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 empty True if NDFrame is entirely empty [no items] flags ftype iat iloc imag is_time_series ix loc ndim real shape size strides values Return Series as ndarray weekday is_copy 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) 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 frame get_ftype_counts() return the counts of ftypes in this frame 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 DataFrame.reset_index function, see docstring there. 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])