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from sklearn.decomposition import PCA
import seaborn as sns

pca = PCA(n_components=2)
X = pca.fit_transform(daily_pca.values[:, :25])
daily_pca['pca1'] = X[:, 0]
daily_pca['pca2'] = X[:, 1]

plt.figure(figsize=(15, 5))
ax = plt.subplot(1, 2, 1)
sns.swarmplot(x='pca1', y='pca2', data=daily_pca, hue='day_of_week')
plt.legend(loc='lower left')
ax = plt.subplot(1, 2, 2)
sns.swarmplot(x='pca1', y='pca2', data=daily_pca, hue='Events')
plt.legend(loc='lower left')
plt.show()

plt.figure(figsize=(15, 5))
ax = plt.subplot(1, 2, 1)
daily_pca.plot('pca1', 'pca2', kind='scatter', c='total_trips', cmap='Greens', ax=ax)
ax = plt.subplot(1, 2, 2)
daily_pca.plot('pca1', 'pca2', kind='scatter', c='Mean_Temperature_F', cmap='Reds', ax=ax)
plt.show()

plt.figure(figsize=(15, 5))
ax = plt.subplot(1, 2, 1)
daily_pca.plot('pca1', 'pca2', kind='scatter', c='Precipitation_In ', cmap='Blues', ax=ax)
plt.show()