<jupyter_start><jupyter_text>Unsupervised Analysis of Days of WeekTreating crossings each day as features to learn about relationships between various days.<jupyter_code>%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn')
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture<jupyter_output><empty_output><jupyter_text>Get Data<jupyter_code>from jupyterworkflow.data import get_fremont_data
data = get_fremont_data()
data.head()<jupyter_output><empty_output><jupyter_text>Data Analysis<jupyter_code>data.resample('W').sum().plot()
data.resample('W').sum().plot()
ax = data.resample('D').sum().rolling(365).sum().plot()
ax.set_ylim(0, None)
data.groupby(data.index.time).mean().plot()
pivoted = data.pivot_table('Total', index=data.index.time, columns=data.index.date)
pivoted.iloc[:5,:5]
pivoted.plot(legend=False, alpha=0.01)<jupyter_output><empty_output><jupyter_text>Principal Component Analysis<jupyter_code>X = pivoted.fillna(0).T.values
X.shape
X2 = PCA(2).fit_transform(X)
X2.shape
plt.scatter(X2[:, 0],X2[:, 1])
gmm = GaussianMixture(2)
gmm.fit(X)
labels = gmm.predict(X)
labels
plt.scatter(X2[:, 0],X2[:, 1], c=labels, cmap='rainbow')
plt.colorbar()
fig, ax = plt.subplots(1, 2, figsize=[14, 6])
pivoted.T[labels == 0].T.plot(legend=False, alpha=0.1, ax = ax[0])
pivoted.T[labels == 1].T.plot(legend=False, alpha=0.1, ax = ax[1])
dayofweek = pd.DatetimeIndex(pivoted.columns).dayofweek
plt.scatter(X2[:, 0],X2[:, 1], c=dayofweek, cmap='rainbow')
plt.colorbar()
dates = pd.DatetimeIndex(pivoted.columns)
dates[(labels == 1) & (dayofweek < 5)]<jupyter_output><empty_output>
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