Series¶

Constructor

Series([data, index, dtype, name, copy, ...])

Snowpark pandas representation of pandas.Series with a lazily-evaluated relational dataset.

Attributes

`Series.index`	Get the index for this DataFrame.
`Series.axes`	Return a list of the row axis labels.
`Series.array`	Return the ExtensionArray of the data backing this Series or Index.
`Series.dtype`	Return the dtype object of the underlying data.
`Series.dtypes`	Return the dtype object of the underlying data.
`Series.duplicated`([keep])	Indicate duplicate Series values.
`Series.empty`	Indicator whether the Series is empty.
`Series.hasnans`	Return True if there are any NaNs.
`Series.name`	Return the name of the Series.
`Series.ndim`	Number of dimensions of the underlying data, by definition 1.
`Series.shape`	Return a tuple of the shape of the underlying data.
`Series.size`	Return an int representing the number of elements in this object.
`Series.T`	Return the transpose, which is by definition self.
`Series.values`	Return a NumPy representation of the dataset.

Snowflake Specific

`Series.to_snowflake`(name[, if_exists, ...])	Save the Snowpark pandas Series as a Snowflake table.
`Series.to_snowpark`([index, index_label])	Convert the Snowpark pandas Series to a Snowpark DataFrame.
`Series.cache_result`([inplace])	Persists the current Snowpark pandas Series to a temporary table to improve the latency of subsequent operations.

Conversion

`Series.astype`(dtype[, copy, errors])	Cast a pandas object to a specified dtype `dtype`.
`Series.convert_dtypes`([infer_objects, ...])	Convert columns to best possible dtypes using dtypes supporting `pd.NA`.
`Series.copy`([deep])	Make a copy of this object's indices and data.
`Series.to_dict`([into])	Convert Series to {label -> value} dict or dict-like object.
`Series.to_list`()	Return a list of the values.
`Series.to_numpy`([dtype, copy, na_value])	A NumPy ndarray representing the values in this Series or Index.
`Series.to_pandas`(*[, statement_params])	Convert Snowpark pandas Series to pandas.Series
`Series.__array__`([dtype])	Return the values as a NumPy array.

Indexing, iteration

`Series.iloc`	Purely integer-location based indexing for selection by position.
`Series.loc`	Access a group of rows and columns by label(s) or a boolean array.
`Series.__iter__`()	Return an iterator of the values.
`Series.keys`()	Return alias for index.

Binary operator functions

`Series.add`(other[, level, fill_value, axis])	Return Addition of series and other, element-wise (binary operator add).
`Series.sub`(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operator sub).
`Series.mul`(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operator mul).
`Series.div`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator truediv).
`Series.truediv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator truediv).
`Series.floordiv`(other[, level, fill_value, axis])	Return Integer division of series and other, element-wise (binary operator floordiv).
`Series.mod`(other[, level, fill_value, axis])	Return Modulo of series and other, element-wise (binary operator mod).
`Series.pow`(other[, level, fill_value, axis])	Return Exponential power of series and other, element-wise (binary operator pow).
`Series.radd`(other[, level, fill_value, axis])	Return Addition of series and other, element-wise (binary operator radd).
`Series.rsub`(other[, level, fill_value, axis])	Return Subtraction of series and other, element-wise (binary operator rsub).
`Series.rmul`(other[, level, fill_value, axis])	Return Multiplication of series and other, element-wise (binary operator rmul).
`Series.rdiv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator rtruediv).
`Series.rtruediv`(other[, level, fill_value, axis])	Return Floating division of series and other, element-wise (binary operator rtruediv).
`Series.rfloordiv`(other[, level, fill_value, ...])	Return Integer division of series and other, element-wise (binary operator rfloordiv).
`Series.rmod`(other[, level, fill_value, axis])	Return Modulo of series and other, element-wise (binary operator rmod).
`Series.rpow`(other[, level, fill_value, axis])	Return Exponential power of series and other, element-wise (binary operator rpow).
`Series.round`([decimals])	Round each value in a Series to the given number of decimals.
`Series.lt`(other[, level, fill_value, axis])	Return Less than of series and other, element-wise (binary operator lt).
`Series.gt`(other[, level, fill_value, axis])	Return Greater than of series and other, element-wise (binary operator gt).
`Series.le`(other[, level, fill_value, axis])	Return Less than or equal to of series and other, element-wise (binary operator le).
`Series.ge`(other[, level, fill_value, axis])	Return Greater than or equal to of series and other, element-wise (binary operator ge).
`Series.ne`(other[, level, fill_value, axis])	Return Not equal to of series and other, element-wise (binary operator ne).
`Series.eq`(other[, level, fill_value, axis])	Return Equal to of series and other, element-wise (binary operator eq).

Function application, GroupBy & window

`Series.apply`(func[, convert_dtype, args])	Invoke function on values of Series.
`Series.agg`([func, axis])	Aggregate using one or more operations over the specified axis.
`Series.aggregate`([func, axis])	Aggregate using one or more operations over the specified axis.
`Series.transform`(func[, axis])	Call `func` on self producing a BasePandasDataset with the same axis shape as self.
`Series.map`(arg[, na_action])	Map values of Series according to an input mapping or function.
`Series.groupby`([by, axis, level, as_index, ...])	Group Series using a mapper or by a Series of columns.
`Series.rolling`(window[, min_periods, ...])	Provide rolling window calculations.

Computations / descriptive stats

`Series.abs`()	Return a Series with absolute numeric value of each element.
`Series.all`([axis, bool_only, skipna])	Return whether all elements are True, potentially over an axis.
`Series.any`([axis, bool_only, skipna])	Return whether any element are True, potentially over an axis.
`Series.count`()	Return number of non-NA/null observations in the Series.
`Series.cummax`([axis, skipna])	Return cumulative maximum over a BasePandasDataset axis.
`Series.cummin`([axis, skipna])	Return cumulative minimum over a BasePandasDataset axis.
`Series.cumsum`([axis, skipna])	Return cumulative sum over a BasePandasDataset axis.
`Series.describe`([percentiles, include, exclude])	Generate descriptive statistics.
`Series.diff`([periods])	First discrete difference of element.
`Series.max`([axis, skipna, numeric_only])	Return the maximum of the values over the requested axis.
`Series.mean`([axis, skipna, numeric_only])	Return the mean of the values over the requested axis.
`Series.median`([axis, skipna, numeric_only])	Return the median of the values over the requested axis.
`Series.min`([axis, skipna, numeric_only])	Return the minimum of the values over the requested axis.
`Series.quantile`([q, interpolation])	Return value at the given quantile.
`Series.rank`([axis, method, numeric_only, ...])	Compute numerical data ranks (1 through n) along axis.
`Series.skew`([axis, skipna, numeric_only])	Return unbiased skew, normalized over n-1
`Series.std`([axis, skipna, ddof, numeric_only])	Return sample standard deviation over requested axis.
`Series.sum`([axis, skipna, numeric_only, ...])	Return the sum of the values over the requested axis.
`Series.var`([axis, skipna, ddof, numeric_only])	Return unbiased variance over requested axis.
`Series.unique`()	Return unique values of Series object.
`Series.nunique`([dropna])	Return number of unique elements in the series.
`Series.is_unique`	Return True if values in the Series are unique.
`Series.value_counts`([normalize, sort, ...])	Return a Series containing counts of unique values.

Reindexing / selection / label manipulation

`Series.drop`([labels, axis, index, columns, ...])	Return Series with specified index labels removed.
`Series.drop_duplicates`([keep, inplace])	Return Series with duplicate values removed.
`Series.get`(key[, default])	Get item from object for given key (ex: DataFrame column).
`Series.head`([n])	Return the first n rows.
`Series.idxmax`([axis, skipna])	Return the row label of the maximum value.
`Series.idxmin`([axis, skipna])	Return the row label of the minimum value.
`Series.isin`(values)	Whether elements in Series are contained in values.
`Series.last`(offset)	Select final periods of time series data based on a date offset.
`Series.rename`([index, axis, copy, inplace, ...])	Alter Series index labels or name.
`Series.rename_axis`([mapper, index, axis, ...])	Set the name of the axis for the index or columns.
`Series.reset_index`([level, drop, name, ...])	Generate a new DataFrame or Series with the index reset.
`Series.sample`([n, frac, replace, weights, ...])	Return a random sample of items from an axis of object.
`Series.set_axis`(labels, *[, axis, copy])	Assign desired index to given axis.
`Series.take`(indices[, axis])	Return the elements in the given positional indices along an axis.
`Series.tail`([n])	Return the last n rows.
`Series.where`(cond[, other, inplace, axis, level])	Replace values where the condition is False.
`Series.mask`(cond[, other, inplace, axis, level])	Replace values where the condition is True.
`Series.add_prefix`(prefix)	Prefix labels with string prefix.
`Series.add_suffix`(suffix)	Suffix labels with string suffix.

Missing data handling

`Series.dropna`(*[, axis, inplace, how])	Return a new Series with missing values removed.
`Series.ffill`([axis, inplace, limit, downcast])	Synonym for `DataFrame.fillna()` with `method='ffill'`.
`Series.fillna`([value, method, axis, ...])	Fill NA/NaN values using the specified method.
`Series.isna`()	Detect missing values.
`Series.isnull`()	Series.isnull is an alias for Series.isna.
`Series.notna`()	Detect non-missing values for an array-like object.
`Series.notnull`()	Detect non-missing values for an array-like object.
`Series.pad`([axis, inplace, limit, downcast])	Synonym for `DataFrame.fillna()` with `method='ffill'`.
`Series.replace`([to_replace, value, inplace, ...])	Replace values given in to_replace with value.

Reshaping, sorting

`Series.sort_values`([axis, ascending, ...])	Sort by the values.
`Series.sort_index`([axis, level, ascending, ...])	Sort object by labels (along an axis).
`Series.squeeze`([axis])	Squeeze 1 dimensional axis objects into scalars.

Combining / comparing / joining / merging

Series.update(other)

Modify Series in place using values from passed Series.

Time Series-related

`Series.shift`([periods, freq, axis, ...])	Shift data by desired number of periods and replace columns with fill_value (default: None).
`Series.first_valid_index`()	Return index for first non-NA value or None, if no non-NA value is found.
`Series.last_valid_index`()	Return index for last non-NA value or None, if no non-NA value is found.
`Series.resample`(rule[, axis, closed, label, ...])	Resample time-series data.

Accessors

`Series.str`	Vectorized string functions for Series and Index.
`Series.dt`	Accessor object for datetimelike properties of the Series values.

All supported Series str APIs

All supported Series dt APIs