In the previous section, we saw the main structure of NumPy. But you python developer might be wondering why not use the native structures of the language to store the data. The answer to that question is because NumPy was designed to be effective on arrays, so. Take up less memory. Data in NumPy is stored in a continuous block of memory, unlike other Python objects. So the library can access this data and modify it very efficiently, a concept called locality of reference in computer science. Also, since business phone number list arrays are not built-in sequences they use a smaller amount of memory. They are faster: The operations that NumPy provides are capable of performing complex processing on data sets, without the need for loops.
Installing NumPy in Python
To be able to take advantage of the operations that NumPy Python makes available, it is necessary to install this library in the environment in which we are working. So let’s get to the code! The installation of NumPy can be performed using the Python library manager, the famous PIP, through the command. With the install command, the latest version of the library is installed in your programming environment. But if for some reason you need to install a specific version of the package, just use the command.
Where to start?
Like any other Python library, to be able to use DV Leads it is necessary to import the package into our programming environment. The import can be performed by the command. Through this command, our environment understands that we will use the operations that are saved in the NumPy Python library. In addition to signaling that we will use the package’s functions, we are assigning a “nickname” to it. This nickname is a convention and speeds up development, as we don’t need to write the entire library name, just np.