How it works...

You may access the three main components of a DataFrame with the index, columns, and values attributes. The output of the columns attribute appears to be just a sequence of the column names. This sequence of column names is technically an Index object. The output of the function type is the fully qualified class name of the object.

The fully qualified class name of the object for the variable columns is pandas.core.indexes.base.Index. It begins with the package name, which is followed by a path of modules and ends with the name of the type. A common way of referring to objects is to include the package name followed by the name of the object type. In this instance, we would refer to the columns as a pandas Index object.

The built-in subclass function checks whether the first argument inherits from the second. The Index and RangeIndex objects are very similar, and in fact, pandas has a number of similar objects reserved specifically for either the index or the columns. The index and the columns must both be some kind of Index object. Essentially, the index and the columns represent the same thing, but along different axes. They’re occasionally referred to as the row index and column index.

In this context, the Index objects refer to all the possible objects that can be used for the index or columns. They are all subclasses of pd.Index. Here is the complete list of the Index objects: CategoricalIndex, MultiIndex, IntervalIndex, Int64Index, UInt64Index, Float64Index, RangeIndex, TimedeltaIndex, DatetimeIndex, PeriodIndex.

A RangeIndex is a special type of Index object that is analogous to Python's range object. Its entire sequence of values is not loaded into memory until it is necessary to do so, thereby saving memory. It is completely defined by its start, stop, and step values.

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How it works...