INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Extract columns names and python typos from metadata | def get_table_columns(metadata):
""" Extract columns names and python typos from metadata
Args:
metadata: Table metadata
Returns:
dict with columns names and python types
"""
cols = OrderedDict()
for col in metadata.c:
name = str(col).rpartition(".")[2]
cols[nam... |
Check query sanity | def check_query(query):
""" Check query sanity
Args:
query: query string
Returns:
None
"""
q = query.lower()
if "select " not in q:
raise InvalidQuery("SELECT word not found in the query: {0}".format(query))
if " from " not in q:
raise InvalidQuery("FROM wor... |
Extract columns names and python typos from query | def get_query_columns(engine, query):
""" Extract columns names and python typos from query
Args:
engine: SQLAlchemy connection engine
query: SQL query
Returns:
dict with columns names and python types
"""
con = engine.connect()
result = con.execute(query).fetchone()
... |
Check partition_column existence and type | def check_partition_column(partition_column, cols):
""" Check partition_column existence and type
Args:
partition_column: partition_column name
cols: dict with columns names and python types
Returns:
None
"""
for k, v in cols.items():
if k == partition_column:
... |
Return a columns name list and the query string | def get_query_info(sql, con, partition_column):
""" Return a columns name list and the query string
Args:
sql: SQL query or table name
con: database connection or url string
partition_column: column used to share the data between the workers
Returns:
Columns name list and q... |
Put bounders in the query | def query_put_bounders(query, partition_column, start, end):
""" Put bounders in the query
Args:
query: SQL query string
partition_column: partition_column name
start: lower_bound
end: upper_bound
Returns:
Query with bounders
"""
where = " WHERE TMP_TABLE.{0... |
Computes the index after a number of rows have been removed. | def compute_index(self, axis, data_object, compute_diff=True):
"""Computes the index after a number of rows have been removed.
Note: In order for this to be used properly, the indexes must not be
changed before you compute this.
Args:
axis: The axis to extract the index... |
Prepares methods given various metadata. Args: pandas_func: The function to prepare. | def _prepare_method(self, pandas_func, **kwargs):
"""Prepares methods given various metadata.
Args:
pandas_func: The function to prepare.
Returns
Helper function which handles potential transpose.
"""
if self._is_transposed:
def helper(df, in... |
Returns the numeric columns of the Manager. | def numeric_columns(self, include_bool=True):
"""Returns the numeric columns of the Manager.
Returns:
List of index names.
"""
columns = []
for col, dtype in zip(self.columns, self.dtypes):
if is_numeric_dtype(dtype) and (
include_bool or ... |
Preprocesses numeric functions to clean dataframe and pick numeric indices. | def numeric_function_clean_dataframe(self, axis):
"""Preprocesses numeric functions to clean dataframe and pick numeric indices.
Args:
axis: '0' if columns and '1' if rows.
Returns:
Tuple with return value(if any), indices to apply func to & cleaned Manager.
"""... |
Joins a pair of index objects ( columns or rows ) by a given strategy. | def _join_index_objects(self, axis, other_index, how, sort=True):
"""Joins a pair of index objects (columns or rows) by a given strategy.
Args:
axis: The axis index object to join (0 for columns, 1 for index).
other_index: The other_index to join on.
how: The type of... |
Joins a list or two objects together. | def join(self, other, **kwargs):
"""Joins a list or two objects together.
Args:
other: The other object(s) to join on.
Returns:
Joined objects.
"""
if not isinstance(other, list):
other = [other]
return self._join_list_of_managers(oth... |
Concatenates two objects together. | def concat(self, axis, other, **kwargs):
"""Concatenates two objects together.
Args:
axis: The axis index object to join (0 for columns, 1 for index).
other: The other_index to concat with.
Returns:
Concatenated objects.
"""
return self._appe... |
Copartition two QueryCompiler objects. | def copartition(self, axis, other, how_to_join, sort, force_repartition=False):
"""Copartition two QueryCompiler objects.
Args:
axis: The axis to copartition along.
other: The other Query Compiler(s) to copartition against.
how_to_join: How to manage joining the inde... |
Converts Modin DataFrame to Pandas DataFrame. | def to_pandas(self):
"""Converts Modin DataFrame to Pandas DataFrame.
Returns:
Pandas DataFrame of the DataManager.
"""
df = self.data.to_pandas(is_transposed=self._is_transposed)
if df.empty:
if len(self.columns) != 0:
df = pandas.DataFra... |
Improve simple Pandas DataFrame to an advanced and superior Modin DataFrame. | def from_pandas(cls, df, block_partitions_cls):
"""Improve simple Pandas DataFrame to an advanced and superior Modin DataFrame.
Args:
cls: DataManger object to convert the DataFrame to.
df: Pandas DataFrame object.
block_partitions_cls: BlockParitions object to store... |
Inter - data operations ( e. g. add sub ). | def _inter_manager_operations(self, other, how_to_join, func):
"""Inter-data operations (e.g. add, sub).
Args:
other: The other Manager for the operation.
how_to_join: The type of join to join to make (e.g. right, outer).
Returns:
New DataManager with new da... |
Helper method for inter - manager and scalar operations. | def _inter_df_op_handler(self, func, other, **kwargs):
"""Helper method for inter-manager and scalar operations.
Args:
func: The function to use on the Manager/scalar.
other: The other Manager/scalar.
Returns:
New DataManager with new data and index.
... |
Perform an operation between two objects. | def binary_op(self, op, other, **kwargs):
"""Perform an operation between two objects.
Note: The list of operations is as follows:
- add
- eq
- floordiv
- ge
- gt
- le
- lt
- mod
- mul
... |
Uses other manager to update corresponding values in this manager. | def update(self, other, **kwargs):
"""Uses other manager to update corresponding values in this manager.
Args:
other: The other manager.
Returns:
New DataManager with updated data and index.
"""
assert isinstance(
other, type(self)
),... |
Gets values from this manager where cond is true else from other. | def where(self, cond, other, **kwargs):
"""Gets values from this manager where cond is true else from other.
Args:
cond: Condition on which to evaluate values.
Returns:
New DataManager with updated data and index.
"""
assert isinstance(
cond... |
Handler for mapping scalar operations across a Manager. | def _scalar_operations(self, axis, scalar, func):
"""Handler for mapping scalar operations across a Manager.
Args:
axis: The axis index object to execute the function on.
scalar: The scalar value to map.
func: The function to use on the Manager with the scalar.
... |
Fits a new index for this Manger. | def reindex(self, axis, labels, **kwargs):
"""Fits a new index for this Manger.
Args:
axis: The axis index object to target the reindex on.
labels: New labels to conform 'axis' on to.
Returns:
A new QueryCompiler with updated data and new index.
"""
... |
Removes all levels from index and sets a default level_0 index. | def reset_index(self, **kwargs):
"""Removes all levels from index and sets a default level_0 index.
Returns:
A new QueryCompiler with updated data and reset index.
"""
drop = kwargs.get("drop", False)
new_index = pandas.RangeIndex(len(self.index))
if not drop... |
Transposes this DataManager. | def transpose(self, *args, **kwargs):
"""Transposes this DataManager.
Returns:
Transposed new DataManager.
"""
new_data = self.data.transpose(*args, **kwargs)
# Switch the index and columns and transpose the
new_manager = self.__constructor__(new_data, self.c... |
Apply function that will reduce the data to a Pandas Series. | def _full_reduce(self, axis, map_func, reduce_func=None):
"""Apply function that will reduce the data to a Pandas Series.
Args:
axis: 0 for columns and 1 for rows. Default is 0.
map_func: Callable function to map the dataframe.
reduce_func: Callable function to reduc... |
Counts the number of non - NaN objects for each column or row. | def count(self, **kwargs):
"""Counts the number of non-NaN objects for each column or row.
Return:
A new QueryCompiler object containing counts of non-NaN objects from each
column or row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis",... |
Returns the mean for each numerical column or row. | def mean(self, **kwargs):
"""Returns the mean for each numerical column or row.
Return:
A new QueryCompiler object containing the mean from each numerical column or
row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
... |
Returns the minimum from each column or row. | def min(self, **kwargs):
"""Returns the minimum from each column or row.
Return:
A new QueryCompiler object with the minimum value from each column or row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
return self.transpose().... |
Calculates the sum or product of the DataFrame. | def _process_sum_prod(self, func, **kwargs):
"""Calculates the sum or product of the DataFrame.
Args:
func: Pandas func to apply to DataFrame.
ignore_axis: Whether to ignore axis when raising TypeError
Return:
A new QueryCompiler object with sum or prod of th... |
Returns the product of each numerical column or row. | def prod(self, **kwargs):
"""Returns the product of each numerical column or row.
Return:
A new QueryCompiler object with the product of each numerical column or row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
return self.t... |
Calculates if any or all the values are true. | def _process_all_any(self, func, **kwargs):
"""Calculates if any or all the values are true.
Return:
A new QueryCompiler object containing boolean values or boolean.
"""
axis = kwargs.get("axis", 0)
axis = 0 if axis is None else axis
kwargs["axis"] = axis
... |
Returns whether all the elements are true potentially over an axis. | def all(self, **kwargs):
"""Returns whether all the elements are true, potentially over an axis.
Return:
A new QueryCompiler object containing boolean values or boolean.
"""
if self._is_transposed:
# Pandas ignores on axis=1
kwargs["bool_only"] = Fals... |
Converts columns dtypes to given dtypes. | def astype(self, col_dtypes, **kwargs):
"""Converts columns dtypes to given dtypes.
Args:
col_dtypes: Dictionary of {col: dtype,...} where col is the column
name and dtype is a numpy dtype.
Returns:
DataFrame with updated dtypes.
"""
# Gr... |
Applies map that reduce Manager to series but require knowledge of full axis. | def _full_axis_reduce(self, axis, func, alternate_index=None):
"""Applies map that reduce Manager to series but require knowledge of full axis.
Args:
func: Function to reduce the Manager by. This function takes in a Manager.
axis: axis to apply the function to.
alter... |
Returns index of first non - NaN/ NULL value. | def first_valid_index(self):
"""Returns index of first non-NaN/NULL value.
Return:
Scalar of index name.
"""
# It may be possible to incrementally check each partition, but this
# computation is fairly cheap.
def first_valid_index_builder(df):
df.... |
Returns the first occurrence of the maximum over requested axis. | def idxmax(self, **kwargs):
"""Returns the first occurrence of the maximum over requested axis.
Returns:
A new QueryCompiler object containing the maximum of each column or axis.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
r... |
Returns the first occurrence of the minimum over requested axis. | def idxmin(self, **kwargs):
"""Returns the first occurrence of the minimum over requested axis.
Returns:
A new QueryCompiler object containing the minimum of each column or axis.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
r... |
Returns index of last non - NaN/ NULL value. | def last_valid_index(self):
"""Returns index of last non-NaN/NULL value.
Return:
Scalar of index name.
"""
def last_valid_index_builder(df):
df.index = pandas.RangeIndex(len(df.index))
return df.apply(lambda df: df.last_valid_index())
func =... |
Returns median of each column or row. | def median(self, **kwargs):
"""Returns median of each column or row.
Returns:
A new QueryCompiler object containing the median of each column or row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
return self.transpose().median... |
Returns the memory usage of each column. | def memory_usage(self, **kwargs):
"""Returns the memory usage of each column.
Returns:
A new QueryCompiler object containing the memory usage of each column.
"""
def memory_usage_builder(df, **kwargs):
return df.memory_usage(**kwargs)
func = self._build... |
Returns quantile of each column or row. | def quantile_for_single_value(self, **kwargs):
"""Returns quantile of each column or row.
Returns:
A new QueryCompiler object containing the quantile of each column or row.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("axis", 0) ^ 1
return ... |
Reduce Manger along select indices using function that needs full axis. | def _full_axis_reduce_along_select_indices(self, func, axis, index):
"""Reduce Manger along select indices using function that needs full axis.
Args:
func: Callable that reduces the dimension of the object and requires full
knowledge of the entire axis.
axis: 0 f... |
Generates descriptive statistics. | def describe(self, **kwargs):
"""Generates descriptive statistics.
Returns:
DataFrame object containing the descriptive statistics of the DataFrame.
"""
# Use pandas to calculate the correct columns
new_columns = (
pandas.DataFrame(columns=self.columns)
... |
Returns a new QueryCompiler with null values dropped along given axis. Return: a new DataManager | def dropna(self, **kwargs):
"""Returns a new QueryCompiler with null values dropped along given axis.
Return:
a new DataManager
"""
axis = kwargs.get("axis", 0)
subset = kwargs.get("subset", None)
thresh = kwargs.get("thresh", None)
how = kwargs.get("h... |
Returns a new QueryCompiler with expr evaluated on columns. | def eval(self, expr, **kwargs):
"""Returns a new QueryCompiler with expr evaluated on columns.
Args:
expr: The string expression to evaluate.
Returns:
A new QueryCompiler with new columns after applying expr.
"""
columns = self.index if self._is_transpos... |
Returns a new QueryCompiler with modes calculated for each label along given axis. | def mode(self, **kwargs):
"""Returns a new QueryCompiler with modes calculated for each label along given axis.
Returns:
A new QueryCompiler with modes calculated.
"""
axis = kwargs.get("axis", 0)
def mode_builder(df, **kwargs):
result = df.mode(**kwargs... |
Replaces NaN values with the method provided. | def fillna(self, **kwargs):
"""Replaces NaN values with the method provided.
Returns:
A new QueryCompiler with null values filled.
"""
axis = kwargs.get("axis", 0)
value = kwargs.get("value")
if isinstance(value, dict):
value = kwargs.pop("value")... |
Query columns of the DataManager with a boolean expression. | def query(self, expr, **kwargs):
"""Query columns of the DataManager with a boolean expression.
Args:
expr: Boolean expression to query the columns with.
Returns:
DataManager containing the rows where the boolean expression is satisfied.
"""
columns = se... |
Computes numerical rank along axis. Equal values are set to the average. | def rank(self, **kwargs):
"""Computes numerical rank along axis. Equal values are set to the average.
Returns:
DataManager containing the ranks of the values along an axis.
"""
axis = kwargs.get("axis", 0)
numeric_only = True if axis else kwargs.get("numeric_only", F... |
Sorts the data with respect to either the columns or the indices. | def sort_index(self, **kwargs):
"""Sorts the data with respect to either the columns or the indices.
Returns:
DataManager containing the data sorted by columns or indices.
"""
axis = kwargs.pop("axis", 0)
index = self.columns if axis else self.index
# sort_i... |
Maps function to select indices along full axis. | def _map_across_full_axis_select_indices(
self, axis, func, indices, keep_remaining=False
):
"""Maps function to select indices along full axis.
Args:
axis: 0 for columns and 1 for rows.
func: Callable mapping function over the BlockParitions.
indices: in... |
Returns Manager containing quantiles along an axis for numeric columns. | def quantile_for_list_of_values(self, **kwargs):
"""Returns Manager containing quantiles along an axis for numeric columns.
Returns:
DataManager containing quantiles of original DataManager along an axis.
"""
if self._is_transposed:
kwargs["axis"] = kwargs.get("a... |
Returns the last n rows. | def tail(self, n):
"""Returns the last n rows.
Args:
n: Integer containing the number of rows to return.
Returns:
DataManager containing the last n rows of the original DataManager.
"""
# See head for an explanation of the transposed behavior
if ... |
Returns the first n columns. | def front(self, n):
"""Returns the first n columns.
Args:
n: Integer containing the number of columns to return.
Returns:
DataManager containing the first n columns of the original DataManager.
"""
new_dtypes = (
self._dtype_cache if self._dt... |
Get column data for target labels. | def getitem_column_array(self, key):
"""Get column data for target labels.
Args:
key: Target labels by which to retrieve data.
Returns:
A new QueryCompiler.
"""
# Convert to list for type checking
numeric_indices = list(self.columns.get_indexer_f... |
Get row data for target labels. | def getitem_row_array(self, key):
"""Get row data for target labels.
Args:
key: Target numeric indices by which to retrieve data.
Returns:
A new QueryCompiler.
"""
# Convert to list for type checking
key = list(key)
def getitem(df, inter... |
Set the column defined by key to the value provided. | def setitem(self, axis, key, value):
"""Set the column defined by `key` to the `value` provided.
Args:
key: The column name to set.
value: The value to set the column to.
Returns:
A new QueryCompiler
"""
def setitem(df, internal_indices=[])... |
Remove row data for target index and columns. | def drop(self, index=None, columns=None):
"""Remove row data for target index and columns.
Args:
index: Target index to drop.
columns: Target columns to drop.
Returns:
A new QueryCompiler.
"""
if self._is_transposed:
return self.t... |
Insert new column data. | def insert(self, loc, column, value):
"""Insert new column data.
Args:
loc: Insertion index.
column: Column labels to insert.
value: Dtype object values to insert.
Returns:
A new PandasQueryCompiler with new data inserted.
"""
if ... |
Apply func across given axis. | def apply(self, func, axis, *args, **kwargs):
"""Apply func across given axis.
Args:
func: The function to apply.
axis: Target axis to apply the function along.
Returns:
A new PandasQueryCompiler.
"""
if callable(func):
return sel... |
Recompute the index after applying function. | def _post_process_apply(self, result_data, axis, try_scale=True):
"""Recompute the index after applying function.
Args:
result_data: a BaseFrameManager object.
axis: Target axis along which function was applied.
Returns:
A new PandasQueryCompiler.
""... |
Apply function to certain indices across given axis. | def _dict_func(self, func, axis, *args, **kwargs):
"""Apply function to certain indices across given axis.
Args:
func: The function to apply.
axis: Target axis to apply the function along.
Returns:
A new PandasQueryCompiler.
"""
if "axis" not... |
Apply list - like function across given axis. | def _list_like_func(self, func, axis, *args, **kwargs):
"""Apply list-like function across given axis.
Args:
func: The function to apply.
axis: Target axis to apply the function along.
Returns:
A new PandasQueryCompiler.
"""
func_prepared = s... |
Apply callable functions across given axis. | def _callable_func(self, func, axis, *args, **kwargs):
"""Apply callable functions across given axis.
Args:
func: The functions to apply.
axis: Target axis to apply the function along.
Returns:
A new PandasQueryCompiler.
"""
def callable_app... |
This method applies all manual partitioning functions. | def _manual_repartition(self, axis, repartition_func, **kwargs):
"""This method applies all manual partitioning functions.
Args:
axis: The axis to shuffle data along.
repartition_func: The function used to repartition data.
Returns:
A `BaseFrameManager` obje... |
Convert categorical variables to dummy variables for certain columns. | def get_dummies(self, columns, **kwargs):
"""Convert categorical variables to dummy variables for certain columns.
Args:
columns: The columns to convert.
Returns:
A new QueryCompiler.
"""
cls = type(self)
# `columns` as None does not mean all col... |
Note: this function involves making copies of the index in memory. | def global_idx_to_numeric_idx(self, axis, indices):
"""
Note: this function involves making copies of the index in memory.
Args:
axis: Axis to extract indices.
indices: Indices to convert to numerical.
Returns:
An Index object.
"""
as... |
Perform the map step | def _get_data(self) -> BaseFrameManager:
"""Perform the map step
Returns:
A BaseFrameManager object.
"""
def iloc(partition, row_internal_indices, col_internal_indices):
return partition.iloc[row_internal_indices, col_internal_indices]
masked_data = sel... |
Gets the lengths of the blocks. | def block_lengths(self):
"""Gets the lengths of the blocks.
Note: This works with the property structure `_lengths_cache` to avoid
having to recompute these values each time they are needed.
"""
if self._lengths_cache is None:
# The first column will have the cor... |
Gets the widths of the blocks. | def block_widths(self):
"""Gets the widths of the blocks.
Note: This works with the property structure `_widths_cache` to avoid
having to recompute these values each time they are needed.
"""
if self._widths_cache is None:
# The first column will have the correct... |
Updates the current DataFrame inplace. Args: new_query_compiler: The new QueryCompiler to use to manage the data | def _update_inplace(self, new_query_compiler):
"""Updates the current DataFrame inplace.
Args:
new_query_compiler: The new QueryCompiler to use to manage the data
"""
old_query_compiler = self._query_compiler
self._query_compiler = new_query_compiler
... |
Helper method to check validity of other in inter - df operations | def _validate_other(
self,
other,
axis,
numeric_only=False,
numeric_or_time_only=False,
numeric_or_object_only=False,
comparison_dtypes_only=False,
):
"""Helper method to check validity of other in inter-df operations"""
axis = self._... |
Helper method to use default pandas function | def _default_to_pandas(self, op, *args, **kwargs):
"""Helper method to use default pandas function"""
empty_self_str = "" if not self.empty else " for empty DataFrame"
ErrorMessage.default_to_pandas(
"`{}.{}`{}".format(
self.__name__,
op if isins... |
Apply an absolute value function to all numeric columns. Returns: A new DataFrame with the applied absolute value. | def abs(self):
"""Apply an absolute value function to all numeric columns.
Returns:
A new DataFrame with the applied absolute value.
"""
self._validate_dtypes(numeric_only=True)
return self.__constructor__(query_compiler=self._query_compiler.abs()) |
Add this DataFrame to another or a scalar/ list. Args: other: What to add this this DataFrame. axis: The axis to apply addition over. Only applicaable to Series or list other. level: A level in the multilevel axis to add over. fill_value: The value to fill NaN. Returns: A new DataFrame with the applied addition. | def add(self, other, axis="columns", level=None, fill_value=None):
"""Add this DataFrame to another or a scalar/list.
Args:
other: What to add this this DataFrame.
axis: The axis to apply addition over. Only applicaable to Series
or list 'other'.
... |
Return whether all elements are True over requested axis Note: If axis = None or axis = 0 this call applies df. all ( axis = 1 ) to the transpose of df. | def all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
"""Return whether all elements are True over requested axis
Note:
If axis=None or axis=0, this call applies df.all(axis=1)
to the transpose of df.
"""
if axis is not None:
... |
Apply a function along input axis of DataFrame. Args: func: The function to apply axis: The axis over which to apply the func. broadcast: Whether or not to broadcast. raw: Whether or not to convert to a Series. reduce: Whether or not to try to apply reduction procedures. Returns: Series or DataFrame depending on func. | def apply(
self,
func,
axis=0,
broadcast=None,
raw=False,
reduce=None,
result_type=None,
convert_dtype=True,
args=(),
**kwds
):
"""Apply a function along input axis of DataFrame.
Args:
func: T... |
Synonym for DataFrame. fillna ( method = bfill ) | def bfill(self, axis=None, inplace=False, limit=None, downcast=None):
"""Synonym for DataFrame.fillna(method='bfill')"""
return self.fillna(
method="bfill", axis=axis, limit=limit, downcast=downcast, inplace=inplace
) |
Return the bool of a single element PandasObject. This must be a boolean scalar value either True or False. Raise a ValueError if the PandasObject does not have exactly 1 element or that element is not boolean | def bool(self):
"""Return the bool of a single element PandasObject.
This must be a boolean scalar value, either True or False. Raise a
ValueError if the PandasObject does not have exactly 1 element, or that
element is not boolean
"""
shape = self.shape
... |
Creates a shallow copy of the DataFrame. Returns: A new DataFrame pointing to the same partitions as this one. | def copy(self, deep=True):
"""Creates a shallow copy of the DataFrame.
Returns:
A new DataFrame pointing to the same partitions as this one.
"""
return self.__constructor__(query_compiler=self._query_compiler.copy()) |
Get the count of non - null objects in the DataFrame. Arguments: axis: 0 or index for row - wise 1 or columns for column - wise. level: If the axis is a MultiIndex ( hierarchical ) count along a particular level collapsing into a DataFrame. numeric_only: Include only float int boolean data Returns: The count in a Serie... | def count(self, axis=0, level=None, numeric_only=False):
"""Get the count of non-null objects in the DataFrame.
Arguments:
axis: 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
level: If the axis is a MultiIndex (hierarchical), count along a
part... |
Perform a cumulative maximum across the DataFrame. Args: axis ( int ): The axis to take maximum on. skipna ( bool ): True to skip NA values false otherwise. Returns: The cumulative maximum of the DataFrame. | def cummax(self, axis=None, skipna=True, *args, **kwargs):
"""Perform a cumulative maximum across the DataFrame.
Args:
axis (int): The axis to take maximum on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
The cumulative maximum of... |
Perform a cumulative product across the DataFrame. Args: axis ( int ): The axis to take product on. skipna ( bool ): True to skip NA values false otherwise. Returns: The cumulative product of the DataFrame. | def cumprod(self, axis=None, skipna=True, *args, **kwargs):
"""Perform a cumulative product across the DataFrame.
Args:
axis (int): The axis to take product on.
skipna (bool): True to skip NA values, false otherwise.
Returns:
The cumulative product o... |
Generates descriptive statistics that summarize the central tendency dispersion and shape of a dataset s distribution excluding NaN values. Args: percentiles ( list - like of numbers optional ): The percentiles to include in the output. include: White - list of data types to include in results exclude: Black - list of ... | def describe(self, percentiles=None, include=None, exclude=None):
"""
Generates descriptive statistics that summarize the central tendency,
dispersion and shape of a dataset's distribution, excluding NaN values.
Args:
percentiles (list-like of numbers, optional):
... |
Finds the difference between elements on the axis requested Args: periods: Periods to shift for forming difference axis: Take difference over rows or columns Returns: DataFrame with the diff applied | def diff(self, periods=1, axis=0):
"""Finds the difference between elements on the axis requested
Args:
periods: Periods to shift for forming difference
axis: Take difference over rows or columns
Returns:
DataFrame with the diff applied
"""
... |
Return new object with labels in requested axis removed. Args: labels: Index or column labels to drop. axis: Whether to drop labels from the index ( 0/ index ) or columns ( 1/ columns ). index columns: Alternative to specifying axis ( labels axis = 1 is equivalent to columns = labels ). level: For MultiIndex inplace: I... | def drop(
self,
labels=None,
axis=0,
index=None,
columns=None,
level=None,
inplace=False,
errors="raise",
):
"""Return new object with labels in requested axis removed.
Args:
labels: Index or column labels to dro... |
Create a new DataFrame from the removed NA values from this one. Args: axis ( int tuple or list ): The axis to apply the drop. how ( str ): How to drop the NA values. all: drop the label if all values are NA. any: drop the label if any values are NA. thresh ( int ): The minimum number of NAs to require. subset ( [ labe... | def dropna(self, axis=0, how="any", thresh=None, subset=None, inplace=False):
"""Create a new DataFrame from the removed NA values from this one.
Args:
axis (int, tuple, or list): The axis to apply the drop.
how (str): How to drop the NA values.
'all': drop... |
Return DataFrame with duplicate rows removed optionally only considering certain columns Args: subset: column label or sequence of labels optional Only consider certain columns for identifying duplicates by default use all of the columns keep: { first last False } default first - first: Drop duplicates except for the f... | def drop_duplicates(self, keep="first", inplace=False, **kwargs):
"""Return DataFrame with duplicate rows removed, optionally only considering certain columns
Args:
subset : column label or sequence of labels, optional
Only consider certain columns for ident... |
Checks element - wise that this is equal to other. Args: other: A DataFrame or Series or scalar to compare to. axis: The axis to perform the eq over. level: The Multilevel index level to apply eq over. Returns: A new DataFrame filled with Booleans. | def eq(self, other, axis="columns", level=None):
"""Checks element-wise that this is equal to other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the eq over.
level: The Multilevel index level to apply eq over.
... |
Fill NA/ NaN values using the specified method. Args: value: Value to use to fill holes. This value cannot be a list. method: Method to use for filling holes in reindexed Series pad. ffill: propagate last valid observation forward to next valid backfill. bfill: use NEXT valid observation to fill gap. axis: 0 or index 1... | def fillna(
self,
value=None,
method=None,
axis=None,
inplace=False,
limit=None,
downcast=None,
**kwargs
):
"""Fill NA/NaN values using the specified method.
Args:
value: Value to use to fill holes. This value ... |
Subset rows or columns based on their labels Args: items ( list ): list of labels to subset like ( string ): retain labels where arg in label == True regex ( string ): retain labels matching regex input axis: axis to filter on Returns: A new DataFrame with the filter applied. | def filter(self, items=None, like=None, regex=None, axis=None):
"""Subset rows or columns based on their labels
Args:
items (list): list of labels to subset
like (string): retain labels where `arg in label == True`
regex (string): retain labels matching regex i... |
Divides this DataFrame against another DataFrame/ Series/ scalar. Args: other: The object to use to apply the divide against this. axis: The axis to divide over. level: The Multilevel index level to apply divide over. fill_value: The value to fill NaNs with. Returns: A new DataFrame with the Divide applied. | def floordiv(self, other, axis="columns", level=None, fill_value=None):
"""Divides this DataFrame against another DataFrame/Series/scalar.
Args:
other: The object to use to apply the divide against this.
axis: The axis to divide over.
level: The Multilevel inde... |
Checks element - wise that this is greater than or equal to other. Args: other: A DataFrame or Series or scalar to compare to. axis: The axis to perform the gt over. level: The Multilevel index level to apply gt over. Returns: A new DataFrame filled with Booleans. | def ge(self, other, axis="columns", level=None):
"""Checks element-wise that this is greater than or equal to other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the gt over.
level: The Multilevel index level to apply gt... |
Get the counts of dtypes in this object. Returns: The counts of dtypes in this object. | def get_dtype_counts(self):
"""Get the counts of dtypes in this object.
Returns:
The counts of dtypes in this object.
"""
if hasattr(self, "dtype"):
return pandas.Series({str(self.dtype): 1})
result = self.dtypes.value_counts()
result.ind... |
Get the counts of ftypes in this object. Returns: The counts of ftypes in this object. | def get_ftype_counts(self):
"""Get the counts of ftypes in this object.
Returns:
The counts of ftypes in this object.
"""
if hasattr(self, "ftype"):
return pandas.Series({self.ftype: 1})
return self.ftypes.value_counts().sort_index() |
Checks element - wise that this is greater than other. Args: other: A DataFrame or Series or scalar to compare to. axis: The axis to perform the gt over. level: The Multilevel index level to apply gt over. Returns: A new DataFrame filled with Booleans. | def gt(self, other, axis="columns", level=None):
"""Checks element-wise that this is greater than other.
Args:
other: A DataFrame or Series or scalar to compare to.
axis: The axis to perform the gt over.
level: The Multilevel index level to apply gt over.
... |
Get the first n rows of the DataFrame. Args: n ( int ): The number of rows to return. Returns: A new DataFrame with the first n rows of the DataFrame. | def head(self, n=5):
"""Get the first n rows of the DataFrame.
Args:
n (int): The number of rows to return.
Returns:
A new DataFrame with the first n rows of the DataFrame.
"""
if n >= len(self.index):
return self.copy()
re... |
Get the index of the first occurrence of the max value of the axis. Args: axis ( int ): Identify the max over the rows ( 1 ) or columns ( 0 ). skipna ( bool ): Whether or not to skip NA values. Returns: A Series with the index for each maximum value for the axis specified. | def idxmax(self, axis=0, skipna=True, *args, **kwargs):
"""Get the index of the first occurrence of the max value of the axis.
Args:
axis (int): Identify the max over the rows (1) or columns (0).
skipna (bool): Whether or not to skip NA values.
Returns:
... |
Fill a DataFrame with booleans for cells contained in values. Args: values ( iterable DataFrame Series or dict ): The values to find. Returns: A new DataFrame with booleans representing whether or not a cell is in values. True: cell is contained in values. False: otherwise | def isin(self, values):
"""Fill a DataFrame with booleans for cells contained in values.
Args:
values (iterable, DataFrame, Series, or dict): The values to find.
Returns:
A new DataFrame with booleans representing whether or not a cell
is in values.
... |
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