<jupyter_start><jupyter_text>EDA UtilitiesIn this section, we will introduce a rich set of plotting functions in orbit for the EDA (exploratory data analysis) purpose. The plots include * Time series heatmap* Correlation heatmap* Dual axis time series plot* Wrap plot<jupyter_code>import seaborn as sns 
from matplotlib import pyplot as plt 
import pandas as pd
import numpy as np

import orbit
from orbit.utils.dataset import load_iclaims
from orbit.eda import eda_plot

from orbit.utils.plot import get_orbit_style
plt.style.use(get_orbit_style())
print(orbit.__version__)
df = load_iclaims()
df['week'] = pd.to_datetime(df['week'])
df.head()<jupyter_output><empty_output><jupyter_text>Time series heat map<jupyter_code>_ = eda_plot.ts_heatmap(df = df, date_col = 'week', value_col='claims')
_ = eda_plot.ts_heatmap(df = df, date_col = 'week', value_col='claims', normalization=True)<jupyter_output><empty_output><jupyter_text>Correlation heatmap<jupyter_code>var_list = ['trend.unemploy', 'trend.filling', 'trend.job', 'sp500', 'vix']
_ = eda_plot.correlation_heatmap(df, var_list = var_list, fig_width=10, fig_height=8)<jupyter_output><empty_output><jupyter_text>Dual axis time series plot<jupyter_code>_ = eda_plot.dual_axis_ts_plot(df=df, var1='trend.unemploy', var2='claims', date_col='week')<jupyter_output><empty_output><jupyter_text>Wrap plots for quick glance of data patterns<jupyter_code>var_list=['week', 'trend.unemploy', 'trend.filling', 'trend.job', 'sp500', 'vix']   
df[var_list].melt(id_vars = ['week'])
_ = eda_plot.wrap_plot_ts(df, 'week', var_list)<jupyter_output><empty_output>
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