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Course Notes

Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! For courses that use data, the datasets will be available in the datasets folder.

# Import any packages you want to use here

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Add notes here about the concepts you've learned and code cells with code you want to keep.

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# Add your code snippets here

houston_pollution = pollution[pollution.city  ==  'Houston']

# Make array orangred for day 330 of year 2014, otherwise lightgray
houston_colors = ['orangered' if (day  ==  330) & (year  ==  2014) else 'lightgray' 
                  for day,year in zip(houston_pollution.day, houston_pollution.year)]

sns.regplot(x = 'NO2',
            y = 'SO2',
            data = houston_pollution,
            fit_reg = False, 
            # Send scatterplot argument to color points 
            scatter_kws = {'facecolors': houston_colors, 'alpha': 0.7})
plt.show()
houston_pollution = pollution[pollution.city  ==  'Houston'].copy()

# Find the highest observed O3 value
max_O3 = houston_pollution.O3.max()

# Make a column that denotes which day had highest O3
houston_pollution['point_type'] = ['Highest O3 Day' if O3  == max_O3 else 'Others' for O3 in houston_pollution.O3]

# Encode the hue of the points with the O3 generated column
sns.scatterplot(x = 'NO2',
                y = 'SO2',
                hue = 'point_type',
                data = houston_pollution)
plt.show()
# Filter dataset to the year 2012
sns.kdeplot(pollution[pollution.year == 2012].O3, 
            # Shade under kde and add a helpful label
            shade = True,
            label = '2012')

# Filter dataset to everything except the year 2012

sns.kdeplot(pollution[pollution.year != 2012].O3, 
            # Again, shade under kde and add a helpful label
            shade = True,
            label = 'other years')

plt.show()
sns.distplot(pollution[pollution.city == 'Vandenberg Air Force Base'].O3, 
             label = 'Vandenberg', 
             # Turn off the histogram and color blue to stand out
             hist = False,
             color = 'steelblue', 
             # Turn on rugplot
             rug = True)

sns.distplot(pollution[pollution.city != 'Vandenberg Air Force Base'].O3, 
             label = 'Other cities',
             # Turn off histogram and color gray
             hist = False,  
             color = 'gray')
plt.show()
# Filter data to just March
pollution_mar = pollution[pollution.month == 3]

# Plot beeswarm with x as O3
sns.swarmplot(y = "city",
              x = 'O3', 
              data = pollution_mar, 
              # Decrease the size of the points to avoid crowding 
              size = 3)

# Give a descriptive title
plt.title('March Ozone levels by city')
plt.show()
# Draw basic scatter plot of pollution data for August
sns.scatterplot(x = 'CO', y = 'SO2', data = pollution[pollution.month  ==  8])

# Label highest SO2 value with text annotation
plt.text(0.57, 41,
         'Cincinnati had highest observed\nSO2 value on Aug 11, 2013',  
         # Set the font to large
         fontdict = {'ha': 'left', 'size': 'large'})
plt.show()
# Query and filter to New Years in Long Beach
jan_pollution = pollution.query("(month  ==  1) & (year  ==  2012)")
lb_newyears = jan_pollution.query("(day  ==  1) & (city  ==  'Long Beach')")

sns.scatterplot(x = 'CO', y = 'NO2',
                data = jan_pollution)

# Point arrow to lb_newyears & place text in lower left 
plt.annotate('Long Beach New Years',
             xy = (lb_newyears.CO, lb_newyears.NO2),
             xytext = (2, 15), 
             # Shrink the arrow to avoid occlusion
             arrowprops = {'facecolor':'gray', 'width': 3, 'shrink': 0.03},
           backgroundcolor=    'white')
plt.show()
# Make a vector where Long Beach is orangered; else lightgray
is_lb = ['orangered' if city  ==  'Long Beach' else 'lightgray' for city in pollution['city']]
# Make a vector where Long Beach is orangered; else lightgray
is_lb = ['orangered' if city  ==  'Long Beach' else 'lightgray' for city in pollution['city']]

# Map facecolors to the list is_lb and set alpha to 0.3
sns.regplot(x = 'CO',
            y = 'O3',
            data = pollution,
            fit_reg = False, 
            scatter_kws = {'facecolors':is_lb, "alpha" :0.3})
plt.show() 
# Make a vector where Long Beach is orangered; else lightgray
is_lb = ['orangered' if city  ==  'Long Beach' else 'lightgray' for city in pollution['city']]

# Map facecolors to the list is_lb and set alpha to 0.3
sns.regplot(x = 'CO',
            y = 'O3',
            data = pollution,
            fit_reg = False,
            scatter_kws = {'facecolors':is_lb, 'alpha': 0.3})

# Add annotation to plot
plt.text(1.6, 0.072, 'April 30th, Bad Day')
plt.show()
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