pd.read_csv()to read CSV files in pandas.
Although NumPy provides fundamental structures and tools that make working with data easier, there are several things that limit its usefulness:
Pandas and NumPy combine to make working with data easier.
The two core pandas types: series and data frames.
How to select data from pandas objects using axis labels.
numpy.genfromtxt()to read in an ndarray.
Why Numpy: The NumPy library lets us write code in Python but take advantage of the performance that C offers. One way NumPy makes our code run quickly is vectorization, which takes advantage of Single Instruction Multiple Data (SIMD) to process data more quickly.
Python has three standard modules that are designed to help to work with dates and times:
The DateTime module contains a number of classes, including:
datetime.datetime: For working with date and time data.
datetime.time: For working with time data only.
datetime.timedelta: For representing time periods.
datetime the class has a number of attributes which make it easy to retrieve the various parts that make up the data stored within the object:
datetime.day: The day of the month.
datetime.month: The month of the year.
datetime.year: The year.
datetime.hour: The hour of the day.
datetime.minute: The minute of the hour.
You can think of methods like special functions that belong to a particular class