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dropdowns = []     #Initialize as an empty list
i = 0   #Intialize a counter

#Iterate through df_dict
for key,value in df_dict.items():
    #add a bar trace to the figure for each indicator
    fig.add_trace(go.Bar(name=key,
                         x=value.iloc[:,0],    #x is the squad name
                         y=value.iloc[:,1],    #y is the value for each indicator
                         visible= (i==0),      #Set only the indicator corresponding to i to be visible as default

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fig.update_layout(
    updatemenus=[
        dict(
        #Specify direction in which you want the dropdown menu to pop up    
        direction="down",
        #(1.18,1) refers to the top right corner of the plot
        x = 1.18,
        y = 1,
        #the list of buttons we created earlier
        buttons = dropdowns)],

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#Number of frames equivalent to number of rows
numOfFrames=df.shape[0]
top_clubs=["Atletico Madrid","Real Madrid","FC Barcelona"]

x_init = np.array([1])
initial_data = []
for club in top_clubs:
    y_axis = np.array(df.loc[0, club])
    initial_data.append(go.Scatter(x =x_init, y = y_axis,mode = "lines",name = club))

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from pandas_datareader import wb
import pandas as pd

#Indicators: GDP, GDP per capita, access to electricity, population, CO2 emissions
indicators = ["NY.GDP.MKTP.CD", "NY.GDP.PCAP.CD", "EG.ELC.ACCS.ZS",
              "SP.POP.TOTL", "EN.ATM.CO2E.KT"]

#ISO Code of countries: Australia, Bhutan, Germany, France, Indonesia, India, Japan,
#Korea, Netherlands, Nepal, Russia, South Africa 
countries = ["AUS", "BTN", "DEU", "FRA", "IDN", "IND", "JPN",

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from pyomo.environ import *

model = ConcreteModel()

#Define variables x and y
model.x = Var (domain = NonNegativeReals)
model.y = Var (domain = NonNegativeReals)

#Define objectives
model.obj = Objective (expr = 90 * model.x + 75 * model.y, sense = maximize)

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plt.figure(figsize = (10,8))
plt.rcParams["font.size"] = 15
plt.axis([0, 50, 0, 50])

#Hours constraint of machine A
x = np.array([0,50]); y = 33 - 1.5*x
plt.plot(x, y, "red", label = "Machine A Hours Constraint")
plt.fill_between([0, 22], [33,0], color = "red", alpha = 0.05)

#Hours constraint of machine B

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using LinearAlgebra
using JuMP
using GLPK
using Plots
pyplot()

model = Model()
set_optimizer(model, GLPK.Optimizer)

#Define variables

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x=LinRange(0, 50, 100)

#Hours constraint for machine A
plot([x], [-1.5x .+ 33], label = "Machine A Constraint", color = "red")
plot!([0,22],[33,0], fillrange = 1, fillalpha = 0.05, fillcolor = "red", label = "")

#Optimal Product Strategy
scatter!([10],[18], marker = true,markercolor = "red", label = "", markersize = 7)
plot!([11,18],[19,25],arrow=true, color=:black,linewidth=2,label="")
annotate!(22, 27, text("Optimal Product Strategy",10))

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plt.rcParams["figure.figsize"] = (12, 6)
plt.rcParams["font.size"] = 14
colors = ["red","green","gold","orange","blue","skyblue","maroon","grey"]

fig, ax = plt.subplots()
df_pop.T.plot.bar(stacked = True, ax = ax, color = colors)
ax.set_ylabel("Population")
ax.xaxis.set_ticklabels(ticklabels = [1990, 2020], rotation = 0)

for i in range(len(df_pop)):          

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fig, ax = plt.subplots(figsize = (10, 8))
    
saarc.plot(color = "whitesmoke", edgecolor = "gray", ax = ax, alpha = 1)
ax.set_title("Population distribution in SAARC countries by gender in 2020", fontsize = 16, fontweight = "bold")

for index, row in saarc.iterrows():
    
    centroid = row.geometry.centroid
    mx, my = centroid.x, centroid.y