# 9th Question:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

temp = [98,87,90,85,95,75]
no_of_cust = [15,12,10,10,16,7]

data = {'Temperature': temp, 'No of Customers':no_of_cust}

df9 = pd.DataFrame(data)

correlation = df9.corr()

print(correlation)

sns.regplot(x='Temperature', y='No of Customers', data=df9)

print('Positive correlation, i.e as the climate gets warmer, the number of Customers increases')

plt.show()
5310abd595c2bc6d5fff67d96f9a9e570eb476ed
<mask token>

<mask token>
print(correlation)
sns.regplot(x='Temperature', y='No of Customers', data=df9)
print(
    'Positive correlation, i.e as the climate gets warmer, the number of Customers increases'
    )
plt.show()

<mask token>
temp = [98, 87, 90, 85, 95, 75]
no_of_cust = [15, 12, 10, 10, 16, 7]
data = {'Temperature': temp, 'No of Customers': no_of_cust}
df9 = pd.DataFrame(data)
correlation = df9.corr()
print(correlation)
sns.regplot(x='Temperature', y='No of Customers', data=df9)
print(
    'Positive correlation, i.e as the climate gets warmer, the number of Customers increases'
    )
plt.show()

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
temp = [98, 87, 90, 85, 95, 75]
no_of_cust = [15, 12, 10, 10, 16, 7]
data = {'Temperature': temp, 'No of Customers': no_of_cust}
df9 = pd.DataFrame(data)
correlation = df9.corr()
print(correlation)
sns.regplot(x='Temperature', y='No of Customers', data=df9)
print(
    'Positive correlation, i.e as the climate gets warmer, the number of Customers increases'
    )
plt.show()

# 9th Question:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

temp = [98,87,90,85,95,75]
no_of_cust = [15,12,10,10,16,7]

data = {'Temperature': temp, 'No of Customers':no_of_cust}

df9 = pd.DataFrame(data)

correlation = df9.corr()

print(correlation)

sns.regplot(x='Temperature', y='No of Customers', data=df9)

print('Positive correlation, i.e as the climate gets warmer, the number of Customers increases')

plt.show()