pandas DataFrames

A pandas DataFrame is a 2D labeled data structure with columns that can be of different types. A DataFrame can be thought of as a dictionary-like container for Series objects, where each key in the dictionary is a column label and each value is a Series.

If you are familiar with relational databases, you’ll notice that a pandas DataFrame is similar to a regular SQL table. The figure below illustrates an

example of a pandas DataFrame.

Notice that the DataFrame includes an index column. Like with Series, pandas uses zero-based numeric indexing for DataFrames by default.

However, you can replace the default index with one or more existing columns. Figure below shows the same DataFrame but with the Date column set as the index.

In this particular example, the index is a column of type date. In fact, pandas allows you to have DataFrame indexes of any type. The most commonly used index types are integers and strings. However, you are not limited to using only simple types. You might define an index of a sequence type, such as List or Tuple, or even use an object type that is not built into Python; this could be a third-party type or even your own object type.