def plot_data_browser(df, title, new_cases, new_deaths):
# Browser group plots:
fig = go.Figure()
fig.add_trace(
go.Bar(x=df['date'],
y=df['cases'],
name='Total Cases',
marker_color='sandybrown'))
fig.add_trace(
go.Bar(x=df['date'],
y=new_cases,
name='New Cases',
marker_color='indianred'))
fig.add_trace(
go.Bar(x=df['date'],
y=df['deaths'],
name='Deaths',
marker_color='black'))
fig.add_trace(
go.Bar(x=df['date'],
y=new_deaths,
name='New Deaths',
marker_color='gray'))
# Here we modify the tickangle of the xaxis, resulting in rotated labels.
fig.update_layout(title=title, barmode='overlay', xaxis_tickangle=-45)
# fig.show()
import plotly.io as pio
pio.write_html(fig, file='index.html', auto_open=True)
import chart_studio.tools as tls
tls.get_embed(
'file:///Users/tim/code/bitbucket/twfenwick/corona_tracker/index.html')
def analysis_playlist():
sp = spotipy.Spotify(auth=session.get('token_info').get('access_token'))
username = session.get('username')
df = md.get_playlist_data(sp, username)
# return render_template('analysis.html',extract='Successfully Extracted')
predicted_df = md.predict_data(df)
predicted_df.to_csv(os.path.join(fileloc, 'predicted_data.csv'),
index=False)
no_of_songs = len(predicted_df['name'])
session['predicted_playlist_data'] = predicted_df.to_json()
plot_df = pd.DataFrame(
predicted_df["Label"].value_counts().reset_index().values,
columns=["Label", "No. of Songs"])
plot1 = px.pie(
plot_df,
values=plot_df['No. of Songs'],
names=plot_df['Label'],
title=(
'Distribution of Playlist according to Emotion\nTotal Songs: {0}'.
format(no_of_songs)))
plot1 = py.plot(plot1, filename='Songs in each emotions', auto_open=False)
fig1 = tls.get_embed(plot1)
plot2 = px.bar(predicted_df,
x=predicted_df['name'],
y=predicted_df['Score'],
color=predicted_df['Label'],
title='Category of Songs with Prediction of Score')
plot2 = py.plot(plot2,
filename='Category of Songs with Prediction Score',
auto_open=False)
fig2 = tls.get_embed(plot2)
features = [
predicted_df['acousticness'], predicted_df['danceability'],
predicted_df['liveness'], predicted_df['loudness'],
predicted_df['speechiness']
]
plot3 = px.bar(predicted_df,
x=predicted_df['name'],
y=features,
title='All Songs with its Features')
plot3 = py.plot(plot3,
filename='All Songs with its Features',
auto_open=False)
fig3 = tls.get_embed(plot3)
return render_template('analysis.html',
extract='Success',
plot1=fig1,
plot2=fig2,
plot3=fig3)
def catCount():
jobs = pd.read_csv(
r'C:\Users\lilyk\Desktop\Capstone_project-master\output.csv')
cat = jobs['category']
count = (cat.value_counts())
a = [
'Artificial Inetlligence', 'Software Engineer', 'Deep Learning',
'Machine Learning'
]
t = []
t.append(count['Artificial Intelligence'])
t.append(count['Software Engineer'])
t.append(count['Deep Learning'])
t.append(count['Machine Learning'])
fig2 = px.bar(jobs,
x=a,
y=t,
color=a,
labels={
'y': 'Frequency',
'x': 'Category'
},
title=' Frequency of Job Categories')
fig2.show()
#This puts it in your cloud for your Chart Studios Account
#You can switch it to be your account/api_key from your account.
tls.set_credentials_file(username='******',
api_key='3ztc7kdqWPHtPtkhusiy')
url = py.plot(fig2, filename='categoryFig', auto_open=True)
return (tls.get_embed(url))
def api(key, fig):
username = '******'
api_key = str(key)
chart_studio.tools.set_credentials_file(username=username, api_key=api_key)
py.plot(fig, filename='nlp_protein_papers', auto_open=False)
print(
tls.get_embed('https://plotly.com/~tadorfer/1/')) #change to your url
def test_get_embed_url_with_share_key(self):
# Check the embed url for url with share_key included
get_embed_return = tls.get_embed(
"https://plot.ly/~neda/6572" + "?share_key=AH4MyPlyDyDWYA2cM2kj2m"
)
expected_get_embed = (
'<iframe id="igraph" scrolling="no" '
'style="border:none;" seamless="seamless" '
'src="{plotly_rest_url}/'
"~{file_owner}/{file_id}.embed?"
'share_key={share_key}" '
'height="{iframe_height}" '
'width="{iframe_width}">'
"</iframe>"
).format(
plotly_rest_url="https://" + "plot.ly",
file_owner="neda",
file_id="6572",
share_key="AH4MyPlyDyDWYA2" + "cM2kj2m",
iframe_height=525,
iframe_width="100%",
)
self.assertEqual(get_embed_return, expected_get_embed)
def plotly_url(data,user, freq='D'):
"""
Takes list of dicts from django model.objects.values iterable and
turns it into a dataframe for use with plotly plotting.
:param data: list containing all values
:param freq: timeperiod, default H:hour
:type data: list
:type freq: str
"""
try:
user_plotly = os.environ.get('PLOTLY_USER')
api = os.environ.get('PLOTLY_API')
assert len(api) > 0
assert len(user_plotly) > 0
# Make dataframe and group by month
df = pd.DataFrame(data)
df['created_at'] = pd.to_datetime(df['created_at'])
if 'user_id' in df.columns:
df.rename(columns={'user_id':'user'})
df['user'] = user
# Set's up series for plotting by grouping datetime
df_u = df.groupby(['user','sentiment',pd.Grouper(key='created_at',freq=freq)])['sentiment'].count().reset_index(name='total')
# Get rolling avgs
df_u['nroll_7'] = df_u[df_u['sentiment']=='Negative'].total.rolling(7,win_type='triang').mean()
df_u['proll_7'] = df_u[df_u['sentiment']=='Positive'].total.rolling(7,win_type='triang').mean()
df_u['nroll_7'] = df_u['nroll_7'].fillna(method='bfill').fillna(method='ffill')
df_u['proll_7'] = df_u['proll_7'].fillna(method='bfill').fillna(method='ffill')
# Plotly
tls.set_credentials_file(username=user_plotly,api_key=api)
user_bar = px.bar(df_u, x="created_at", y="total", color="sentiment",barmode="group", category_orders=co,labels={'created_at':'Date','total':'Total'}, title=f'{user}: Tweets',hover_data=df_u.columns)
# Plot rolling averages
user_bar.add_trace(go.Scatter(x=df_u[df_u['sentiment']=='Negative']['created_at'],y=df_u[df_u['sentiment']=='Negative']['nroll_7'], name='nroll_7'))
user_bar.add_trace(go.Scatter(x=df_u[df_u['sentiment']=='Positive']['created_at'],y=df_u[df_u['sentiment']=='Positive']['proll_7'], name='proll_7'))
url = py.plot(user_bar, filename=f'{user}_tweets_{freq}',auto_open=False)
time.sleep(5)
return tls.get_embed(url)
except Exception as e:
logger.error(f'plotly_url error: {e}')
def create_portfolio_pie(portfolio, portfolio_id):
if portfolio.shape[0]:
df = pd.DataFrame({
"assets": portfolio.iloc[-1]["assets"],
"weights": portfolio.iloc[-1]["weights"]
})
df = df[df["weights"] > 0]
fig = go.Figure(data=go.Pie(labels=df["assets"], values=df["weights"]))
fig_url = py.plot(
fig,
filename=f"portfolio_pie_{portfolio_id}",
auto_open=False,
)
plot_html = tls.get_embed(fig_url)
print(fig_url)
# print("Portfolio ID (pie):", portfolio_id)
return plot_html
def salaries():
jobs = pd.read_csv(
r'C:\Users\lilyk\Desktop\Capstone_project-master\output.csv')
salary = jobs['salary'].dropna()
category = jobs['category'].dropna().head(200)
salary = np.array(salary)
titles = jobs['jobTitle'].dropna().head(200)
sals = []
for i in range(len(salary)):
test = (salary[i].split())
nums = []
for i in test:
if i.startswith('$'):
number = (i.strip('$'))
number = number.replace(",", '')
number = float(number)
nums.append(number)
else:
sals.append(0.0)
sals.append(statistics.mean(nums))
fig3 = px.bar(jobs,
x=category,
y=sals,
color=sals,
labels={
'y': 'Salaries',
'x': 'Category'
},
title='Salaries for Different Job Categories',
height=400)
tls.set_credentials_file(username='******',
api_key='3ztc7kdqWPHtPtkhusiy')
url = py.plot(fig3, filename='salFig', auto_open=True)
return (tls.get_embed(url))
def test_get_embed_url_with_share_key(self):
# Check the embed url for url with share_key included
get_embed_return = tls.get_embed('https://plot.ly/~neda/6572' +
'?share_key=AH4MyPlyDyDWYA2cM2kj2m')
expected_get_embed = ("<iframe id=\"igraph\" scrolling=\"no\" "
"style=\"border:none;\" seamless=\"seamless\" "
"src=\"{plotly_rest_url}/"
"~{file_owner}/{file_id}.embed?"
"share_key={share_key}\" "
"height=\"{iframe_height}\" "
"width=\"{iframe_width}\">"
"</iframe>").format(plotly_rest_url="https://" +
"plot.ly",
file_owner="neda",
file_id="6572",
share_key="AH4MyPlyDyDWYA2" +
"cM2kj2m",
iframe_height=525,
iframe_width="100%")
self.assertEqual(get_embed_return, expected_get_embed)
def edus():
jobs = pd.read_csv(
r'C:\Users\lilyk\Desktop\Capstone_project-master\output.csv')
edus = (jobs['education'].dropna())
edus = edus.str.split(',')
#GETS THE UNIQUE EDUS
u = []
for x in edus:
for i in x:
i = i.replace('bachelors', "bachelor's")
u.append(i)
countDict = (Counter(u))
vals = countDict.values()
keys = countDict.keys()
keys, values = zip(*countDict.items())
fig = px.bar(jobs,
x=keys,
y=values,
color=keys,
labels={
'y': 'Frequency',
'x': 'Education'
},
title='Most Desired Education Levels')
#fig.show()
#This puts it in your cloud for your Chart Studios Account
#You can switch it to be your account/api_key from your account.
tls.set_credentials_file(username='******',
api_key='3ztc7kdqWPHtPtkhusiy')
url = py.plot(fig, filename='eduFig', auto_open=True)
return (tls.get_embed(url))
def create_portfolio_graph(portfolio, spy, etf, rfr, portfolio_id):
# print(portfolio)
if portfolio.shape[0]:
# Render a graph and return the URL
# layout = go.Layout(yaxis=dict(tickformat=".2%"))
spy["close"] = spy["close"] * portfolio.iloc[0]["value"] / spy.iloc[0][
"close"]
spy = spy.sort_values(by="date").groupby("date").last().reset_index()
etf["adj_close"] = etf["close"] * 0.6 + rfr["close"] * 0.4
etf["adj_close"] = etf["adj_close"] * portfolio.iloc[0][
"value"] / etf.iloc[0]["adj_close"]
fig = go.Figure()
# fig = go.Figure(filename=f"portfolio_value_{portf_name}")
fig.add_trace(
go.Scattergl(x=portfolio["date"],
y=portfolio["value"],
mode="lines",
name="Portfolio Value")) #, layout=layout)
fig.add_trace(
go.Scattergl(x=spy["date"],
y=spy["close"],
mode="lines",
name="SPY")) #, layout=layout)
fig.add_trace(
go.Scattergl(x=etf["date"],
y=etf["adj_close"],
mode="lines",
name="40% Risk Free/60% Dow Jones"))
fig.update_xaxes(title_text='Date')
fig.update_yaxes(title_text='Portfolio Value')
portfolio_graph_url = get_portfolio_graph_url(fig, name=portfolio_id)
print(portfolio_graph_url)
plot_html = tls.get_embed(portfolio_graph_url)
return plot_html
def test_get_invalid_embed():
url = 'https://plot.ly/~PlotBot/a/'
tls.get_embed(url)
import chart_studio.plotly as py
import chart_studio.tools as tls
import plotly.graph_objects as go
# Create random data with numpy
import numpy as np
'''
x = np.arange(10)
fig = go.Figure(data=go.Scatter(x=x, y=x**2))
fig.show()
'''
N = 500
random_x = np.linspace(0, 1, N)
random_y = np.random.randn(N)
# Create a trace
data = go.Scatter(x=random_x, y=random_y)
tls.get_embed("https://plot.ly/~blaesting/0")
fig = go.Figure(data)
#fig.show()
def edu_sal():
jobs = pd.read_csv(
r'C:\Users\lilyk\Desktop\Capstone_project-master\output.csv')
#makes it so u can graph, but messes up data
edus = (jobs['education'].fillna('Not Specified').head(235))
edus = edus.str.split(',')
#GETS THE UNIQUE EDUS
u = []
for x in edus:
for i in x:
i = i.replace('bachelors', "bachelor's")
u.append(i)
print(len(u))
#keys,values = zip(*countDict.items())
##KEYS IS EDUS
salary = jobs['salary'].dropna()
salary = np.array(salary)
sals = []
for i in range(len(salary)):
# print (salary[i])
test = (salary[i].split())
nums = []
for i in test:
if i.startswith('$'):
number = (i.strip('$'))
number = number.replace(",", '')
#print (number)
number = float(number)
#print (number)
nums.append(number)
else:
pass
try:
sals.append(statistics.mean(nums))
except:
sals.append(nums)
#print (sals)
print(len(sals))
fig = px.scatter(jobs,
x=u,
y=sals,
color=u,
labels={
'y': 'Sal',
'x': 'Education'
},
title='edu/sal',
size=sals)
tls.set_credentials_file(username='******',
api_key='3ztc7kdqWPHtPtkhusiy')
url = py.plot(fig, filename='scatEduSal', auto_open=True)
return (tls.get_embed(url))
def test_get_invalid_embed():
url = "https://plotly.com/~PlotBot/a/"
with pytest.raises(PlotlyError):
tls.get_embed(url)
def test_get_valid_embed():
url = "https://plotly.com/~PlotBot/82/"
tls.get_embed(url)
URL = 'https://scholar.google.com/citations?user=RgxdCXQAAAAJ&hl=en'
page = requests.get(URL)
soup = BeautifulSoup(page.content, 'html.parser')
df = pd.DataFrame()
df['Year'] = pd.to_numeric(
[i.get_text() for i in soup.find_all(class_='gsc_g_t')])
df['Citations'] = pd.to_numeric(
[i.get_text() for i in soup.find_all(class_='gsc_g_al')])
total_citations = df['Citations'].sum()
'''
=========================================================================================
'''
fig = px.bar(df,
x='Year',
y='Citations',
title='Total Citations: {}'.format(total_citations))
fig.update_traces(marker_color='#990A00', marker_line_width=1)
fig.update_layout({'plot_bgcolor': '#ACC1D5'})
fig.show()
'''
=========================================================================================
'''
chart_studio.tools.set_credentials_file(username='******',
api_key='zrVsh0tcfILLshqesfga')
name = py.plot(fig, filename='Citations', auto_open=False)
url = str(name)
a = tls.get_embed(url)
print(a)
def visualize_audio_similarities(df, cs_username, cs_api_key,
similar_song_ids):
songs = []
artists = []
for i in similar_song_ids[::-3]:
artists.append(i)
artist_cleaned = []
for i in artists:
res = str(i)[1:-1]
artist_cleaned.append(res)
for i in similar_song_ids[1::3]:
songs.append(i)
"""
"""
vals = [36, 72, 108, 144, 180, 216, 252, 288, 324, 360]
theta = [23, 59, 95, 131, 167, 203, 239, 275, 311, 347]
k = 4
k2 = 8
k3 = 12
k4 = 16
k5 = 20
k6 = 24
theta2 = [x + k for x in theta]
theta3 = [x + k2 for x in theta]
theta4 = [x + k3 for x in theta]
theta5 = [x + k4 for x in theta]
theta6 = [x + k5 for x in theta]
theta7 = [x + k6 for x in theta]
width = 7
fig = go.Figure()
fig.add_trace(
go.Barpolar(
r=df.iloc[0],
theta=theta2,
width=width,
marker_color='red',
marker_line_color="black",
marker_line_width=2,
opacity=0.9,
name='Searched Song',
hovertext=df.iloc[12],
hoverinfo="text",
))
fig.add_trace(
go.Barpolar(
r=df.iloc[2],
theta=theta3,
width=width,
marker_color='gray',
marker_line_color="black",
marker_line_width=2,
opacity=0.9,
name=(f"{songs[0]} by {artist_cleaned[0]}"),
hovertext=df.iloc[7],
hoverinfo="text",
))
fig.add_trace(
go.Barpolar(
r=df.iloc[3],
theta=theta4,
width=width,
marker_color='violet',
marker_line_color="black",
marker_line_width=2,
opacity=0.9,
name=(f"{songs[1]} by {artist_cleaned[1]}"),
hovertext=df.iloc[8],
hoverinfo="text",
))
fig.add_trace(
go.Barpolar(
r=df.iloc[4],
theta=theta5,
width=width,
marker_color='purple',
marker_line_color="black",
marker_line_width=2,
opacity=0.9,
name=(f"{songs[2]} by {artist_cleaned[2]}"),
hovertext=df.iloc[9],
hoverinfo="text",
))
fig.add_trace(
go.Barpolar(
r=df.iloc[5],
theta=theta6,
width=width,
marker_color='blue',
marker_line_color="black",
marker_line_width=2,
opacity=0.9,
name=(f"{songs[3]} by {artist_cleaned[3]}"),
hovertext=df.iloc[10],
hoverinfo="text",
))
fig.add_trace(
go.Barpolar(
r=df.iloc[6],
theta=theta7,
width=width,
marker_color='orange',
marker_line_color="black",
marker_line_width=2,
opacity=0.9,
name=(f"{songs[4]} by {artist_cleaned[4]}"),
hovertext=df.iloc[11],
hoverinfo="text",
))
fig.update_layout(
template='none',
width=800,
height=800,
hoverlabel=dict(
namelength=-1,
bgcolor="white",
bordercolor='black',
font_size=16,
font_family="Rockwell",
),
polar=dict(
radialaxis=dict(
range=[df[0:7].values.min(), df[0:7].values.max() + .1],
showticklabels=False,
ticks=''),
angularaxis=dict(showticklabels=True,
tickmode='array',
tickvals=vals,
ticktext=df.columns,
ticks=''),
),
title={
'text': "<b>Comparing Audio Features</b>",
'y': 0.95,
'x': 0.51,
'xanchor': 'center',
'yanchor': 'top',
'font': {
'size': 20
}
},
legend={
'y': -0.33,
'x': 0.49,
'xanchor': 'center',
'yanchor': 'bottom'
},
)
chart_studio.tools.set_credentials_file(username=cs_username,
api_key=cs_api_key)
embed_var = py.plot(fig,
filename='pleasedonotbreakkk',
auto_open=True,
fileopt='new')
embed_link = tls.get_embed(embed_var)
return embed_link
def plot_pie(dataframe, name, value, title):
plot1 = px.pie(dataframe, names=name, values=value, title=title)
fig1 = py.plot(plot1, filename=title, auto_open=False)
return tls.get_embed(fig1)
def plot_bar(dataframe, x, y, title):
plot1 = px.bar(dataframe, x=x, y=y, color=x, title=title)
fig1 = py.plot(plot1, filename=title, auto_open=False)
return tls.get_embed(fig1)
import plotly.express as px
import pandas as pd
import csv
import plotly.graph_objects as go
import plotly
import chart_studio.tools as tls
'''
tls.get_embed('https://plot.ly/~test/1')
#request filename
#filen
#file = '../files/{}'.format(filename)
#file = f'../files/{filename}'
#df = pd.read_csv('df.csv')
df = pd.read_csv('dftest.csv', na_values=['#VALUE!', '#DIV/0!', '#NUM!', '#ERROR!'])
#r = lambda x: x.isnumeric()
#df[df.logFC.apply(r)]
fc = df['logFC']
df = df['logpv']
def r(file, col):
l = file[col]
for i in l:
if i == '#DIV/0!':
l[l.index(i)] = 5
return l
n = r(df, 'logFC')
df['logFC1'] = n
# Using graph_objects
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv(
'https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv'
)
fig = go.Figure([go.Scatter(x=df['Date'], y=df['AAPL.High'])])
fig.show()
py.plot(fig, filename='dummy_graph', auto_open=True)
import chart_studio.tools as tls
tls.get_embed('https://plotly.com/~abbybatinga/3/')
df = px.data.tips()
fig = px.pie(df,
values='tip',
names='day',
color='day',
color_discrete_map={
'Thur': 'peachpuff',
'Fri': 'honeydew',
'Sat': 'mediumspringgreen',
'Sun': 'lavender'
})
fig.show()
py.plot(fig, filename='dummy_pie', auto_open=True)
def mapping():
df = pd.read_csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv",
usecols = ["location","iso_code","date","total_vaccinations"])
df = df[df["location"] != "World"]
df.fillna(method="ffill",inplace=True)
df.reset_index(drop=True,inplace=True)
str_min_date = df["date"].min()
df["date"] = df["date"].apply(lambda x: datetime.datetime.strptime(x,"%Y-%m-%d"))
min_date = df["date"].min()
max_date = df["date"].max()
country_list = list(dict.fromkeys(zip(df["location"],df["iso_code"])))
for i in country_list:
date_temp = min_date
country_df = df[df["location"]==i[0]]
country_min = country_df["date"].min()
country_max = country_df["date"].max()
vaccine_max = int(country_df[df["date"]==country_max]["total_vaccinations"])
while date_temp <= max_date:
if date_temp < country_min:
df.loc[len(df)] = [i[0],i[1],date_temp,0]
elif date_temp > country_max:
df.loc[len(df)] = [i[0],i[1],date_temp, vaccine_max]
else:
if date_temp not in country_df["date"].unique():
df.loc[len(df)] = [i[0],i[1],date_temp, np.nan]
date_temp += datetime.timedelta(days=1)
df.fillna(method="ffill",inplace=True)
df.reset_index(drop=True,inplace=True)
df = df.sort_values(by=["location","date"]).reset_index(drop=True)
people = df[df["date"]==max_date]["total_vaccinations"].sum()
country = len(df["location"].unique())
days = (max_date - min_date).days
ppd = round(int(people)/int(days),1)
df["date"] = df["date"].apply(lambda x: str(x)[:10])
fig = px.scatter_geo(df, locations="iso_code", locationmode="ISO-3",
size="total_vaccinations",
hover_name="location",
animation_frame="date",
animation_group="location",
range_color=[833471,833471],
width=1200, height=900)
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 10
fig.layout.updatemenus[0].buttons[0].args[1]['transition']['duration'] = 10
fig.update_layout({
"plot_bgcolor": "rgba(0,0,0,0)",
"paper_bgcolor": "rgba(0,0,0,0)"
})
fig.update_layout(geo=dict(bgcolor= 'rgba(0,0,0,0)'))
username = '******' # your username
api_key = 'A9iEh0PmfbcEmki2cptp' # your api key - go to profile > settings > regenerate key
chart_studio.tools.set_credentials_file(username=username, api_key=api_key)
a = py.plot(fig, filename = 'tempp', auto_open=False)
iframe = tls.get_embed(a, height = "100%")
return str_min_date, iframe, people, country, ppd
bandyaxis = go.layout.YAxis(
title="$ E - E_f \quad / \quad \\text{eV} $", # 费米能级对其
titlefont=dict(size=20),
range=[emin, emax],
showgrid=True,
showline=True,
zeroline=True,
mirror="ticks",
ticks="inside",
tickfont=dict(family='Times New Roman', size=20),
linewidth=2,
tickwidth=2,
zerolinewidth=2)
bandlayout = go.Layout(title="Bands diagram of Al",
titlefont=dict(size=22,
family='Times New Roman',
color='grey'),
xaxis=bandxaxis,
yaxis=bandyaxis,
annotations=annotations)
bandfig = go.Figure(data=bandTraces + vlines, layout=bandlayout)
plot_url = pltly.plot(bandfig, filename="Bands_Al", auto_open=True)
print(tls.get_embed(plot_url))
# plotly.offline.iplot(bandfig, filename='Bands_Al', image='jpeg')
# plotly.offline.iplot(bandfig, filename='Bands_Al')
print(bandfig)
plotly.io.write_image(bandfig, 'Bands_Al.jpeg')
