CustomerID,Count,Country,State,City,Zip Code,Lat Long,Latitude,Longitude,Gender,Senior Citizen,Partner,Dependents,Tenure Months,Phone Service,Multiple Lines,Internet Service,Online Security,Online Backup,Device Protection,Tech Support,Streaming TV,Streaming Movies,Contract,Paperless Billing,Payment Method,Monthly Charges,Total Charges,Churn Label,Churn Value,Churn Score,CLTV,Churn Reason
3668-QPYBK,1,United States,California,Los Angeles,90003,"33.964131, -118.272783",33.964131,-118.272783,Male,No,No,No,2,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,53.85,108.15,Yes,1,86,3239,Competitor made better offer
9237-HQITU,1,United States,California,Los Angeles,90005,"34.059281, -118.30742",34.059281,-118.307420,Female,No,No,Yes,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.7,151.65,Yes,1,67,2701,Moved

For a dataset which looks like above and has no empty values, please perform the following analytics in Python:

1. Descriptive Statistics
Customer Demographics: Analyze the distribution of customers by gender, age (Senior Citizen), and geographic location (Country, State, City).
Service Usage: Summarize the usage of different services (Phone Service, Internet Service, etc.) and additional features (Online Security, Streaming TV, etc.).
Billing and Payment Methods: Examine the distribution of billing types (Paperless Billing) and payment methods.
2. Churn Analysis
Churn Rate: Calculate the overall churn rate and segment it by different demographic and service usage categories.
Churn Reasons: Analyze the reasons for churn (Churn Reason) to identify common issues leading to customer attrition.
Churn Score and CLTV: Evaluate the relationship between Churn Score, Customer Lifetime Value (CLTV), and actual churn (Churn Label).
3. Segmentation Analysis
Geographic Segmentation: Compare churn rates and service usage across different geographic regions.
Demographic Segmentation: Analyze churn rates and service preferences by gender, senior citizen status, and presence of partners/dependents.
Service Segmentation: Segment customers based on the types of services they use and analyze churn rates within these segments.
4. Tenure Analysis
Tenure vs. Churn: Investigate the relationship between tenure (Tenure Months) and churn rate.
Tenure vs. Service Usage: Analyze how service usage patterns change with tenure.
5. Feature Importance
Correlation Analysis: Identify correlations between different features (e.g., Monthly Charges, Total Charges, Contract Type) and churn.
Predictive Modeling: Build predictive models (e.g., logistic regression, decision trees) to identify the most important features contributing to churn.

7. Customer Lifetime Value (CLTV) Analysis
CLTV Distribution: Analyze the distribution of CLTV across different customer segments.
CLTV vs. Churn: Investigate the relationship between CLTV and churn rate to identify high-value customers at risk of churning.
8. Geospatial Analysis
Churn Hotspots: Use Latitude and Longitude data to identify geographic hotspots with high churn rates.
Service Availability: Analyze the availability and usage of different services across geographic regions.
9. Time Series Analysis
Trend Analysis: Analyze trends in churn rates, service usage, and customer demographics over time.
Seasonality: Identify any seasonal patterns in churn rates and service usage.
10. Customer Feedback Analysis
Sentiment Analysis: If available, analyze customer feedback and sentiment to understand the reasons behind churn and identify areas for improvement.