# Time Series Plot
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

data = {
    'date': pd.date_range(start='2023-01-01', periods=100, freq='D'),
    'value': np.random.randn(100).cumsum()
}
df = pd.DataFrame(data)
sns.lineplot(x='date', y='value', data=df)
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Time Series Plot')
plt.show()

# Customizing the Time Series Plot
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

data = {
    'date': pd.date_range(start='2023-01-01', periods=100, freq='D'),
    'value': np.random.randn(100).cumsum()
}
df = pd.DataFrame(data)
sns.lineplot(x='date', y='value', data=df, linestyle='--', marker='o', color='blue')
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Customized Time Series Plot')
plt.show()

# Plotting Multiple Time Series
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

data = {
    'date': pd.date_range(start='2023-01-01', periods=100, freq='D'),
    'value1': np.random.randn(100).cumsum(),
    'value2': np.random.randn(100).cumsum(),
    'value3': np.random.randn(100).cumsum(),
}
df = pd.DataFrame(data).melt('date', var_name='series', value_name='value')
sns.lineplot(x='date', y='value', hue='series', data=df)
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Multiple Time Series Plot')
plt.show()