Series — pandas 0.25.3 documentation (original) (raw)
Constructor¶
| Series([data, index, dtype, name, copy, …]) | One-dimensional ndarray with axis labels (including time series). |
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Attributes¶
Axes
| Series.index | The index (axis labels) of the Series. |
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| Series.array | The ExtensionArray of the data backing this Series or Index. |
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| Series.values | Return Series as ndarray or ndarray-like depending on the dtype. |
| Series.dtype | Return the dtype object of the underlying data. |
| Series.ftype | (DEPRECATED) Return if the data is sparse|dense. |
| Series.shape | Return a tuple of the shape of the underlying data. |
| Series.nbytes | Return the number of bytes in the underlying data. |
| Series.ndim | Number of dimensions of the underlying data, by definition 1. |
| Series.size | Return the number of elements in the underlying data. |
| Series.strides | (DEPRECATED) Return the strides of the underlying data. |
| Series.itemsize | (DEPRECATED) Return the size of the dtype of the item of the underlying data. |
| Series.base | (DEPRECATED) Return the base object if the memory of the underlying data is shared. |
| Series.T | Return the transpose, which is by |
| Series.memory_usage(self[, index, deep]) | Return the memory usage of the Series. |
| Series.hasnans | Return if I have any nans; enables various perf speedups. |
| Series.flags | (DEPRECATED) |
| Series.empty | |
| Series.dtypes | Return the dtype object of the underlying data. |
| Series.ftypes | (DEPRECATED) Return if the data is sparse|dense. |
| Series.data | (DEPRECATED) Return the data pointer of the underlying data. |
| Series.is_copy | Return the copy. |
| Series.name | Return name of the Series. |
| Series.put(self, \*args, \*\*kwargs) | (DEPRECATED) Apply the put method to its values attribute if it has one. |
Conversion¶
| Series.astype(self, dtype[, copy, errors]) | Cast a pandas object to a specified dtype dtype. |
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| Series.infer_objects(self) | Attempt to infer better dtypes for object columns. |
| Series.copy(self[, deep]) | Make a copy of this object’s indices and data. |
| Series.bool(self) | Return the bool of a single element PandasObject. |
| Series.to_numpy(self[, dtype, copy]) | A NumPy ndarray representing the values in this Series or Index. |
| Series.to_period(self[, freq, copy]) | Convert Series from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed). |
| Series.to_timestamp(self[, freq, how, copy]) | Cast to DatetimeIndex of Timestamps, at beginning of period. |
| Series.to_list(self) | Return a list of the values. |
| Series.get_values(self) | (DEPRECATED) Same as values (but handles sparseness conversions); is a view. |
| Series.__array__(self[, dtype]) | Return the values as a NumPy array. |
Indexing, iteration¶
| Series.get(self, key[, default]) | Get item from object for given key (ex: DataFrame column). |
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| Series.at | Access a single value for a row/column label pair. |
| Series.iat | Access a single value for a row/column pair by integer position. |
| Series.loc | Access a group of rows and columns by label(s) or a boolean array. |
| Series.iloc | Purely integer-location based indexing for selection by position. |
| Series.__iter__(self) | Return an iterator of the values. |
| Series.items(self) | Lazily iterate over (index, value) tuples. |
| Series.iteritems(self) | Lazily iterate over (index, value) tuples. |
| Series.keys(self) | Return alias for index. |
| Series.pop(self, item) | Return item and drop from frame. |
| Series.item(self) | Return the first element of the underlying data as a python scalar. |
| Series.xs(self, key[, axis, level, drop_level]) | Return cross-section from the Series/DataFrame. |
For more information on .at, .iat, .loc, and.iloc, see the indexing documentation.
Binary operator functions¶
| Series.add(self, other[, level, fill_value, …]) | Return Addition of series and other, element-wise (binary operator add). |
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| Series.sub(self, other[, level, fill_value, …]) | Return Subtraction of series and other, element-wise (binary operator sub). |
| Series.mul(self, other[, level, fill_value, …]) | Return Multiplication of series and other, element-wise (binary operator mul). |
| Series.div(self, other[, level, fill_value, …]) | Return Floating division of series and other, element-wise (binary operator truediv). |
| Series.truediv(self, other[, level, …]) | Return Floating division of series and other, element-wise (binary operator truediv). |
| Series.floordiv(self, other[, level, …]) | Return Integer division of series and other, element-wise (binary operator floordiv). |
| Series.mod(self, other[, level, fill_value, …]) | Return Modulo of series and other, element-wise (binary operator mod). |
| Series.pow(self, other[, level, fill_value, …]) | Return Exponential power of series and other, element-wise (binary operator pow). |
| Series.radd(self, other[, level, …]) | Return Addition of series and other, element-wise (binary operator radd). |
| Series.rsub(self, other[, level, …]) | Return Subtraction of series and other, element-wise (binary operator rsub). |
| Series.rmul(self, other[, level, …]) | Return Multiplication of series and other, element-wise (binary operator rmul). |
| Series.rdiv(self, other[, level, …]) | Return Floating division of series and other, element-wise (binary operator rtruediv). |
| Series.rtruediv(self, other[, level, …]) | Return Floating division of series and other, element-wise (binary operator rtruediv). |
| Series.rfloordiv(self, other[, level, …]) | Return Integer division of series and other, element-wise (binary operator rfloordiv). |
| Series.rmod(self, other[, level, …]) | Return Modulo of series and other, element-wise (binary operator rmod). |
| Series.rpow(self, other[, level, …]) | Return Exponential power of series and other, element-wise (binary operator rpow). |
| Series.combine(self, other, func[, fill_value]) | Combine the Series with a Series or scalar according to func. |
| Series.combine_first(self, other) | Combine Series values, choosing the calling Series’s values first. |
| Series.round(self[, decimals]) | Round each value in a Series to the given number of decimals. |
| Series.lt(self, other[, level, fill_value, axis]) | Return Less than of series and other, element-wise (binary operator lt). |
| Series.gt(self, other[, level, fill_value, axis]) | Return Greater than of series and other, element-wise (binary operator gt). |
| Series.le(self, other[, level, fill_value, axis]) | Return Less than or equal to of series and other, element-wise (binary operator le). |
| Series.ge(self, other[, level, fill_value, axis]) | Return Greater than or equal to of series and other, element-wise (binary operator ge). |
| Series.ne(self, other[, level, fill_value, axis]) | Return Not equal to of series and other, element-wise (binary operator ne). |
| Series.eq(self, other[, level, fill_value, axis]) | Return Equal to of series and other, element-wise (binary operator eq). |
| Series.product(self[, axis, skipna, level, …]) | Return the product of the values for the requested axis. |
| Series.dot(self, other) | Compute the dot product between the Series and the columns of other. |
Function application, groupby & window¶
| Series.apply(self, func[, convert_dtype, args]) | Invoke function on values of Series. |
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| Series.agg(self, func[, axis]) | Aggregate using one or more operations over the specified axis. |
| Series.aggregate(self, func[, axis]) | Aggregate using one or more operations over the specified axis. |
| Series.transform(self, func[, axis]) | Call func on self producing a Series with transformed values and that has the same axis length as self. |
| Series.map(self, arg[, na_action]) | Map values of Series according to input correspondence. |
| Series.groupby(self[, by, axis, level, …]) | Group DataFrame or Series using a mapper or by a Series of columns. |
| Series.rolling(self, window[, min_periods, …]) | Provide rolling window calculations. |
| Series.expanding(self[, min_periods, …]) | Provide expanding transformations. |
| Series.ewm(self[, com, span, halflife, …]) | Provide exponential weighted functions. |
| Series.pipe(self, func, \*args, \*\*kwargs) | Apply func(self, *args, **kwargs). |
Computations / descriptive stats¶
| Series.abs(self) | Return a Series/DataFrame with absolute numeric value of each element. |
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| Series.all(self[, axis, bool_only, skipna, …]) | Return whether all elements are True, potentially over an axis. |
| Series.any(self[, axis, bool_only, skipna, …]) | Return whether any element is True, potentially over an axis. |
| Series.autocorr(self[, lag]) | Compute the lag-N autocorrelation. |
| Series.between(self, left, right[, inclusive]) | Return boolean Series equivalent to left <= series <= right. |
| Series.clip(self[, lower, upper, axis, inplace]) | Trim values at input threshold(s). |
| Series.clip_lower(self, threshold[, axis, …]) | (DEPRECATED) Trim values below a given threshold. |
| Series.clip_upper(self, threshold[, axis, …]) | (DEPRECATED) Trim values above a given threshold. |
| Series.corr(self, other[, method, min_periods]) | Compute correlation with other Series, excluding missing values. |
| Series.count(self[, level]) | Return number of non-NA/null observations in the Series. |
| Series.cov(self, other[, min_periods]) | Compute covariance with Series, excluding missing values. |
| Series.cummax(self[, axis, skipna]) | Return cumulative maximum over a DataFrame or Series axis. |
| Series.cummin(self[, axis, skipna]) | Return cumulative minimum over a DataFrame or Series axis. |
| Series.cumprod(self[, axis, skipna]) | Return cumulative product over a DataFrame or Series axis. |
| Series.cumsum(self[, axis, skipna]) | Return cumulative sum over a DataFrame or Series axis. |
| Series.describe(self[, percentiles, …]) | Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. |
| Series.diff(self[, periods]) | First discrete difference of element. |
| Series.factorize(self[, sort, na_sentinel]) | Encode the object as an enumerated type or categorical variable. |
| Series.kurt(self[, axis, skipna, level, …]) | Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). |
| Series.mad(self[, axis, skipna, level]) | Return the mean absolute deviation of the values for the requested axis. |
| Series.max(self[, axis, skipna, level, …]) | Return the maximum of the values for the requested axis. |
| Series.mean(self[, axis, skipna, level, …]) | Return the mean of the values for the requested axis. |
| Series.median(self[, axis, skipna, level, …]) | Return the median of the values for the requested axis. |
| Series.min(self[, axis, skipna, level, …]) | Return the minimum of the values for the requested axis. |
| Series.mode(self[, dropna]) | Return the mode(s) of the dataset. |
| Series.nlargest(self[, n, keep]) | Return the largest n elements. |
| Series.nsmallest(self[, n, keep]) | Return the smallest n elements. |
| Series.pct_change(self[, periods, …]) | Percentage change between the current and a prior element. |
| Series.prod(self[, axis, skipna, level, …]) | Return the product of the values for the requested axis. |
| Series.quantile(self[, q, interpolation]) | Return value at the given quantile. |
| Series.rank(self[, axis, method, …]) | Compute numerical data ranks (1 through n) along axis. |
| Series.sem(self[, axis, skipna, level, …]) | Return unbiased standard error of the mean over requested axis. |
| Series.skew(self[, axis, skipna, level, …]) | Return unbiased skew over requested axis Normalized by N-1. |
| Series.std(self[, axis, skipna, level, …]) | Return sample standard deviation over requested axis. |
| Series.sum(self[, axis, skipna, level, …]) | Return the sum of the values for the requested axis. |
| Series.var(self[, axis, skipna, level, …]) | Return unbiased variance over requested axis. |
| Series.kurtosis(self[, axis, skipna, level, …]) | Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). |
| Series.unique(self) | Return unique values of Series object. |
| Series.nunique(self[, dropna]) | Return number of unique elements in the object. |
| Series.is_unique | Return boolean if values in the object are unique. |
| Series.is_monotonic | Return boolean if values in the object are monotonic_increasing. |
| Series.is_monotonic_increasing | Return boolean if values in the object are monotonic_increasing. |
| Series.is_monotonic_decreasing | Return boolean if values in the object are monotonic_decreasing. |
| Series.value_counts(self[, normalize, sort, …]) | Return a Series containing counts of unique values. |
| Series.compound(self[, axis, skipna, level]) | (DEPRECATED) Return the compound percentage of the values for the requested axis. |
Reindexing / selection / label manipulation¶
| Series.align(self, other[, join, axis, …]) | Align two objects on their axes with the specified join method for each axis Index. |
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| Series.drop(self[, labels, axis, index, …]) | Return Series with specified index labels removed. |
| Series.droplevel(self, level[, axis]) | Return DataFrame with requested index / column level(s) removed. |
| Series.drop_duplicates(self[, keep, inplace]) | Return Series with duplicate values removed. |
| Series.duplicated(self[, keep]) | Indicate duplicate Series values. |
| Series.equals(self, other) | Test whether two objects contain the same elements. |
| Series.first(self, offset) | Convenience method for subsetting initial periods of time series data based on a date offset. |
| Series.head(self[, n]) | Return the first n rows. |
| Series.idxmax(self[, axis, skipna]) | Return the row label of the maximum value. |
| Series.idxmin(self[, axis, skipna]) | Return the row label of the minimum value. |
| Series.isin(self, values) | Check whether values are contained in Series. |
| Series.last(self, offset) | Convenience method for subsetting final periods of time series data based on a date offset. |
| Series.reindex(self[, index]) | Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. |
| Series.reindex_like(self, other[, method, …]) | Return an object with matching indices as other object. |
| Series.rename(self[, index]) | Alter Series index labels or name. |
| Series.rename_axis(self[, mapper, index, …]) | Set the name of the axis for the index or columns. |
| Series.reset_index(self[, level, drop, …]) | Generate a new DataFrame or Series with the index reset. |
| Series.sample(self[, n, frac, replace, …]) | Return a random sample of items from an axis of object. |
| Series.set_axis(self, labels[, axis, inplace]) | Assign desired index to given axis. |
| Series.take(self, indices[, axis, is_copy]) | Return the elements in the given positional indices along an axis. |
| Series.tail(self[, n]) | Return the last n rows. |
| Series.truncate(self[, before, after, axis, …]) | Truncate a Series or DataFrame before and after some index value. |
| Series.where(self, cond[, other, inplace, …]) | Replace values where the condition is False. |
| Series.mask(self, cond[, other, inplace, …]) | Replace values where the condition is True. |
| Series.add_prefix(self, prefix) | Prefix labels with string prefix. |
| Series.add_suffix(self, suffix) | Suffix labels with string suffix. |
| Series.filter(self[, items, like, regex, axis]) | Subset rows or columns of dataframe according to labels in the specified index. |
Missing data handling¶
| Series.isna(self) | Detect missing values. |
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| Series.notna(self) | Detect existing (non-missing) values. |
| Series.dropna(self[, axis, inplace]) | Return a new Series with missing values removed. |
| Series.fillna(self[, value, method, axis, …]) | Fill NA/NaN values using the specified method. |
| Series.interpolate(self[, method, axis, …]) | Interpolate values according to different methods. |
Reshaping, sorting¶
| Series.argsort(self[, axis, kind, order]) | Override ndarray.argsort. |
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| Series.argmin(self[, axis, skipna]) | (DEPRECATED) Return the row label of the minimum value. |
| Series.argmax(self[, axis, skipna]) | (DEPRECATED) Return the row label of the maximum value. |
| Series.reorder_levels(self, order) | Rearrange index levels using input order. |
| Series.sort_values(self[, axis, ascending, …]) | Sort by the values. |
| Series.sort_index(self[, axis, level, …]) | Sort Series by index labels. |
| Series.swaplevel(self[, i, j, copy]) | Swap levels i and j in a MultiIndex. |
| Series.unstack(self[, level, fill_value]) | Unstack, a.k.a. |
| Series.explode(self) | Transform each element of a list-like to a row, replicating the index values. |
| Series.searchsorted(self, value[, side, sorter]) | Find indices where elements should be inserted to maintain order. |
| Series.ravel(self[, order]) | Return the flattened underlying data as an ndarray. |
| Series.repeat(self, repeats[, axis]) | Repeat elements of a Series. |
| Series.squeeze(self[, axis]) | Squeeze 1 dimensional axis objects into scalars. |
| Series.view(self[, dtype]) | Create a new view of the Series. |
Combining / joining / merging¶
| Series.append(self, to_append[, …]) | Concatenate two or more Series. |
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| Series.replace(self[, to_replace, value, …]) | Replace values given in to_replace with value. |
| Series.update(self, other) | Modify Series in place using non-NA values from passed Series. |
Accessors¶
Pandas provides dtype-specific methods under various accessors. These are separate namespaces within Series that only apply to specific data types.
| Data Type | Accessor |
|---|---|
| Datetime, Timedelta, Period | dt |
| String | str |
| Categorical | cat |
| Sparse | sparse |
Datetimelike properties¶
Series.dt can be used to access the values of the series as datetimelike and return several properties. These can be accessed like Series.dt.<property>.
Datetime methods¶
| Series.dt.to_period(self, \*args, \*\*kwargs) | Cast to PeriodArray/Index at a particular frequency. |
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| Series.dt.to_pydatetime(self) | Return the data as an array of native Python datetime objects. |
| Series.dt.tz_localize(self, \*args, \*\*kwargs) | Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index. |
| Series.dt.tz_convert(self, \*args, \*\*kwargs) | Convert tz-aware Datetime Array/Index from one time zone to another. |
| Series.dt.normalize(self, \*args, \*\*kwargs) | Convert times to midnight. |
| Series.dt.strftime(self, \*args, \*\*kwargs) | Convert to Index using specified date_format. |
| Series.dt.round(self, \*args, \*\*kwargs) | Perform round operation on the data to the specified freq. |
| Series.dt.floor(self, \*args, \*\*kwargs) | Perform floor operation on the data to the specified freq. |
| Series.dt.ceil(self, \*args, \*\*kwargs) | Perform ceil operation on the data to the specified freq. |
| Series.dt.month_name(self, \*args, \*\*kwargs) | Return the month names of the DateTimeIndex with specified locale. |
| Series.dt.day_name(self, \*args, \*\*kwargs) | Return the day names of the DateTimeIndex with specified locale. |
String handling¶
Series.str can be used to access the values of the series as strings and apply several methods to it. These can be accessed likeSeries.str.<function/property>.
| Series.str.capitalize(self) | Convert strings in the Series/Index to be capitalized. |
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| Series.str.casefold(self) | Convert strings in the Series/Index to be casefolded. |
| Series.str.cat(self[, others, sep, na_rep, join]) | Concatenate strings in the Series/Index with given separator. |
| Series.str.center(self, width[, fillchar]) | Filling left and right side of strings in the Series/Index with an additional character. |
| Series.str.contains(self, pat[, case, …]) | Test if pattern or regex is contained within a string of a Series or Index. |
| Series.str.count(self, pat[, flags]) | Count occurrences of pattern in each string of the Series/Index. |
| Series.str.decode(self, encoding[, errors]) | Decode character string in the Series/Index using indicated encoding. |
| Series.str.encode(self, encoding[, errors]) | Encode character string in the Series/Index using indicated encoding. |
| Series.str.endswith(self, pat[, na]) | Test if the end of each string element matches a pattern. |
| Series.str.extract(self, pat[, flags, expand]) | Extract capture groups in the regex pat as columns in a DataFrame. |
| Series.str.extractall(self, pat[, flags]) | For each subject string in the Series, extract groups from all matches of regular expression pat. |
| Series.str.find(self, sub[, start, end]) | Return lowest indexes in each strings in the Series/Index where the substring is fully contained between [start:end]. |
| Series.str.findall(self, pat[, flags]) | Find all occurrences of pattern or regular expression in the Series/Index. |
| Series.str.get(self, i) | Extract element from each component at specified position. |
| Series.str.index(self, sub[, start, end]) | Return lowest indexes in each strings where the substring is fully contained between [start:end]. |
| Series.str.join(self, sep) | Join lists contained as elements in the Series/Index with passed delimiter. |
| Series.str.len(self) | Compute the length of each element in the Series/Index. |
| Series.str.ljust(self, width[, fillchar]) | Filling right side of strings in the Series/Index with an additional character. |
| Series.str.lower(self) | Convert strings in the Series/Index to lowercase. |
| Series.str.lstrip(self[, to_strip]) | Remove leading and trailing characters. |
| Series.str.match(self, pat[, case, flags, na]) | Determine if each string matches a regular expression. |
| Series.str.normalize(self, form) | Return the Unicode normal form for the strings in the Series/Index. |
| Series.str.pad(self, width[, side, fillchar]) | Pad strings in the Series/Index up to width. |
| Series.str.partition(self[, sep, expand]) | Split the string at the first occurrence of sep. |
| Series.str.repeat(self, repeats) | Duplicate each string in the Series or Index. |
| Series.str.replace(self, pat, repl[, n, …]) | Replace occurrences of pattern/regex in the Series/Index with some other string. |
| Series.str.rfind(self, sub[, start, end]) | Return highest indexes in each strings in the Series/Index where the substring is fully contained between [start:end]. |
| Series.str.rindex(self, sub[, start, end]) | Return highest indexes in each strings where the substring is fully contained between [start:end]. |
| Series.str.rjust(self, width[, fillchar]) | Filling left side of strings in the Series/Index with an additional character. |
| Series.str.rpartition(self[, sep, expand]) | Split the string at the last occurrence of sep. |
| Series.str.rstrip(self[, to_strip]) | Remove leading and trailing characters. |
| Series.str.slice(self[, start, stop, step]) | Slice substrings from each element in the Series or Index. |
| Series.str.slice_replace(self[, start, …]) | Replace a positional slice of a string with another value. |
| Series.str.split(self[, pat, n, expand]) | Split strings around given separator/delimiter. |
| Series.str.rsplit(self[, pat, n, expand]) | Split strings around given separator/delimiter. |
| Series.str.startswith(self, pat[, na]) | Test if the start of each string element matches a pattern. |
| Series.str.strip(self[, to_strip]) | Remove leading and trailing characters. |
| Series.str.swapcase(self) | Convert strings in the Series/Index to be swapcased. |
| Series.str.title(self) | Convert strings in the Series/Index to titlecase. |
| Series.str.translate(self, table) | Map all characters in the string through the given mapping table. |
| Series.str.upper(self) | Convert strings in the Series/Index to uppercase. |
| Series.str.wrap(self, width, \*\*kwargs) | Wrap long strings in the Series/Index to be formatted in paragraphs with length less than a given width. |
| Series.str.zfill(self, width) | Pad strings in the Series/Index by prepending ‘0’ characters. |
| Series.str.isalnum(self) | Check whether all characters in each string are alphanumeric. |
| Series.str.isalpha(self) | Check whether all characters in each string are alphabetic. |
| Series.str.isdigit(self) | Check whether all characters in each string are digits. |
| Series.str.isspace(self) | Check whether all characters in each string are whitespace. |
| Series.str.islower(self) | Check whether all characters in each string are lowercase. |
| Series.str.isupper(self) | Check whether all characters in each string are uppercase. |
| Series.str.istitle(self) | Check whether all characters in each string are titlecase. |
| Series.str.isnumeric(self) | Check whether all characters in each string are numeric. |
| Series.str.isdecimal(self) | Check whether all characters in each string are decimal. |
| Series.str.get_dummies(self[, sep]) | Split each string in the Series by sep and return a DataFrame of dummy/indicator variables. |
Categorical accessor¶
Categorical-dtype specific methods and attributes are available under the Series.cat accessor.
| Series.cat.categories | The categories of this categorical. |
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| Series.cat.ordered | Whether the categories have an ordered relationship. |
| Series.cat.codes | Return Series of codes as well as the index. |
| Series.cat.rename_categories(self, \*args, …) | Rename categories. |
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| Series.cat.reorder_categories(self, \*args, …) | Reorder categories as specified in new_categories. |
| Series.cat.add_categories(self, \*args, …) | Add new categories. |
| Series.cat.remove_categories(self, \*args, …) | Remove the specified categories. |
| Series.cat.remove_unused_categories(self, …) | Remove categories which are not used. |
| Series.cat.set_categories(self, \*args, …) | Set the categories to the specified new_categories. |
| Series.cat.as_ordered(self, \*args, \*\*kwargs) | Set the Categorical to be ordered. |
| Series.cat.as_unordered(self, \*args, \*\*kwargs) | Set the Categorical to be unordered. |
Plotting¶
Series.plot is both a callable method and a namespace attribute for specific plotting methods of the form Series.plot.<kind>.
| Series.plot([kind, ax, figsize, ….]) | Series plotting accessor and method |
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| Series.plot.area(self[, x, y]) | Draw a stacked area plot. |
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| Series.plot.bar(self[, x, y]) | Vertical bar plot. |
| Series.plot.barh(self[, x, y]) | Make a horizontal bar plot. |
| Series.plot.box(self[, by]) | Make a box plot of the DataFrame columns. |
| Series.plot.density(self[, bw_method, ind]) | Generate Kernel Density Estimate plot using Gaussian kernels. |
| Series.plot.hist(self[, by, bins]) | Draw one histogram of the DataFrame’s columns. |
| Series.plot.kde(self[, bw_method, ind]) | Generate Kernel Density Estimate plot using Gaussian kernels. |
| Series.plot.line(self[, x, y]) | Plot Series or DataFrame as lines. |
| Series.plot.pie(self, \*\*kwargs) | Generate a pie plot. |
| Series.hist(self[, by, ax, grid, …]) | Draw histogram of the input series using matplotlib. |
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Serialization / IO / conversion¶
| Series.to_pickle(self, path[, compression, …]) | Pickle (serialize) object to file. |
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| Series.to_csv(self, \*args, \*\*kwargs) | Write object to a comma-separated values (csv) file. |
| Series.to_dict(self[, into]) | Convert Series to {label -> value} dict or dict-like object. |
| Series.to_excel(self, excel_writer[, …]) | Write object to an Excel sheet. |
| Series.to_frame(self[, name]) | Convert Series to DataFrame. |
| Series.to_xarray(self) | Return an xarray object from the pandas object. |
| Series.to_hdf(self, path_or_buf, key, \*\*kwargs) | Write the contained data to an HDF5 file using HDFStore. |
| Series.to_sql(self, name, con[, schema, …]) | Write records stored in a DataFrame to a SQL database. |
| Series.to_msgpack(self[, path_or_buf, encoding]) | (DEPRECATED) Serialize object to input file path using msgpack format. |
| Series.to_json(self[, path_or_buf, orient, …]) | Convert the object to a JSON string. |
| Series.to_sparse(self[, kind, fill_value]) | (DEPRECATED) Convert Series to SparseSeries. |
| Series.to_dense(self) | (DEPRECATED) Return dense representation of Series/DataFrame (as opposed to sparse). |
| Series.to_string(self[, buf, na_rep, …]) | Render a string representation of the Series. |
| Series.to_clipboard(self[, excel, sep]) | Copy object to the system clipboard. |
| Series.to_latex(self[, buf, columns, …]) | Render an object to a LaTeX tabular environment table. |
Sparse¶
| SparseSeries.to_coo(self[, row_levels, …]) | Create a scipy.sparse.coo_matrix from a SparseSeries with MultiIndex. |
|---|---|
| SparseSeries.from_coo(A[, dense_index]) | Create a SparseSeries from a scipy.sparse.coo_matrix. |