Exercise: Visualize the evolution of the temperature anomaly monthly average over time with a timeseries chart
Tips:
import pandas as pd
pd.read_csv()
pd.to_datetime()
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import pandas as pd
from bokeh.charts import TimeSeries, output_notebook, show
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output_notebook()
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# Get data
be = pd.read_csv('data/Land_Ocean_Monthly_Anomaly_Average.csv',
parse_dates=[0])
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be.head()
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be.datetime[:10]
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# Process data
be.datetime = pd.to_datetime(be.datetime)
be = be[['anomaly','datetime']]
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# Output option
output_notebook()
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# Create timeseries chart
t = TimeSeries(be, x='datetime', y='anomaly')
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# Show chart
show(t)
Exercise: Style your plot
Ideas:
Charts arguments can be found: http://bokeh.pydata.org/en/latest/docs/user_guide/charts.html#generic-arguments
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# Style your timeseries chart
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# Show new chart
Exercise: Add the moving annual average to your chart
Tips:
pd.rolling_mean()
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# Compute moving average
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# Create chart with moving average
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# Show chart with moving average