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# 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 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 : | 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 np.arange(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` | support for compatiblity
  `at` |
  `axes` |
  `base` |
  `blocks` | Internal property, property synonym for as_blocks()
  `data` |
  `dtype` |
  `dtypes` | for compat
  `empty` | True if NDFrame is entirely empty [no items]
  `flags` |
  `ftype` |
  `ftypes` | for compat
  `iat` |
  `iloc` |
  `imag` |
  `is_time_series` |
  `ix` |
  `loc` |
  `ndim` |
  `real` |
  `shape` |
  `size` |
  `strides` |
  `values` | Return Series as ndarray
  `weekday` |

  is_copy |
  str |

  Methods

  `abs`() | Return an object with absolute value taken.
  `add`(other[, level, fill_value, axis]) | Binary operator add with support to substitute a fill_value for missing data
  `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, out]) | Returns True if all elements evaluate to True.
  `any`([axis, out]) | Returns True if any of the elements of a evaluate to True.
  `append`(to_append[, verify_integrity]) | Concatenate two or more Series. The indexes must not overlap
  `apply`(func[, convert_dtype, args]) | Invoke function on values of Series. Can be ufunc (a NumPy function
  `argmax`([axis, out, skipna]) | Index of first occurrence of maximum of values.
  `argmin`([axis, out, skipna]) | Index of first occurrence of minimum of values.
  `argsort`([axis, kind, order]) | Overrides ndarray.argsort.
  `as_blocks`([columns]) | Convert the frame to a dict of dtype -> Constructor Types that each has
  `as_matrix`([columns]) | Convert the frame to its Numpy-array matrix representation. Columns
  `asfreq`(freq[, method, how, normalize]) | Convert all TimeSeries inside to specified frequency using DateOffset
  `asof`(where) | Return last good (non-NaN) value in TimeSeries if value is NaN for
  `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-1 autocorrelation
  `between`(left, right[, inclusive]) | Return boolean Series equivalent to left <= series <= right. NA values
  `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
  `clip`([lower, upper, out]) | Trim values at input threshold(s)
  `clip_lower`(threshold) | Return copy of the input with values below given value truncated
  `clip_upper`(threshold) | Return copy of input with values above given value 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
  `compound`([axis, skipna, level]) | Return the compound percentage of the values for the requested axis
  `consolidate`([inplace]) | Compute NDFrame with “consolidated” internals (data of each dtype
  `convert_objects`([convert_dates, ...]) | Attempt to infer better dtype for object columns
  `copy`([deep]) | Make a copy of this object
  `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, dtype, out, skipna]) | Return cumulative max over requested axis.
  `cummin`([axis, dtype, out, skipna]) | Return cumulative min over requested axis.
  `cumprod`([axis, dtype, out, skipna]) | Return cumulative prod over requested axis.
  `cumsum`([axis, dtype, out, skipna]) | Return cumulative sum over requested axis.
  `describe`([percentile_width]) | Generate various summary statistics of Series, excluding NaN
  `diff`([periods]) | 1st discrete difference of object
  `div`(other[, level, fill_value, axis]) | Binary operator truediv with support to substitute a fill_value for missing data
  `divide`(other[, level, fill_value, axis]) | Binary operator truediv with support to substitute a fill_value for missing data
  `dot`(other) | Matrix multiplication with DataFrame or inner-product with Series
  `drop`(labels[, axis, level, inplace]) | Return new object with labels in requested axis removed
  `drop_duplicates`([take_last, inplace]) | Return Series with duplicate values removed
  `dropna`([axis, inplace]) | Return Series without null values
  `duplicated`([take_last]) | Return boolean Series denoting duplicate values
  `eq`(other) |
  `equals`(other) | Determines if two NDFrame objects contain the same elements. NaNs in the
  `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]) | Restrict the info axis to set of items or wildcard
  `first`(offset) | Convenience method for subsetting initial periods of time series data
  `first_valid_index`() | Return label for first non-NA/null value
  `floordiv`(other[, level, fill_value, axis]) | Binary operator floordiv with support to substitute a fill_value for missing data
  `from_array`(arr[, index, name, copy, fastpath]) |
  `from_csv`(path[, sep, parse_dates, header, ...]) | Read delimited file into Series
  `ge`(other) |
  `get`(label[, default]) | Returns value occupying requested label, default to specified missing value if not present.
  `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) | 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
  `gt`(other) |
  `head`([n]) | Returns first n rows
  `hist`([by, ax, grid, xlabelsize, xrot, ...]) | Draw histogram of the input series using matplotlib
  `idxmax`([axis, out, skipna]) | Index of first occurrence of maximum of values.
  `idxmin`([axis, out, skipna]) | Index of first occurrence of minimum of values.
  `iget`(i[, axis]) | Return the i-th value or values in the Series by location
  `iget_value`(i[, axis]) | Return the i-th value or values in the Series by location
  `interpolate`([method, axis, limit, inplace, ...]) | Interpolate values according to different methods.
  `irow`(i[, axis]) | Return the i-th value or values in the Series by location
  `isin`(values) | Return a boolean `Series` showing whether each element
  `isnull`() | Return a boolean same-sized object indicating if the values are null
  `item`() |
  `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
  `kurtosis`([axis, skipna, level, numeric_only]) | Return unbiased kurtosis over requested axis
  `last`(offset) | Convenience method for subsetting final periods of time series data
  `last_valid_index`() | Return label for last non-NA/null value
  `le`(other) |
  `load`(path) | Deprecated.
  `lt`(other) |
  `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) | Returns copy whose values are replaced with nan if the
  `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
  `min`([axis, skipna, level, numeric_only]) | This method returns the minimum of the values in the object.
  `mod`(other[, level, fill_value, axis]) | Binary operator mod with support to substitute a fill_value for missing data
  `mode`() | Returns the mode(s) of the dataset.
  `mul`(other[, level, fill_value, axis]) | Binary operator mul with support to substitute a fill_value for missing data
  `multiply`(other[, level, fill_value, axis]) | Binary operator mul with support to substitute a fill_value for missing data
  `ne`(other) |
  `nonzero`() | numpy like, returns same as nonzero
  `notnull`() | Return a boolean same-sized object indicating if the values are
  `nunique`() | Return count of unique elements in the Series
  `order`([na_last, ascending, kind]) | Sorts Series object, by value, maintaining index-value link
  `pct_change`([periods, fill_method, limit, freq]) | Percent change over given number of periods
  `plot`(series[, label, kind, use_index, rot, ...]) | Plot the input series with the index on the x-axis using matplotlib
  `pop`(item) | Return item and drop from frame.
  `pow`(other[, level, fill_value, axis]) | Binary operator pow with support to substitute a fill_value for missing data
  `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, out]) |
  `put`(*args, **kwargs) |
  `quantile`([q]) | Return value at the given quantile, a la scoreatpercentile in
  `radd`(other[, level, fill_value, axis]) | Binary operator radd with support to substitute a fill_value for missing data
  `rank`([method, na_option, ascending]) | Compute data ranks (1 through n).
  `ravel`([order]) |
  `rdiv`(other[, level, fill_value, axis]) | Binary operator rtruediv with support to substitute a fill_value for missing data
  `reindex`([index]) | Conform Series to new index with optional filling logic, placing
  `reindex_axis`(labels[, axis]) | for compatibility with higher dims
  `reindex_like`(other[, method, copy, limit]) | return an object with matching indicies 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) | See ndarray.repeat
  `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 regular time-series data.
  `reset_index`([level, drop, name, inplace]) | Analogous to the `pandas.DataFrame.reset_index()` function, see
  `reshape`(*args, **kwargs) | See numpy.ndarray.reshape
  `rfloordiv`(other[, level, fill_value, axis]) | Binary operator rfloordiv with support to substitute a fill_value for missing data
  `rmod`(other[, level, fill_value, axis]) | Binary operator rmod with support to substitute a fill_value for missing data
  `rmul`(other[, level, fill_value, axis]) | Binary operator rmul with support to substitute a fill_value for missing data
  `round`([decimals, out]) | Return a with each element rounded to the given number of decimals.
  `rpow`(other[, level, fill_value, axis]) | Binary operator rpow with support to substitute a fill_value for missing data
  `rsub`(other[, level, fill_value, axis]) | Binary operator rsub with support to substitute a fill_value for missing data
  `rtruediv`(other[, level, fill_value, axis]) | Binary operator rtruediv with support to substitute a fill_value for missing data
  `save`(path) | Deprecated.
  `select`(crit[, axis]) | Return data corresponding to axis labels matching criteria
  `set_value`(label, value) | 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
  `sort`([axis, kind, order, ascending]) | Sort values and index labels by value, in place.
  `sort_index`([ascending]) | Sort object by labels (along an axis)
  `sortlevel`([level, ascending]) | Sort Series with MultiIndex by chosen level. Data will be
  `squeeze`() | squeeze length 1 dimensions
  `std`([axis, skipna, level, ddof]) | Return unbiased standard deviation over requested axis
  `sub`(other[, level, fill_value, axis]) | Binary operator sub with support to substitute a fill_value for missing data
  `subtract`(other[, level, fill_value, axis]) | Binary operator sub with support to substitute a fill_value for missing data
  `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]) | Analogous to ndarray.take, return Series corresponding to requested
  `to_clipboard`([excel, sep]) | Attempt to write text representation of object to the system clipboard
  `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) | activate the HDFStore
  `to_json`([path_or_buf, orient, date_format, ...]) | Convert the object to a JSON string.
  `to_msgpack`([path_or_buf]) | msgpack (serialize) object to input file path
  `to_period`([freq, copy]) | Convert TimeSeries 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_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
  `tolist`() | Convert Series to a nested list
  `transpose`() | support for compatiblity
  `truediv`(other[, level, fill_value, axis]) | Binary operator truediv with support to substitute a fill_value for missing data
  `truncate`([before, after, axis, copy]) | Truncates a sorted NDFrame before and/or after some particular
  `tshift`([periods, freq, axis]) | Shift the time index, using the index’s frequency if available
  `tz_convert`(tz[, copy]) | Convert TimeSeries to target time zone
  `tz_localize`(tz[, copy, infer_dst]) | Localize tz-naive TimeSeries to target time zone
  `unique`() | Return array of unique values in the Series. Significantly faster than
  `unstack`([level]) | Unstack, a.k.a.
  `update`(other) | Modify Series in place using non-NA values from passed
  `valid`([inplace]) |
  `value_counts`([normalize, sort, ascending, bins]) | Returns Series containing counts of unique values. The resulting Series
  `var`([axis, skipna, level, ddof]) | 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
  `xs`(key[, axis, level, copy, drop_level]) | Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.

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