Series()

class pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False) [source]

One-dimensional ndarray with axis labels (including time series).

Labels need not be unique but must be any hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index. Statistical methods from ndarray have been overridden to automatically exclude missing data (currently represented as NaN)

Operations between Series (+, -, /, , *) align values based on their associated index values? they need not be the same length. The result index will be the sorted union of the two indexes.

Parameters:

Parameters:	data : array-like, dict, or scalar value Contains data stored in Series index : array-like or Index (1d) Values must be unique and hashable, same length as data. Index object (or other iterable of same length as data) Will default to RangeIndex(len(data)) if not provided. If both a dict and index sequence are used, the index will override the keys found in the dict. dtype : numpy.dtype or None If None, dtype will be inferred copy : boolean, default False Copy input data

data : array-like, dict, or scalar value

Contains data stored in Series

index : array-like or Index (1d)

Values must be unique and hashable, same length as data. Index object (or other iterable of same length as data) Will default to RangeIndex(len(data)) if not provided. If both a dict and index sequence are used, the index will override the keys found in the dict.

dtype : numpy.dtype or None

If None, dtype will be inferred

copy : boolean, default False

Copy input data

Attributes

`T`	return the transpose, which is by definition self
`asobject`	return object Series which contains boxed values
`at`	Fast label-based scalar accessor
`axes`	Return a list of the row axis labels
`base`	return the base object if the memory of the underlying data is
`blocks`	Internal property, property synonym for as_blocks()
`data`	return the data pointer of the underlying data
`dtype`	return the dtype object of the underlying data
`dtypes`	return the dtype object of the underlying data
`empty`	True if NDFrame is entirely empty [no items], meaning any of the axes are of length 0.
`flags`
`ftype`	return if the data is sparse\|dense
`ftypes`	return if the data is sparse\|dense
`hasnans`
`iat`	Fast integer location scalar accessor.
`iloc`	Purely integer-location based indexing for selection by position.
`imag`
`is_copy`
`is_monotonic`	Return boolean if values in the object are
`is_monotonic_decreasing`	Return boolean if values in the object are
`is_monotonic_increasing`	Return boolean if values in the object are
`is_time_series`
`is_unique`	Return boolean if values in the object are unique
`itemsize`	return the size of the dtype of the item of the underlying data
`ix`	A primarily label-location based indexer, with integer position fallback.
`loc`	Purely label-location based indexer for selection by label.
`name`
`nbytes`	return the number of bytes in the underlying data
`ndim`	return the number of dimensions of the underlying data,
`real`
`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 Series as ndarray or ndarray-like

Methods

`abs`()	Return an object with absolute value taken?only applicable to objects that are all numeric.
`add`(other[, level, fill_value, axis])	Addition of series and other, element-wise (binary operator `add`).
`add_prefix`(prefix)	Concatenate prefix string with panel items names.
`add_suffix`(suffix)	Concatenate suffix string with panel items names.
`align`(other[, join, axis, level, copy, ...])	Align two object on their axes with the
`all`([axis, bool_only, skipna, level])	Return whether all elements are True over requested axis
`any`([axis, bool_only, skipna, level])	Return whether any element is True over requested axis
`append`(to_append[, ignore_index, ...])	Concatenate two or more Series.
`apply`(func[, convert_dtype, args])	Invoke function on values of Series.
`argmax`([axis, skipna])	Index of first occurrence of maximum of values.
`argmin`([axis, skipna])	Index of first occurrence of minimum of values.
`argsort`([axis, kind, order])	Overrides ndarray.argsort.
`as_blocks`([copy])	Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype.
`as_matrix`([columns])	Convert the frame to its Numpy-array representation.
`asfreq`(freq[, method, how, normalize])	Convert TimeSeries to specified frequency.
`asof`(where[, subset])	The last row without any NaN is taken (or the last row without
`astype`(dtype[, copy, raise_on_error])	Cast object to input numpy.dtype
`at_time`(time[, asof])	Select values at particular time of day (e.g.
`autocorr`([lag])	Lag-N autocorrelation
`between`(left, right[, inclusive])	Return boolean Series equivalent to left <= series <= right.
`between_time`(start_time, end_time[, ...])	Select values between particular times of the day (e.g., 9:00-9:30 AM).
`bfill`([axis, inplace, limit, downcast])	Synonym for NDFrame.fillna(method=?bfill?)
`bool`()	Return the bool of a single element PandasObject.
`cat`	alias of `CategoricalAccessor`
`clip`([lower, upper, axis])	Trim values at input threshold(s).
`clip_lower`(threshold[, axis])	Return copy of the input with values below given value(s) truncated.
`clip_upper`(threshold[, axis])	Return copy of input with values above given value(s) truncated.
`combine`(other, func[, fill_value])	Perform elementwise binary operation on two Series using given function
`combine_first`(other)	Combine Series values, choosing the calling Series?s values first.
`compound`([axis, skipna, level])	Return the compound percentage of the values for the requested axis
`compress`(condition, \args, \\*kwargs)	Return selected slices of an array along given axis as a Series
`consolidate`([inplace])	Compute NDFrame with ?consolidated? internals (data of each dtype grouped together in a single ndarray).
`convert_objects`([convert_dates, ...])	Deprecated.
`copy`([deep])	Make a copy of this objects data.
`corr`(other[, method, min_periods])	Compute correlation with `other` Series, excluding missing values
`count`([level])	Return number of non-NA/null observations in the Series
`cov`(other[, min_periods])	Compute covariance with Series, excluding missing values
`cummax`([axis, skipna])	Return cumulative max over requested axis.
`cummin`([axis, skipna])	Return cumulative minimum over requested axis.
`cumprod`([axis, skipna])	Return cumulative product over requested axis.
`cumsum`([axis, skipna])	Return cumulative sum over requested axis.
`describe`([percentiles, include, exclude])	Generate various summary statistics, excluding NaN values.
`diff`([periods])	1st discrete difference of object
`div`(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator `truediv`).
`divide`(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator `truediv`).
`dot`(other)	Matrix multiplication with DataFrame or inner-product with Series
`drop`(labels[, axis, level, inplace, errors])	Return new object with labels in requested axis removed.
`drop_duplicates`(\args, \\*kwargs)	Return Series with duplicate values removed
`dropna`([axis, inplace])	Return Series without null values
`dt`	alias of `CombinedDatetimelikeProperties`
`duplicated`(\args, \\*kwargs)	Return boolean Series denoting duplicate values
`eq`(other[, level, fill_value, axis])	Equal to of series and other, element-wise (binary operator `eq`).
`equals`(other)	Determines if two NDFrame objects contain the same elements.
`ewm`([com, span, halflife, alpha, ...])	Provides exponential weighted functions
`expanding`([min_periods, freq, center, axis])	Provides expanding transformations.
`factorize`([sort, na_sentinel])	Encode the object as an enumerated type or categorical variable
`ffill`([axis, inplace, limit, downcast])	Synonym for NDFrame.fillna(method=?ffill?)
`fillna`([value, method, axis, inplace, ...])	Fill NA/NaN values using the specified method
`filter`([items, like, regex, axis])	Subset rows or columns of dataframe according to labels in the specified index.
`first`(offset)	Convenience method for subsetting initial periods of time series data based on a date offset.
`first_valid_index`()	Return label for first non-NA/null value
`floordiv`(other[, level, fill_value, axis])	Integer division of series and other, element-wise (binary operator `floordiv`).
`from_array`(arr[, index, name, dtype, copy, ...])
`from_csv`(path[, sep, parse_dates, header, ...])	Read CSV file (DISCOURAGED, please use `pandas.read_csv()` instead).
`ge`(other[, level, fill_value, axis])	Greater than or equal to of series and other, element-wise (binary operator `ge`).
`get`(key[, default])	Get item from object for given key (DataFrame column, Panel slice, etc.).
`get_dtype_counts`()	Return the counts of dtypes in this object.
`get_ftype_counts`()	Return the counts of ftypes in this object.
`get_value`(label[, takeable])	Quickly retrieve single value at passed index label
`get_values`()	same as values (but handles sparseness conversions); is a view
`groupby`([by, axis, level, as_index, sort, ...])	Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns.
`gt`(other[, level, fill_value, axis])	Greater than of series and other, element-wise (binary operator `gt`).
`head`([n])	Returns first n rows
`hist`([by, ax, grid, xlabelsize, xrot, ...])	Draw histogram of the input series using matplotlib
`idxmax`([axis, skipna])	Index of first occurrence of maximum of values.
`idxmin`([axis, skipna])	Index of first occurrence of minimum of values.
`iget`(i[, axis])	DEPRECATED.
`iget_value`(i[, axis])	DEPRECATED.
`interpolate`([method, axis, limit, inplace, ...])	Interpolate values according to different methods.
`irow`(i[, axis])	DEPRECATED.
`isin`(values)	Return a boolean `Series` showing whether each element in the `Series` is exactly contained in the passed sequence of `values`.
`isnull`()	Return a boolean same-sized object indicating if the values are null.
`item`()	return the first element of the underlying data as a python
`iteritems`()	Lazily iterate over (index, value) tuples
`iterkv`(\args, \\*kwargs)	iteritems alias used to get around 2to3. Deprecated
`keys`()	Alias for index
`kurt`([axis, skipna, level, numeric_only])	Return unbiased kurtosis over requested axis using Fisher?s definition of kurtosis (kurtosis of normal == 0.0).
`kurtosis`([axis, skipna, level, numeric_only])	Return unbiased kurtosis over requested axis using Fisher?s definition of kurtosis (kurtosis of normal == 0.0).
`last`(offset)	Convenience method for subsetting final periods of time series data based on a date offset.
`last_valid_index`()	Return label for last non-NA/null value
`le`(other[, level, fill_value, axis])	Less than or equal to of series and other, element-wise (binary operator `le`).
`lt`(other[, level, fill_value, axis])	Less than of series and other, element-wise (binary operator `lt`).
`mad`([axis, skipna, level])	Return the mean absolute deviation of the values for the requested axis
`map`(arg[, na_action])	Map values of Series using input correspondence (which can be
`mask`(cond[, other, inplace, axis, level, ...])	Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other.
`max`([axis, skipna, level, numeric_only])	This method returns the maximum of the values in the object.
`mean`([axis, skipna, level, numeric_only])	Return the mean of the values for the requested axis
`median`([axis, skipna, level, numeric_only])	Return the median of the values for the requested axis
`memory_usage`([index, deep])	Memory usage of the Series
`min`([axis, skipna, level, numeric_only])	This method returns the minimum of the values in the object.
`mod`(other[, level, fill_value, axis])	Modulo of series and other, element-wise (binary operator `mod`).
`mode`()	Returns the mode(s) of the dataset.
`mul`(other[, level, fill_value, axis])	Multiplication of series and other, element-wise (binary operator `mul`).
`multiply`(other[, level, fill_value, axis])	Multiplication of series and other, element-wise (binary operator `mul`).
`ne`(other[, level, fill_value, axis])	Not equal to of series and other, element-wise (binary operator `ne`).
`nlargest`(\args, \\*kwargs)	Return the largest `n` elements.
`nonzero`()	Return the indices of the elements that are non-zero
`notnull`()	Return a boolean same-sized object indicating if the values are not null.
`nsmallest`(\args, \\*kwargs)	Return the smallest `n` elements.
`nunique`([dropna])	Return number of unique elements in the object.
`order`([na_last, ascending, kind, ...])	DEPRECATED: use `Series.sort_values()`
`pct_change`([periods, fill_method, limit, freq])	Percent change over given number of periods.
`pipe`(func, \args, \\*kwargs)	Apply func(self, args, *kwargs)
`plot`	alias of `SeriesPlotMethods`
`pop`(item)	Return item and drop from frame.
`pow`(other[, level, fill_value, axis])	Exponential power of series and other, element-wise (binary operator `pow`).
`prod`([axis, skipna, level, numeric_only])	Return the product of the values for the requested axis
`product`([axis, skipna, level, numeric_only])	Return the product of the values for the requested axis
`ptp`([axis, skipna, level, numeric_only])	Returns the difference between the maximum value and the minimum value in the object.
`put`(\args, \\*kwargs)	Applies the `put` method to its `values` attribute if it has one.
`quantile`([q, interpolation])	Return value at the given quantile, a la numpy.percentile.
`radd`(other[, level, fill_value, axis])	Addition of series and other, element-wise (binary operator `radd`).
`rank`([axis, method, numeric_only, ...])	Compute numerical data ranks (1 through n) along axis.
`ravel`([order])	Return the flattened underlying data as an ndarray
`rdiv`(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator `rtruediv`).
`reindex`([index])	Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
`reindex_axis`(labels[, axis])	for compatibility with higher dims
`reindex_like`(other[, method, copy, limit, ...])	Return an object with matching indices to myself.
`rename`([index])	Alter axes input function or functions.
`rename_axis`(mapper[, axis, copy, inplace])	Alter index and / or columns using input function or functions.
`reorder_levels`(order)	Rearrange index levels using input order.
`repeat`(reps, \args, \\*kwargs)	Repeat elements of an Series.
`replace`([to_replace, value, inplace, limit, ...])	Replace values given in ?to_replace? with ?value?.
`resample`(rule[, how, axis, fill_method, ...])	Convenience method for frequency conversion and resampling of time series.
`reset_index`([level, drop, name, inplace])	Analogous to the `pandas.DataFrame.reset_index()` function, see docstring there.
`reshape`(\args, \\*kwargs)	DEPRECATED: calling this method will raise an error in a future release.
`rfloordiv`(other[, level, fill_value, axis])	Integer division of series and other, element-wise (binary operator `rfloordiv`).
`rmod`(other[, level, fill_value, axis])	Modulo of series and other, element-wise (binary operator `rmod`).
`rmul`(other[, level, fill_value, axis])	Multiplication of series and other, element-wise (binary operator `rmul`).
`rolling`(window[, min_periods, freq, center, ...])	Provides rolling window calculcations.
`round`([decimals])	Round each value in a Series to the given number of decimals.
`rpow`(other[, level, fill_value, axis])	Exponential power of series and other, element-wise (binary operator `rpow`).
`rsub`(other[, level, fill_value, axis])	Subtraction of series and other, element-wise (binary operator `rsub`).
`rtruediv`(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator `rtruediv`).
`sample`([n, frac, replace, weights, ...])	Returns a random sample of items from an axis of object.
`searchsorted`(v[, side, sorter])	Find indices where elements should be inserted to maintain order.
`select`(crit[, axis])	Return data corresponding to axis labels matching criteria
`sem`([axis, skipna, level, ddof, numeric_only])	Return unbiased standard error of the mean over requested axis.
`set_axis`(axis, labels)	public verson of axis assignment
`set_value`(label, value[, takeable])	Quickly set single value at passed label.
`shift`([periods, freq, axis])	Shift index by desired number of periods with an optional time freq
`skew`([axis, skipna, level, numeric_only])	Return unbiased skew over requested axis
`slice_shift`([periods, axis])	Equivalent to `shift` without copying data.
`sort`([axis, ascending, kind, na_position, ...])	DEPRECATED: use `Series.sort_values(inplace=True)()` for INPLACE
`sort_index`([axis, level, ascending, ...])	Sort object by labels (along an axis)
`sort_values`([axis, ascending, inplace, ...])	Sort by the values along either axis
`sortlevel`([level, ascending, sort_remaining])	Sort Series with MultiIndex by chosen level.
`squeeze`(\\kwargs)	Squeeze length 1 dimensions.
`std`([axis, skipna, level, ddof, numeric_only])	Return sample standard deviation over requested axis.
`str`	alias of `StringMethods`
`sub`(other[, level, fill_value, axis])	Subtraction of series and other, element-wise (binary operator `sub`).
`subtract`(other[, level, fill_value, axis])	Subtraction of series and other, element-wise (binary operator `sub`).
`sum`([axis, skipna, level, numeric_only])	Return the sum of the values for the requested axis
`swapaxes`(axis1, axis2[, copy])	Interchange axes and swap values axes appropriately
`swaplevel`([i, j, copy])	Swap levels i and j in a MultiIndex
`tail`([n])	Returns last n rows
`take`(indices[, axis, convert, is_copy])	return Series corresponding to requested indices
`to_clipboard`([excel, sep])	Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example.
`to_csv`([path, index, sep, na_rep, ...])	Write Series to a comma-separated values (csv) file
`to_dense`()	Return dense representation of NDFrame (as opposed to sparse)
`to_dict`()	Convert Series to {label -> value} dict
`to_frame`([name])	Convert Series to DataFrame
`to_hdf`(path_or_buf, key, \\kwargs)	Write the contained data to an HDF5 file using HDFStore.
`to_json`([path_or_buf, orient, date_format, ...])	Convert the object to a JSON string.
`to_msgpack`([path_or_buf, encoding])	msgpack (serialize) object to input file path
`to_period`([freq, copy])	Convert Series from DatetimeIndex to PeriodIndex with desired
`to_pickle`(path)	Pickle (serialize) object to input file path.
`to_sparse`([kind, fill_value])	Convert Series to SparseSeries
`to_sql`(name, con[, flavor, schema, ...])	Write records stored in a DataFrame to a SQL database.
`to_string`([buf, na_rep, float_format, ...])	Render a string representation of the Series
`to_timestamp`([freq, how, copy])	Cast to datetimeindex of timestamps, at beginning of period
`to_xarray`()	Return an xarray object from the pandas object.
`tolist`()	Convert Series to a nested list
`transpose`(\args, \\*kwargs)	return the transpose, which is by definition self
`truediv`(other[, level, fill_value, axis])	Floating division of series and other, element-wise (binary operator `truediv`).
`truncate`([before, after, axis, copy])	Truncates a sorted NDFrame before and/or after some particular index value.
`tshift`([periods, freq, axis])	Shift the time index, using the index?s frequency if available.
`tz_convert`(tz[, axis, level, copy])	Convert tz-aware axis to target time zone.
`tz_localize`(\args, \\*kwargs)	Localize tz-naive TimeSeries to target time zone.
`unique`()	Return np.ndarray of unique values in the object.
`unstack`([level, fill_value])	Unstack, a.k.a.
`update`(other)	Modify Series in place using non-NA values from passed Series.
`valid`([inplace])
`value_counts`([normalize, sort, ascending, ...])	Returns object containing counts of unique values.
`var`([axis, skipna, level, ddof, numeric_only])	Return unbiased variance over requested axis.
`view`([dtype])
`where`(cond[, other, inplace, axis, level, ...])	Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.
`xs`(key[, axis, level, drop_level])	Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.

Links:

http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.Series.html

doc_Pandas

2017-01-12 04:53:11

Comments