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import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns

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plt.figure(figsize=(14,6))

plt.title("Daily Global Streams of Popular Songs in 2017-2018")

sns.lineplot(data=spotify_data['Shape of You'], label="Shape of You")
sns.lineplot(data=spotify_data['Despacito'], label="Despacito")

plt.xlabel("Date")

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plt.figure(figsize=(14,7))

sns.barplot(x=flight_data.index, y=flight_data['NK'])
plt.ylabel("Arrival delay (in minutes)")

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sns.heatmap(data=flight_data, annot=True)

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# color by smoker categorical data
sns.scatterplot(x=insurance_data['bmi'], y=insurance_data['charges'], hue=insurance_data['smoker'])

# add regression line
sns.regplot(x=insurance_data['bmi'], y=insurance_data['charges'])
# colored by smoker categorical data, create 2 regression line
sns.lmplot(x="bmi", y="charges", hue="smoker", data=insurance_data)

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# smoker=category, charges=numeric
sns.swarmplot(x=insurance_data['smoker'],
              y=insurance_data['charges'])

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sns.histplot(data=iris_data, x='Petal Length (cm)', hue='Species')

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sns.kdeplot(data=iris_data, x='Petal Length (cm)', hue='Species', shade=True)

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sns.jointplot(x=iris_data['Petal Length (cm)'], y=iris_data['Sepal Width (cm)'], kind="kde")