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ca_international_mig_2018 = px.line(california, x = 'Area_Name', y='INTERNATIONAL_MIG_2018', title="International Migration to Califonia 2018")
ca_international_mig_2018.show()

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tx_international_mig_2018 = px.line(texas, x = 'Area_Name', y='INTERNATIONAL_MIG_2018', title="International Migration to Texas 2018")
tx_international_mig_2018.show()

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trace0 = go.Scatter(
    x = years_mig,
    y = fl_international_mig,
    mode = "lines",
    name = "Florida"
)

trace1 = go.Scatter(
    x = years_mig,
    y = ny_international_mig,

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fl = population_data.loc[population_data['Area_Name'] == 'Florida']
fl_2010 = fl['INTERNATIONAL_MIG_2010'].tolist()
fl_2011 = fl['INTERNATIONAL_MIG_2011'].tolist()
fl_2012 = fl['INTERNATIONAL_MIG_2012'].tolist()
fl_2013 = fl['INTERNATIONAL_MIG_2013'].tolist()
fl_2014 = fl['INTERNATIONAL_MIG_2014'].tolist()
fl_2015 = fl['INTERNATIONAL_MIG_2015'].tolist()
fl_2016 = fl['INTERNATIONAL_MIG_2016'].tolist()
fl_2017 = fl['INTERNATIONAL_MIG_2017'].tolist()
fl_2018 = fl['INTERNATIONAL_MIG_2018'].tolist()

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X = fl_df.iloc[:, 0].values.reshape(-1, 1)  # values converts it into a numpy array
Y = fl_df.iloc[:, 1].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column
linear_regressor = LinearRegression()  # create object for the class
linear_regressor.fit(X, Y)  # perform linear regression
Y_pred = linear_regressor.predict(X)  # make predictions

plt.scatter(X, Y)
plt.plot(X, Y_pred, color='red')
plt.show()

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florida['text'] = "International Migration 2018"+"\
    "+ florida["INTERNATIONAL_MIG_2018"].astype(str) + " " +"County:" +" \
    "+ florida["Area_Name"]

values = florida['INTERNATIONAL_MIG_2018'].tolist()
fips = florida['FIPS'].tolist()

endpts = list(np.mgrid[min(values):max(values):4j])

colorscale = [

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x = florida['FIPS'].tolist()
fips = [str(i) for i in x]
values = florida['INTERNATIONAL_MIG_2018'].tolist()

fig = ff.create_choropleth(fips=fips, values=values)
fig.layout.template = None
fig.show()

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from matplotlib.pyplot import figure
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from pandas import DataFrame
from plotly.subplots import make_subplots
import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go

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#Read csv file and load to population data  data frame
population_data = pd.read_csv(".../population_estimates.csv")

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population_data.head()