# pandas.Series

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

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

  Labels need not be unique but must be a 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:

  data : array-like, dict, or scalar value

  > Contains data stored in Series

  index : array-like or Index (1d)

  > Values must be hashable and have the same length as data. Non-unique index values are allowed. 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` |
  `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_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.
  `agg`(func[, axis]) | Aggregate using callable, string, dict, or list of string/callables
  `aggregate`(func[, axis]) | Aggregate using callable, string, dict, or list of string/callables
  `align`(other[, join, axis, level, copy, …]) | Align two objects 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`(*args, **kwargs) |
  `argmin`(*args, **kwargs) |
  `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`(*args, **kwargs) | Cast a pandas object to a specified dtype `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 `DataFrame.fillna(method='bfill')`
  `bool`() | Return the bool of a single element PandasObject.
  `cat` | alias of `pandas.core.categorical.CategoricalAccessor`
  `clip`([lower, upper, axis, inplace]) | Trim values at input threshold(s).
  `clip_lower`(threshold[, axis, inplace]) | Return copy of the input with values below given value(s) truncated.
  `clip_upper`(threshold[, axis, inplace]) | 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]) | DEPRECATED: consolidate will be an internal implementation only.
  `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]) | Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, 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, index, columns, level, …]) | Return new object with labels in requested axis removed.
  `drop_duplicates`([keep, inplace]) | Return Series with duplicate values removed
  `dropna`([axis, inplace]) | Return Series without null values
  `dt` | alias of `pandas.core.indexes.accessors.CombinedDatetimelikeProperties`
  `duplicated`([keep]) | 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 `DataFrame.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 index 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 (DEPRECATED, 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]) | Return the first n rows.
  `hist`([by, ax, grid, xlabelsize, xrot, …]) | Draw histogram of the input series using matplotlib
  `idxmax`([axis, skipna]) | Index label of the first occurrence of maximum of values.
  `idxmin`([axis, skipna]) | Index label of the first occurrence of minimum of values.
  `infer_objects`() | Attempt to infer better dtypes for object columns.
  `interpolate`([method, axis, limit, inplace, …]) | Interpolate values according to different methods.
  `isin`(values) | Return a boolean `Series` showing whether each element in the `Series` is exactly contained in the passed sequence of `values`.
  `isna`() | Return a boolean same-sized object indicating if the values are NA.
  `isnull`() | Return a boolean same-sized object indicating if the values are NA.
  `item`() | return the first element of the underlying data as a python
  `items`() | Lazily iterate over (index, value) tuples
  `iteritems`() | Lazily iterate over (index, value) tuples
  `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 index 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`() | Return 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`([n, keep]) | Return the largest n elements.
  `nonzero`() | Return the indices of the elements that are non-zero
  `notna`() | Return a boolean same-sized object indicating if the values are not NA.
  `notnull`() | Return a boolean same-sized object indicating if the values are not NA.
  `nsmallest`([n, keep]) | Return the smallest n elements.
  `nunique`([dropna]) | Return number of unique elements in the object.
  `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 `pandas.plotting._core.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 Series index labels or name
  `rename_axis`(mapper[, axis, copy, inplace]) | Alter the name of the index or columns.
  `reorder_levels`(order) | Rearrange index levels using input order.
  `repeat`(*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 since version 0.19.0.
  `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 calculations.
  `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`(*args, **kwargs) | 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`(labels[, axis, inplace]) | Assign desired index to given axis
  `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_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]) | DEPRECATED: use `Series.sort_index()`
  `squeeze`([axis]) | Squeeze length 1 dimensions.
  `std`([axis, skipna, level, ddof, numeric_only]) | Return sample standard deviation over requested axis.
  `str` | alias of `pandas.core.strings.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]) | Return the last n rows.
  `take`(indices[, axis, convert, is_copy]) | Return the elements in the given positional indices along an axis.
  `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`([into]) | Convert Series to {label -> value} dict or dict-like object.
  `to_excel`(excel_writer[, sheet_name, na_rep, …]) | Write Series to an excel sheet
  `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_latex`([buf, columns, col_space, header, …]) | Render an object to a tabular environment table.
  `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[, compression, protocol]) | 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`() | Return a list of the values.
  `transform`(func, *args, **kwargs) | Call function producing a like-indexed NDFrame
  `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 DataFrame/Series 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 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.

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