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Top Questions People Ask About Pandas, NumPy, Matplotlib & Scikit-learn — Answered!

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 Whether you're a beginner or brushing up on your skills, these are the real-world questions Python learners ask most about key libraries in data science. Let’s dive in! 🐍 🐼 Pandas: Data Manipulation Made Easy 1. How do I handle missing data in a DataFrame? df.fillna( 0 ) # Replace NaNs with 0 df.dropna() # Remove rows with NaNs df.isna(). sum () # Count missing values per column 2. How can I merge or join two DataFrames? pd.merge(df1, df2, on= 'id' , how= 'inner' ) # inner, left, right, outer 3. What is the difference between loc[] and iloc[] ? loc[] uses labels (e.g., column names) iloc[] uses integer positions df.loc[ 0 , 'name' ] # label-based df.iloc[ 0 , 1 ] # index-based 4. How do I group data and perform aggregation? df.groupby( 'category' )[ 'sales' ]. sum () 5. How can I convert a column to datetime format? df[ 'date' ] = pd.to_datetime(df[ 'date' ]) ...
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IoT real concept for auto insurance

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IoT in insurance is a connected car which helps transforming how insurance premiums can be calculated.

With the help of a small wireless device that plugs into the diagnostic port, Metromile offers a “per-mile” usage-based insurance. Often, low-mileage drivers overpay for insurance because they’re subsidizing those who drive the most.

Insurance Calculation

But since the number one risk indicator for drivers is time on the road, Metromile can offer insurance pro-rata by tracking the miles driven.
Mile-based insurance an application of IoT
#Mile-based insurance an application of IoT:
According to wiki: Usage-based insurance (UBI) -also known as pay as you drive (PAYD) and pay how you drive (PHYD) and mile-based auto insurance is a type of vehicle insurance whereby the costs are dependent upon the type of vehicle used, measured against time, distance, behavior, and place.

This differs from traditional insurance, which attempts to differentiate and reward "safe" drivers, giving them lower premiums and/or a no-claims bonus.


However, conventional differentiation is a reflection of history rather than present patterns of behavior.


This means that it may take a long time before safer (or more reckless) patterns of driving and changes in lifestyle feed through into premiums.

Usage of IoT technique - Pay as you drive (PAYD) means that the insurance premium is calculated dynamically, typically according to the amount driven. There are three types of usage-based insurance:
  • Coverage is based on the odometer reading of the vehicle. 
  • Coverage is based on mileage aggregated from GPS data, or the number of minutes the vehicle is being used as recorded by a vehicle-independent module transmitting data via cellphone or RF technology. 
  • Coverage is based on other data collected from the vehicle, including speed and time-of-day information, the historic riskiness of the road, driving actions in addition to distance or time traveled. IoT Beginner to an expert in a nutshell
The formula can be a simple function of the number of miles driven or can vary according to the type of driving or the identity of the driver. Once the basic scheme is in place, it is possible to add further details, such as an extra risk premium if someone drives too long without a break, uses their mobile phone while driving, or travels at an excessive speed.

Telematic usage-based insurance (i.e. the latter two types, in which vehicle information is automatically transmitted to the system) provides a much more immediate feedback loop to the driver, by changing the cost of insurance dynamically with a change of risk. This means drivers have a stronger incentive to adopt safer practices.

For example, if a commuter switches to public transport or to working at home, this immediately reduces the risk of rush hour accidents. With usage-based insurance, this reduction would be immediately reflected in the cost of car insurance for that month. The smartphone as measurement probe for insurance telematics has been surveyed.

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