• Select columns, rows and individual items using their integer location.
  • Use pd.read_csv() to read CSV files in pandas.
  • Work with integer axis labels.
  • How to use pandas methods to produce boolean arrays.
  • Use boolean operators to combine boolean comparisons to perform more complex analysis.
  • Use index labels to align data.
  • Use aggregation to perform advanced analysis using loops.
  • How to select data from pandas objects using boolean arrays.
  • How to assign data using labels and boolean arrays.
  • How to create new rows and columns in pandas.
  • Many new methods to make data analysis easier in pandas.

Although NumPy provides fundamental structures and tools that make working with data easier, there are several things that limit its usefulness:

  • The lack of support for column names forces us to frame questions as multi-dimensional array operations.
  • Support for only one data type per ndarray makes it more difficult to work with data that contains both numeric and string data.
  • There are lots of low level methods, but there are many common analysis patterns that don’t have pre-built methods.

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.

  • How to use numpy.genfromtxt() to read in an ndarray.
  • About NaN values.
  • What a boolean array is, and how to create one.
  • How to use boolean indexing to filter values in one and two-dimensional ndarrays.
  • How to assign one or more new values to an ndarray based on their locations.
  • How to assign one or more new values to an ndarray based on their values.

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 calendar module
  • The time module
  • The datetime module

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.

The 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

  • What objects, classes, methods, and attributes are.
  • How to create a class and instantiate a new object.
  • How to store attributes inside objects using the init method.
  • How to create methods to transform data and update attributes

Concillier Kitungulu (Coco)

Passionate about product management and people. Raising the bar on functionality, flow, usability, and simplicity. Information Systems Masters, Lund University.

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