<jupyter_start><jupyter_text>Plotting the Data* Use proper labeling of the plots using plot titles (including date of analysis) and axes labels.* Save the plotted figures as .pngs.  Latitude vs. Temperature Plot<jupyter_code># set x,y values
x_values = city_weather_clean['Lat']
y_values = city_weather_clean['Max Temp']

# add grid to plot
plt.grid()

# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Cities vs. Cities Max Temperature (F) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Max Temperature (F)')<jupyter_output><empty_output><jupyter_text>Latitude vs. Humidity Plot<jupyter_code># set x,y values
x_values = city_weather_clean['Lat']
y_values = city_weather_clean['Humidity']

# add grid to plot
plt.grid()

# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Cities vs. Cities Humidity (%) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Humidity (%)')<jupyter_output><empty_output><jupyter_text>Latitude vs. Cloudiness Plot<jupyter_code># set x,y values
x_values = city_weather_clean['Lat']
y_values = city_weather_clean['Cloudiness']

# add grid to plot
plt.grid()

# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Cities vs. Cities Cloudiness (%) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Cloudiness (%)')<jupyter_output><empty_output><jupyter_text>Latitude vs. Wind Speed Plot<jupyter_code># set x,y values
x_values = city_weather_clean['Lat']
y_values = city_weather_clean['Wind Speed']

# add grid to plot
plt.grid()

# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Cities vs. Cities Wind Speed (mph) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Wind Speed')<jupyter_output><empty_output><jupyter_text>Linear Regression<jupyter_code># Create Northern and Southern Hemisphere DataFrames

Northern_Hemi = city_weather_clean.loc[city_weather_clean['Lat'] > 0]

Southern_Hemi = city_weather_clean.loc[city_weather_clean['Lat'] < 0]<jupyter_output><empty_output><jupyter_text>Northern Hemisphere - Max Temp vs. Latitude Linear Regression<jupyter_code># set x,y values
x_values = Northern_Hemi['Lat']
y_values = Northern_Hemi['Max Temp']


# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Northern Hemisphere Cities vs. Max Temperature (F) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Max Temperature (F)')

#Regeression Analysis Time (and line formula - good ol' y=mx+b) !!!
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
regress_values = x_values * slope + intercept
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept, 2))

plt.annotate(line_eq, (0,40), fontsize = 15, color="red")

plt.plot(x_values, regress_values, "r-")

print(f"The r-squared is: {rvalue}")

plt.show()<jupyter_output>The r-squared is: -0.7309617769574978<jupyter_text>Southern Hemisphere - Max Temp vs. Latitude Linear Regression<jupyter_code># set x,y values
x_values = Southern_Hemi['Lat']
y_values = Southern_Hemi['Max Temp']


# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Southern Hemisphere Cities vs. Max Temperature (F) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Max Temperature (F)')

#Regeression Analysis Time (and line formula - good ol' y=mx+b) !!!
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
regress_values = x_values * slope + intercept
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept, 2))

plt.annotate(line_eq, (-55,95), fontsize = 15, color="red")

plt.plot(x_values, regress_values, "r-")

print(f"The r-squared is: {rvalue}")

plt.show()<jupyter_output>The r-squared is: 0.7888283081312039<jupyter_text>Northern Hemisphere - Humidity (%) vs. Latitude Linear Regression<jupyter_code># set x,y values
x_values = Northern_Hemi['Lat']
y_values = Northern_Hemi['Humidity']


# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Northern Hemisphere Cities vs. Humidity (%) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Humidity (%)')

#Regeression Analysis Time (and line formula - good ol' y=mx+b) !!!
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
regress_values = x_values * slope + intercept
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept, 2))

plt.annotate(line_eq, (45,15), fontsize = 15, color="red")

plt.plot(x_values, regress_values, "r-")

print(f"The r-squared is: {rvalue}")

plt.show()<jupyter_output>The r-squared is: 0.03405892128591604<jupyter_text>Southern Hemisphere - Humidity (%) vs. Latitude Linear Regression<jupyter_code># set x,y values
x_values = Southern_Hemi['Lat']
y_values = Southern_Hemi['Humidity']


# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Southern Hemisphere Cities vs. Humidity (%) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Humidity (%)')

#Regeression Analysis Time (and line formula - good ol' y=mx+b) !!!
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
regress_values = x_values * slope + intercept
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept, 2))

plt.annotate(line_eq, (-55,20), fontsize = 15, color="red")

plt.plot(x_values, regress_values, "r-")

print(f"The r-squared is: {rvalue}")

plt.show()<jupyter_output>The r-squared is: -0.11625288866041122<jupyter_text>Northern Hemisphere - Cloudiness (%) vs. Latitude Linear Regression<jupyter_code># set x,y values
x_values = Northern_Hemi['Lat']
y_values = Northern_Hemi['Cloudiness']


# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Northern Hemisphere Cities vs. Cloudiness (%) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Cloudiness (%)')

#Regeression Analysis Time (and line formula - good ol' y=mx+b) !!!
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
regress_values = x_values * slope + intercept
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept, 2))

plt.annotate(line_eq, (0,7), fontsize = 15, color="red")

plt.plot(x_values, regress_values, "r-")

print(f"The r-squared is: {rvalue}")

plt.show()<jupyter_output>The r-squared is: -0.039409896681170176<jupyter_text>Southern Hemisphere - Cloudiness (%) vs. Latitude Linear Regression<jupyter_code># set x,y values
x_values = Southern_Hemi['Lat']
y_values = Southern_Hemi['Cloudiness']


# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Southern Hemisphere Cities vs. Cloudiness (%) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Cloudiness (%)')

#Regeression Analysis Time (and line formula - good ol' y=mx+b) !!!
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
regress_values = x_values * slope + intercept
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept, 2))

plt.annotate(line_eq, (-56,27), fontsize = 15, color="red")

plt.plot(x_values, regress_values, "r-")

print(f"The r-squared is: {rvalue}")

plt.show()<jupyter_output>The r-squared is: -0.30963510220393814<jupyter_text>Northern Hemisphere - Wind Speed (mph) vs. Latitude Linear Regression<jupyter_code># set x,y values
x_values = Northern_Hemi['Lat']
y_values = Northern_Hemi['Wind Speed']


# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Northern Hemisphere Cities vs. Wind Speed (mph) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Wind Speed (mph)')

#Regeression Analysis Time (and line formula - good ol' y=mx+b) !!!
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
regress_values = x_values * slope + intercept
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept, 2))

plt.annotate(line_eq, (40,40), fontsize = 15, color="red")

plt.plot(x_values, regress_values, "r-")

print(f"The r-squared is: {rvalue}")

plt.show()<jupyter_output>The r-squared is: -0.055741870810576986<jupyter_text>Southern Hemisphere - Wind Speed (mph) vs. Latitude Linear Regression<jupyter_code># set x,y values
x_values = Southern_Hemi['Lat']
y_values = Southern_Hemi['Wind Speed']


# Create a scatter plot
plt.scatter(x_values, y_values, facecolors = 'tab:blue', edgecolors = 'dimgray')
plt.title(f'Latitude of Southern Hemisphere Cities vs. Wind Speed (mph) ({date})')
plt.xlabel('Latitude')
plt.ylabel('Wind Speed (mph)')

#Regeression Analysis Time (and line formula - good ol' y=mx+b) !!!
(slope, intercept, rvalue, pvalue, stderr) = linregress(x_values, y_values)
regress_values = x_values * slope + intercept
line_eq = "y = " + str(round(slope,2)) + "x + " + str(round(intercept, 2))

plt.annotate(line_eq, (-23,30), fontsize = 15, color="red")

plt.plot(x_values, regress_values, "r-")

print(f"The r-squared is: {rvalue}")

plt.show()<jupyter_output>The r-squared is: -0.09861916543627866
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