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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 <img src=images/continuum_analytics_bw.png align="left" width="15%" style="margin-right
Step1:  Exercise
Step2:  Exercise
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Python Code:
import pandas as pd
from bokeh.charts import TimeSeries, output_notebook, show
# Get data
df = pd.read_csv('data/Land_Ocean_Monthly_Anomaly_Average.csv')
# Process data
df['datetime'] = pd.to_datetime(df['datetime'])
df = df[['anomaly','datetime']]
# Output option
output_notebook()
# Create timeseries chart
t = TimeSeries(df, x='datetime')
# Show chart
show(t)
Explanation: <img src=images/continuum_analytics_bw.png align="left" width="15%" style="margin-right:15%">
<h1 align='center'>Berkeley Earth</h1>
1.1 Charts - Timeseries
Exercise: Visualize the evolution of the temperature anomaly monthly average over time with a timeseries chart
Data: 'data/Land_Ocean_Monthly_Anomaly_Average.csv'
Tips:
import pandas as pd
pd.read_csv()
pd.to_datetime()
End of explanation
# Style your timeseries chart 
t = TimeSeries(df, x='datetime', xlabel='time', ylabel='Anomaly(ºC)',
               xgrid = False, ygrid=True, tools=False, width=950, height=300,
               title="Temperature Anomaly(ºC) Monthly Average", palette=["grey"])
# Show new chart
show(t)
Explanation: Exercise: Style your plot
Ideas:
Add a title
Add axis labels
Change width and height
Deactivate toolbox or customize available tools
Change line color
Charts arguments can be found: http://bokeh.pydata.org/en/latest/docs/user_guide/charts.html#generic-arguments
End of explanation
# Compute moving average
df['moving_average'] = pd.rolling_mean(df['anomaly'], 12)
# Create chart with moving average
t = TimeSeries(df, x='datetime', xlabel='time', ylabel='Anomaly(ºC)',
               xgrid = False, ygrid=True, tools=False, width=950, height=300, legend="bottom_right",
               title="Temperature Anomaly(ºC) Monthly Average", palette=["grey", "red"])
# Show chart with moving average
show(t)
Explanation: Exercise: Add the moving annual average to your chart
Tips:
pd.rolling_mean()
End of explanation
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