In [1]:
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
shapefile = '/Users/vanle/Documents/Obesity data/ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp'
datafile = '/Users/vanle/Documents/Obesity data/obesity.csv'

gdf = gpd.read_file(shapefile)[['ADMIN', 'ADM0_A3', 'geometry']]
gdf.columns = ['country', 'country_code', 'geometry']
gdf.head()
Out[1]:
country country_code geometry
0 Fiji FJI MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1 United Republic of Tanzania TZA POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2 Western Sahara SAH POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3 Canada CAN MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4 United States of America USA MULTIPOLYGON (((-122.84000 49.00000, -120.0000...
In [2]:
print(gdf[gdf['country'] == 'Antarctica'])
gdf = gdf.drop(gdf.index[159])

        country country_code  \
159  Antarctica          ATA   

                                              geometry  
159  MULTIPOLYGON (((-48.66062 -78.04702, -48.15140...
In [3]:
gdf.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
Int64Index: 176 entries, 0 to 176
Data columns (total 3 columns):
 #   Column        Non-Null Count  Dtype   
---  ------        --------------  -----   
 0   country       176 non-null    object  
 1   country_code  176 non-null    object  
 2   geometry      176 non-null    geometry
dtypes: geometry(1), object(2)
memory usage: 5.5+ KB
In [4]:
print(gdf[gdf['country'] == 'Antarctica'])

Empty GeoDataFrame
Columns: [country, country_code, geometry]
Index: []
In [5]:
import pandas as pd

datafile = '/Users/vanle/Documents/Obesity data/obesity.csv'

df = pd.read_csv(datafile, names = ['entity', 'code', 'year', 'per_cent_obesity'], skiprows = 1)
df.head()
Out[5]:
entity code year per_cent_obesity
0 Afghanistan AFG 1975 0.4
1 Afghanistan AFG 1976 0.4
2 Afghanistan AFG 1977 0.5
3 Afghanistan AFG 1978 0.5
4 Afghanistan AFG 1979 0.5
In [6]:
df_2016 = df[df['year'] == 2016]

#Perform left merge to preserve every row in gdf.
merged = gdf.merge(df_2016, left_on = 'country_code', right_on = 'code', how = 'left')

#Replace NaN values to string 'No data'.
merged.fillna('No data', inplace = True)
In [7]:
import json

#Read data to json
merged_json = json.loads(merged.to_json())

#Convert to str like object
json_data = json.dumps(merged_json)
In [8]:
from bokeh.io import output_notebook, show, output_file
from bokeh.plotting import figure
from bokeh.models import GeoJSONDataSource, LinearColorMapper, ColorBar
from bokeh.palettes import brewer

#Input GeoJSON source that contains features for plotting.
geosource = GeoJSONDataSource(geojson = json_data)

#Define a sequential multi-hue color palette.
palette = brewer['YlGnBu'][8]

#Reverse color order so that dark blue is highest obesity.
palette = palette[::-1]

#Instantiate LinearColorMapper that linearly maps numbers in a range, into a sequence of colors.
color_mapper = LinearColorMapper(palette = palette, low = 0, high = 40)

#Define custom tick labels for color bar.
tick_labels = {'0': '0%', '5': '5%', '10':'10%', '15':'15%', '20':'20%', '25':'25%', '30':'30%','35':'35%', '40': '>40%'}

#Create color bar. 
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,
border_line_color=None,location = (0,0), orientation = 'horizontal', major_label_overrides = tick_labels)

#Create figure object.
p = figure(title = 'Share of adults who are obese, 2016', plot_height = 600 , plot_width = 950, toolbar_location = None)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None

#Add patch renderer to figure. 
p.patches('xs','ys', source = geosource,fill_color = {'field' :'per_cent_obesity', 'transform' : color_mapper},
          line_color = 'black', line_width = 0.25, fill_alpha = 1)

#Specify figure layout.
p.add_layout(color_bar, 'below')

#Display figure inline in Jupyter Notebook.
output_notebook()

#Display figure.
show(p)
Loading BokehJS ...
In [9]:
from bokeh.io import curdoc, output_notebook
from bokeh.models import Slider, HoverTool
from bokeh.layouts import widgetbox, row, column

#Define function that returns json_data for year selected by user.
    
def json_data(selectedYear):
    yr = selectedYear
    df_yr = df[df['year'] == yr]
    merged = gdf.merge(df_yr, left_on = 'country_code', right_on = 'code', how = 'left')
    merged.fillna('No data', inplace = True)
    merged_json = json.loads(merged.to_json())
    json_data = json.dumps(merged_json)
    return json_data

#Input GeoJSON source that contains features for plotting.
geosource = GeoJSONDataSource(geojson = json_data(2016))

#Define a sequential multi-hue color palette.
palette = brewer['PRGn'][8]

#Reverse color order so that dark blue is highest obesity.
palette = palette[::-1]

#Instantiate LinearColorMapper that linearly maps numbers in a range, into a sequence of colors. Input nan_color.
color_mapper = LinearColorMapper(palette = palette, low = 0, high = 40, nan_color = '#d9d9d9')

#Define custom tick labels for color bar.
tick_labels = {'0': '0%', '5': '5%', '10':'10%', '15':'15%', '20':'20%', '25':'25%', '30':'30%','35':'35%', '40': '>40%'}

#Add hover tool
hover = HoverTool(tooltips = [ ('Country/region','@country'),('% obesity', '@per_cent_obesity')])


#Create color bar. 
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,
                     border_line_color=None,location = (0,0), orientation = 'horizontal', major_label_overrides = tick_labels)


#Create figure object.
p = figure(title = 'Share of adults who are obese, 2016', plot_height = 600 , plot_width = 950, toolbar_location = None, tools = [hover])
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None

#Add patch renderer to figure. 
p.patches('xs','ys', source = geosource,fill_color = {'field' :'per_cent_obesity', 'transform' : color_mapper},
          line_color = 'black', line_width = 0.25, fill_alpha = 1)
p.add_layout(color_bar, 'below')

# Define the callback function: update_plot
def update_plot(attr, old, new):
    yr = slider.value
    new_data = json_data(yr)
    geosource.geojson = new_data
    p.title.text = 'Share of adults who are obese, %d' %yr
    
# Make a slider object: slider 
slider = Slider(title = 'Year',start = 1975, end = 2016, step = 1, value = 2016)
slider.on_change('value', update_plot)

# Make a column layout of widgetbox(slider) and plot, and add it to the current document
layout = column(p,widgetbox(slider))
curdoc().add_root(layout)

#Display plot inline in Jupyter notebook
output_notebook()

#Display plot
show(layout)

#export file
#from bokeh.io import export_svg

#export_svg(p, filename='/Users/vanle/Documents/Obesity data/images/worldmap.pdf')

BokehDeprecationWarning: 'WidgetBox' is deprecated and will be removed in Bokeh 3.0, use 'bokeh.models.Column' instead
Loading BokehJS ... 
WARNING:bokeh.embed.util:
You are generating standalone HTML/JS output, but trying to use real Python
callbacks (i.e. with on_change or on_event). This combination cannot work.

Only JavaScript callbacks may be used with standalone output. For more
information on JavaScript callbacks with Bokeh, see:

    https://docs.bokeh.org/en/latest/docs/user_guide/interaction/callbacks.html

Alternatively, to use real Python callbacks, a Bokeh server application may
be used. For more information on building and running Bokeh applications, see:

    https://docs.bokeh.org/en/latest/docs/user_guide/server.html
In [10]:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import seaborn as sns
plt.style.use("seaborn")
sns.set()
%matplotlib notebook
In [11]:
import plotly.express as px
import matplotlib.pyplot as plt
df10 = pd.read_csv('/Users/vanle/Documents/Obesity data/obese_pop.csv')
fig = px.area(df10, x="year", y="obese_pop", color="country",color_discrete_sequence= ['#9970ab','#d9f0d3','#1b7837','blue'], 
              labels={
                     "country": "Country Classification",
                     "year": "Year",
                     "obese_pop": "Number of Obese Adults"
                 },
                title="Obese population from 1975 to 2016")
fig.update_layout({'plot_bgcolor': 'rgba(0,0,0,0)', 'paper_bgcolor': 'rgba(0,0,0,0)'}, #this for transparent
    legend=dict(
        x=0.05,  # value must be between 0 to 1.
        y=0.9,   # value must be between 0 to 1.
        traceorder="normal",
        font=dict(
            family="sans-serif",
            size=12,
            color="black"
        ),
    )
)
fig.show()
#fig.write_image('/Users/vanle/Documents/Obesity data/images/trans_world_pop.eps')
In [12]:
gender = pd.read_csv("/Users/vanle/Documents/Obesity data/obesity_gender02.csv")
gender.dropna()
Out[12]:
country year code pct_obese gender total
0 Afghanistan 2016 AFG 7.6 female 10.8
1 Afghanistan 2016 AFG 3.2 male 10.8
2 Albania 2016 ALB 21.6 male 43.4
3 Albania 2016 ALB 21.8 female 43.4
4 Algeria 2016 DZA 19.9 male 54.8
... ... ... ... ... ... ...
371 Yemen, Rep. 2016 YEM 22.0 female 34.0
372 Zambia 2016 ZMB 3.6 male 16.0
373 Zambia 2016 ZMB 12.4 female 16.0
374 Zimbabwe 2016 ZWE 25.3 female 30.0
375 Zimbabwe 2016 ZWE 4.7 male 30.0

376 rows × 6 columns

In [13]:
countries = ['United States','Egypt','Turkey','Chile','Argentina','Mexico','Canada','Australia','United Kingdom',
'South Africa','Venezuela','Irag','Czechia','Spain','Iran','Poland','Russia','Algeria','Kazakhstan','Hungary','France','Italy','Morocco','Germany','Brazil','Colombia','Peru','Ukraine','Netherlands','Uzbekistan','Malaysia','Nigeria','Thailand','Sudan','China','Kenya','Tanzania','Sri Lanka','Pakistan','Indonesia','Philippines','South Korea',
'Mozambique','Congo','Uganda','Nepal' ,'Myanmar','India','Ethiopia','Japan','Afghanistan','Bangladesh','North Korea','Vietnam']
In [14]:
gender_2016 = gender[["country",'gender','pct_obese','total']][gender["year"] == 2016]
gender_2016 = gender_2016.loc[gender_2016["country"].isin(countries)]
gender_2016 = gender_2016.sort_values(by= 'total',ascending=False)  
#gender_2016.head()
In [15]:
sns.set(style="ticks")
sns.set_style("darkgrid",{'axes.grid' : False})
g = sns.catplot(data=gender_2016,x="pct_obese", y="country" , hue="gender", s = 8, palette="PRGn", height=10, aspect = 0.8)
g.set(xlabel='Prevalence of Obesity')
g.set(ylabel= None)
#plt.legend(title='Gender',loc = 'center right', labels=['Men', 'Women'], bbox_to_anchor=(1,1))
plt.show()
#plt.savefig('/Users/vanle/Documents/Obesity data/images/pattern.eps', bbox_inches = 'tight',transparent=True)
In [16]:
df3 = pd.read_csv("/Users/vanle/Documents/Obesity data/country_obesity.csv")
country_16 = df3.loc[df3['year'] == 2016]
country_16 = country_16.dropna(axis=1)
country_16 = country_16.sort_values(by= 'pct_obese',ascending=True)
country_16['country_duplicate'] = country_16.loc[:,'country']
country_16 = country_16.set_index('code')
In [17]:
plt.figure(figsize=(12,5))
ax=sns.barplot(x = 'country',
            y = 'pct_obese',
            data = country_16,edgecolor='none')
ax.set(ylabel= None) 
ax.set(xlabel = None)
ax.yaxis.set_label_position("right")
ax.xaxis.set_ticklabels ([]) #remove x axis ticks
ax.yaxis.tick_right()
for bar in ax.patches:
    bar.set_facecolor('#bfbfbf')

#highlight 
pos01 = country_16.index.get_loc('VNM')
ax.patches[pos01].set_facecolor('#762a83')
#Annotate: https://mode.com/example-gallery/python_chart_annotations/
ax.axvline(x=0, linestyle='--', alpha=0.5,color='#762a83')
ax.text(x=1, y=40, s='Vietnam (2.1%)', alpha=0.7, color='#762a83')

pos02 = country_16.index.get_loc('KHM')
ax.patches[pos02].set_facecolor('#762a83')


pos03 = country_16.index.get_loc('LAO')
ax.patches[pos03].set_facecolor('#762a83')


pos04 = country_16.index.get_loc('MYS')
ax.patches[pos04].set_facecolor('#762a83')
ax.axvline(x=71, linestyle='--', alpha=0.5,color='#762a83')
ax.text(x=72, y=40, s='Malaysia (15.3%)', alpha=0.7, color='#762a83')

pos05 = country_16.index.get_loc('PHL')
ax.patches[pos05].set_facecolor('#762a83')

pos06 = country_16.index.get_loc('SGP')
ax.patches[pos06].set_facecolor('#762a83')

pos07 = country_16.index.get_loc('IDN')
ax.patches[pos07].set_facecolor('#762a83')

pos08 = country_16.index.get_loc('THA')
ax.patches[pos08].set_facecolor('#762a83')

pos09 = country_16.index.get_loc('MMR')
ax.patches[pos09].set_facecolor('#762a83')

pos09 = country_16.index.get_loc('OWID_WRL')
ax.patches[pos09].set_facecolor('#5ca964')

ax.axvline(x=63, linestyle='-', alpha=0.5, color ='#018837')
ax.text(x=43, y=30, s='World (13.2%)', alpha=0.7, color='#018837')

ax.legend()
#Show the plot
plt.tight_layout()
plt.ylabel('Prevalence of obese adults')
plt.title("SEA countries vs Others (2016)")
plt.show()
#plt.savefig('/Users/vanle/Documents/Obesity data/images/SEA-Others.eps', bbox_inches = 'tight',transparent=True)

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
In [18]:
df3 = pd.read_csv("/Users/vanle/Documents/Obesity data/country_obesity.csv")
df4 = df3.loc[df3["code"].isin(['VNM','KHM','MMR','IDN','LAO','MYS','SGP','THA','PHL'])]
In [19]:
#fig = px.area(df6, x='year', y='pct_obese', color='country') #for area chart
fig = px.line(df4, x='year', y='pct_obese', color='country', title = 'Obesity growing rate in SEA countries',
             color_discrete_sequence= ['#9970ab','#5ca964','#9970ab','#5ca964','#9970ab','#9970ab','#9970ab','#5ca964','#9970ab'], 
              labels={
                     "country": "Country",
                     "year": "Year",
                     "pct_obese": "Prevalence of obese adults"
                 },
                )
fig.add_annotation(x=2016, y=10,
            text="THA-CAGR: 6.9%",
            showarrow=False,
            yshift=10)
fig.add_annotation(x=2016, y=15.6,
            text="MYS-CAGR: 6.2% ",
            showarrow=False,
            yshift=10)
fig.add_annotation(x=2016, y=6.9,
            text="IDN-CAGR: 7.19%",
            showarrow=False,
            yshift=10)
#fig.update_layout(showlegend=False)
fig.update_layout(
#     {'plot_bgcolor': 'rgba(0,0,0,0)',
#     'paper_bgcolor': 'rgba(0,0,0,0)'},
    legend=dict(
        x=0.05,  # value must be between 0 to 1.
        y=0.9,   # value must be between 0 to 1.
        traceorder="normal",
        font=dict(
            family="sans-serif",
            size=12,
            color="black"
        ),
    )
)
fig.show()
#plt.savefig('/Users/vanle/Documents/Obesity data/images/SEA-growing.pdf', bbox_inches = 'tight')
#fig.write_image('/Users/vanle/Documents/Obesity data/images/SEA-growing.eps',width=1000, height=500)
In [20]:
df5 = pd.read_csv("/Users/vanle/Documents/Obesity data/SEA_age_sorted.csv")
tha = df5[(df5.code == 'THA') & (df5.gender == 'Total')]
In [21]:
fig = px.line(tha, x="year", y="pct_obese", color = 'age',color_discrete_sequence= ['#9970ab','blue','#1b7837'], labels = {'year': 'Year','pct_obese' : 'prevalence of obesity', 'age': 'Age Group'},title='Obesity rate in Thailand for different age groups')
fig.update_layout(
    #{'plot_bgcolor': 'rgba(0,0,0,0)',
    #'paper_bgcolor': 'rgba(0,0,0,0)'},
    legend=dict(
        x=0.05,  # value must be between 0 to 1.
        y=0.9,   # value must be between 0 to 1.
        traceorder="normal",
        font=dict(
            family="sans-serif",
            size=12,
            color="black"
        ),
    )
)
fig.show()
#plt.savefig('/Users/vanle/Documents/Obesity data/images/SEA-growing.pdf', bbox_inches = 'tight')
#fig.write_image('/Users/vanle/Documents/Obesity data/images/thai.eps',width=1000, height=500)
In [22]:
import plotly.express as px
from plotly.subplots import make_subplots

# data for px.sunburst1
data1 = dict(character=[ "Total GDP 2016", ' ',"Obesity"],
             parent=["", "Total GDP 2016",   "Total GDP 2016"],
             value=[519.6,513,6.6])

# extract data and structure FROM px.sunburst1
sb1 = px.sunburst(data1,
                  names='character',
                  parents='parent',
                  values='value',
                  branchvalues="total",
                  color_discrete_sequence = ['#9970ab','#d9f0d3','#5aae61']
                  )

# data for px.sunburst2
# data for px.sunburst2
data2 = dict(character=[ "Total GDP 2056", ' ',"Obesity"],
             parent=["", "Total GDP 2056", "Total GDP 2056"],
             value=[519.6,494.24,25.36])

# extract data and structure FROM px.sunburst2
sb2 = px.sunburst(data2,
                  names='character',
                  parents='parent',
                  values='value',
                  branchvalues="total",
                  color_discrete_sequence = ['#9970ab','#d9f0d3','#5aae61']
                  )

fig = make_subplots(rows=1, cols=2, specs=[
    [{"type": "sunburst"}, {"type": "sunburst"}]
])
fig.add_trace(sb1.data[0], row=1, col=1)
fig.add_trace(sb2.data[0], row=1, col=2)
fig.show()
In [23]:
sea_country = ['Vietnam','Cambodia','Myanmar','Laos','Philippines','Indonesia','Singapore','Thailand','Malaysia']
obese_pop = [1355023,368182,1952005,206979,3884363,11727354,283080,5379703,3266162]
data = pd.DataFrame(list(zip(sea_country, obese_pop)),
               columns =['country', 'obese_pop'])
fig = px.treemap(data,
                 path= [px.Constant("SEA"),'country'],
                 values='obese_pop',
                 color='obese_pop',
                 color_continuous_scale='PRGn',
                  )

fig.update_layout(title="SEA Obesity Popuplation Distribution",
                  width=1000, height=600
                  #, uniformtext=dict(minsize=8, mode='hide')
                 )

fig.show()
fig.write_image('/Users/vanle/Documents/Obesity data/images/treemap_sea.eps', width = 720, height = 400)

/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/plotly/express/_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/plotly/express/_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
In [24]:
df7 = pd.read_csv('/Users/vanle/Documents/Obesity data/annual-working-hours-per-worker.csv')
In [25]:
hours = df7[df7.Year == 2017]
hours= hours.sort_values(by= 'Average annual working hours per worker',ascending=False)  
hours.reset_index()
Out[25]:
index Entity Code Year Average annual working hours per worker
0 460 Cambodia KHM 2017 2455.5508
1 2026 Myanmar MMR 2017 2437.8633
2 1978 Mexico MEX 2017 2255.0000
3 1886 Malaysia MYS 2017 2238.2729
4 2606 Singapore SGP 2017 2237.7263
... ... ... ... ... ...
61 1376 Iceland ISL 2017 1493.3651
62 2101 Netherlands NLD 2017 1430.0229
63 2225 Norway NOR 2017 1417.4723
64 895 Denmark DNK 2017 1400.3815
65 1159 Germany DEU 2017 1353.8868

66 rows × 5 columns

In [26]:
hours[hours.Code == 'THA']
Out[26]:
Entity Code Year Average annual working hours per worker
3127 Thailand THA 2017 2185.4456
In [27]:
sns.set(style="ticks")
sns.set_style("darkgrid",{'axes.grid' : False})
g = sns.catplot(data=hours,x="Average annual working hours per worker", y="Entity",palette=['seagreen'], alpha=0.5,s = 8, height=10, aspect = 0.8)
g.set(xlabel='Average annual working hours per worker')
g.set(ylabel= None)
plt.axhline(y=9, color='r', linestyle='--')
#plt.legend(title='Gender',loc = 'center right', labels=['Men', 'Women'], bbox_to_anchor=(1,1))
#g.axvline(x=2185.4456, linestyle='-', alpha=0.5, color ='#018837')
plt.text(x=2225, y = 8, s='2185.4456 hrs/year', alpha=0.7, color='r')
plt.show()
plt.savefig('/Users/vanle/Documents/Obesity data/images/working_hour.eps', bbox_inches = 'tight',transparent=True)

The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.
In [29]:
food_ads = pd.read_csv('/Users/vanle/Documents/Obesity data/Food ads.csv')
In [30]:
food_ads
Out[30]:
Topic F&B category F&B types Rate of advertisements (ads per channel hour (n))
0 Total 997 Food ads Non-core and unhealthy food High sugar and/or low fibre cereals 0.1
1 Total 997 Food ads Non-core and unhealthy food Instant rice and noodle products 0.2
2 Total 997 Food ads Non-core and unhealthy food Sweet breads, cakes, muffins, etc. 0.1
3 Total 997 Food ads Non-core and unhealthy food Sweet snack foods 0.4
4 Total 997 Food ads Non-core and unhealthy food Savoury snack foods 0.4
5 Total 997 Food ads Non-core and unhealthy food Fruit juice/drinks 0.1
6 Total 997 Food ads Non-core and unhealthy food Full cream milks and yoghurts 0.5
7 Total 997 Food ads Non-core and unhealthy food Desserts and Sweets 0.1
8 Total 997 Food ads Non-core and unhealthy food Fast food 0.2
9 Total 997 Food ads Non-core and unhealthy food Other high fat/salt products 0.1
10 Total 997 Food ads Non-core and unhealthy food Sugar sweetened drinks 0.7
11 Total 997 Food ads Core and healthy food Milks and yoghurts 0.1
12 Total 997 Food ads Core and healthy food Bottled water 0.1
13 Total 997 Food ads Miscellaneous food/food-related Recipe additions 0.1
14 Total 997 Food ads Miscellaneous food/food-related Vitamin/mineral 0.2
15 Total 997 Food ads Miscellaneous food/food-related Tea and coffee 0.1
16 Total 997 Food ads Miscellaneous food/food-related Baby and toddler milk formulae 0.2
17 Total 997 Food ads Miscellaneous food/food-related Fast food 0.2
18 Total 997 Food ads Miscellaneous food/food-related Fast-food restaurant 0.1
In [31]:
fig = px.sunburst(food_ads,
                  path=["F&B category", "F&B types"],
                  values='Rate of advertisements (ads per channel hour (n))',
                  title="Starbucks Store Count Distribution World Wide [Country, State, City]",
                  width=750, height=750)
fig.show()

/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/plotly/express/_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.

/Users/vanle/opt/anaconda3/lib/python3.9/site-packages/plotly/express/_core.py:1637: FutureWarning:

The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
In [ ]: