Index
pandas-on-Spark Index that corresponds to pandas Index logically.
Index.is_monotonic
Return boolean if values in the object are monotonically increasing.
Index.is_monotonic_increasing
Index.is_monotonic_decreasing
Return boolean if values in the object are monotonically decreasing.
Index.is_unique
Return if the index has unique values.
Index.has_duplicates
If index has duplicates, return True, otherwise False.
Index.hasnans
Return True if it has any missing values.
Index.dtype
Return the dtype object of the underlying data.
Index.inferred_type
Return a string of the type inferred from the values.
Index.is_all_dates
Return if all data types of the index are datetime.
Index.shape
Return a tuple of the shape of the underlying data.
Index.name
Return name of the Index.
Index.names
Return names of the Index.
Index.ndim
Return an int representing the number of array dimensions.
Index.size
Return an int representing the number of elements in this object.
Index.nlevels
Number of levels in Index & MultiIndex.
Index.empty
Returns true if the current object is empty.
Index.T
Return the transpose, For index, It will be index itself.
Index.values
Return an array representing the data in the Index.
Index.all([axis, skipna])
Index.all
Return whether all elements are True.
Index.any([axis])
Index.any
Return whether any element is True.
Index.argmin()
Index.argmin
Return a minimum argument indexer.
Index.argmax()
Index.argmax
Return a maximum argument indexer.
Index.copy([name, deep])
Index.copy
Make a copy of this object.
Index.delete(loc)
Index.delete
Make new Index with passed location(-s) deleted.
Index.equals(other)
Index.equals
Determine if two Index objects contain the same elements.
Index.factorize([sort, na_sentinel])
Index.factorize
Encode the object as an enumerated type or categorical variable.
Index.identical(other)
Index.identical
Similar to equals, but check that other comparable attributes are also equal.
Index.insert(loc, item)
Index.insert
Make new Index inserting new item at location.
Index.is_boolean()
Index.is_boolean
Return if the current index type is a boolean type.
Index.is_categorical()
Index.is_categorical
Return if the current index type is a categorical type.
Index.is_floating()
Index.is_floating
Return if the current index type is a floating type.
Index.is_integer()
Index.is_integer
Return if the current index type is a integer type.
Index.is_interval()
Index.is_interval
Return if the current index type is an interval type.
Index.is_numeric()
Index.is_numeric
Return if the current index type is a numeric type.
Index.is_object()
Index.is_object
Return if the current index type is a object type.
Index.drop(labels)
Index.drop
Make new Index with passed list of labels deleted.
Index.drop_duplicates()
Index.drop_duplicates
Return Index with duplicate values removed.
Index.min()
Index.min
Return the minimum value of the Index.
Index.max()
Index.max
Return the maximum value of the Index.
Index.map(mapper[, na_action])
Index.map
Map values using input correspondence (a dict, Series, or function).
Index.rename(name[, inplace])
Index.rename
Alter Index or MultiIndex name.
Index.repeat(repeats)
Index.repeat
Repeat elements of a Index/MultiIndex.
Index.take(indices)
Index.take
Return the elements in the given positional indices along an axis.
Index.unique([level])
Index.unique
Return unique values in the index.
Index.nunique([dropna, approx, rsd])
Index.nunique
Return number of unique elements in the object.
Index.value_counts([normalize, sort, …])
Index.value_counts
Return a Series containing counts of unique values.
Index.set_names(names[, level, inplace])
Index.set_names
Set Index or MultiIndex name.
Index.droplevel(level)
Index.droplevel
Return index with requested level(s) removed.
Index.fillna(value)
Index.fillna
Fill NA/NaN values with the specified value.
Index.dropna()
Index.dropna
Return Index or MultiIndex without NA/NaN values
Index.isna()
Index.isna
Detect existing (non-missing) values.
Index.notna()
Index.notna
Index.astype(dtype)
Index.astype
Cast a pandas-on-Spark object to a specified dtype dtype.
dtype
Index.item()
Index.item
Return the first element of the underlying data as a python scalar.
Index.to_list()
Index.to_list
Return a list of the values.
Index.to_series([name])
Index.to_series
Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index.
Index.to_frame([index, name])
Index.to_frame
Create a DataFrame with a column containing the Index.
Index.view()
Index.view
this is defined as a copy with the same identity
Index.to_numpy([dtype, copy])
Index.to_numpy
A NumPy ndarray representing the values in this Index or MultiIndex.
Index.spark provides features that does not exist in pandas but in Spark. These can be accessed by Index.spark.<function/property>.
Index.spark
Index.spark.<function/property>
Index.spark.column
Spark Column object representing the Series/Index.
Index.spark.transform(func)
Index.spark.transform
Applies a function that takes and returns a Spark column.
Index.sort_values([ascending])
Index.sort_values
Return a sorted copy of the index.
Index.shift([periods, fill_value])
Index.shift
Shift Series/Index by desired number of periods.
Index.append(other)
Index.append
Append a collection of Index options together.
Index.intersection(other)
Index.intersection
Form the intersection of two Index objects.
Index.union(other[, sort])
Index.union
Form the union of two Index objects.
Index.difference(other[, sort])
Index.difference
Return a new Index with elements from the index that are not in other.
Index.symmetric_difference(other[, …])
Index.symmetric_difference
Compute the symmetric difference of two Index objects.
Index.asof(label)
Index.asof
Return the label from the index, or, if not present, the previous one.
Index.isin(values)
Index.isin
Check whether values are contained in Series or Index.
Int64Index
Immutable sequence used for indexing and alignment.
Float64Index
CategoricalIndex
Index based on an underlying Categorical.
CategoricalIndex.codes
The category codes of this categorical.
CategoricalIndex.categories
The categories of this categorical.
CategoricalIndex.ordered
Whether the categories have an ordered relationship.
CategoricalIndex.rename_categories(…[, …])
CategoricalIndex.rename_categories
Rename categories.
CategoricalIndex.reorder_categories(…[, …])
CategoricalIndex.reorder_categories
Reorder categories as specified in new_categories.
CategoricalIndex.add_categories(new_categories)
CategoricalIndex.add_categories
Add new categories.
CategoricalIndex.remove_categories(removals)
CategoricalIndex.remove_categories
Remove the specified categories.
CategoricalIndex.remove_unused_categories([…])
CategoricalIndex.remove_unused_categories
Remove categories which are not used.
CategoricalIndex.set_categories(new_categories)
CategoricalIndex.set_categories
Set the categories to the specified new_categories.
CategoricalIndex.as_ordered([inplace])
CategoricalIndex.as_ordered
Set the Categorical to be ordered.
CategoricalIndex.as_unordered([inplace])
CategoricalIndex.as_unordered
Set the Categorical to be unordered.
CategoricalIndex.map(mapper)
CategoricalIndex.map
MultiIndex
pandas-on-Spark MultiIndex that corresponds to pandas MultiIndex logically.
MultiIndex.from_arrays(arrays[, sortorder, …])
MultiIndex.from_arrays
Convert arrays to MultiIndex.
MultiIndex.from_tuples(tuples[, sortorder, …])
MultiIndex.from_tuples
Convert list of tuples to MultiIndex.
MultiIndex.from_product(iterables[, …])
MultiIndex.from_product
Make a MultiIndex from the cartesian product of multiple iterables.
MultiIndex.from_frame(df[, names])
MultiIndex.from_frame
Make a MultiIndex from a DataFrame.
MultiIndex.has_duplicates
MultiIndex.hasnans
MultiIndex.inferred_type
MultiIndex.is_all_dates
is_all_dates always returns False for MultiIndex
MultiIndex.shape
MultiIndex.names
MultiIndex.ndim
MultiIndex.empty
MultiIndex.T
MultiIndex.size
MultiIndex.nlevels
MultiIndex.levshape
A tuple with the length of each level.
MultiIndex.values
MultiIndex.dtypes
Return the dtypes as a Series for the underlying MultiIndex.
MultiIndex.swaplevel([i, j])
MultiIndex.swaplevel
Swap level i with level j.
MultiIndex.droplevel(level)
MultiIndex.droplevel
MultiIndex.fillna(value)
MultiIndex.fillna
MultiIndex.dropna()
MultiIndex.dropna
MultiIndex.equals(other)
MultiIndex.equals
MultiIndex.equal_levels(other)
MultiIndex.equal_levels
Return True if the levels of both MultiIndex objects are the same
MultiIndex.identical(other)
MultiIndex.identical
MultiIndex.insert(loc, item)
MultiIndex.insert
Make new MultiIndex inserting new item at location.
MultiIndex.drop(codes[, level])
MultiIndex.drop
Make new MultiIndex with passed list of labels deleted
MultiIndex.copy([deep])
MultiIndex.copy
MultiIndex.delete(loc)
MultiIndex.delete
MultiIndex.rename(name[, inplace])
MultiIndex.rename
MultiIndex.repeat(repeats)
MultiIndex.repeat
MultiIndex.take(indices)
MultiIndex.take
MultiIndex.unique([level])
MultiIndex.unique
MultiIndex.min()
MultiIndex.min
MultiIndex.max()
MultiIndex.max
MultiIndex.value_counts([normalize, sort, …])
MultiIndex.value_counts
MultiIndex.append(other)
MultiIndex.append
MultiIndex.intersection(other)
MultiIndex.intersection
MultiIndex.union(other[, sort])
MultiIndex.union
MultiIndex.difference(other[, sort])
MultiIndex.difference
MultiIndex.symmetric_difference(other[, …])
MultiIndex.symmetric_difference
Compute the symmetric difference of two MultiIndex objects.
MultiIndex.astype(dtype)
MultiIndex.astype
MultiIndex.item()
MultiIndex.item
Return the first element of the underlying data as a python tuple.
MultiIndex.to_list()
MultiIndex.to_list
MultiIndex.to_series([name])
MultiIndex.to_series
MultiIndex.to_frame([index, name])
MultiIndex.to_frame
Create a DataFrame with the levels of the MultiIndex as columns.
MultiIndex.view()
MultiIndex.view
MultiIndex.to_numpy([dtype, copy])
MultiIndex.to_numpy
MultiIndex.spark provides features that does not exist in pandas but in Spark. These can be accessed by MultiIndex.spark.<function/property>.
MultiIndex.spark
MultiIndex.spark.<function/property>
MultiIndex.spark.data_type
Returns the data type as defined by Spark, as a Spark DataType object.
MultiIndex.spark.column
MultiIndex.spark.transform(func)
MultiIndex.spark.transform
MultiIndex.sort_values([ascending])
MultiIndex.sort_values
DatetimeIndex
Immutable ndarray-like of datetime64 data.
DatetimeIndex.year
The year of the datetime.
DatetimeIndex.month
The month of the timestamp as January = 1 December = 12.
DatetimeIndex.day
The days of the datetime.
DatetimeIndex.hour
The hours of the datetime.
DatetimeIndex.minute
The minutes of the datetime.
DatetimeIndex.second
The seconds of the datetime.
DatetimeIndex.microsecond
The microseconds of the datetime.
DatetimeIndex.week
The week ordinal of the year.
DatetimeIndex.weekofyear
DatetimeIndex.dayofweek
The day of the week with Monday=0, Sunday=6.
DatetimeIndex.day_of_week
DatetimeIndex.weekday
DatetimeIndex.dayofyear
The ordinal day of the year.
DatetimeIndex.day_of_year
DatetimeIndex.quarter
The quarter of the date.
DatetimeIndex.is_month_start
Indicates whether the date is the first day of the month.
DatetimeIndex.is_month_end
Indicates whether the date is the last day of the month.
DatetimeIndex.is_quarter_start
Indicator for whether the date is the first day of a quarter.
DatetimeIndex.is_quarter_end
Indicator for whether the date is the last day of a quarter.
DatetimeIndex.is_year_start
Indicate whether the date is the first day of a year.
DatetimeIndex.is_year_end
Indicate whether the date is the last day of the year.
DatetimeIndex.is_leap_year
Boolean indicator if the date belongs to a leap year.
DatetimeIndex.daysinmonth
The number of days in the month.
DatetimeIndex.days_in_month
DatetimeIndex.indexer_between_time(…[, …])
DatetimeIndex.indexer_between_time
Return index locations of values between particular times of day (example: 9:00-9:30AM).
DatetimeIndex.indexer_at_time(time[, asof])
DatetimeIndex.indexer_at_time
Return index locations of values at particular time of day (example: 9:30AM).
DatetimeIndex.normalize()
DatetimeIndex.normalize
Convert times to midnight.
DatetimeIndex.strftime(date_format)
DatetimeIndex.strftime
Convert to a string Index using specified date_format.
DatetimeIndex.round(freq, *args, **kwargs)
DatetimeIndex.round
Perform round operation on the data to the specified freq.
DatetimeIndex.floor(freq, *args, **kwargs)
DatetimeIndex.floor
Perform floor operation on the data to the specified freq.
DatetimeIndex.ceil(freq, *args, **kwargs)
DatetimeIndex.ceil
Perform ceil operation on the data to the specified freq.
DatetimeIndex.month_name([locale])
DatetimeIndex.month_name
Return the month names of the DatetimeIndex with specified locale.
DatetimeIndex.day_name([locale])
DatetimeIndex.day_name
Return the day names of the series with specified locale.
TimedeltaIndex
Immutable ndarray-like of timedelta64 data, represented internally as int64, and which can be boxed to timedelta objects.
TimedeltaIndex.days
Number of days for each element.
TimedeltaIndex.seconds
Number of seconds (>= 0 and less than 1 day) for each element.
TimedeltaIndex.microseconds
Number of microseconds (>= 0 and less than 1 second) for each element.