Index objects#

Constructor#

Index(data, **_)

Properties#

`Index.is_monotonic`	Return boolean scalar if values in the object are monotonic_increasing.
`Index.is_monotonic_decreasing`	Return boolean scalar if values in the object are monotonic_decreasing.
`Index.is_monotonic_increasing`	Return boolean scalar if values in the object are monotonic_increasing.
`Index.name`
`Index.names`
`Index.ndim`
`Index.size`
`Index.T`	Return the transpose, which is by definition self.
`Index.memory_usage`([deep])	Memory usage of the values.

Modifying and computations#

`Index.all`()
`Index.any`()
`Index.drop`(labels[, errors])	Make new Index with passed list of labels deleted.
`Index.drop_duplicates`([keep, method])	Return Index with duplicate values removed.
`Index.duplicated`([keep])	Indicate duplicate index values.
`Index.max`([axis, skipna])
`Index.min`([axis, skipna])
`Index.rename`(name[, inplace])	Alter Index or MultiIndex name.

Compatibility with MultiIndex#

Index.set_names(names[, level, inplace])

Set Index or MultiIndex name.

Missing values#

`Index.fillna`([value, downcast])	Fill NA/NaN values with the specified value.
`Index.dropna`([how])	Return Index without NA/NaN values.
`Index.isna`()	Detect missing values.
`Index.notna`()	Detect existing (non-missing) values.

Conversion#

`Index.astype`(dtype[, copy])	Create an Index with values cast to dtypes.
`Index.map`(mapper[, na_action, dtype, ...])	Map values using input correspondence (a dict, Series, or function).
`Index.to_frame`([index, name])	Create a DataFrame with a column containing the Index.
`Index.to_series`([index, name])	Create a Series with both index and values equal to the index keys.