Correlation in data

Dataset: Our dataset was cobbled together from monthly average Ice Cream Production in 2011 and the average monthly temperature for the US.File: correlation/weatherIceCream.csv 
File: Correlation/scatters1.py#Import Libraries
from bokeh.models import HoverTool
from bokeh.plotting import figure, show, output_file
import pandas as pd
# Read Data
df = pd.read_csv("correlation/weatherIceCream.csv", usecols=['Date','AVG temp C','Ice Cream production'])
hover = HoverTool(tooltips=[
("(Temp,Ice Cream Production", "($x, $y)")
])
p = figure(x_range=(-10, 30),y_range=(35, 90), tools=[hover])# Main chart definition
p.scatter(df['AVG temp C'], df['Ice Cream production'],size=10)
p.background_fill_color = "mintcream"
p.background_fill_alpha = 0.2
p.xaxis.axis_label = "Avg Temp C"
p.yaxis.axis_label = "Ice Cream Production (1000, Gallons)"
show(p)
Check the live chart or run it from the code sample/github, you can hover over the data points,which makes it easier to understand the relationships.

Pearsons Correlation Coefficient ( PCC ) ,Pearson’s r or simply correlation coefficient.

File: pearsonr.pyfrom scipy.stats import pearsonr
import pandas as pd
# Read Data
df = pd.read_csv("correlation/weatherIceCream.csv", usecols=['Date','AVG temp C','Ice Cream production'])
# pearson in the hauz !
print(pearsonr(df['AVG temp C'],df['Ice Cream production']))

Causation and other traps

Conclusion

About the Author :

AI, Software Developer, Designer : www.k3no.com

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