Import Data

Import and take a fist glimpse of the geospatial data.

We'll cover the following...

Now that we’ve gone over the theory, it’s time to get started. At the end of the day, the most important part of any data science project is the data. Our boss has already compiled the data in a .csv file and handed it over to us. Now, take a first look at the provided .csv file, which consists of three columns: Store, lat, and lon.

The MoscowMcD.csv File

Store

lat

long

StoreA

55.8335718446519

37.64083664695893

StoreB

55.801166340384995

37.8118113719255

StoreC

55.78302291327372

37.68066208490295

StoreD

55.76873384364866

37.6854686032755

StoreE

55.77916151917434

37.631910255695615

StoreF

55.771823816133235

37.6016978544967

StoreG

55.7803199775828

37.58590500841545

StoreH

55.79769272132777

37.55500596173475

StoreI

55.7416861350695

37.412183701521705

StoreJ

55.65655688623046

37.59002488130621

StoreK

55.65616951170943

37.74452011470974

StoreL

55.74477825112604

37.62367050991409

StoreM

55.76255316385133

37.63465683762279

No matter in which format the data is delivered to us, we’ll have to read it in Python.

Create DataFrame

An example of the pragmatic didactic approach of this course is the use of import statements. Usually, it’s common to import all used Python packages at the beginning of a project. This course deviates from this approach, so any import statement used will be explained right before it is used. This approach is intended to improve code understanding and simplify traceability.

We’ll use the pandas package to import this data file via the internet. The pandas Python library ...