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154 lines
6.2 KiB
Plaintext
154 lines
6.2 KiB
Plaintext
---
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slug: 2016/01/cloudy-in-seattle
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title: Cloudy in Seattle
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date: 2016-01-23 12:00:00
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authors: [bspeice]
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tags: []
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---
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Building on prior analysis, is Seattle's reputation as a depressing city actually well-earned?
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<!-- truncate -->
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```python
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import pickle
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import pandas as pd
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import numpy as np
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from bokeh.plotting import output_notebook, figure, show
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from bokeh.palettes import RdBu4 as Palette
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from datetime import datetime
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import warnings
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output_notebook()
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```
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```
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BokehJS successfully loaded.
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```
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## Examining other cities
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After taking some time to explore how the weather in North Carolina stacked up over the past years, I was interested in doing the same analysis for other cities. Growing up with family from Binghamton, NY I was always told it was very cloudy there. And Seattle has a nasty reputation for being very depressing and cloudy. All said, the cities I want to examine are:
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- Binghamton, NY
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- Cary, NC
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- Seattle, WA
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- New York City, NY
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I'd be interested to try this analysis worldwide at some point - comparing London and Seattle might be an interesting analysis. For now though, we'll stick with trying out the US data.
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There will be plenty of charts. I want to know: **How has average cloud cover and precipitation chance changed over the years for each city mentioned?** This will hopefully tell us whether Seattle has actually earned its reputation for being a depressing city.
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```python
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city_forecasts = pickle.load(open('city_forecasts.p', 'rb'))
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forecasts_df = pd.DataFrame.from_dict(city_forecasts)
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```
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```python
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cities = ['binghamton', 'cary', 'nyc', 'seattle']
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city_colors = {cities[i]: Palette[i] for i in range(0, 4)}
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def safe_cover(frame):
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if frame and 'cloudCover' in frame:
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return frame['cloudCover']
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else:
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return np.NaN
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def monthly_avg_cloudcover(city, year, month):
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dates = pd.DatetimeIndex(start=datetime(year, month, 1, 12),
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end=datetime(year, month + 1, 1, 12),
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freq='D', closed='left')
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cloud_cover_vals = list(map(lambda x: safe_cover(forecasts_df[city][x]['currently']), dates))
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cloud_cover_samples = len(list(filter(lambda x: x is not np.NaN, cloud_cover_vals)))
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# Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.
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with warnings.catch_warnings():
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warnings.simplefilter('ignore')
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return np.nanmean(cloud_cover_vals), cloud_cover_samples
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```
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```python
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years = range(1990, 2016)
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def city_avg_cc(city, month):
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return [monthly_avg_cloudcover(city, y, month) for y in years]
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months = [
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('July', 7),
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('August', 8),
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('September', 9),
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('October', 10),
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('November', 11)
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]
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for month, month_id in months:
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month_averages = {city: city_avg_cc(city, month_id) for city in cities}
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f = figure(title="{} Average Cloud Cover".format(month),
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x_axis_label='Year',
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y_axis_label='Cloud Cover Percentage')
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for city in cities:
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f.line(years, [x[0] for x in month_averages[city]],
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legend=city, color=city_colors[city])
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show(f)
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```
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Well, as it so happens it looks like there are some data issues. July's data is a bit sporadic, and 2013 seems to be missing from most months as well. I think really only two things can really be confirmed here:
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- Seattle, specifically for the months of October and November, is in fact significantly more cloudy on average than are other cities
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- All cities surveyed have seen average cloud cover decline over the months studied. There are data issues, but the trend seems clear.
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Let's now move from cloud cover data to looking at average rainfall chance.
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```python
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def safe_precip(frame):
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if frame and 'precipProbability' in frame:
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return frame['precipProbability']
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else:
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return np.NaN
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def monthly_avg_precip(city, year, month):
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dates = pd.DatetimeIndex(start=datetime(year, month, 1, 12),
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end=datetime(year, month + 1, 1, 12),
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freq='D', closed='left')
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precip_vals = list(map(lambda x: safe_precip(forecasts_df[city][x]['currently']), dates))
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precip_samples = len(list(filter(lambda x: x is not np.NaN, precip_vals)))
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# Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.
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with warnings.catch_warnings():
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warnings.simplefilter('ignore')
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return np.nanmean(precip_vals), precip_samples
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def city_avg_precip(city, month):
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return [monthly_avg_precip(city, y, month) for y in years]
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for month, month_id in months:
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month_averages = {city: city_avg_cc(city, month_id) for city in cities}
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f = figure(title="{} Average Precipitation Chance".format(month),
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x_axis_label='Year',
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y_axis_label='Precipitation Chance Percentage')
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for city in cities:
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f.line(years, [x[0] for x in month_averages[city]],
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legend=city, color=city_colors[city])
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show(f)
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```
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The same data issue caveats apply here: 2013 seems to be missing some data, and July has some issues as well. However, this seems to confirm the trends we saw with cloud cover:
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- Seattle, specifically for the months of August, October, and November has had a consistently higher chance of rain than other cities surveyed.
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- Average precipitation chance, just like average cloud cover, has been trending down over time.
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## Conclusion
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I have to admit I was a bit surprised after doing this analysis. Seattle showed a higher average cloud cover and average precipitation chance than did the other cities surveyed. Maybe Seattle is actually an objectively more depressing city to live in.
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Well that's all for weather data at the moment. It's been a great experiment, but I think this is about as far as I'll be able to get with weather data without some domain knowledge. Talk again soon! |