def make_traces(self,
main_chart=ChartType.kline,
sub_chart="bar",
yaxis="y",
scale_value=None,
**kwargs):
traces = []
sub_traces = []
for entity_id, df in self.main_data.entity_map_df.items():
df = df.select_dtypes(np.number)
df = df.copy()
if scale_value:
for col in df.columns:
first = None
for i in range(0, len(df)):
first = df[col][i]
if first != 0:
break
if first == 0:
continue
scale = scale_value / first
df[col] = df[col] * scale
code = entity_id
try:
_, _, code = decode_entity_id(entity_id)
except Exception:
pass
# 构造主图
if main_chart == ChartType.bar:
for col in df.columns:
trace_name = "{}_{}".format(code, col)
ydata = df[col].values.tolist()
traces.append(
go.Bar(x=df.index,
y=ydata,
name=trace_name,
yaxis=yaxis,
**kwargs))
elif main_chart == ChartType.kline:
trace_name = "{}_kdata".format(code)
trace = go.Candlestick(
x=df.index,
open=df["open"],
close=df["close"],
low=df["low"],
high=df["high"],
name=trace_name,
yaxis=yaxis,
**kwargs,
)
traces.append(trace)
elif main_chart in [
ChartType.scatter, ChartType.line, ChartType.area
]:
mode = _zvt_chart_type_map_scatter_mode.get(main_chart)
for col in df.columns:
trace_name = "{}_{}".format(code, col)
ydata = df[col].values.tolist()
traces.append(
go.Scatter(x=df.index,
y=ydata,
mode=mode,
name=trace_name,
yaxis=yaxis,
**kwargs))
elif main_chart == ChartType.histogram:
for col in df.columns:
trace_name = "{}_{}".format(code, col)
x = df[col].tolist()
trace = go.Histogram(x=x, name=trace_name, **kwargs)
traces.append(trace)
annotation = [
dict(
entity_id=entity_id,
timestamp=x[-1],
value=0,
flag=f"{trace_name}:{x[-1]}",
)
]
annotation_df = pd.DataFrame.from_records(
annotation, index=["entity_id", "timestamp"])
if pd_is_not_null(self.annotation_df):
self.annotation_df = pd.concat(
[self.annotation_df, annotation_df])
else:
self.annotation_df = annotation_df
elif main_chart == ChartType.pie:
for _, row in df.iterrows():
traces.append(
go.Pie(name=entity_id,
labels=df.columns.tolist(),
values=row.tolist(),
**kwargs))
else:
assert False
# 构造主图指标
if self.factor_data_list:
for factor_data in self.factor_data_list:
if not factor_data.empty():
factor_df = factor_data.entity_map_df.get(entity_id)
factor_df = factor_df.select_dtypes(np.number)
if pd_is_not_null(factor_df):
for col in factor_df.columns:
trace_name = "{}_{}".format(code, col)
ydata = factor_df[col].values.tolist()
line = go.Scatter(x=factor_df.index,
y=ydata,
mode="lines",
name=trace_name,
yaxis=yaxis,
**kwargs)
traces.append(line)
# 构造幅图
if self.has_sub_plot():
for sub_data in self.sub_data_list:
sub_df = sub_data.entity_map_df.get(entity_id)
if pd_is_not_null(sub_df):
sub_df = sub_df.select_dtypes(np.number)
for col in sub_df.columns:
trace_name = "{}_{}".format(code, col)
ydata = sub_df[col].values.tolist()
def color(i):
if i > 0:
return "red"
else:
return "green"
colors = [color(i) for i in ydata]
the_sub_chart = None
if self.sub_col_chart is not None:
the_sub_chart = self.sub_col_chart.get(col)
if not the_sub_chart:
the_sub_chart = sub_chart
if the_sub_chart == ChartType.line:
sub_trace = go.Scatter(
x=sub_df.index,
y=ydata,
name=trace_name,
yaxis="y2",
marker=dict(color=colors))
else:
sub_trace = go.Bar(x=sub_df.index,
y=ydata,
name=trace_name,
yaxis="y2",
marker=dict(color=colors))
sub_traces.append(sub_trace)
return traces, sub_traces
## Describing data according to the Feedback
Alexa_train.groupby('feedback').describe()
### Data Visulization
## Distribution of ratings of Alexa
ratings = Alexa_train['rating'].value_counts()
label_rating = ratings.index
size_rating = ratings.values
Colors = ['pink', 'lightblue', 'aqua', 'gold', 'crimson']
rating_Piechart = go.Pie(labels=label_rating,
values=size_rating,
marker=dict(colors=Colors),
name='Alexa',
hole=0.3)
df = [rating_Piechart]
layout = go.Layout(title='Distribution of Ratings for Alexa')
fig = go.Figure(data=df, layout=layout)
#fig=go.Figure(data=[go.Pie(labels=label_rating,values=size_rating,marker=dict(colors=Colors),name='Alexa',hole=0.3)],layout=layout)
py.offline.plot(fig, filename='Pie_Chart1.html')
### BAR PLOT
#linespace
color = plt.cm.copper(np.linspace(0, 1, 15))
Alexa_train['variation'].value_counts().plot.bar(color=color, figsize=(15, 9))
{
'data': [Q2_Top5Plot_Bronx],
'layout': {
'title': 'Top 5 complaints for the borough of Bronx',
'margin': {
'l': '180'
}
}
},
filename='Q2_Top5_Bronx')
# Q3. Proportion of Open, Closed and Pending Complaints
ComplaintStatus_Q3 = spark.sql(
'SELECT Status,COUNT(Status) AS Counts FROM TABLE GROUP BY STATUS')
Q3_PieComplaintStatus = go.Pie(labels=ComplaintStatus_Q3.toPandas()['Status'],
values=ComplaintStatus_Q3.toPandas()['Counts'],
textinfo='label+percent')
py.plot(
{
'data': [Q3_PieComplaintStatus],
'layout': {
'title': 'Proportion of Closed and Open complaints'
}
},
filename='Q3_ComplaintStatus',
image='png')
# Q4. Total Complaints over the years
Q4_TotalComplaints = spark.sql(
'SELECT COUNT(*) AS `Number of Complaints`,SUBSTRING(`Created Date`,0,4) AS Year FROM TABLE GROUP BY SUBSTRING(`Created Date`,0,4) ORDER BY Year desc'
)
def interactive():
response = ''
while True:
response = input('Enter a command: ')
if response.strip() == '':
continue
if response == 'help':
print(help_text)
continue
elif response == 'exit':
print("bye")
break
elif response[:
24] == 'tv popularity and rating' or response[:
21] == 'tv country and rating' or response[:
22] == 'tv #released per month' or response[:
20] == 'tv ratings and month':
try:
tuple = process_command(response)
except:
print("Command not recognized: " + response)
continue
length_format = []
if not tuple:
print("No matched results")
continue
if response[:24] == 'tv popularity and rating':
tuple_print = [('TvName', 'Popularity', 'Rating')] + tuple
elif response[:21] == 'tv country and rating':
tuple_print = [('Country', 'Rating')] + tuple
elif response[:22] == 'tv #released per month':
tuple_print = [('Month', 'Count')] + tuple
elif response[:20] == 'tv ratings and month':
tuple_print = [('Month', 'Avg Rating')] + tuple
else:
continue
# print out results
for index in range(len(tuple_print[0])):
maxlen = -1
for row1 in tuple_print:
if len(str(row1[index])) > maxlen:
maxlen = len(str(row1[index]))
length_format.append(maxlen)
for line_temp in tuple_print:
for j in range(len(line_temp)):
format_string = "{0:<" + str(length_format[j]) + "s}"
print(format_string.format(str(line_temp[j])), end=" ")
print("")
if response[:24] == 'tv popularity and rating':
x_rating = []
y_popularity = []
for line_data in tuple:
y_popularity.append(line_data[1])
x_rating.append(line_data[2])
trace = go.Scatter(x=x_rating, y=y_popularity, mode='markers')
layout = dict(title='Population v.s. rating',
xaxis_title="Rating",
yaxis_title="Popularity")
data = [trace]
fig = go.Figure(data=data)
fig = fig.update_layout(layout)
fig.write_html('scatter.html', auto_open=True)
elif response[:21] == 'tv country and rating':
x_bar = [] # rating
y_bar = [] # country
for line_data in tuple:
x_bar.insert(0, line_data[1])
y_bar.insert(0, line_data[0])
trace = go.Bar(x=x_bar,
y=y_bar,
orientation='h',
marker=dict(color='rgba(246, 78, 139, 0.8)', ))
layout = dict(title='Barplot of country with Tv rating',
xaxis_title="Rating",
yaxis_title="Country")
data = [trace]
fig = go.Figure(data=data)
fig = fig.update_layout(layout)
fig.write_html('bar.html', auto_open=True)
elif response[:22] == 'tv #released per month':
values_pie = []
labels_pie = [
'January', 'February', 'March', 'April', 'May', 'June',
'July', 'August', 'September', 'October', 'November',
'December'
]
for line_data in tuple:
values_pie.append(line_data[1])
trace = go.Pie(
labels=labels_pie,
values=values_pie,
sort=False,
)
layout = dict(
title='Pie chart of the number of TVs per month', )
data = [trace]
fig = go.Figure(data=data)
fig = fig.update_layout(layout)
fig.write_html('pie.html', auto_open=True)
elif response[:20] == 'tv ratings and month':
x_line = []
y_line = []
for line_data in tuple:
x_line.append(line_data[0])
y_line.append(line_data[1])
trace = go.Scatter(x=x_line,
y=y_line,
mode='lines+markers',
name='lines+markers')
layout = dict(title='Line chart of ratings v.s. month in 2018',
xaxis_title="Month",
yaxis_title="Rating")
data = [trace]
fig = go.Figure(data=data)
fig = fig.update_layout(layout)
fig.write_html('line.html', auto_open=True)
else:
print("Command not recognized: " + response)
Header(),
#dcc.Link('', href='/jrc/job_analysis', className="tab"),
# Row 3
html.Div([
html.Div([
html.H6(["Distribution of job as per Years of Experience"],
className="gs-header gs-table-header padded")
],
className="row "),
html.Div([
dcc.Graph(id='graph-4',
figure={
'data': [
go.Pie(labels=labels,
values=val,
name="Years of Experience",
hoverinfo='label+value+percent+name')
],
'layout': {
'height': 500,
'clickmode': 'event+select'
}
}),
],
className="row ")
],
className="subpage1")
])
#Layout for time series trend for job openings
tra = html.Div([
def update_revenue(year, region, state, service):
filtered_data = car_data
if year != 'All':
filtered_data = filtered_data[filtered_data['invoice_year'] == int(
year)]
if region != 'All':
filtered_data = filtered_data[filtered_data['Region'] == region]
if state != 'All':
filtered_data = filtered_data[filtered_data['State'] == state]
if service != 'All':
filtered_data = filtered_data[filtered_data['Order Type'] == service]
revenue_data = filtered_data
class_revenue = pd.DataFrame(
revenue_data.groupby([
'Car_Class'
])['Total Amt Wtd Tax.'].sum()).sort_values(by='Total Amt Wtd Tax.',
ascending=False)
fig_1 = go.Figure(data=[
go.Pie(labels=class_revenue.index,
values=class_revenue['Total Amt Wtd Tax.'])
])
fig_1.update_traces(hoverinfo='label+value',
textinfo='percent',
textfont_size=20,
hole=0.4)
fig_1.update_layout(title_text="Distribution of Revenue by Car Class",
annotations=[
dict(text=state,
x=0.5,
y=0.5,
font_size=15,
showarrow=False)
])
make_count = pd.DataFrame(
revenue_data.groupby([
'Make'
])['Total Amt Wtd Tax.'].sum()).sort_values(by='Total Amt Wtd Tax.',
ascending=False).head(15)
fig_2 = go.Figure(
data=[go.Bar(x=make_count.index, y=make_count['Total Amt Wtd Tax.'])])
fig_2.update_layout(title_text="Revenue by Car Companies",
xaxis=dict(title='Car Make',
categoryorder='total descending'),
yaxis=dict(title='Revenue'))
# fig_2 = go.Figure(data=[go.Pie(labels = make_count.index,
# values=make_count['Regn No'])])
# fig_2.update_traces(hoverinfo='label+value', textinfo='percent', textfont_size=20, hole = 0.4)
# fig_2.update_layout(title_text = "Car Make: {}".format(state), annotations=[dict(text = state, x = 0.5, y = 0.5, font_size=15, showarrow=False)])
model_count = pd.DataFrame(
revenue_data.groupby([
'Model'
])['Total Amt Wtd Tax.'].sum()).sort_values(by='Total Amt Wtd Tax.',
ascending=False).head(15)
fig_3 = go.Figure(data=[
go.Bar(x=model_count.index, y=model_count['Total Amt Wtd Tax.'])
])
fig_3.update_layout(title_text="Revenue by Car Models",
xaxis=dict(title='Car Model',
categoryorder='total descending'),
yaxis=dict(title='Revenue'))
return fig_1, fig_2, fig_3
#pie_data = [
# {"company": "Company X", "market_share": 0.55},
# {"company": "Company Y", "market_share": 0.30},
# {"company": "Company Z", "market_share": 0.15}
#]
#print("----------------")
#print("GENERATING PIE CHART...")
#print(pie_data) # TODO: create a pie chart based on the pie_data
import plotly
import plotly.graph_objs as go
colors = ['gold', 'mediumturquoise', 'darkorange']
fig = go.Figure(data=[
go.Pie(labels=['Company X', 'Company Y', 'Company Z'],
values=[0.55, 0.30, 0.15])
])
#layout=(title="Pie Chart"))])
#layout(title="Pie Chart")
fig.update_traces(hoverinfo='label+percent',
textinfo='value',
textfont_size=20,
marker=dict(colors=colors,
line=dict(color='#000000', width=2)))
fig.show()
#
# CHART 2 (LINE)
#
#line_data = [
value='Claim Amount'),
className='col-3'),
html.Div([
## Graph Pie
dcc.Graph(
id='contoh-graph-pie',
figure={
'data': [
go.Pie(labels=[
'{}'.format(i) for i in
list(dfTsa_plot[
'Claim Type'].unique())
],
values=[
dfTsa_plot.groupby(
'Claim Type').
mean()
['Claim Amount'][i]
for i in
list(dfTsa_plot[
'Claim Type'].
unique())
],
sort=False)
],
'layout': {
'title': 'Mean Pie Chart'
}
})
])
])
],
def update_graph(select_index):
if select_index == '1':
figure = {
'data': [
go.Scatter(
x=d1['日期'].tolist(),
y=d1[yi].tolist(),
mode='lines',
name=city+yi
) for yi in ['最高温度','最低温度','紫外线强度指数']
],
'layout': [
go.Layout(
height=350,
hovermode='closest',
title='折线图'
)
]
}
return figure
elif select_index == '2':
figure = {
'data': [
go.Bar(
x=d1['日期'].tolist(),
y=d1[yi].tolist(),
name=city+yi
) for yi in ['相对湿度','能见度(公里)','风速(公里/小时)']],
'layout': [
go.Layout(
height=350,
hovermode='closest',
title='条形图',
)
]
}
return figure
elif select_index=='3':
figure = {
'data': [
go.Pie(
labels=quality_level,
values=quality_count,
name='legend'
)],
'layout': [
go.Layout(
height=350,
hovermode='closest',
title='饼状图'
)
]
}
return figure
elif select_index=='4':
figure = {
'data': [
go.Scatter(
x=df3['Longitude'].tolist(),
y=df3['Latitude'].tolist(),
text = df3['text'].tolist(),
mode='markers',
marker={'size': 15},
name='legend'
)],
'layout': [
go.Layout(
height=350,
hovermode='closest',
title ='散点图'
)
]
}
return figure
top5_average_rating = round(top5_rating_sum/top5_num,2)
#calculate all average rating
all_num=0
all_rating_sum=0
for i in range(0,10):
all_num+=all_value_lst[i]
for i in range(0,10):
all_rating_sum+=float(all_value_lst[i])*float(all_key_lst[i])
all_average_rating = round(all_rating_sum/all_num,2)
#save chart to image and render
trace1 = go.Pie(
labels=top5_key_lst,
values=top5_value_lst,
domain=dict(x=[0,0.4], y=[0,0.4]),
sort=False,
hole=0.4
)
trace2 = go.Bar(
x=top5_key_lst,
y=top5_value_lst,
xaxis='x2',
yaxis='y2',
marker=dict(color="maroon"),
showlegend=False
)
trace3 = go.Pie(
labels=all_key_lst,
values=all_value_lst,
domain=dict(x=[0,0.4], y=[0.6,1]),
state_average_funding = pd.DataFrame({'Donor State':donor_state_amount_project['Donor State'][-5:][::-1],'Average Funding':val[-5:][::-1]})
sns.barplot(x="Donor State",y="Average Funding",data=state_average_funding)
# # <a id='4-6'>4.6 Percentage of donors as teachers</a>
# In[ ]:
per_teacher_as_donor = donors['Donor Is Teacher'].value_counts().to_frame().reset_index()
per_teacher_as_donor = per_teacher_as_donor.rename(columns= {'index': 'Types'})
labels = ['Donor is a Teacher','Donor is not a Teacher']
values = per_teacher_as_donor['Donor Is Teacher'].tolist()
colors = ['#96D38C', '#E1396C']
trace = go.Pie(labels=labels, values=values,
hoverinfo='label+percent', textinfo='value',
textfont=dict(size=20),
marker=dict(colors=colors,
line=dict(color='#000000', width=2)))
py.iplot([trace], filename='styled_pie_chart')
# # <a id='4-7'>4.7 Top Donor Checked Out carts</a>
# In[ ]:
temp = donors_donations['Donor Cart Sequence'].value_counts().head()
df = pd.DataFrame({'labels': temp.index,'values': temp.values})
labels = df['labels'].tolist()
values = df['values'].tolist()
colors = ['#96D38C', '#E1396C','#C0C0C0','#FF0000','#F08080']
trace = go.Pie(labels=labels, values=values,
"""# Participación de los Cuentahabientes Garantizados"""
import plotly.graph_objects as go
fig = go.Figure()
x1 = ['BANPRO', 'BANCENTRO', 'BAC', 'BDF', 'FICOHSA', 'AVANZ', 'ATLANTIDA']
y1 = [33.9, 35.6, 22.5, 4.0, 1.5, 2.5, 0.007]
colors_bancos = [
'#66A731', '#006837', '#E3002B', '#0075BF', '#07C0DB', '#F48028', '#A61A11'
]
fig.add_trace(
go.Pie(labels=x1, values=y1, hole=.4, marker_colors=colors_bancos))
fig.update_layout(
barmode='stack',
xaxis_type='category',
title=
'<b></b><b>Participación de los Cuentahabientes Garantizados</b><br><i>Porcentajes</i><br><i>31 de Agosto 2020</i>',
paper_bgcolor='rgb(255, 255, 255)',
plot_bgcolor='rgb(255, 255, 255)',
font=dict(family="Courier New, monospace", size=18, color="#000000"),
annotations=[
dict(
x=1, #0.5,
y=-0.20, #-0.15,
showarrow=False,
text="Fuente: FOGADE",
us = csv.reader(userfile)
next(us, None)
countmovies = len(list(mv))
countusers = len(list(us))
moviefile.seek(0)
userfile.seek(0)
def Cloning(li1):
li_copy = list(li1)
return li_copy
moviefile.seek(0)
gencount = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
genre = [
'Biography', 'Horror', 'Crime', 'Animation', 'Musical', 'Romance',
'Family', 'Comedy', 'Thriller', 'Mystery', 'History', 'Sci-Fi', 'Fantasy',
'Drama', 'Sport', 'War'
]
listd = []
for row in mv:
listd = (row[8])
for j in range(0, 16):
if listd.find(genre[j]) != -1:
gencount[j] += 1
trace = go.Pie(labels=genre, values=gencount)
py.offline.plot([trace], filename='genre_splitup.html', auto_open=False)
print("Generated Genre Splitup")
movies.movie_name,
COUNT(cinema_session.ticket_number_fk)
FROM
cinema_session INNER JOIN movies ON cinema_session.movie_id_fk = movies.movie_id
GROUP BY
movies.movie_name
""")
movies = []
movie_tickets_count = []
for row in cursor.fetchall():
movies.append(row[0])
movie_tickets_count.append(row[1])
pie = go.Pie(labels=movies, values=movie_tickets_count)
movies_proportion_url = py.plot([pie], filename='movies-proportion')
""" create plot 3 """
cursor.execute("""
SELECT
cinema_session.buy_date, COUNT(cinema_session.ticket_number_fk)
FROM
cinema_session
GROUP BY
cinema_session.buy_date
ORDER BY
cinema_session.buy_date
""")
buy_dates = []
title="Fare Distribution",
xaxis_title="Passanger ID",
yaxis_title="Fare"
)
),
config={
'displayModeBar': False
}
),
], className="six columns"),
html.Div([
dcc.Graph(
id='graph3',
figure=go.Figure(
data=[go.Pie(
labels=barData.json()['labels'],
values=barData.json()['values']
)],
layout=go.Layout(
title="Passanger Class Distribution",
)
),
config={
'displayModeBar': False
}
)
], className="six columns")
], className="row")
])
'plot_bgcolor': colors['plot_color'],
'paper_bgcolor': colors['paper_color'],
'title': 'Visitors Count',
'font':{
'color': colors['text']
}
}
}
)
]),
html.Div([
dcc.Graph(
id = 'pie',
figure=go.Figure(
data = [ go.Pie(labels = data_gen.states, values = data_gen.states_cnt)],
layout= go.Layout(
title = 'Statewise vistors'
)
)
)
]),
html.Div([
dcc.Graph(
id = 'page-visits',
figure=go.Figure(
data = [ go.Bar(x = data_gen.pages, y = data_gen.pg_cnt)],
layout= go.Layout(
title = 'Statewise vistors'
)
sns.scatterplot(x=Combined_data['latitude'], y=Combined_data['longitude'])
plt.show()
sns.catplot(x='room_type', kind='count', data=Combined_data)
fig = plt.gcf()
fig.set_size_inches(8, 6)
plt.show()
import plotly.offline as pyo
import plotly.graph_objs as go
roomdf = Combined_data.groupby(
'room_type').size() / Combined_data['room_type'].count() * 100
labels = roomdf.index
values = roomdf.values
# Use `hole` to create a donut-like pie chart
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.6)])
plt.show()
plt.figure(figsize=(10, 6))
sns.countplot(x='room_type', hue="neighbourhood", data=Combined_data)
plt.title("Room types occupied by the neighbourhood")
plt.show()
#catplot room type and price
plt.figure(figsize=(10, 6))
sns.catplot(x="room_type", y="price", data=Combined_data)
plt.ioff()
fig, axes = plt.subplots(1, 2, figsize=(21, 6))
sns.distplot(Combined_data['minimum_nights'],
# Create the option to display charts that show the stats
# for gender representation for the last 120 years
if st.sidebar.checkbox("Gender Representation"):
male = df[df["Sex"] == "M"]["ID"].agg("count")
female = df[df["Sex"] == "F"]["ID"].agg("count")
fig1 = px.histogram(df,
x="Year",
color="Sex",
title="Gender Representation 1896-2016")
st.plotly_chart(fig1)
labels = ["Male Participants", "Female Participants"]
values = [male, female]
layout = go.Layout(title="Total Ratio Of Participants")
fig2 = go.Figure(data=[go.Pie(labels=labels, values=values, hole=0.5)],
layout=layout)
st.plotly_chart(fig2)
# Create the option to display information about the
# yearly total of participants
if st.sidebar.checkbox("Participants: Yearly Total"):
df["Games"] = df["Games"].fillna("0")
df = pd.DataFrame(df)
games = df["Games"].value_counts()
trace = go.Bar(
x=games.index,
y=games.values,
marker=dict(color=games.values, colorscale="Plotly3", showscale=True),
)
# EDA
percentual_sucess = round(df["state"].value_counts() / len(df["state"]) * 100,
2)
print("State Percent in %: ")
print(percentual_sucess)
# In[15]:
state = round(df["state"].value_counts() / len(df["state"]) * 100, 2)
labels = list(state.index)
values = list(state.values)
trace1 = go.Pie(labels=labels, values=values, marker=dict(colors=['red']))
layout = go.Layout(title='State Distribution', legend=dict(orientation="h"))
fig = go.Figure(data=[trace1], layout=layout)
iplot(fig)
# In[16]:
# Only consider failed or successful projects
# Exclude suspended, cancelled, and other misc states
df = df[(df['state'] == "failed") | (df['state'] == "successful")]
# In[17]:
state = round(df["state"].value_counts() / len(df["state"]) * 100, 2)
##auth = dash_auth.BasicAuth(app,USERNAME_PASSWORD_PAIRS)
server = app.server
colors = {
'chartbackground': '#ffffff',
'background':'#EEF1F6',
'text': '#000000'
}
piecolours=['#7BA3B7','#6C8FA0','#5C7A89','#4D6672','#3D525B','#2E3D45','1F292E','0F1417']
graph1 = [
dcc.Graph(
id='piechart',
figure={
'data': [go.Pie(labels=labels, values=sizes,
marker={'colors': ['#C0D2D8','#425772']}, textinfo='label')],
'layout': go.Layout(legend={'x': 1, 'y': 0.7},
plot_bgcolor = colors['background'], paper_bgcolor = colors['chartbackground'],
title='<b>Total Messages Per User</b>')
}
),
]
graph2 = [
dcc.Graph(
id='linegraph',
figure={
'data': [
{'x': data0['date'], 'y': data0['jodi_count'], 'type': 'line', 'name': 'Jodi'},
{'x': data0['date'], 'y': data0['richard_count'], 'type': 'line', 'name': 'Richard'},
],
def update_pie_chart(sentiment_term):
# get data from cache
c = conn.cursor()
import numpy as np
'''df = pd.read_sql(
"SELECT COUNT(*)(sentiment) FROM sentiment WHERE sentiment BETWEEN 0.000001 and 1 and country LIKE ? ORDER BY unix DESC LIMIT 1000 ;",
conn, params=('%' + sentiment_term + '%',))'''
# df = pd.read_sql("SELECT *(sentiment) FROM sentiment WHERE sentiment BETWEEN 0.000001 and 1 ;",conn)
df = pd.read_sql(
"SELECT COUNT(*)FROM sentiment WHERE sentiment BETWEEN 0.000001 and 1 and country LIKE ? ORDER BY unix DESC LIMIT 1000 ;",
conn, params=('%' + sentiment_term + '%',))
df2 = pd.read_sql(
"SELECT COUNT(sentiment) FROM sentiment WHERE sentiment BETWEEN -1 and -0.0001 and country LIKE ? ORDER BY unix DESC LIMIT 1000 ;",
conn, params=('%' + sentiment_term + '%',))
df3 = pd.read_sql(
"SELECT COUNT(sentiment) FROM sentiment WHERE sentiment =0 and country LIKE ? ORDER BY unix DESC LIMIT 1000 ;",
conn, params=('%' + sentiment_term + '%',))
# print(df2)
'''print(df.dtypes)
df_value=df.values
x = df_value.astype(float)
print('df.dtypes')
print('x=',x)
'''
df_value = df.values
x = df_value.astype(float)
x = x.astype(int)
df_value2 = df2.values
y = df_value2.astype(float)
y = y.astype(int)
df_value3 = df3.values
q = df_value3.astype(float)
q = q.astype(int)
# print(df)
'''print(df2.values)
x=df.values
y=df.values
print(x)
print(y)
'''
import plotly.graph_objects as go
labels =['Positive','Negative','natural']
values = [int(x), int(y),int(q)]
# fig = go.Figure(data=[go.Pie(labels=labels, values=values)])
colors = ['#007F25', '#800000']
trace = go.Pie(labels=labels, values=values,
hoverinfo='label+percent', textinfo='value',
textfont=dict(size=20, color=app_colors['text']),
marker=dict(colors=colors,
line=dict(color=app_colors['background'], width=2)))
return {"data": [trace],
'layout': go.Layout(
title='Positive vs Negative sentiment for "{}" (longer-term)'.format(sentiment_term),
font={'color': app_colors['text']},
plot_bgcolor=app_colors['background'],
paper_bgcolor=app_colors['background'],
showlegend=True),}
def write_html_tol(fpath, df_sell, max_huice):
if len(df_sell) == 0:
return
m_data = df_sell[len(df_sell) - 1]
datetime_x = [d['datetime'] for d in df_sell]
datediff = relativedelta(m_data['datetime'], df_sell[0]['datetime'])
datediff = "历经{}年{}个月{}天".format(datediff.years, datediff.months,
datediff.days)
labels = ["盈利", "亏损", "持平"]
trade_num = m_data['ex_buy_count']
trade_avg_ex = m_data['ex_zdf_count'] / trade_num
trade_days = len(df_sell)
trade_avg_day = m_data['day_sj_zdf_count'] / trade_days
trade_tol_yl = m_data['zyk']
trade_ex_sz = m_data['ex_sz_count']
trade_ex_xd = m_data['ex_xd_count']
trade_day_sz = m_data['day_sz']
trade_day_xd = m_data['day_xd']
fig = make_subplots(
rows=3,
cols=2,
row_heights=[0.5, 0.3, 0.2],
shared_xaxes=True,
vertical_spacing=0.1,
specs=[
[{
'type': 'xy',
'colspan': 2
}, None],
[{
'type': 'xy',
'colspan': 2
}, None],
[{
'type': 'domain'
}, {
'type': 'domain'
}],
],
subplot_titles=[
'收益（{:.2f}%）'.format(trade_tol_yl),
'每日明细（{}）'.format(datediff),
'次数（{}） 平均涨幅 {:.2f}%'.format(trade_num, trade_avg_ex),
'天数（{}） 平均涨幅 {:.2f}%'.format(trade_days, trade_avg_day),
])
fig.add_trace(
go.Scatter(x=datetime_x,
y=[d['zyk'] for d in df_sell],
mode='lines+markers',
name="收益"), 1, 1)
fig.add_trace(
go.Scatter(x=datetime_x,
y=[d['day_sj_zdf_count'] for d in df_sell],
mode='markers',
name="每日明细"), 2, 1)
fig.add_trace(
go.Pie(labels=labels,
values=[
trade_ex_sz, trade_ex_xd,
trade_num - (trade_ex_sz + trade_ex_xd)
],
hole=.3,
textinfo='label+percent+value',
name='交易次数'), 3, 1)
fig.add_trace(
go.Pie(labels=labels,
values=[
trade_day_sz, trade_day_xd,
trade_days - (trade_day_sz + trade_day_xd)
],
hole=.3,
textinfo='label+percent+value',
name='交易天数'), 3, 2)
fig.update_layout(showlegend=False,
height=1000,
title_text='最大回测：{} 夏普率：{} 年华收益：{} '.format(
max_huice, 0, 0))
fig.update_yaxes(zeroline=True, zerolinewidth=2, zerolinecolor='LightPink')
fig.write_html(fpath, full_html=False)
def update_heatmap_confidence(n, current_time, footage, threshold):
layout = go.Layout(
showlegend=False,
paper_bgcolor="rgb(249,249,249)",
plot_bgcolor="rgb(249,249,249)",
autosize=False,
margin=dict(l=10, r=10, b=20, t=20, pad=4),
)
if current_time is not None:
current_frame = round(current_time * FRAMERATE)
if n > 0 and current_frame > 0:
# Load variables from the data dictionary
video_info_df = data_dict[footage]["video_info_df"]
classes_padded = data_dict[footage]["classes_padded"]
root_round = data_dict[footage]["root_round"]
classes_matrix = data_dict[footage]["classes_matrix"]
# Select the subset of the dataset that correspond to the current frame
frame_df = video_info_df[video_info_df["frame"] == current_frame]
# Select only the frames above the threshold
threshold_dec = threshold / 100
frame_df = frame_df[frame_df["score"] > threshold_dec]
# Remove duplicate, keep the top result
frame_no_dup = frame_df[["class_str", "score"]].drop_duplicates("class_str")
frame_no_dup.set_index("class_str", inplace=True)
# The list of scores
score_list = []
for el in classes_padded:
if el in frame_no_dup.index.values:
score_list.append(frame_no_dup.loc[el][0])
else:
score_list.append(0)
# Generate the score matrix, and flip it for visual
score_matrix = np.reshape(score_list, (-1, int(root_round)))
score_matrix = np.flip(score_matrix, axis=0)
# We set the color scale to white if there's nothing in the frame_no_dup
if frame_no_dup.shape != (0, 1):
colorscale = [[0, "#f9f9f9"], [1, "#fa4f56"]]
else:
colorscale = [[0, "#f9f9f9"], [1, "#f9f9f9"]]
hover_text = [f"{score * 100:.2f}% confidence" for score in score_list]
hover_text = np.reshape(hover_text, (-1, int(root_round)))
hover_text = np.flip(hover_text, axis=0)
# Add linebreak for multi-word annotation
classes_matrix = classes_matrix.astype(dtype="|U40")
for index, row in enumerate(classes_matrix):
row = list(map(lambda x: "<br>".join(x.split()), row))
classes_matrix[index] = row
# Set up annotation text
annotation = []
for y_cord in range(int(root_round)):
for x_cord in range(int(root_round)):
annotation_dict = dict(
showarrow=False,
text=classes_matrix[y_cord][x_cord],
xref="x",
yref="y",
x=x_cord,
y=y_cord,
)
if score_matrix[y_cord][x_cord] > 0:
annotation_dict["font"] = {"color": "#F9F9F9", "size": "11"}
else:
annotation_dict["font"] = {"color": "#606060", "size": "11"}
annotation.append(annotation_dict)
# Generate heatmap figure
figure = {
"data": [
{
"colorscale": colorscale,
"showscale": False,
"hoverinfo": "text",
"text": hover_text,
"type": "heatmap",
"zmin": 0,
"zmax": 1,
"xgap": 1,
"ygap": 1,
"z": score_matrix,
}
],
"layout": {
"showlegend": False,
"autosize": False,
"paper_bgcolor": "rgb(249,249,249)",
"plot_bgcolor": "rgb(249,249,249)",
"margin": {"l": 10, "r": 10, "b": 20, "t": 20, "pad": 2},
"annotations": annotation,
"xaxis": {
"showticklabels": False,
"showgrid": False,
"showline": False,
"zeroline": False,
"side": "top",
"ticks": "",
},
"yaxis": {
"showticklabels": False,
"showgrid": False,
"showline": False,
"zeroline": False,
"side": "left",
"ticks": "",
},
},
}
return figure
# Returns empty figure
return go.Figure(data=[go.Pie()], layout=layout)
pie_data = [{
"category": "housing",
"spend_pct": spend_housing
}, {
"category": "food",
"spend_pct": spend_food
}, {
"category": "personal",
"spend_pct": spend_personal
}, {
"category": "transportation",
"spend_pct": spend_transportation
}, {
"category": "other",
"spend_pct": spend_other
}]
print("----------------")
print("GENERATING PIE CHART...")
labels = [pie_data["category"] for pie_data in pie_data]
values = [pie_data["spend_pct"] for pie_data in pie_data]
trace = go.Pie(labels=labels, values=values) #, title="Spending by Category")
layout_pie = go.Layout(title="Spending by Category")
plotly.offline.plot({
"data": trace,
"layout": layout_pie
},
filename="spending_by_category.html",
auto_open=True)
plt.show()
#Method2
import matplotlib.pyplot as plt
labels = ['Cookies', 'Jellybean', 'Milkshake', 'Cheesecake']
sizes = [38.4, 40.6, 20.7, 10.3]
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
patches, texts = plt.pie(sizes, colors=colors, shadow=True, startangle=90)
plt.legend(patches, labels, loc="best")
plt.axis('equal')
plt.tight_layout()
plt.show()
#Method2
import plotly
plotly.tools.set_credentials_file(username='******', api_key='0zZ5FLEa1sM0EfqsMK9R')
import plotly as py1
import plotly.plotly as py
import plotly.graph_objs as go
labels = ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']
values = [4500,2500,1053,500]
trace = go.Pie(labels=labels, values=values)
py.iplot([trace], filename='basic_pie_chart')
def update_charts(goodness, skew, threshold):
df = generate_roc_curve_df(goodness, skew)
hist_kwargs = dict(opacity=0.75,
autobinx=False,
xbins=dict(start=-10.0, end=10, size=0.5))
return html.Div([
html.Div([
dcc.Graph(id='classes',
figure=dict(data=[
go.Histogram(x=df[df.pos == 1].score, **hist_kwargs),
go.Histogram(x=df[df.neg == 1].score, **hist_kwargs)
],
layout=go.Layout(
barmode='overlay',
xaxis=dict(range=[-10, 10]),
yaxis=dict(range=[0, 200]),
width=400,
height=400,
autosize=False,
legend=dict(orientation="h"),
margin=go.Margin(l=40, t=40)))),
dcc.Graph(id='rates',
figure=dict(data=[
go.Scatter(x=df.score, y=df.tpr),
go.Scatter(x=df.score, y=df.fpr),
],
layout=go.Layout(
xaxis=dict(range=[-10, 10],
zeroline=False),
yaxis=dict(range=[0, 1]),
width=400,
height=400,
autosize=False,
legend=dict(orientation="h"),
margin=go.Margin(l=40, t=40)))),
],
style={'columnCount': 2}),
html.Div([
dcc.Graph(id='roc',
figure=dict(data=[go.Scatter(x=df.fpr, y=df.tpr)],
layout=go.Layout(
xaxis=dict(range=[0, 1]),
yaxis=dict(range=[0, 1]),
width=400,
height=400,
autosize=False,
legend=dict(orientation="h"),
margin=go.Margin(l=40, t=40)))),
dcc.Graph(
id='confusion',
figure=dict(data=[
go.Pie(labels=['tp', 'fp', 'tn', 'fn'],
values=df.iloc[threshold][['tp', 'fp', 'tn', 'fn']],
sort=False)
],
layout=go.Layout(width=400,
height=400,
autosize=False,
legend=dict(orientation="h"),
margin=go.Margin(l=40, t=40)))),
],
style={'columnCount': 2})
],
style={'width': "600px"})
def plots_actualize(df2):
fig_map = go.Figure(data=go.Scattermapbox(lat=df2["latitude"],
lon=df2["longitude"],
mode="markers",
marker=dict(color="#5A6FF9")),
layout=go.Layout(autosize=True,
margin=go.layout.Margin(l=0,
r=0,
t=0,
b=0),
showlegend=False,
mapbox=dict(
accesstoken=mapbox_access_token,
style="dark",
center={
'lat': 39,
'lon': -9.2
},
zoom=8.5,
)))
pie_colors = ["#636EFA", "#4296f5", "#42ddf5"]
unique_rooms = list(df2.room_type.unique())
if len(unique_rooms) == 1:
if unique_rooms[0] == 'Private room':
pie_colors = ["#4296f5"]
elif unique_rooms[0] == 'Shared room':
pie_colors = ["#42ddf5"]
fig_pie = go.Figure(data=go.Pie(
labels=df2['room_type'].value_counts().index,
values=df2['room_type'].value_counts().values,
textinfo='text+value+percent',
text=df2['room_type'].value_counts().index,
hoverinfo='label',
showlegend=False,
textfont=dict(color="white"),
marker=(dict(colors=pie_colors, line=dict(color="white", width=0.5)))),
layout=go.Layout(
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
margin=go.layout.Margin(l=0, r=0, t=0, b=0),
))
fig_bar = go.Figure(data=go.Bar(
x=df2['ordinal_rating'].value_counts().values,
y=df2['ordinal_rating'].value_counts().index,
orientation='h',
marker=dict(line=dict(color="white"))),
layout=go.Layout(
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
margin=go.layout.Margin(l=0, r=0, t=0, b=0),
clickmode='event+select',
font=dict(color="white"),
xaxis_title='Number of Listings',
))
fig_hist = go.Figure(data=go.Histogram(
x=df2['price'],
histnorm="",
xbins=dict(size=30, end=800),
marker=dict(line=dict(color="white", width=0.25))),
layout=go.Layout(margin=go.layout.Margin(l=0,
r=0,
t=0,
b=0),
clickmode='event+select',
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
xaxis_title='Price (€)',
yaxis_title='Absolute frequencies',
showlegend=False,
font=dict(color="white")))
return (fig_map, fig_pie, fig_bar, fig_hist)
def update_graph(hotel_group_id, hotel_id, hotel__city, start_date, end_date,
**kwargs):
# get metadata for current user
current_user = kwargs['user']
user_id = current_user.pk
user_metadata = UserMetadata.objects.get(user_id=user_id)
hotel_group_filter = {} if hotel_group_id == None else {
"hotel_group_id": hotel_group_id
}
hotel_filter = {} if hotel_id == None else {"hotel_id": hotel_id}
hotel__city_filter = {} if hotel__city == None else {
"hotel__city": hotel__city
}
booking_start_date_filter = {} if start_date == None else {
"start_date__gte": dt.strptime(start_date.split(' ')[0], '%Y-%m-%d')
}
booking_end_date_filter = {} if end_date == None else {
"end_date__lte": dt.strptime(end_date.split(' ')[0], '%Y-%m-%d')
}
# if group or hotel then apply filter
if current_user.groups.filter(name='Hotel Group Administrators').exists():
hotel_group_id = user_metadata.hotel_group_id
# bookings = Booking.objects.filter(hotel_group_id=hotel_group_id)
bookings = Booking.objects.filter(hotel_group_id=hotel_group_id).filter(**hotel__city_filter).filter(**hotel_filter).filter(**booking_start_date_filter)\
.filter(**booking_end_date_filter)\
.values('start_date') \
.annotate(total=Count('hotel_id'))
elif current_user.groups.filter(name='Hotel Administrators').exists():
hotel_id = user_metadata.hotel_id
bookings = Booking.objects.filter(hotel_id=hotel_id).filter(**booking_start_date_filter).filter(**booking_end_date_filter)\
.values('start_date') \
.annotate(total=Count('hotel_id'))
else:
bookings = Booking.objects.filter(**hotel_group_filter).filter(**hotel__city_filter).filter(**hotel_filter).filter(**booking_start_date_filter)\
.filter(**booking_end_date_filter)\
.values('start_date') \
.annotate(total=Count('hotel_id'))
if len(bookings) == 0:
return {
"data": [go.Pie(labels=[], values=[], textinfo='label')],
"layout":
go.Layout({
"annotations": [{
"text": "No matching data found",
"showarrow": False
}]
})
}
else:
#values=[booking['total'] for booking in list(bookings)]
df = pd.DataFrame(list(bookings))
#labels=[booking['start_date'] for booking in list(bookings)]
return {
"data": [go.Scatter(x=df.start_date, y=df['total'])],
"layout":
go.Layout(
margin=dict(t=20, l=50, r=50, pad=0),
autosize=True,
width=350,
)
}
movies.movie_name,
COUNT(watched_movies.review_id_fk)
FROM
watched_movies INNER JOIN movies ON watched_movies.movie_id_fk = movies.movie_id
GROUP BY
movies.movie_name
""")
movies = []
movie_reviews_count = []
for row in cursor.fetchall():
movies.append(row[0])
movie_reviews_count.append(row[1])
pie = go.Pie(labels=movies, values=movie_reviews_count)
movies_reviews_proportion_url = py.plot([pie], filename='reviews-count')
""" create plot 3 """
cursor.execute("""
SELECT
reviews.review_date, COUNT(DISTINCT profiles.profile_id)
FROM
profiles INNER JOIN watched_movies ON profiles.profile_id = watched_movies.profile_id_fk
INNER JOIN reviews ON reviews.review_id = watched_movies.review_id_fk
GROUP BY
reviews.review_date
ORDER BY
reviews.review_date
""")
df_info = pd.concat(_temp,axis=1).T
df_info.index.name = '指标'
df_info = df_info.round(decimals=3)
import plotly.offline as pyof
from plotly import figure_factory as ff
table = ff.create_table(df_info,index=True, index_title='指标')
pyof.plot(table, filename='产品组合信息表.html')
'''画饼图'''
import plotly.graph_objs as go
labels=df_info.columns[:-1]
values=w
trace=go.Pie(labels=labels,values=values,text='哈哈')
layout = dict(title = '产品组合成分图')
fig = dict(data=[trace],layout=layout)
pyof.plot(fig,filename='组合成分图.html')
'''画产品对比图'''
import tushare as ts
data_hs300 = ts.get_hist_data('hs300')
data_hs300 = data_hs300.rename_axis(lambda x:pd.to_datetime(x))
df_contra = pd.concat([data_hs300.close,df['组合']],axis=1,join='inner')
df_contra = df_contra / df_contra.iloc[0,:]
df_contra.rename(columns={'close':'沪深300'},inplace=True)
import plotly.graph_objs as go
