pandas.Index¶
- class pandas.Index¶
Immutable ndarray implementing an ordered, sliceable set. The basic object storing axis labels for all pandas objects
Parameters: data : array-like (1-dimensional)
dtype : NumPy dtype (default: object)
copy : bool
Make a copy of input ndarray
name : object
Name to be stored in the index
tupleize_cols : bool (default: True)
When True, attempt to create a MultiIndex if possible
Notes
An Index instance can only contain hashable objects
Attributes
T return the transpose, which is by definition self asi8 base return the base object if the memory of the underlying data is shared data return the data pointer of the underlying data dtype dtype_str flags has_duplicates hasnans inferred_type is_all_dates is_monotonic alias for is_monotonic_increasing (deprecated) is_monotonic_decreasing return if the index is monotonic decreasing (only equal or is_monotonic_increasing return if the index is monotonic increasing (only equal or is_unique itemsize return the size of the dtype of the item of the underlying data name names nbytes return the number of bytes in the underlying data ndim return the number of dimensions of the underlying data, by definition 1 nlevels shape return a tuple of the shape of the underlying data size return the number of elements in the underlying data strides return the strides of the underlying data values return the underlying data as an ndarray Methods
all(*args, **kwargs) Return whether all elements are True any(*args, **kwargs) Return whether any element is True append(other) Append a collection of Index options together argmax([axis]) return a ndarray of the maximum argument indexer argmin([axis]) return a ndarray of the minimum argument indexer argsort(*args, **kwargs) return an ndarray indexer of the underlying data asof(label) For a sorted index, return the most recent label up to and including the passed label. asof_locs(where, mask) where : array of timestamps astype(dtype) copy([names, name, dtype, deep]) Make a copy of this object. delete(loc) Make new Index with passed location(-s) deleted diff(*args, **kwargs) difference(other) Return a new Index with elements from the index that are not in other. drop(labels[, errors]) Make new Index with passed list of labels deleted drop_duplicates(*args, **kwargs) Return Index with duplicate values removed duplicated(*args, **kwargs) Return boolean np.array denoting duplicate values equals(other) Determines if two Index objects contain the same elements. factorize([sort, na_sentinel]) Encode the object as an enumerated type or categorical variable fillna([value, downcast]) Fill NA/NaN values with the specified value format([name, formatter]) Render a string representation of the Index get_duplicates() get_indexer(target[, method, limit, tolerance]) Compute indexer and mask for new index given the current index. get_indexer_for(target, **kwargs) guaranteed return of an indexer even when non-unique get_indexer_non_unique(target) return an indexer suitable for taking from a non unique index get_level_values(level) Return vector of label values for requested level, equal to the length get_loc(key[, method, tolerance]) Get integer location for requested label get_slice_bound(label, side, kind) Calculate slice bound that corresponds to given label. get_value(series, key) Fast lookup of value from 1-dimensional ndarray. get_values() return the underlying data as an ndarray groupby(to_groupby) Group the index labels by a given array of values. holds_integer() identical(other) Similar to equals, but check that other comparable attributes are insert(loc, item) Make new Index inserting new item at location. intersection(other) Form the intersection of two Index objects. is_(other) More flexible, faster check like is but that works through views is_boolean() is_categorical() is_floating() is_integer() is_lexsorted_for_tuple(tup) is_mixed() is_numeric() is_object() is_type_compatible(kind) isin(values[, level]) Compute boolean array of whether each index value is found in the passed set of values. item() return the first element of the underlying data as a python scalar join(other[, how, level, return_indexers]) this is an internal non-public method map(mapper) max() The maximum value of the object memory_usage([deep]) Memory usage of my values min() The minimum value of the object nunique([dropna]) Return number of unique elements in the object. order([return_indexer, ascending]) Return sorted copy of Index putmask(mask, value) return a new Index of the values set with the mask ravel([order]) return an ndarray of the flattened values of the underlying data reindex(target[, method, level, limit, ...]) Create index with target’s values (move/add/delete values as necessary) rename(name[, inplace]) Set new names on index. repeat(n) return a new Index of the values repeated n times searchsorted(key[, side]) np.ndarray searchsorted compat set_names(names[, level, inplace]) Set new names on index. set_value(arr, key, value) Fast lookup of value from 1-dimensional ndarray. shift([periods, freq]) Shift Index containing datetime objects by input number of periods and slice_indexer([start, end, step, kind]) For an ordered Index, compute the slice indexer for input labels and slice_locs([start, end, step, kind]) Compute slice locations for input labels. sort(*args, **kwargs) sort_values([return_indexer, ascending]) Return sorted copy of Index sortlevel([level, ascending, sort_remaining]) For internal compatibility with with the Index API str alias of StringMethods summary([name]) sym_diff(other[, result_name]) Compute the sorted symmetric difference of two Index objects. take(indices[, axis, allow_fill, fill_value]) return a new Index of the values selected by the indexer to_datetime([dayfirst]) For an Index containing strings or datetime.datetime objects, attempt to_native_types([slicer]) slice and dice then format to_series(**kwargs) Create a Series with both index and values equal to the index keys tolist() return a list of the Index values transpose() return the transpose, which is by definition self union(other) Form the union of two Index objects and sorts if possible. unique() Return array of unique values in the object. value_counts([normalize, sort, ascending, ...]) Returns object containing counts of unique values. view([cls])