def figure(self) -> go.Figure:
return (go.Figure(
go.Waterfall(
orientation="v",
measure=self.MEASURES,
x=self.ORDER,
textposition="outside",
text=self.create_bartext(),
y=[self.qc_vols[key] for key in self.ORDER],
connector={"mode": "spanning"},
)).update_layout(
plot_bgcolor="white",
title="Waterfall chart showing changes from SWATINIT to SWAT",
margin={
"t": 50,
"b": 50,
"l": 50,
"r": 50
},
).update_yaxes(title="Water Volume (Mrm3)",
range=self.range,
**axis_defaults()).update_xaxes(
type="category",
tickangle=-45,
tickfont_size=17,
**axis_defaults(showgrid=False),
))
def _plot_force_df_coef(self, force_df, name=None):
"""
Plot forced with coef column (useful in regressions)
"""
height = max(600, int(len(force_df) / 3 * 100))
measure = ["relative"] * len(force_df)
x, text, y = [], [], []
for i, row in force_df.iterrows():
y.append(row['feature'][:100])
text.append(f"{row['overall']} ({row['diff_perc']:+.2f}%)")
x.append(row['diff_perc'])
fig = go.Figure(go.Waterfall(
name="20", orientation="h",
measure=["relative"] * len(force_df),
x=x,
textposition="outside",
text=text,
y=y,
connector={"line": {"color": "rgb(63, 63, 63)"}},
))
title = "Analysis " if isNone(name) else f"Analysis of {name}"
fig.update_layout(
title=title,
showlegend=False,
height=height,
)
fig.update_yaxes(showticklabels=False)
fig.update_xaxes(showticklabels=False)
return fig
def update_graph(options_chosen):
dff = df[df['Source'].isin(options_chosen)]
print(dff['Source'].unique())
fig = go.Figure(
go.Waterfall(
name="20",
orientation="v",
measure=dff['Measure'],
x=dff['Source'],
textposition="inside",
text=dff['Contribution'],
y=dff['Contribution'],
increasing={"marker": {
"color": "#8F2738"
}},
decreasing={"marker": {
"color": "Teal"
}},
totals={
"marker": {
"color": "#ffb01f",
"line": {
"color": "gold",
"width": 3
}
}
},
connector={"line": {
"color": "#C0C0C0"
}},
))
fig.update_yaxes(visible=True, title_text='W/m<sup>2</sup>')
return (fig)
def _plot_force_df_perc(self, force_df, name=None):
"""
Plot forced with perc column (useful in biclassifications)
"""
learn = self.learn
height = max(600, int(len(force_df) / 3 * 100))
x, text, y = [], [], []
for i, row in force_df.iterrows():
y.append(row['feature'][:100])
text.append(f"{row['overall']:.5f} ({row['diff_perc']:+.2f}%)")
x.append(row['overall_diff'])
fig = go.Figure(go.Waterfall(
name="20", orientation="h",
measure=["relative"] * len(force_df),
x=x,
textposition="outside",
text=text,
y=y,
connector={"line": {"color": "rgb(63, 63, 63)"}},
))
clss = learn.dls.vocab[0]
title = "" if isNone(name) else f"Analysis of {name}"
title = f"{title}\n Probability of item is '{clss}'"
fig.update_layout(
title=title,
showlegend=False,
height=height,
)
fig.update_yaxes(showticklabels=False)
return fig
def waterfall(df_in, date, interval):
x_list, y_list, y_range = calculations.waterfall_data_prep(df_in, date)
fig_waterfall = go.Figure()
fig_waterfall.add_trace(go.Waterfall(
name = 'networth', orientation = "v",
measure = ["absolute", "relative", "relative", "relative", "relative", "relative", "relative", "relative", "total"],
x = x_list,
textposition = "outside",
#text = ["", "+80", "", "-40", "-20", "Total"],
y = y_list,
connector = {"line":{"color":"rgb(63, 63, 63)"}},))
#fig_waterfall.update_xaxes(showgrid=False, showline=True, linewidth=2, linecolor='#404b6b', mirror=True, tickfont=dict(family='sans-serif',color='#fffef9',size=14))
fig_waterfall.update_yaxes(showgrid=True, showline=False, zeroline=False, gridcolor='#d5d7d8',
tickfont=dict(family='sans-serif',color='black',size=14),
range=y_range)
fig_waterfall.update_layout(margin={'l':40,'b':0,'r':0,'t':20,}, yaxis={'type':'linear'},plot_bgcolor='white',paper_bgcolor='white',
height=260,width=580, hoverlabel=dict(
bgcolor="white",
font_size=16,
font_family="Rockwell"
))
return fig_waterfall
def closed_income_composition_graph_html(portfolio_rows, base_currency):
profits = {}
courses = _get_current_currency_courses(base_currency)
for row in portfolio_rows:
profit = row.income
profits[row.ticker] = round(
profit *
_get_current_currency_course(courses, base_currency, row.currency),
1)
profits = dict(
sorted(profits.items(), key=lambda item: item[1], reverse=True))
fig = go.Figure(
go.Waterfall(
orientation="v",
measure=["relative"
for i in range(len(list(profits.keys())))] + ['total'],
x=list(profits.keys()) + ['total'],
textposition="outside",
textangle=-90,
texttemplate='%{text:.2s}',
text=list(profits.values()) + [sum(list(profits.values()))],
y=list(profits.values()) + [sum(list(profits.values()))],
))
fig.update_layout(title="Состав дохода (закрытые позиции)",
showlegend=False)
return fig.to_html(full_html=False, include_plotlyjs=False)
def waterfall_plot(cashflow, label=""):
n = cashflow.shape[0]
fig = go.Figure(
go.Waterfall(name="",
orientation="v",
measure=["absolute"] * n,
x=["Mês " + str(x) for x in range(n)],
textposition="outside",
y=cashflow,
connector={"line": {
"width": 0,
"color": "black"
}},
totals={
"marker": {
"color": "#ff774a",
"line": {
"color": "black",
"width": 1
}
}
}))
fig.update_layout(
title={
"text": label,
"x": 0.5,
"xanchor": "center"
},
showlegend=False,
)
fig.show()
def waterfall(df_in, date):
x_list, y_list, y_range = calculations.waterfall_data_prep(df_in, date)
fig_waterfall = go.Figure()
fig_waterfall.add_trace(go.Waterfall(
name = 'networth', orientation = "v",
measure = ["absolute", "relative", "relative", "relative", "relative", "relative", "relative", "relative", "total"],
x = x_list,
textposition = "outside",
y = y_list,
connector = {"line":{"color":"rgb(63, 63, 63)"}},))
fig_waterfall.update_xaxes(autorange=True, showline=True, title="")
fig_waterfall.update_yaxes(autorange=False, range=y_range,
gridcolor="rgba(127, 127, 127, 0.2)",
mirror=False,
nticks=4,
showgrid=True,
showline=True,
linewidth=1,
ticklen=5,
ticks='outside',
#title="",
type="linear",
zeroline=False,
zerolinewidth=4,)
fig_waterfall.update_layout(margin={'l':10,'b':10,'r':0,'t':20,}, yaxis={'type':'linear'},plot_bgcolor='white',paper_bgcolor='white',
height=200,width=330, autosize=False,hovermode='closest')
return fig_waterfall
def plot_waterfall(measures,
dropdown_tag,
x_labels,
y_values,
disp_values,
dicts_ar,
title=None):
"""
Function to display waterfall plots with a dropdown selector in plotly.
Different data slices can be selected by timestamp from a dropdown selection menu.
These slices of data are contained within dictionary objects passes as a list to fig.update_layout.
Code assumes that dropdown information shouldn't be plotted.
Args:
measures (list): Defines the type of column to plot in the waterfall - 'absolute' or 'relative'.
dropdown_tag (str): The column that contains the timestamp values.
x_labels (list): Defines the names for the columns to plot.
y_labels (list): Initial data to plot.
disp_values (numpy.ndarray) Initial data to display.
dicts_ar (list): List of dicts containing data to be passed to fig.update_layout.
title (str): Plot title to be displayed.
Returns:
None
"""
# Define the initial plot
fig = go.Figure(
go.Waterfall(
orientation="v",
measure=measures,
x=x_labels,
textposition="outside",
y=y_values,
text=disp_values,
connector={"line": {
"color": "rgb(63, 63, 63)"
}},
))
# Add dynamic plot that is built from the dicts inside dicts_ar
fig.update_layout(updatemenus=[
dict(buttons=dicts_ar,
direction="down",
pad={
"r": 10,
"t": 10
},
showactive=True,
xanchor="auto",
x=0.25,
yanchor="top",
y=1.2),
])
fig.update_layout(title=title, showlegend=False)
fig.show()
def getwaterfall(batch=2017, base=3000):
frame = grouped[grouped.year == batch].copy()
first = str(batch)
first = first[2:]
second = str(int(first) + 1)
title_ = 'O Level Batch of ' + str(batch) + '-' + second
nine = int(frame['Number of Students in 9-C'])
ten = grouped[grouped.year == batch + 1]
dropouts_in_10c = nine - int(ten['Students retained in 10-C'])
new_students_in_10c = int(ten['New Students in 10-C'])
total_in_10c = int(ten['Number of Students in 10-C'])
eleven = grouped[grouped.year == batch + 2]
dropouts_in_11c = total_in_10c - int(eleven['Students retained in 11-C'])
new_students_in_11c = int(eleven['New students in 11-C'])
total_in_11c = int(eleven['Number of Students in 11-C'])
numbers = [
nine - base, dropouts_in_10c * -1, new_students_in_10c, None,
dropouts_in_11c * -1, new_students_in_11c, None
]
texts = [
nine, dropouts_in_10c, new_students_in_10c, total_in_10c,
dropouts_in_11c, new_students_in_11c, total_in_11c
]
texts = [str(i) for i in texts]
fig = go.Figure(
go.Waterfall(
x=[["9-C", "10-C", "10-C", "10-C", "11-C", "11-C", "11-C"],
[
"Enrolled in 9-C", "Dropouts", "New", "total", "Dropouts",
"New", "total"
]],
measure=[
"absolute", "relative", "relative", "total", "relative",
"relative", "total"
],
y=numbers,
base=base,
text=texts,
textposition="auto",
decreasing={"marker": {
"color": "#DC143C"
}},
increasing={"marker": {
"color": "#50C878"
}},
totals={"marker": {
"color": "deep sky blue"
}}))
fig.update_layout(title=title_,
waterfallgap=0.5,
width=1000,
paper_bgcolor="white")
fig.update_yaxes(title_text='Number of Students')
fig.show()
def waterfall_plot_transactions(transactions: Transactions, plot_dates=True): fig = go.Figure() fig.add_trace( go.Waterfall( x = [t.date for t in transactions] if plot_dates else None, y = [t.ammount for t in transactions], text = [str(t.date) + '<br>Comment: ' + t.comment + '<br>Tags: ' + str(t.tags) for t in transactions], offset = 0, # See https://plot.ly/python/reference/#waterfall-offset ) ) return fig
def is_waterfall_plot(df, index_no):
measure_list = np.array(["relative", "relative", "total", "relative","total","relative",'relative',"total",'relative',"total"])
if len(index_no) > 0:
measure_list = measure_list[np.arange(len(measure_list)) != index_no]
fig = go.Figure()
fig.add_trace(go.Waterfall(
orientation='v',
measure = measure_list,
x = np.array(df['index']),
y = df.Amount))
return st.plotly_chart(fig) #fig.show()
def simple_waterfall_chart(x_list, y_list):
fig = go.Figure(go.Waterfall(
name="20", orientation="v",
measure=["relative"] * (len(x_list) - 1) + ["total"],
x=x_list,
textposition="outside",
y=y_list,
connector={"line": {"color": "rgb(63, 63, 63)"}},
))
fig.update_layout(
showlegend=False
)
fig.update_layout(margin=dict(l=0, r=0, t=0, b=0), )
return fig
def drawWaterfall(total_list, path):
fig = go.Figure()
fig.add_trace(go.Waterfall(
x = list(range(len(total_list))),
orientation = "h",
measure=['absolute'] + ['relative' for i in range(len(total_list) - 1)],
base=0,
decreasing = {"marker":{"color":"Maroon", "line":{"color":"red", "width":2}}},
increasing = {"marker":{"color":"Teal"}},
))
fig.update_layout(title="Waterfall", waterfallgap=0.3)
# fig.show()
fig.write_image(path + "_waterfall.png")
def attribution_waterfall_chart(factor_selection,to_plot):
n = len(factor_selection)
str1 = to_plot
import plotly.graph_objects as go
df4=make_dataset(factor_selection)
fig = go.Figure(go.Waterfall(
name = "Absolute " + str1 + " Attribution", orientation = "h", measure = ["relative"]*(n+1) + ["total"],
y = factor_selection + ["Alpha","Total Returns"],
x = list(df4[str1]),
connector = {"mode":"between", "line":{"width":4, "color":"rgb(0, 0, 0)", "dash":"solid"}}
))
fig.update_layout(title={'text': "Absolute " + str1 + " Attribution", 'y':0.97, 'x':0.5,'xanchor': 'center','yanchor': 'top'},
font=dict(size=18,),
margin = dict(t=60,r=0,b=0,pad=0),
height = 350, width = 700,
xaxis= {'showspikes': True, 'title': '%age returns'},
yaxis= {'showspikes': True},
spikedistance = 10)
return fig
def _waterfall_df(df,
x,
y,
theme,
agg_func='sum',
orientation='v',
show_label=False):
dff = df.groupby(x, as_index=True,
sort=False).agg({y: agg_func})[y].reset_index()
initial = dff[y].iloc[0]
value_sum = dff[y].sum()
dff[y] = dff[y].diff(periods=1)
dff[y].iloc[0] = initial
dff = dff.append({x: 'Total', y: value_sum}, ignore_index=True)
text = dff[y].astype(
str).tolist()[:-1] + ['Total'] if show_label == True else None
fig = go.Figure(
go.Waterfall(
orientation=orientation,
measure=sum([['relative']
for _ in range(df[x].unique().shape[0])], []) +
['total'],
x=dff[x],
textposition="outside",
text=text,
y=dff[y],
connector={"line": {
"color": "rgb(63, 63, 63)"
}}))
fig.update_layout(title=None,
showlegend=False,
template=theme,
waterfallgap=0.3)
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
return fig
go.Waterfall(
name="20",
orientation="v",
measure=[
"relative", "relative", "total", "relative", "relative", "total",
"relative", "total"
],
x=[
"Revenue", "Cost of Revenue", "Gross Profit", "R&D Expenses",
"SG&A Expense", "Operating Expenses", "Interest Expense",
"Earnings before Tax", "Income Tax Expense", "Net Income Com"
],
textposition="outside",
text=[
REV / 1000, COGs / 1000, GrossProfit / 1000, RD / 1000, GA / 1000,
OperExp / 1000, INT / 1000, EBT / 1000, IncTax / 1000,
NetInc / 1000
],
y=[REV, COGs, GrossProfit, RD, GA, OperExp, INT, EBT, IncTax, NetInc],
connector={"line": {
"color": "rgba(63, 63, 63, 0.7)"
}},
decreasing={"marker": {
"color": "rgba(219, 64, 82, 0.7)"
}},
increasing={"marker": {
"color": "rgba(50, 171, 96, 0.7)"
}},
totals={"marker": {
"color": "rgba(55, 128, 192, 0.7)"
}},
))
def visualizations(self, data, base_year, end_year, loa, model,
energy_type, rename_dict):
"""Visualize additive LMDI results in a waterfall chart, opens in internet browsers and
user must save manually (from plotly save button)
"""
data = data[data['@filter|Model'] == model.capitalize()]
structure_cols = []
for column in data.columns:
if column.endswith('Structure'):
structure_cols.append(column)
if len(structure_cols) == 1:
data = data.rename(
columns={structure_cols[0]: '@filter|Measure|Structure'})
elif len(structure_cols) > 1:
data['@filter|Measure|Structure'] = data[structure_cols].sum(
axis=1) # Current level total structure
to_drop = [
s for s in structure_cols if s != '@filter|Measure|Structure'
]
data = data.drop(to_drop, axis=1)
x_data = []
if '@filter|Measure|Structure' in data.columns:
x_data.append('@filter|Measure|Structure')
else:
for c in data.columns:
if 'Structure' in c:
x_data.append(c)
if "@filter|Measure|Intensity" in data.columns:
x_data.append("@filter|Measure|Intensity")
else:
for c in data.columns:
if 'Intensity' in c:
x_data.append(c)
if "@filter|Measure|Activity" in data.columns:
x_data.append("@filter|Measure|Activity")
else:
for c in data.columns:
if c.endswith('Activity'):
x_data.append(c)
loa = [l.replace("_", " ") for l in loa]
if loa[0] == loa[-1]:
loa = [loa[0]]
else:
loa = [loa[0], loa[-1]]
final_year = max(data['@timeseries|Year'])
data_base = data[data['@timeseries|Year'] == base_year][x_data]
data_base['intial_energy'] = self.energy_data.loc[base_year,
self.total_label]
data = data[data['@timeseries|Year'] == end_year][x_data]
if self.end_year in self.energy_data.index:
data['final_energy'] = self.energy_data.loc[end_year,
self.total_label]
else:
data['final_energy'] = self.energy_data.loc[
max(self.energy_data.index), self.total_label]
x_data = ['intial_energy'] + x_data + ['final_energy']
y_data = pd.concat([data_base, data],
ignore_index=True,
axis=0,
sort=False).fillna(0)
y_data = y_data[x_data]
y_data.loc[:, 'final_energy'] = 0
y_data = y_data.sum(axis=0).values.tolist()
y_labels = [self.format_y_vals(y) for y in y_data]
x_labels = [x_.replace('@filter|Measure|', '') for x_ in x_data]
x_labels = [x.replace("_", " ").title() for x in x_labels]
measure = ['relative'] * 4 + ['total']
fig = go.Figure(
go.Waterfall(name="Change",
orientation="v",
measure=measure,
x=x_labels,
textposition="outside",
y=y_data,
text=y_labels,
connector={"line": {
"color": "rgb(63, 63, 63)"
}}))
title = f"Change in {energy_type.capitalize()} Energy Use (TBtu) {base_year}-{final_year} {' '.join([l.title() for l in loa])}"
fig.update_layout(title=title, font=dict(size=18), showlegend=True)
fig.show()
def main(stackname, profile='default', region='us-east-2'):
session = boto3.session.Session(profile_name=profile, region_name=region)
cloudformation_client = session.client('cloudformation')
paginator = cloudformation_client.get_paginator('describe_stack_events')
response = paginator.paginate(StackName=stackname)
first = True
start = None
data = {}
# Drop it, flip it, and reverse it.
events = order_events([e for e in response])
for page in events:
for event in page['StackEvents']:
if first:
start = event['Timestamp']
first = False
# Initialize the waterfall for this Resource
if event['LogicalResourceId'] not in data:
base = event['Timestamp'] - start
data[event['LogicalResourceId']] = {
'result': {
'x': [],
'y': [],
'text': [],
'textfont': {
"family": "Open Sans, light",
"color": "black"
},
'textposition': "outside",
'width': 0.5,
'base': base.seconds,
'measure': [],
'increasing': {
"marker": {
"color": "LightBlue"
}
}
},
'start_time': event['Timestamp']
}
# Calculate the distance from stack start
duration = event['Timestamp'] - \
data[event['LogicalResourceId']]['start_time']
# If there are no values recorded for this resource ID, set the first
# one as an absolute. TODO: this may need to change.
if len(data[event['LogicalResourceId']]['result']['measure']) == 0:
data[event['LogicalResourceId']]['result']['measure'].append(
'absolute')
# Otherwise set it as relative
else:
data[event['LogicalResourceId']]['result']['measure'].append(
'relative')
# Record the results
data[event['LogicalResourceId']]['result']['x'].append(
duration.seconds)
data[event['LogicalResourceId']]['result']['y'].append(
event['LogicalResourceId'])
# If this is the last event then set a text label for the
# total time the resource took to deploy.
if event['ResourceStatus'] == 'CREATE_COMPLETE':
resource_duration = sum(
data[event['LogicalResourceId']]['result']['x'])
data[event['LogicalResourceId']]['result']['text'].append(
format_time_from_seconds(resource_duration))
else:
data[event['LogicalResourceId']]['result']['text'].append('')
# Format the total run time for display
total_time = format_time_from_seconds(data[stackname]["result"]["x"][-1])
fig = go.Figure()
fig.update_layout(title={
'text':
f'<span style="color:#000000">CloudFormation Waterfall - {stackname}<br /><b>Total Time: {total_time}</b></span>'
},
showlegend=False,
height=(len(data) * 30),
font={
'family': 'Open Sans, light',
'color': 'black',
'size': 14
},
plot_bgcolor='#FFF')
fig.update_xaxes(title="Time in Seconds",
tickangle=-45,
tickfont=dict(family='Open Sans, light',
color='black',
size=12))
fig.update_yaxes(title="CloudFormation Resources",
tickangle=0,
tickfont=dict(family='Open Sans, light',
color='black',
size=12),
linecolor='#000')
for k, v in data.items():
fig.add_trace(go.Waterfall(orientation='h', **v['result']))
fig.show()
def test_pop_data(self):
fig = go.Figure(data=go.Waterfall(y=[2, 1, 3]))
self.assertEqual(fig.pop("data"), (go.Waterfall(y=[2, 1, 3]), ))
self.assertEqual(fig.data, ())
def test_scalar_trace_as_data(self):
fig = go.Figure(data=go.Waterfall(y=[2, 1, 3]))
self.assertEqual(fig.data, (go.Waterfall(y=[2, 1, 3]), ))
fig = go.Figure(data=dict(type="waterfall", y=[2, 1, 3]))
self.assertEqual(fig.data, (go.Waterfall(y=[2, 1, 3]), ))
print(p.value(x), p.value(y), p.value(z), p.value(a),
p.value(hosp_prob.objective))
#-------------------------------VISUALISATION-----------------------------------
fig = go.Figure(
go.Waterfall(
name="20",
orientation="v",
measure=['relative', 'relative', 'relative', 'relative', 'total'],
x=[
'Medical Staff', 'Nursing Staff', 'Admin Staff', 'Cleaning Staff',
'Total Cost'
],
textposition="outside",
text=[(p.value(x) * med_cost), (p.value(y) * nurs_cost),
(p.value(z) * admin_cost), (p.value(a) * clean_cost),
p.value(hosp_prob.objective)],
y=[(p.value(x) * med_cost), (p.value(y) * nurs_cost),
(p.value(z) * admin_cost), (p.value(a) * clean_cost),
p.value(hosp_prob.objective)],
connector={"line": {
"color": "rgb(63, 63, 63)"
}},
))
fig.update_layout(title="Optimised hospital staffing expenditure",
showlegend=False)
fig.show()
def update_graph3(option_slctd1, option_slctd2, option_slctd3):
print('*' * 100)
# Initial Filter
df_bs = df_fin_filt_1.copy()
df_bs = df_bs[df_bs["Sector"] == option_slctd1]
df_bs = df_bs[df_bs["SGX_CoyName"] == option_slctd3]
df_bs = df_bs[[option_slctd2, "Info_Label", "Statement_Type"]]
df_bs[option_slctd2] = df_bs[option_slctd2] #.abs()
df_bs = df_bs[
df_bs["Statement_Type"] ==
'Cash_Flow'] # dff = dff[dff["Statement_Type"] == "Income_Statement"]]
label_sort_ = [
{
'netIncome': 'absolute'
},
{
'totalCashFromOperatingActivities': 'relative'
},
{
'totalCashflowsFromInvestingActivities': 'relative'
},
{
'totalCashFromFinancingActivities': 'relative'
},
]
df_corr = pd.DataFrame({
'netIncome': [1],
'totalCashFromOperatingActivities': [1],
'totalCashflowsFromInvestingActivities': [1],
'totalCashFromFinancingActivities': [1],
})
# ERROR req_labels = list(set().union(*(d.keys() for d in label_sort_)))
req_labels = [i for s in [d.keys() for d in label_sort_] for i in s]
req_measures = [i for s in [d.values() for d in label_sort_] for i in s]
# Filter only required
df_bs_filt = df_bs[df_bs["Info_Label"].isin(req_labels)]
df_corr = df_corr[list(df_bs_filt["Info_Label"])].T.reset_index()
df_corr.columns = ['Info_Label', 'Corrector']
df_bs_filt_2 = pd.merge(df_bs_filt, df_corr, on=['Info_Label'])
df_bs_filt_2 = convert_df_cols_to_float(df_bs_filt_2, [option_slctd2])
# Order
sorterIndex = dict(zip(req_labels, range(len(req_labels))))
df_bs_filt_2['Order_id'] = df_bs_filt_2['Info_Label'].map(sorterIndex)
df_bs_filt_2.sort_values(['Order_id'], inplace=True)
req_vals_cor = [
a * b for a, b in zip(list(df_bs_filt_2[option_slctd2]),
list(df_bs_filt_2['Corrector']))
]
df_bs_filt_2[option_slctd2] = req_vals_cor
# Set Chart X-values / Y-values
x_labels = list(df_bs_filt_2['Info_Label']) # req_labels
y_labels = list(df_bs_filt_2[option_slctd2])
# # adjust due to yahoo error
# pos0 = 1
# adjust0 = y_labels[x_labels.index("totalAssets")]-y_labels[x_labels.index("totalCurrentAssets")]
# y_labels[pos0:pos0] = [adjust0]
# x_labels[pos0:pos0] = ['totalNonCurrentAssets']
# req_measures[pos0:pos0] = ['relative']
# # y_labels[pos0:pos0] = [y_labels[x_labels.index("totalCurrentAssets")]]
# pos1 = 3
# adjust1 = y_labels[x_labels.index("totalLiab")]-y_labels[x_labels.index("totalCurrentLiabilities")]
# y_labels[pos1:pos1] = [-adjust1]
# x_labels[pos1:pos1] = ['totalNonCurrentLiabilities']
# req_measures[pos1:pos1] = ['relative']
# y_labels[4] = -y_labels[x_labels.index("totalCurrentLiabilities")]
# #y_labels[pos1 + 2] = [-y_labels[x_labels.index("totalCurrentLiabilities")]]
# # adjust due to yahoo error
# pos2 = 6
# adjust2 = y_labels[x_labels.index("totalStockholderEquity")] - y_labels[x_labels.index("commonStock")]
# y_labels[pos2:pos2] = [adjust2]
# x_labels[pos2:pos2] = ['retainedEarnings']
# req_measures[pos2:pos2] = ['relative']
# adjust due to yahoo error
pos3 = len(y_labels)
# adjust3 = y_labels[x_labels.index("netIncome")]+y_labels[x_labels.index("totalCashFromOperatingActivities")]
adjust3 = sum(map(float, y_labels))
y_labels[pos3:pos3] = [adjust3]
x_labels[pos3:pos3] = ['Cash']
req_measures[pos3:pos3] = ['total']
# Graph
import plotly.graph_objects as go
fig = go.Figure(
go.Waterfall(
name="20",
orientation="v",
measure=req_measures,
x=x_labels,
textposition="outside",
# text=["+60", "+80", "", "-40", "-20", "Total"],
y=y_labels,
connector={"line": {
"color": "rgb(63, 63, 63)"
}},
))
title_ = 'Cash flow for {}'.format(option_slctd3)
fig.update_layout(
title=title_,
# showlegend=True,
width=700,
height=500,
xaxis_title="Content",
yaxis_title="Value",
template='plotly_dark')
return fig
import plotly.express as px
import plotly.graph_objects as go
fig = go.Figure(
go.Waterfall(name="20",
orientation="v",
measure=[
"relative", "relative", "relative", "relative",
"relative", "total"
],
x=["Exp1", "Exp2", "Exp3", "Exp4", "Exp5", "Exp6"],
textposition="outside",
text=["100", "50", "130", "200", "40", "Total"],
y=[100, +50, 130, 200, 40, 0],
connector={"line": {
"color": "rgb(63, 63, 63)"
}},
increasing={"marker": {
"color": "green"
}},
totals={"marker": {
"color": "blue"
}}))
fig.update_layout(
title="Waterfall Chart",
showlegend=True,
xaxis_title='X Axis Title',
yaxis_title='Y Axis Title',
)
fig.layout.template = 'plotly_dark'
import plotly.graph_objects as go
fig = go.Figure(
go.Waterfall(
name="20",
orientation="v",
measure=[
"relative", "relative", "total", "relative", "relative", "total"
],
x=[
"Sales", "Consulting", "Net revenue", "Purchases",
"Other expenses", "Profit before tax"
],
textposition="outside",
text=["+60", "+80", "", "-40", "-20", "Total"],
y=[60, 80, 0, -40, -20, 0],
connector={"line": {
"color": "rgb(63, 63, 63)"
}},
))
fig.update_layout(title="Profit and loss statement 2018", showlegend=True)
fig.show()
go.Waterfall(
name="2018",
orientation="h",
measure=[
"relative",
"relative",
"relative",
"relative",
"relative",
"relative",
"relative",
],
y=[
x[1] for x in [
["Выпуск", "Output (X)"],
["Импорт", "Import (IM)"],
["Статистическое расхождение", "Statistical discrepancy"],
["Экспорт", "Export (EX)"],
["Инвестиции", "Investment (I)"],
["Конечное потребление", "Final consumption (C)"],
["Промежуточное потребление", "Intermediate consumption (AX)"],
]
],
x=[196.6, 21.6, -0.6, -31.9, -23.6, -69.3, -92.7],
connector={
"mode": "between",
"line": {
"width": 2,
"color": "rgb(0, 0, 0)",
"dash": "solid"
},
},
))
import pandas as pd
#df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')
df = pd.read_csv('./data/finance-charts-apple.csv')
# 1) Simple Waterfall Chart
fig = go.Figure(
go.Waterfall(
name="20",
orientation="v",
measure=[
"relative", "relative", "total", "relative", "relative", "total"
],
x=[
"Продажи", "Консультации", "Чистый доход", "Покупки",
"Прочие расходы", "Прибыль до вычета налога"
],
textposition="outside",
text=["+60", "+80", "", "-40", "-20", "Total"],
y=[60, 80, 0, -40, -20, 0],
connector={"line": {
"color": "rgb(63, 63, 63)"
}},
))
fig.update_layout(title="Отчет о прибылях и убытках 2018", showlegend=True)
fig.show()
# 2) Multi Category Waterfall Chart
fig = go.Figure()
def update_graph(option_slctd1, option_slctd2, option_slctd3):
# Initial Filter
df_is = df_fin_filt_1.copy()
df_is = df_is[df_is["Sector"] == option_slctd1]
df_is = df_is[df_is["SGX_CoyName"] == option_slctd3]
df_is = df_is[[option_slctd2, "Info_Label", "Statement_Type"]]
df_is[option_slctd2] = df_is[option_slctd2].abs()
df_is = df_is[
df_is["Statement_Type"] ==
'Income_Statement'] # dff = dff[dff["Statement_Type"] == "Income_Statement"]
# print('Incme Statement')
# print(df_is)
# Measure dictionary
# label_sort_ = [
# {'totalRevenue': 'absolute'}, {'costOfRevenue': 'relative'},
# {'grossProfit': 'total'}, {'totalOperatingExpenses': 'relative'}, {'totalOtherIncomeExpenseNet': 'relative'},
# {'operatingIncome': 'total'},
# {'interestExpense': 'relative'}, {'researchDevelopment': 'relative'},
# {'incomeBeforeTax': 'total'}, {'incomeTaxExpense': 'relative'},
# {'netIncome': 'absolute'},
# ]
# df_corr = pd.DataFrame({
# 'totalRevenue': [1], 'costOfRevenue': [-1],
# 'grossProfit': [1], 'totalOperatingExpenses': [-1],'totalOtherIncomeExpenseNet': [-1],
# 'operatingIncome': [1],
# 'interestExpense': [-1], 'researchDevelopment': [-1],
# 'incomeBeforeTax': [1], 'incomeTaxExpense': [-1],
# 'netIncome': [1]
# })
# label_sort_ = [
# {'totalRevenue': 'absolute'}, {'costOfRevenue': 'relative'},
# {'grossProfit': 'total'},
# {'operatingIncome': 'total'},
# {'interestExpense': 'relative'}, {'researchDevelopment': 'relative'},
# {'incomeBeforeTax': 'total'}, {'incomeTaxExpense': 'relative'},
# {'netIncome': 'absolute'},
# ]
# df_corr = pd.DataFrame({
# 'totalRevenue': [1], 'costOfRevenue': [-1],
# 'grossProfit': [1],
# 'operatingIncome': [1],
# 'interestExpense': [-1], 'researchDevelopment': [-1],
# 'incomeBeforeTax': [1], 'incomeTaxExpense': [-1],
# 'netIncome': [1]
# })
label_sort_ = [{
'totalRevenue': 'absolute'
}, {
'grossProfit': 'total'
}, {
'operatingIncome': 'total'
}, {
'incomeBeforeTax': 'total'
}, {
'netIncome': 'total'
}]
df_is_corr = pd.DataFrame({
'totalRevenue': [1],
'grossProfit': [1],
'operatingIncome': [1],
'incomeBeforeTax': [1],
'netIncome': [1]
})
# ERROR req_labels = list(set().union(*(d.keys() for d in label_sort_)))
req_labels = [i for s in [d.keys() for d in label_sort_] for i in s]
req_measures = [i for s in [d.values() for d in label_sort_] for i in s]
# req_correctors = [i for s in [d.values() for d in label_correction] for i in s]
df_is_filt = df_is[df_is["Info_Label"].isin(req_labels)]
df_is_corr = df_is_corr[list(df_is_filt["Info_Label"])].T.reset_index()
df_is_corr.columns = ['Info_Label', 'Corrector']
df_is_filt_2 = pd.merge(df_is_filt, df_is_corr, on=['Info_Label'])
df_is_filt_2 = convert_df_cols_to_float(df_is_filt_2, [option_slctd2])
sorterIndex = dict(zip(req_labels, range(len(req_labels))))
df_is_filt_2['Order_id'] = df_is_filt_2['Info_Label'].map(sorterIndex)
df_is_filt_2.sort_values(['Order_id'], inplace=True)
req_vals_cor = [
a * b for a, b in zip(list(df_is_filt_2[option_slctd2]),
list(df_is_filt_2['Corrector']))
]
df_is_filt_2[option_slctd2] = req_vals_cor
x_labels = list(df_is_filt_2['Info_Label']) # req_labels
y_labels = list(df_is_filt_2[option_slctd2])
# adjust due to yahoo error
pos0 = 1
adjust0 = y_labels[x_labels.index("grossProfit")] - y_labels[
x_labels.index("totalRevenue")]
y_labels[pos0:pos0] = [adjust0]
x_labels[pos0:pos0] = ['costOfRevenue']
req_measures[pos0:pos0] = ['relative']
adjust1 = y_labels[x_labels.index("operatingIncome")] - y_labels[
x_labels.index("grossProfit")]
y_labels[3:3] = [adjust1]
x_labels[3:3] = ['operatingExpense']
req_measures[3:3] = ['relative']
pos2 = 5
adjust2 = y_labels[x_labels.index("incomeBeforeTax")] - y_labels[
x_labels.index("operatingIncome")]
y_labels[pos2:pos2] = [adjust2]
x_labels[pos2:pos2] = ['interestExpense']
req_measures[pos2:pos2] = ['relative']
pos3 = 7
adjust3 = y_labels[x_labels.index("netIncome")] - y_labels[x_labels.index(
"incomeBeforeTax")]
y_labels[pos3:pos3] = [adjust3]
x_labels[pos3:pos3] = ['taxExpense']
req_measures[pos3:pos3] = ['relative']
# Graph
import plotly.graph_objects as go
fig = go.Figure(
go.Waterfall(
name="20",
orientation="v",
measure=req_measures,
x=x_labels,
textposition="outside",
# text=["+60", "+80", "", "-40", "-20", "Total"],
y=y_labels,
connector={"line": {
"color": "rgb(63, 63, 63)"
}},
))
# title_ = "Balance Sheet {}".format(option_slctd2)
# fig.update_layout(
# title=title_,
#
# )
cht1_title = 'Income Statement for {}'.format(option_slctd3)
# layout = Layout(paper_bgcolor='rgb(0,0,0,0',plot_bgcolor='rgb(0,0,0,0')
fig.update_layout(
title=cht1_title,
xaxis_title="Content",
yaxis_title="Value",
# showlegend=True,
width=700,
height=500,
template='plotly_dark')
return fig
confirmados_age_m = melter(df, df.columns[23:40:2], 'data_dados')
total_confirmed = list(df_dict['confirmados'].values())[-1]
total_deaths = list(df_dict['obitos'].values())[-1]
external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
fig_confirmados_age_f = px.line(confirmados_age_f, x='data_dados', y='value', color='variable')
fig_confirmados_age_m = px.line(confirmados_age_m, x='data_dados', y='value', color='variable')
waterfall_fig = go.Figure(go.Waterfall(x=df['data_dados'], y=df['confirmados_novos']))
app.layout = html.Div([
html.H1('Portugal COVID Tracker'),
html.Div([
html.Div('Total confirmed: %d' % total_confirmed),
html.Div('Total deaths: %d' % total_deaths)
]),
html.Div([
dcc.Checklist(
id='radio_conf',
options=[{'label' : i, 'value': 'confirmados_' + i} for i in ['arsnorte', 'arscentro', 'arslvt', 'arsalentejo', 'arsalgarve', 'acores', 'madeira']],
value=['confirmados_arslvt']
),
dcc.Graph(id='line-graph'),
html.Div([
dcc.Markdown('''The next graph is a waterfall chart that shows the last 12 month movements by industry.
This way you can quickly see what has been growing (green) vs. going backwards (red) which provides a good starting point for a deep dive analysis. '''),
html.Div([html.H3("Australian GDP Year on Year Chain Volume Measure Movements by Industry")], style={"textAlign": "center"}),
html.Div([html.H4("(Last 12 months from Decemeber 2019 to Previous 12 months)")], style={"textAlign": "center"}),
dcc.Graph(
figure= go.Figure(
data=[
go.Waterfall(
orientation = 'h',
measure=measure,
x=x,
y=y,
connector=connector
)
],
layout = layoutWF
)
), dcc.Markdown('''Hopefully, with these two graphs you gain a quicker insight into what’s happening in the Australian economy over the longer term (compared to what’s normally reported).
I will endeavour to keep this up to date as new figures from the ABS is released. ''')
]
)
