = build_formatters(data)
ret_data = dict(
data={},
min={
col: fmt(data[col].min(), None)
for _, col, fmt in range_f.fmts if col in [x_col] + final_cols
},
max={
col: fmt(data[col].max(), None)
for _, col, fmt in range_f.fmts if col in [x_col] + final_cols
},
)
dtypes = get_dtypes(data)
group_fmts = {
c: find_dtype_formatter(dtypes[c], overrides=group_fmt_overrides)
for c in group_col
}
def _load_groups(df):
for group_val, grp in df.groupby(group_col):
def _group_filter():
for gv, gc in zip(make_list(group_val), group_col):
classifier = classify_type(dtypes[gc])
yield group_filter_handler(
gc, group_fmts[gc](gv, as_string=True), classifier)
final_group_filter, final_group_label = [], []
for gf, gl in _group_filter():
final_group_filter.append(gf)
final_group_label.append(gl)
group_filter = " and ".join(final_group_filter)
group_label = "({})".format(", ".join(final_group_label))
data = data_f.format_lists(grp)
data["_filter_"] = group_filter
yield group_label, data
if animate_by is not None:
frame_fmt = find_dtype_formatter(dtypes[animate_by],
overrides=group_fmt_overrides)
ret_data["frames"] = []
for frame_key, frame in data.sort_values(animate_by).groupby(
animate_by):
ret_data["frames"].append(
dict(
data=dict(_load_groups(frame)),
name=frame_fmt(frame_key, as_string=True),
))
ret_data["data"] = copy.deepcopy(ret_data["frames"][-1]["data"])
else:
ret_data["data"] = dict(_load_groups(data))
return ret_data, code
main_group = [x]
if animate_by is not None:
main_group = [animate_by] + main_group
sort_cols = main_group + sort_cols
data = data.sort_values(sort_cols)
code.append("chart_data = chart_data.sort_values(['{cols}'])".format(
cols="', '".join(sort_cols)))
check_all_nan(data)
data = data[main_group + final_cols]
data = data.rename(columns={x: x_col})
main_group = [x_col if c == x else c for c in main_group]
code.append("chart_data = chart_data.rename(columns={'" + x + "': '" +
x_col + "'})")
# convert booleans into integers for aggregation
for col in z_cols or y_cols:
classifier = classify_type(find_dtype(data[col]))
if classifier == "B":
data.loc[:, col] = data[col].astype("int")
if agg is not None or len(extended_aggregation):
data, agg_code, final_cols = build_agg_data(
data,
x_col,
y_cols,
kwargs,
agg,
z=z_col,
animate_by=animate_by,
extended_aggregation=extended_aggregation,
)
code += agg_code
data = data.dropna()
if return_raw:
return data.rename(columns={x_col: x})
code.append("chart_data = chart_data.dropna()")
dupe_cols = main_group + y_group_cols
data_limit = global_state.get_chart_settings(
)["3d_points" if is_z or animate_by is not None else "scatter_points"]
check_exceptions(
data[dupe_cols].rename(columns={x_col: x}),
allow_duplicates or agg in ["raw", "drop_duplicates"],
unlimited_data=unlimited_data,
data_limit=data_limit,
)
data_f, range_f = build_formatters(data)
ret_data = dict(
min={
col: fmt(data[col].min(), None)
for _, col, fmt in range_f.fmts
if col in [x_col] + y_group_cols + final_cols
},
max={
col: fmt(data[col].max(), None)
for _, col, fmt in range_f.fmts
if col in [x_col] + y_group_cols + final_cols
},
)
if animate_by is not None:
frame_fmt = find_dtype_formatter(find_dtype(data[animate_by]),
overrides=group_fmt_overrides)
ret_data["frames"] = []
for frame_key, frame in data.sort_values(animate_by).groupby(
animate_by):
ret_data["frames"].append(
dict(
data={str("all"): data_f.format_lists(frame)},
name=frame_fmt(frame_key, as_string=True),
))
ret_data["data"] = copy.deepcopy(ret_data["frames"][-1]["data"])
else:
ret_data["data"] = {str("all"): data_f.format_lists(data)}
return ret_data, code
def build_base_chart(raw_data,
x,
y,
group_col=None,
group_val=None,
agg=None,
allow_duplicates=False,
return_raw=False,
unlimited_data=False,
**kwargs):
"""
Helper function to return data for 'chart-data' & 'correlations-ts' endpoints. Will return a dictionary of
dictionaries (one for each series) which contain the data for the x & y axes of the chart as well as the minimum &
maximum of all the series for the y-axis. If there is only one series (no group_col specified) the only key in the
dictionary of series data will be 'all' otherwise the keys will be the values of the groups.
:param raw_data: dataframe to be used for chart
:type raw_data: :class:`pandas:pandas.DataFrame`
:param x: column to be used as x-axis of chart
:type x: str
:param y: column to be used as y-axis of chart
:type y: list of strings
:param group: comma-separated string of columns to group chart data by
:type group: str, optional
:param agg: points to a specific function that can be applied to
:func: pandas.core.groupby.DataFrameGroupBy. Possible values are: count, first, last mean,
median, min, max, std, var, mad, prod, sum
:type agg: str, optional
:param allow_duplicates: flag to allow duplicates to be ignored (usually for scatter plots)
:type allow_duplicates: bool, optional
:return: dict
"""
data, code = retrieve_chart_data(raw_data,
x,
y,
kwargs.get('z'),
group_col,
group_val=group_val)
x_col = str('x')
y_cols = make_list(y)
z_col = kwargs.get('z')
z_cols = make_list(z_col)
if group_col is not None and len(group_col):
data = data.sort_values(group_col + [x])
code.append("chart_data = chart_data.sort_values(['{cols}'])".format(
cols="', '".join(group_col + [x])))
check_all_nan(data, [x] + y_cols)
data = data.rename(columns={x: x_col})
code.append("chart_data = chart_data.rename(columns={'" + x + "': '" +
x_col + "'})")
if agg is not None and agg != 'raw':
data = data.groupby(group_col + [x_col])
data = getattr(data, agg)().reset_index()
code.append(
"chart_data = chart_data.groupby(['{cols}']).{agg}().reset_index()"
.format(cols="', '".join(group_col + [x]), agg=agg))
MAX_GROUPS = 30
group_vals = data[group_col].drop_duplicates()
if len(group_vals) > MAX_GROUPS:
dtypes = get_dtypes(group_vals)
group_fmt_overrides = {
'I':
lambda v, as_string: json_int(v, as_string=as_string, fmt='{}')
}
group_fmts = {
c: find_dtype_formatter(dtypes[c],
overrides=group_fmt_overrides)
for c in group_col
}
group_f, _ = build_formatters(group_vals)
group_vals = group_f.format_lists(group_vals)
group_vals = pd.DataFrame(group_vals, columns=group_col)
msg = (
'Group ({}) contains more than {} unique values, more groups than that will make the chart unreadable. '
'You can choose specific groups to display from then "Group(s)" dropdown above. The available group(s) '
'are listed below:').format(', '.join(group_col), MAX_GROUPS,
group_vals.to_string(index=False))
raise ChartBuildingError(msg, group_vals.to_string(index=False))
data = data.dropna()
if return_raw:
return data.rename(columns={x_col: x})
code.append("chart_data = chart_data.dropna()")
data_f, range_f = build_formatters(data)
ret_data = dict(
data={},
min={
col: fmt(data[col].min(), None)
for _, col, fmt in range_f.fmts if col in [x_col] + y_cols
},
max={
col: fmt(data[col].max(), None)
for _, col, fmt in range_f.fmts if col in [x_col] + y_cols
},
)
dtypes = get_dtypes(data)
group_fmt_overrides = {
'I':
lambda v, as_string: json_int(v, as_string=as_string, fmt='{}')
}
group_fmts = {
c: find_dtype_formatter(dtypes[c], overrides=group_fmt_overrides)
for c in group_col
}
for group_val, grp in data.groupby(group_col):
def _group_filter():
for gv, gc in zip(make_list(group_val), group_col):
classifier = classify_type(dtypes[gc])
yield group_filter_handler(
gc, group_fmts[gc](gv, as_string=True), classifier)
group_filter = ' and '.join(list(_group_filter()))
ret_data['data'][group_filter] = data_f.format_lists(grp)
return ret_data, code
sort_cols = [x] + (y_cols if len(z_cols) else [])
data = data.sort_values(sort_cols)
code.append("chart_data = chart_data.sort_values(['{cols}'])".format(
cols="', '".join(sort_cols)))
check_all_nan(data, [x] + y_cols + z_cols)
y_cols = [str(y_col) for y_col in y_cols]
data.columns = [x_col] + y_cols + z_cols
code.append("chart_data.columns = ['{cols}']".format(
cols="', '".join([x_col] + y_cols + z_cols)))
if agg is not None:
data, agg_code = build_agg_data(data,
x_col,
y_cols,
kwargs,
agg,
z=z_col)
code += agg_code
data = data.dropna()
if return_raw:
return data.rename(columns={x_col: x})
code.append("chart_data = chart_data.dropna()")
dupe_cols = [x_col] + (y_cols if len(z_cols) else [])
check_exceptions(data[dupe_cols].rename(columns={'x': x}),
allow_duplicates or agg == 'raw',
unlimited_data=unlimited_data,
data_limit=40000 if len(z_cols) else 15000)
data_f, range_f = build_formatters(data)
ret_data = dict(data={str('all'): data_f.format_lists(data)},
min={
col: fmt(data[col].min(), None)
for _, col, fmt in range_f.fmts
if col in [x_col] + y_cols + z_cols
},
max={
col: fmt(data[col].max(), None)
for _, col, fmt in range_f.fmts
if col in [x_col] + y_cols + z_cols
})
return ret_data, code
