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Hello! Opal here~ While looking at the syntax I noticed there seemed to be a section for average age, but it didn’t seem like there was anything in place to visualize the data. I just wanted to know if there was a way to visualize it? (I tried to use a codebyte but I couldn’t figure out what language I was using). I figured it might be best to apply the same bell curve like the one found in Numerical Variable, but I had trouble making it work.
### Average
ages = churn_data.groupby(churn_data["Exited"])["Age"].mean()
print("Looking at statistics \n")
print("The average age of someone who left the bank is", round(ages[0], 2), "years old. \n")
print("The average age of someone who is still with the bank is", round(ages[1], 2), "years old. \n")
### Visualizing a Numerical Variable
sns.displot(churn_data["CreditScore"], kde=False)
plt.title("Analysis of a Numerical Variable")
plt.show()
plt.clf()
This is the whole data set:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import codecademylib3
# Loading in and showing the data
churn_data = pd.read_csv("churn.csv")
print(churn_data.head())
### Checks for any missing data
print(churn_data.isnull().any())
### Remove columns we do not need
churn_data.drop(["RowNumber","CustomerId","Surname"], axis = 1 , inplace = True)
# show data with removed columns
print(churn_data.head())
### Visualizing a Categorical Variable
sns.countplot(churn_data["Geography"])
plt.title("Analysis of a Categorical Variable")
plt.show()
plt.clf()
### Visualizing a Numerical Variable
sns.displot(churn_data["CreditScore"], kde=False)
plt.title("Analysis of a Numerical Variable")
plt.show()
plt.clf()
### Visualizing the Effect of a Categorical Variable on the Exited Variable
sns.countplot(y = churn_data["Gender"], hue = churn_data["Exited"])
plt.title("Effect of a Categorical Variable on the Exited Variable")
plt.show()
plt.clf()
### Average
ages = churn_data.groupby(churn_data["Exited"])["Age"].mean()
print("Looking at statistics \n")
print("The average age of someone who left the bank is", round(ages[0], 2), "years old. \n")
print("The average age of someone who is still with the bank is", round(ages[1], 2), "years old. \n")
### Creating a Correlation Matrix
var_list = ["CreditScore","Age","Tenure","Balance","NumOfProducts","EstimatedSalary","Exited"]
sns.heatmap(churn_data[var_list].corr(), annot = True)
plt.title("Correlation Matrix")
plt.tight_layout()
plt.show()
plt.clf()
) below.
) to up-vote the contribution!