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pandas.DataFrame.rank
`pandas.DataFrame.rank` Compute numerical data ranks (1 through n) along axis. ``` >>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog', ... 'spider', 'snake'], ... 'Number_legs': [4, 2, 4, 8, np.nan]}) >>> df Animal Number_legs 0 cat 4.0 1 penguin 2.0 2 dog 4.0 3 spider 8.0 4 snake NaN ```
DataFrame.rank(axis=0, method='average', numeric_only=_NoDefault.no_default, na_option='keep', ascending=True, pct=False)[source]# Compute numerical data ranks (1 through n) along axis. By default, equal values are assigned a rank that is the average of the ranks of those values. Parameters axis{0 or ‘index’, 1 or ‘columns’}, default 0Index to direct ranking. For Series this parameter is unused and defaults to 0. method{‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’How to rank the group of records that have the same value (i.e. ties): average: average rank of the group min: lowest rank in the group max: highest rank in the group first: ranks assigned in order they appear in the array dense: like ‘min’, but rank always increases by 1 between groups. numeric_onlybool, optionalFor DataFrame objects, rank only numeric columns if set to True. na_option{‘keep’, ‘top’, ‘bottom’}, default ‘keep’How to rank NaN values: keep: assign NaN rank to NaN values top: assign lowest rank to NaN values bottom: assign highest rank to NaN values ascendingbool, default TrueWhether or not the elements should be ranked in ascending order. pctbool, default FalseWhether or not to display the returned rankings in percentile form. Returns same type as callerReturn a Series or DataFrame with data ranks as values. See also core.groupby.GroupBy.rankRank of values within each group. Examples >>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog', ... 'spider', 'snake'], ... 'Number_legs': [4, 2, 4, 8, np.nan]}) >>> df Animal Number_legs 0 cat 4.0 1 penguin 2.0 2 dog 4.0 3 spider 8.0 4 snake NaN Ties are assigned the mean of the ranks (by default) for the group. >>> s = pd.Series(range(5), index=list("abcde")) >>> s["d"] = s["b"] >>> s.rank() a 1.0 b 2.5 c 4.0 d 2.5 e 5.0 dtype: float64 The following example shows how the method behaves with the above parameters: default_rank: this is the default behaviour obtained without using any parameter. max_rank: setting method = 'max' the records that have the same values are ranked using the highest rank (e.g.: since ‘cat’ and ‘dog’ are both in the 2nd and 3rd position, rank 3 is assigned.) NA_bottom: choosing na_option = 'bottom', if there are records with NaN values they are placed at the bottom of the ranking. pct_rank: when setting pct = True, the ranking is expressed as percentile rank. >>> df['default_rank'] = df['Number_legs'].rank() >>> df['max_rank'] = df['Number_legs'].rank(method='max') >>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom') >>> df['pct_rank'] = df['Number_legs'].rank(pct=True) >>> df Animal Number_legs default_rank max_rank NA_bottom pct_rank 0 cat 4.0 2.5 3.0 2.5 0.625 1 penguin 2.0 1.0 1.0 1.0 0.250 2 dog 4.0 2.5 3.0 2.5 0.625 3 spider 8.0 4.0 4.0 4.0 1.000 4 snake NaN NaN NaN 5.0 NaN
reference/api/pandas.DataFrame.rank.html
pandas.tseries.offsets.Minute.normalize
pandas.tseries.offsets.Minute.normalize
Minute.normalize#
reference/api/pandas.tseries.offsets.Minute.normalize.html
pandas.tseries.offsets.CustomBusinessMonthBegin.normalize
pandas.tseries.offsets.CustomBusinessMonthBegin.normalize
CustomBusinessMonthBegin.normalize#
reference/api/pandas.tseries.offsets.CustomBusinessMonthBegin.normalize.html
pandas.io.formats.style.Styler.concat
`pandas.io.formats.style.Styler.concat` Append another Styler to combine the output into a single table. ``` >>> df = DataFrame([[4, 6], [1, 9], [3, 4], [5, 5], [9,6]], ... columns=["Mike", "Jim"], ... index=["Mon", "Tue", "Wed", "Thurs", "Fri"]) >>> styler = df.style.concat(df.agg(["sum"]).style) ```
Styler.concat(other)[source]# Append another Styler to combine the output into a single table. New in version 1.5.0. Parameters otherStylerThe other Styler object which has already been styled and formatted. The data for this Styler must have the same columns as the original, and the number of index levels must also be the same to render correctly. Returns selfStyler Notes The purpose of this method is to extend existing styled dataframes with other metrics that may be useful but may not conform to the original’s structure. For example adding a sub total row, or displaying metrics such as means, variance or counts. Styles that are applied using the apply, applymap, apply_index and applymap_index, and formatting applied with format and format_index will be preserved. Warning Only the output methods to_html, to_string and to_latex currently work with concatenated Stylers. Other output methods, including to_excel, do not work with concatenated Stylers. The following should be noted: table_styles, table_attributes, caption and uuid are all inherited from the original Styler and not other. hidden columns and hidden index levels will be inherited from the original Styler css will be inherited from the original Styler, and the value of keys data, row_heading and row will be prepended with foot0_. If more concats are chained, their styles will be prepended with foot1_, ‘’foot_2’’, etc., and if a concatenated style have another concatanated style, the second style will be prepended with foot{parent}_foot{child}_. A common use case is to concatenate user defined functions with DataFrame.agg or with described statistics via DataFrame.describe. See examples. Examples A common use case is adding totals rows, or otherwise, via methods calculated in DataFrame.agg. >>> df = DataFrame([[4, 6], [1, 9], [3, 4], [5, 5], [9,6]], ... columns=["Mike", "Jim"], ... index=["Mon", "Tue", "Wed", "Thurs", "Fri"]) >>> styler = df.style.concat(df.agg(["sum"]).style) Since the concatenated object is a Styler the existing functionality can be used to conditionally format it as well as the original. >>> descriptors = df.agg(["sum", "mean", lambda s: s.dtype]) >>> descriptors.index = ["Total", "Average", "dtype"] >>> other = (descriptors.style ... .highlight_max(axis=1, subset=(["Total", "Average"], slice(None))) ... .format(subset=("Average", slice(None)), precision=2, decimal=",") ... .applymap(lambda v: "font-weight: bold;")) >>> styler = (df.style ... .highlight_max(color="salmon") ... .set_table_styles([{"selector": ".foot_row0", ... "props": "border-top: 1px solid black;"}])) >>> styler.concat(other) When other has fewer index levels than the original Styler it is possible to extend the index in other, with placeholder levels. >>> df = DataFrame([[1], [2]], index=pd.MultiIndex.from_product([[0], [1, 2]])) >>> descriptors = df.agg(["sum"]) >>> descriptors.index = pd.MultiIndex.from_product([[""], descriptors.index]) >>> df.style.concat(descriptors.style)
reference/api/pandas.io.formats.style.Styler.concat.html
pandas.io.formats.style.Styler.to_string
`pandas.io.formats.style.Styler.to_string` Write Styler to a file, buffer or string in text format.
Styler.to_string(buf=None, *, encoding=None, sparse_index=None, sparse_columns=None, max_rows=None, max_columns=None, delimiter=' ')[source]# Write Styler to a file, buffer or string in text format. New in version 1.5.0. Parameters bufstr, path object, file-like object, optionalString, path object (implementing os.PathLike[str]), or file-like object implementing a string write() function. If None, the result is returned as a string. encodingstr, optionalCharacter encoding setting for file output (and meta tags if available). Defaults to pandas.options.styler.render.encoding value of “utf-8”. sparse_indexbool, optionalWhether to sparsify the display of a hierarchical index. Setting to False will display each explicit level element in a hierarchical key for each row. Defaults to pandas.options.styler.sparse.index value. sparse_columnsbool, optionalWhether to sparsify the display of a hierarchical index. Setting to False will display each explicit level element in a hierarchical key for each column. Defaults to pandas.options.styler.sparse.columns value. max_rowsint, optionalThe maximum number of rows that will be rendered. Defaults to pandas.options.styler.render.max_rows, which is None. max_columnsint, optionalThe maximum number of columns that will be rendered. Defaults to pandas.options.styler.render.max_columns, which is None. Rows and columns may be reduced if the number of total elements is large. This value is set to pandas.options.styler.render.max_elements, which is 262144 (18 bit browser rendering). delimiterstr, default single spaceThe separator between data elements. Returns str or NoneIf buf is None, returns the result as a string. Otherwise returns None.
reference/api/pandas.io.formats.style.Styler.to_string.html
pandas.tseries.offsets.WeekOfMonth.is_year_start
`pandas.tseries.offsets.WeekOfMonth.is_year_start` Return boolean whether a timestamp occurs on the year start. ``` >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_year_start(ts) True ```
WeekOfMonth.is_year_start()# Return boolean whether a timestamp occurs on the year start. Examples >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_year_start(ts) True
reference/api/pandas.tseries.offsets.WeekOfMonth.is_year_start.html
pandas.api.extensions.ExtensionArray.take
`pandas.api.extensions.ExtensionArray.take` Take elements from an array. Indices to be taken.
ExtensionArray.take(indices, *, allow_fill=False, fill_value=None)[source]# Take elements from an array. Parameters indicessequence of int or one-dimensional np.ndarray of intIndices to be taken. allow_fillbool, default FalseHow to handle negative values in indices. False: negative values in indices indicate positional indices from the right (the default). This is similar to numpy.take(). True: negative values in indices indicate missing values. These values are set to fill_value. Any other other negative values raise a ValueError. fill_valueany, optionalFill value to use for NA-indices when allow_fill is True. This may be None, in which case the default NA value for the type, self.dtype.na_value, is used. For many ExtensionArrays, there will be two representations of fill_value: a user-facing “boxed” scalar, and a low-level physical NA value. fill_value should be the user-facing version, and the implementation should handle translating that to the physical version for processing the take if necessary. Returns ExtensionArray Raises IndexErrorWhen the indices are out of bounds for the array. ValueErrorWhen indices contains negative values other than -1 and allow_fill is True. See also numpy.takeTake elements from an array along an axis. api.extensions.takeTake elements from an array. Notes ExtensionArray.take is called by Series.__getitem__, .loc, iloc, when indices is a sequence of values. Additionally, it’s called by Series.reindex(), or any other method that causes realignment, with a fill_value. Examples Here’s an example implementation, which relies on casting the extension array to object dtype. This uses the helper method pandas.api.extensions.take(). def take(self, indices, allow_fill=False, fill_value=None): from pandas.core.algorithms import take # If the ExtensionArray is backed by an ndarray, then # just pass that here instead of coercing to object. data = self.astype(object) if allow_fill and fill_value is None: fill_value = self.dtype.na_value # fill value should always be translated from the scalar # type for the array, to the physical storage type for # the data, before passing to take. result = take(data, indices, fill_value=fill_value, allow_fill=allow_fill) return self._from_sequence(result, dtype=self.dtype)
reference/api/pandas.api.extensions.ExtensionArray.take.html
pandas.tseries.offsets.SemiMonthBegin.is_year_end
`pandas.tseries.offsets.SemiMonthBegin.is_year_end` Return boolean whether a timestamp occurs on the year end. Examples ``` >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_year_end(ts) False ```
SemiMonthBegin.is_year_end()# Return boolean whether a timestamp occurs on the year end. Examples >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_year_end(ts) False
reference/api/pandas.tseries.offsets.SemiMonthBegin.is_year_end.html
pandas.tseries.offsets.Milli.base
`pandas.tseries.offsets.Milli.base` Returns a copy of the calling offset object with n=1 and all other attributes equal.
Milli.base# Returns a copy of the calling offset object with n=1 and all other attributes equal.
reference/api/pandas.tseries.offsets.Milli.base.html
pandas.Timestamp.is_month_start
`pandas.Timestamp.is_month_start` Return True if date is first day of month. Examples ``` >>> ts = pd.Timestamp(2020, 3, 14) >>> ts.is_month_start False ```
Timestamp.is_month_start# Return True if date is first day of month. Examples >>> ts = pd.Timestamp(2020, 3, 14) >>> ts.is_month_start False >>> ts = pd.Timestamp(2020, 1, 1) >>> ts.is_month_start True
reference/api/pandas.Timestamp.is_month_start.html
pandas.CategoricalIndex.reorder_categories
`pandas.CategoricalIndex.reorder_categories` Reorder categories as specified in new_categories. new_categories need to include all old categories and no new category items.
CategoricalIndex.reorder_categories(*args, **kwargs)[source]# Reorder categories as specified in new_categories. new_categories need to include all old categories and no new category items. Parameters new_categoriesIndex-likeThe categories in new order. orderedbool, optionalWhether or not the categorical is treated as a ordered categorical. If not given, do not change the ordered information. inplacebool, default FalseWhether or not to reorder the categories inplace or return a copy of this categorical with reordered categories. Deprecated since version 1.3.0. Returns catCategorical or NoneCategorical with removed categories or None if inplace=True. Raises ValueErrorIf the new categories do not contain all old category items or any new ones See also rename_categoriesRename categories. add_categoriesAdd new categories. remove_categoriesRemove the specified categories. remove_unused_categoriesRemove categories which are not used. set_categoriesSet the categories to the specified ones.
reference/api/pandas.CategoricalIndex.reorder_categories.html
pandas.tseries.offsets.CustomBusinessMonthBegin.apply
pandas.tseries.offsets.CustomBusinessMonthBegin.apply
CustomBusinessMonthBegin.apply()#
reference/api/pandas.tseries.offsets.CustomBusinessMonthBegin.apply.html
pandas.Interval.mid
`pandas.Interval.mid` Return the midpoint of the Interval.
Interval.mid# Return the midpoint of the Interval.
reference/api/pandas.Interval.mid.html
pandas.io.formats.style.Styler.bar
`pandas.io.formats.style.Styler.bar` Draw bar chart in the cell backgrounds.
Styler.bar(subset=None, axis=0, *, color=None, cmap=None, width=100, height=100, align='mid', vmin=None, vmax=None, props='width: 10em;')[source]# Draw bar chart in the cell backgrounds. Changed in version 1.4.0. Parameters subsetlabel, array-like, IndexSlice, optionalA valid 2d input to DataFrame.loc[<subset>], or, in the case of a 1d input or single key, to DataFrame.loc[:, <subset>] where the columns are prioritised, to limit data to before applying the function. axis{0 or ‘index’, 1 or ‘columns’, None}, default 0Apply to each column (axis=0 or 'index'), to each row (axis=1 or 'columns'), or to the entire DataFrame at once with axis=None. colorstr or 2-tuple/listIf a str is passed, the color is the same for both negative and positive numbers. If 2-tuple/list is used, the first element is the color_negative and the second is the color_positive (eg: [‘#d65f5f’, ‘#5fba7d’]). cmapstr, matplotlib.cm.ColorMapA string name of a matplotlib Colormap, or a Colormap object. Cannot be used together with color. New in version 1.4.0. widthfloat, default 100The percentage of the cell, measured from the left, in which to draw the bars, in [0, 100]. heightfloat, default 100The percentage height of the bar in the cell, centrally aligned, in [0,100]. New in version 1.4.0. alignstr, int, float, callable, default ‘mid’How to align the bars within the cells relative to a width adjusted center. If string must be one of: ‘left’ : bars are drawn rightwards from the minimum data value. ‘right’ : bars are drawn leftwards from the maximum data value. ‘zero’ : a value of zero is located at the center of the cell. ‘mid’ : a value of (max-min)/2 is located at the center of the cell, or if all values are negative (positive) the zero is aligned at the right (left) of the cell. ‘mean’ : the mean value of the data is located at the center of the cell. If a float or integer is given this will indicate the center of the cell. If a callable should take a 1d or 2d array and return a scalar. Changed in version 1.4.0. vminfloat, optionalMinimum bar value, defining the left hand limit of the bar drawing range, lower values are clipped to vmin. When None (default): the minimum value of the data will be used. vmaxfloat, optionalMaximum bar value, defining the right hand limit of the bar drawing range, higher values are clipped to vmax. When None (default): the maximum value of the data will be used. propsstr, optionalThe base CSS of the cell that is extended to add the bar chart. Defaults to “width: 10em;”. New in version 1.4.0. Returns selfStyler Notes This section of the user guide: Table Visualization gives a number of examples for different settings and color coordination.
reference/api/pandas.io.formats.style.Styler.bar.html
pandas.core.groupby.DataFrameGroupBy.count
`pandas.core.groupby.DataFrameGroupBy.count` Compute count of group, excluding missing values. Count of values within each group.
DataFrameGroupBy.count()[source]# Compute count of group, excluding missing values. Returns Series or DataFrameCount of values within each group. See also Series.groupbyApply a function groupby to a Series. DataFrame.groupbyApply a function groupby to each row or column of a DataFrame.
reference/api/pandas.core.groupby.DataFrameGroupBy.count.html
Input/output
Input/output
Pickling# read_pickle(filepath_or_buffer[, ...]) Load pickled pandas object (or any object) from file. DataFrame.to_pickle(path[, compression, ...]) Pickle (serialize) object to file. Flat file# read_table(filepath_or_buffer, *[, sep, ...]) Read general delimited file into DataFrame. read_csv(filepath_or_buffer, *[, sep, ...]) Read a comma-separated values (csv) file into DataFrame. DataFrame.to_csv([path_or_buf, sep, na_rep, ...]) Write object to a comma-separated values (csv) file. read_fwf(filepath_or_buffer, *[, colspecs, ...]) Read a table of fixed-width formatted lines into DataFrame. Clipboard# read_clipboard([sep]) Read text from clipboard and pass to read_csv. DataFrame.to_clipboard([excel, sep]) Copy object to the system clipboard. Excel# read_excel(io[, sheet_name, header, names, ...]) Read an Excel file into a pandas DataFrame. DataFrame.to_excel(excel_writer[, ...]) Write object to an Excel sheet. ExcelFile.parse([sheet_name, header, names, ...]) Parse specified sheet(s) into a DataFrame. Styler.to_excel(excel_writer[, sheet_name, ...]) Write Styler to an Excel sheet. ExcelWriter(path[, engine, date_format, ...]) Class for writing DataFrame objects into excel sheets. JSON# read_json(path_or_buf, *[, orient, typ, ...]) Convert a JSON string to pandas object. json_normalize(data[, record_path, meta, ...]) Normalize semi-structured JSON data into a flat table. DataFrame.to_json([path_or_buf, orient, ...]) Convert the object to a JSON string. build_table_schema(data[, index, ...]) Create a Table schema from data. HTML# read_html(io, *[, match, flavor, header, ...]) Read HTML tables into a list of DataFrame objects. DataFrame.to_html([buf, columns, col_space, ...]) Render a DataFrame as an HTML table. Styler.to_html([buf, table_uuid, ...]) Write Styler to a file, buffer or string in HTML-CSS format. XML# read_xml(path_or_buffer, *[, xpath, ...]) Read XML document into a DataFrame object. DataFrame.to_xml([path_or_buffer, index, ...]) Render a DataFrame to an XML document. Latex# DataFrame.to_latex([buf, columns, ...]) Render object to a LaTeX tabular, longtable, or nested table. Styler.to_latex([buf, column_format, ...]) Write Styler to a file, buffer or string in LaTeX format. HDFStore: PyTables (HDF5)# read_hdf(path_or_buf[, key, mode, errors, ...]) Read from the store, close it if we opened it. HDFStore.put(key, value[, format, index, ...]) Store object in HDFStore. HDFStore.append(key, value[, format, axes, ...]) Append to Table in file. HDFStore.get(key) Retrieve pandas object stored in file. HDFStore.select(key[, where, start, stop, ...]) Retrieve pandas object stored in file, optionally based on where criteria. HDFStore.info() Print detailed information on the store. HDFStore.keys([include]) Return a list of keys corresponding to objects stored in HDFStore. HDFStore.groups() Return a list of all the top-level nodes. HDFStore.walk([where]) Walk the pytables group hierarchy for pandas objects. Warning One can store a subclass of DataFrame or Series to HDF5, but the type of the subclass is lost upon storing. Feather# read_feather(path[, columns, use_threads, ...]) Load a feather-format object from the file path. DataFrame.to_feather(path, **kwargs) Write a DataFrame to the binary Feather format. Parquet# read_parquet(path[, engine, columns, ...]) Load a parquet object from the file path, returning a DataFrame. DataFrame.to_parquet([path, engine, ...]) Write a DataFrame to the binary parquet format. ORC# read_orc(path[, columns]) Load an ORC object from the file path, returning a DataFrame. DataFrame.to_orc([path, engine, index, ...]) Write a DataFrame to the ORC format. SAS# read_sas(filepath_or_buffer, *[, format, ...]) Read SAS files stored as either XPORT or SAS7BDAT format files. SPSS# read_spss(path[, usecols, convert_categoricals]) Load an SPSS file from the file path, returning a DataFrame. SQL# read_sql_table(table_name, con[, schema, ...]) Read SQL database table into a DataFrame. read_sql_query(sql, con[, index_col, ...]) Read SQL query into a DataFrame. read_sql(sql, con[, index_col, ...]) Read SQL query or database table into a DataFrame. DataFrame.to_sql(name, con[, schema, ...]) Write records stored in a DataFrame to a SQL database. Google BigQuery# read_gbq(query[, project_id, index_col, ...]) Load data from Google BigQuery. STATA# read_stata(filepath_or_buffer, *[, ...]) Read Stata file into DataFrame. DataFrame.to_stata(path, *[, convert_dates, ...]) Export DataFrame object to Stata dta format. StataReader.data_label Return data label of Stata file. StataReader.value_labels() Return a nested dict associating each variable name to its value and label. StataReader.variable_labels() Return a dict associating each variable name with corresponding label. StataWriter.write_file() Export DataFrame object to Stata dta format.
reference/io.html
pandas.tseries.offsets.YearEnd.__call__
`pandas.tseries.offsets.YearEnd.__call__` Call self as a function.
YearEnd.__call__(*args, **kwargs)# Call self as a function.
reference/api/pandas.tseries.offsets.YearEnd.__call__.html
pandas.tseries.offsets.Easter.rollforward
`pandas.tseries.offsets.Easter.rollforward` Roll provided date forward to next offset only if not on offset.
Easter.rollforward()# Roll provided date forward to next offset only if not on offset. Returns TimeStampRolled timestamp if not on offset, otherwise unchanged timestamp.
reference/api/pandas.tseries.offsets.Easter.rollforward.html
pandas.TimedeltaIndex.days
`pandas.TimedeltaIndex.days` Number of days for each element.
property TimedeltaIndex.days[source]# Number of days for each element.
reference/api/pandas.TimedeltaIndex.days.html
pandas.tseries.offsets.Week.is_anchored
`pandas.tseries.offsets.Week.is_anchored` Return boolean whether the frequency is a unit frequency (n=1). ``` >>> pd.DateOffset().is_anchored() True >>> pd.DateOffset(2).is_anchored() False ```
Week.is_anchored()# Return boolean whether the frequency is a unit frequency (n=1). Examples >>> pd.DateOffset().is_anchored() True >>> pd.DateOffset(2).is_anchored() False
reference/api/pandas.tseries.offsets.Week.is_anchored.html
pandas arrays, scalars, and data types
pandas arrays, scalars, and data types For most data types, pandas uses NumPy arrays as the concrete objects contained with a Index, Series, or DataFrame. For some data types, pandas extends NumPy’s type system. String aliases for these types can be found at dtypes. Kind of Data pandas Data Type Scalar
Objects# For most data types, pandas uses NumPy arrays as the concrete objects contained with a Index, Series, or DataFrame. For some data types, pandas extends NumPy’s type system. String aliases for these types can be found at dtypes. Kind of Data pandas Data Type Scalar Array TZ-aware datetime DatetimeTZDtype Timestamp Datetimes Timedeltas (none) Timedelta Timedeltas Period (time spans) PeriodDtype Period Periods Intervals IntervalDtype Interval Intervals Nullable Integer Int64Dtype, … (none) Nullable integer Categorical CategoricalDtype (none) Categoricals Sparse SparseDtype (none) Sparse Strings StringDtype str Strings Boolean (with NA) BooleanDtype bool Nullable Boolean PyArrow ArrowDtype Python Scalars or NA PyArrow pandas and third-party libraries can extend NumPy’s type system (see Extension types). The top-level array() method can be used to create a new array, which may be stored in a Series, Index, or as a column in a DataFrame. array(data[, dtype, copy]) Create an array. PyArrow# Warning This feature is experimental, and the API can change in a future release without warning. The arrays.ArrowExtensionArray is backed by a pyarrow.ChunkedArray with a pyarrow.DataType instead of a NumPy array and data type. The .dtype of a arrays.ArrowExtensionArray is an ArrowDtype. Pyarrow provides similar array and data type support as NumPy including first-class nullability support for all data types, immutability and more. Note For string types (pyarrow.string(), string[pyarrow]), PyArrow support is still facilitated by arrays.ArrowStringArray and StringDtype("pyarrow"). See the string section below. While individual values in an arrays.ArrowExtensionArray are stored as a PyArrow objects, scalars are returned as Python scalars corresponding to the data type, e.g. a PyArrow int64 will be returned as Python int, or NA for missing values. arrays.ArrowExtensionArray(values) Pandas ExtensionArray backed by a PyArrow ChunkedArray. ArrowDtype(pyarrow_dtype) An ExtensionDtype for PyArrow data types. Datetimes# NumPy cannot natively represent timezone-aware datetimes. pandas supports this with the arrays.DatetimeArray extension array, which can hold timezone-naive or timezone-aware values. Timestamp, a subclass of datetime.datetime, is pandas’ scalar type for timezone-naive or timezone-aware datetime data. Timestamp([ts_input, freq, tz, unit, year, ...]) Pandas replacement for python datetime.datetime object. Properties# Timestamp.asm8 Return numpy datetime64 format in nanoseconds. Timestamp.day Timestamp.dayofweek Return day of the week. Timestamp.day_of_week Return day of the week. Timestamp.dayofyear Return the day of the year. Timestamp.day_of_year Return the day of the year. Timestamp.days_in_month Return the number of days in the month. Timestamp.daysinmonth Return the number of days in the month. Timestamp.fold Timestamp.hour Timestamp.is_leap_year Return True if year is a leap year. Timestamp.is_month_end Return True if date is last day of month. Timestamp.is_month_start Return True if date is first day of month. Timestamp.is_quarter_end Return True if date is last day of the quarter. Timestamp.is_quarter_start Return True if date is first day of the quarter. Timestamp.is_year_end Return True if date is last day of the year. Timestamp.is_year_start Return True if date is first day of the year. Timestamp.max Timestamp.microsecond Timestamp.min Timestamp.minute Timestamp.month Timestamp.nanosecond Timestamp.quarter Return the quarter of the year. Timestamp.resolution Timestamp.second Timestamp.tz Alias for tzinfo. Timestamp.tzinfo Timestamp.value Timestamp.week Return the week number of the year. Timestamp.weekofyear Return the week number of the year. Timestamp.year Methods# Timestamp.astimezone(tz) Convert timezone-aware Timestamp to another time zone. Timestamp.ceil(freq[, ambiguous, nonexistent]) Return a new Timestamp ceiled to this resolution. Timestamp.combine(date, time) Combine date, time into datetime with same date and time fields. Timestamp.ctime Return ctime() style string. Timestamp.date Return date object with same year, month and day. Timestamp.day_name Return the day name of the Timestamp with specified locale. Timestamp.dst Return self.tzinfo.dst(self). Timestamp.floor(freq[, ambiguous, nonexistent]) Return a new Timestamp floored to this resolution. Timestamp.freq Timestamp.freqstr Return the total number of days in the month. Timestamp.fromordinal(ordinal[, freq, tz]) Construct a timestamp from a a proleptic Gregorian ordinal. Timestamp.fromtimestamp(ts) Transform timestamp[, tz] to tz's local time from POSIX timestamp. Timestamp.isocalendar Return a 3-tuple containing ISO year, week number, and weekday. Timestamp.isoformat Return the time formatted according to ISO 8610. Timestamp.isoweekday() Return the day of the week represented by the date. Timestamp.month_name Return the month name of the Timestamp with specified locale. Timestamp.normalize Normalize Timestamp to midnight, preserving tz information. Timestamp.now([tz]) Return new Timestamp object representing current time local to tz. Timestamp.replace([year, month, day, hour, ...]) Implements datetime.replace, handles nanoseconds. Timestamp.round(freq[, ambiguous, nonexistent]) Round the Timestamp to the specified resolution. Timestamp.strftime(format) Return a formatted string of the Timestamp. Timestamp.strptime(string, format) Function is not implemented. Timestamp.time Return time object with same time but with tzinfo=None. Timestamp.timestamp Return POSIX timestamp as float. Timestamp.timetuple Return time tuple, compatible with time.localtime(). Timestamp.timetz Return time object with same time and tzinfo. Timestamp.to_datetime64 Return a numpy.datetime64 object with 'ns' precision. Timestamp.to_numpy Convert the Timestamp to a NumPy datetime64. Timestamp.to_julian_date() Convert TimeStamp to a Julian Date. Timestamp.to_period Return an period of which this timestamp is an observation. Timestamp.to_pydatetime Convert a Timestamp object to a native Python datetime object. Timestamp.today([tz]) Return the current time in the local timezone. Timestamp.toordinal Return proleptic Gregorian ordinal. Timestamp.tz_convert(tz) Convert timezone-aware Timestamp to another time zone. Timestamp.tz_localize(tz[, ambiguous, ...]) Localize the Timestamp to a timezone. Timestamp.tzname Return self.tzinfo.tzname(self). Timestamp.utcfromtimestamp(ts) Construct a naive UTC datetime from a POSIX timestamp. Timestamp.utcnow() Return a new Timestamp representing UTC day and time. Timestamp.utcoffset Return self.tzinfo.utcoffset(self). Timestamp.utctimetuple Return UTC time tuple, compatible with time.localtime(). Timestamp.weekday() Return the day of the week represented by the date. A collection of timestamps may be stored in a arrays.DatetimeArray. For timezone-aware data, the .dtype of a arrays.DatetimeArray is a DatetimeTZDtype. For timezone-naive data, np.dtype("datetime64[ns]") is used. If the data are timezone-aware, then every value in the array must have the same timezone. arrays.DatetimeArray(values[, dtype, freq, copy]) Pandas ExtensionArray for tz-naive or tz-aware datetime data. DatetimeTZDtype([unit, tz]) An ExtensionDtype for timezone-aware datetime data. Timedeltas# NumPy can natively represent timedeltas. pandas provides Timedelta for symmetry with Timestamp. Timedelta([value, unit]) Represents a duration, the difference between two dates or times. Properties# Timedelta.asm8 Return a numpy timedelta64 array scalar view. Timedelta.components Return a components namedtuple-like. Timedelta.days Timedelta.delta (DEPRECATED) Return the timedelta in nanoseconds (ns), for internal compatibility. Timedelta.freq (DEPRECATED) Freq property. Timedelta.is_populated (DEPRECATED) Is_populated property. Timedelta.max Timedelta.microseconds Timedelta.min Timedelta.nanoseconds Return the number of nanoseconds (n), where 0 <= n < 1 microsecond. Timedelta.resolution Timedelta.seconds Timedelta.value Timedelta.view Array view compatibility. Methods# Timedelta.ceil(freq) Return a new Timedelta ceiled to this resolution. Timedelta.floor(freq) Return a new Timedelta floored to this resolution. Timedelta.isoformat Format the Timedelta as ISO 8601 Duration. Timedelta.round(freq) Round the Timedelta to the specified resolution. Timedelta.to_pytimedelta Convert a pandas Timedelta object into a python datetime.timedelta object. Timedelta.to_timedelta64 Return a numpy.timedelta64 object with 'ns' precision. Timedelta.to_numpy Convert the Timedelta to a NumPy timedelta64. Timedelta.total_seconds Total seconds in the duration. A collection of Timedelta may be stored in a TimedeltaArray. arrays.TimedeltaArray(values[, dtype, freq, ...]) Pandas ExtensionArray for timedelta data. Periods# pandas represents spans of times as Period objects. Period# Period([value, freq, ordinal, year, month, ...]) Represents a period of time. Properties# Period.day Get day of the month that a Period falls on. Period.dayofweek Day of the week the period lies in, with Monday=0 and Sunday=6. Period.day_of_week Day of the week the period lies in, with Monday=0 and Sunday=6. Period.dayofyear Return the day of the year. Period.day_of_year Return the day of the year. Period.days_in_month Get the total number of days in the month that this period falls on. Period.daysinmonth Get the total number of days of the month that this period falls on. Period.end_time Get the Timestamp for the end of the period. Period.freq Period.freqstr Return a string representation of the frequency. Period.hour Get the hour of the day component of the Period. Period.is_leap_year Return True if the period's year is in a leap year. Period.minute Get minute of the hour component of the Period. Period.month Return the month this Period falls on. Period.ordinal Period.quarter Return the quarter this Period falls on. Period.qyear Fiscal year the Period lies in according to its starting-quarter. Period.second Get the second component of the Period. Period.start_time Get the Timestamp for the start of the period. Period.week Get the week of the year on the given Period. Period.weekday Day of the week the period lies in, with Monday=0 and Sunday=6. Period.weekofyear Get the week of the year on the given Period. Period.year Return the year this Period falls on. Methods# Period.asfreq Convert Period to desired frequency, at the start or end of the interval. Period.now Return the period of now's date. Period.strftime Returns a formatted string representation of the Period. Period.to_timestamp Return the Timestamp representation of the Period. A collection of Period may be stored in a arrays.PeriodArray. Every period in a arrays.PeriodArray must have the same freq. arrays.PeriodArray(values[, dtype, freq, copy]) Pandas ExtensionArray for storing Period data. PeriodDtype([freq]) An ExtensionDtype for Period data. Intervals# Arbitrary intervals can be represented as Interval objects. Interval Immutable object implementing an Interval, a bounded slice-like interval. Properties# Interval.closed String describing the inclusive side the intervals. Interval.closed_left Check if the interval is closed on the left side. Interval.closed_right Check if the interval is closed on the right side. Interval.is_empty Indicates if an interval is empty, meaning it contains no points. Interval.left Left bound for the interval. Interval.length Return the length of the Interval. Interval.mid Return the midpoint of the Interval. Interval.open_left Check if the interval is open on the left side. Interval.open_right Check if the interval is open on the right side. Interval.overlaps Check whether two Interval objects overlap. Interval.right Right bound for the interval. A collection of intervals may be stored in an arrays.IntervalArray. arrays.IntervalArray(data[, closed, dtype, ...]) Pandas array for interval data that are closed on the same side. IntervalDtype([subtype, closed]) An ExtensionDtype for Interval data. Nullable integer# numpy.ndarray cannot natively represent integer-data with missing values. pandas provides this through arrays.IntegerArray. arrays.IntegerArray(values, mask[, copy]) Array of integer (optional missing) values. Int8Dtype() An ExtensionDtype for int8 integer data. Int16Dtype() An ExtensionDtype for int16 integer data. Int32Dtype() An ExtensionDtype for int32 integer data. Int64Dtype() An ExtensionDtype for int64 integer data. UInt8Dtype() An ExtensionDtype for uint8 integer data. UInt16Dtype() An ExtensionDtype for uint16 integer data. UInt32Dtype() An ExtensionDtype for uint32 integer data. UInt64Dtype() An ExtensionDtype for uint64 integer data. Categoricals# pandas defines a custom data type for representing data that can take only a limited, fixed set of values. The dtype of a Categorical can be described by a CategoricalDtype. CategoricalDtype([categories, ordered]) Type for categorical data with the categories and orderedness. CategoricalDtype.categories An Index containing the unique categories allowed. CategoricalDtype.ordered Whether the categories have an ordered relationship. Categorical data can be stored in a pandas.Categorical Categorical(values[, categories, ordered, ...]) Represent a categorical variable in classic R / S-plus fashion. The alternative Categorical.from_codes() constructor can be used when you have the categories and integer codes already: Categorical.from_codes(codes[, categories, ...]) Make a Categorical type from codes and categories or dtype. The dtype information is available on the Categorical Categorical.dtype The CategoricalDtype for this instance. Categorical.categories The categories of this categorical. Categorical.ordered Whether the categories have an ordered relationship. Categorical.codes The category codes of this categorical. np.asarray(categorical) works by implementing the array interface. Be aware, that this converts the Categorical back to a NumPy array, so categories and order information is not preserved! Categorical.__array__([dtype]) The numpy array interface. A Categorical can be stored in a Series or DataFrame. To create a Series of dtype category, use cat = s.astype(dtype) or Series(..., dtype=dtype) where dtype is either the string 'category' an instance of CategoricalDtype. If the Series is of dtype CategoricalDtype, Series.cat can be used to change the categorical data. See Categorical accessor for more. Sparse# Data where a single value is repeated many times (e.g. 0 or NaN) may be stored efficiently as a arrays.SparseArray. arrays.SparseArray(data[, sparse_index, ...]) An ExtensionArray for storing sparse data. SparseDtype([dtype, fill_value]) Dtype for data stored in SparseArray. The Series.sparse accessor may be used to access sparse-specific attributes and methods if the Series contains sparse values. See Sparse accessor and the user guide for more. Strings# When working with text data, where each valid element is a string or missing, we recommend using StringDtype (with the alias "string"). arrays.StringArray(values[, copy]) Extension array for string data. arrays.ArrowStringArray(values) Extension array for string data in a pyarrow.ChunkedArray. StringDtype([storage]) Extension dtype for string data. The Series.str accessor is available for Series backed by a arrays.StringArray. See String handling for more. Nullable Boolean# The boolean dtype (with the alias "boolean") provides support for storing boolean data (True, False) with missing values, which is not possible with a bool numpy.ndarray. arrays.BooleanArray(values, mask[, copy]) Array of boolean (True/False) data with missing values. BooleanDtype() Extension dtype for boolean data. Utilities# Constructors# api.types.union_categoricals(to_union[, ...]) Combine list-like of Categorical-like, unioning categories. api.types.infer_dtype Return a string label of the type of a scalar or list-like of values. api.types.pandas_dtype(dtype) Convert input into a pandas only dtype object or a numpy dtype object. Data type introspection# api.types.is_bool_dtype(arr_or_dtype) Check whether the provided array or dtype is of a boolean dtype. api.types.is_categorical_dtype(arr_or_dtype) Check whether an array-like or dtype is of the Categorical dtype. api.types.is_complex_dtype(arr_or_dtype) Check whether the provided array or dtype is of a complex dtype. api.types.is_datetime64_any_dtype(arr_or_dtype) Check whether the provided array or dtype is of the datetime64 dtype. api.types.is_datetime64_dtype(arr_or_dtype) Check whether an array-like or dtype is of the datetime64 dtype. api.types.is_datetime64_ns_dtype(arr_or_dtype) Check whether the provided array or dtype is of the datetime64[ns] dtype. api.types.is_datetime64tz_dtype(arr_or_dtype) Check whether an array-like or dtype is of a DatetimeTZDtype dtype. api.types.is_extension_type(arr) (DEPRECATED) Check whether an array-like is of a pandas extension class instance. api.types.is_extension_array_dtype(arr_or_dtype) Check if an object is a pandas extension array type. api.types.is_float_dtype(arr_or_dtype) Check whether the provided array or dtype is of a float dtype. api.types.is_int64_dtype(arr_or_dtype) Check whether the provided array or dtype is of the int64 dtype. api.types.is_integer_dtype(arr_or_dtype) Check whether the provided array or dtype is of an integer dtype. api.types.is_interval_dtype(arr_or_dtype) Check whether an array-like or dtype is of the Interval dtype. api.types.is_numeric_dtype(arr_or_dtype) Check whether the provided array or dtype is of a numeric dtype. api.types.is_object_dtype(arr_or_dtype) Check whether an array-like or dtype is of the object dtype. api.types.is_period_dtype(arr_or_dtype) Check whether an array-like or dtype is of the Period dtype. api.types.is_signed_integer_dtype(arr_or_dtype) Check whether the provided array or dtype is of a signed integer dtype. api.types.is_string_dtype(arr_or_dtype) Check whether the provided array or dtype is of the string dtype. api.types.is_timedelta64_dtype(arr_or_dtype) Check whether an array-like or dtype is of the timedelta64 dtype. api.types.is_timedelta64_ns_dtype(arr_or_dtype) Check whether the provided array or dtype is of the timedelta64[ns] dtype. api.types.is_unsigned_integer_dtype(arr_or_dtype) Check whether the provided array or dtype is of an unsigned integer dtype. api.types.is_sparse(arr) Check whether an array-like is a 1-D pandas sparse array. Iterable introspection# api.types.is_dict_like(obj) Check if the object is dict-like. api.types.is_file_like(obj) Check if the object is a file-like object. api.types.is_list_like Check if the object is list-like. api.types.is_named_tuple(obj) Check if the object is a named tuple. api.types.is_iterator Check if the object is an iterator. Scalar introspection# api.types.is_bool Return True if given object is boolean. api.types.is_categorical(arr) (DEPRECATED) Check whether an array-like is a Categorical instance. api.types.is_complex Return True if given object is complex. api.types.is_float Return True if given object is float. api.types.is_hashable(obj) Return True if hash(obj) will succeed, False otherwise. api.types.is_integer Return True if given object is integer. api.types.is_interval api.types.is_number(obj) Check if the object is a number. api.types.is_re(obj) Check if the object is a regex pattern instance. api.types.is_re_compilable(obj) Check if the object can be compiled into a regex pattern instance. api.types.is_scalar Return True if given object is scalar.
reference/arrays.html
pandas.tseries.offsets.YearEnd.copy
`pandas.tseries.offsets.YearEnd.copy` Return a copy of the frequency. Examples ``` >>> freq = pd.DateOffset(1) >>> freq_copy = freq.copy() >>> freq is freq_copy False ```
YearEnd.copy()# Return a copy of the frequency. Examples >>> freq = pd.DateOffset(1) >>> freq_copy = freq.copy() >>> freq is freq_copy False
reference/api/pandas.tseries.offsets.YearEnd.copy.html
pandas.PeriodIndex.start_time
`pandas.PeriodIndex.start_time` Get the Timestamp for the start of the period. ``` >>> period = pd.Period('2012-1-1', freq='D') >>> period Period('2012-01-01', 'D') ```
property PeriodIndex.start_time[source]# Get the Timestamp for the start of the period. Returns Timestamp See also Period.end_timeReturn the end Timestamp. Period.dayofyearReturn the day of year. Period.daysinmonthReturn the days in that month. Period.dayofweekReturn the day of the week. Examples >>> period = pd.Period('2012-1-1', freq='D') >>> period Period('2012-01-01', 'D') >>> period.start_time Timestamp('2012-01-01 00:00:00') >>> period.end_time Timestamp('2012-01-01 23:59:59.999999999')
reference/api/pandas.PeriodIndex.start_time.html
pandas.Index.shape
`pandas.Index.shape` Return a tuple of the shape of the underlying data.
property Index.shape[source]# Return a tuple of the shape of the underlying data.
reference/api/pandas.Index.shape.html
pandas.Timestamp.to_julian_date
`pandas.Timestamp.to_julian_date` Convert TimeStamp to a Julian Date. ``` >>> ts = pd.Timestamp('2020-03-14T15:32:52') >>> ts.to_julian_date() 2458923.147824074 ```
Timestamp.to_julian_date()# Convert TimeStamp to a Julian Date. 0 Julian date is noon January 1, 4713 BC. Examples >>> ts = pd.Timestamp('2020-03-14T15:32:52') >>> ts.to_julian_date() 2458923.147824074
reference/api/pandas.Timestamp.to_julian_date.html
pandas.Series.cumsum
`pandas.Series.cumsum` Return cumulative sum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative sum. ``` >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 ```
Series.cumsum(axis=None, skipna=True, *args, **kwargs)[source]# Return cumulative sum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative sum. Parameters axis{0 or ‘index’, 1 or ‘columns’}, default 0The index or the name of the axis. 0 is equivalent to None or ‘index’. For Series this parameter is unused and defaults to 0. skipnabool, default TrueExclude NA/null values. If an entire row/column is NA, the result will be NA. *args, **kwargsAdditional keywords have no effect but might be accepted for compatibility with NumPy. Returns scalar or SeriesReturn cumulative sum of scalar or Series. See also core.window.expanding.Expanding.sumSimilar functionality but ignores NaN values. Series.sumReturn the sum over Series axis. Series.cummaxReturn cumulative maximum over Series axis. Series.cumminReturn cumulative minimum over Series axis. Series.cumsumReturn cumulative sum over Series axis. Series.cumprodReturn cumulative product over Series axis. Examples Series >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cumsum() 0 2.0 1 NaN 2 7.0 3 6.0 4 6.0 dtype: float64 To include NA values in the operation, use skipna=False >>> s.cumsum(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 DataFrame >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the sum in each column. This is equivalent to axis=None or axis='index'. >>> df.cumsum() A B 0 2.0 1.0 1 5.0 NaN 2 6.0 1.0 To iterate over columns and find the sum in each row, use axis=1 >>> df.cumsum(axis=1) A B 0 2.0 3.0 1 3.0 NaN 2 1.0 1.0
reference/api/pandas.Series.cumsum.html
pandas.RangeIndex.from_range
`pandas.RangeIndex.from_range` Create RangeIndex from a range object.
classmethod RangeIndex.from_range(data, name=None, dtype=None)[source]# Create RangeIndex from a range object. Returns RangeIndex
reference/api/pandas.RangeIndex.from_range.html
pandas.Series.mul
`pandas.Series.mul` Return Multiplication of series and other, element-wise (binary operator mul). ``` >>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) >>> a a 1.0 b 1.0 c 1.0 d NaN dtype: float64 >>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) >>> b a 1.0 b NaN d 1.0 e NaN dtype: float64 >>> a.multiply(b, fill_value=0) a 1.0 b 0.0 c 0.0 d 0.0 e NaN dtype: float64 ```
Series.mul(other, level=None, fill_value=None, axis=0)[source]# Return Multiplication of series and other, element-wise (binary operator mul). Equivalent to series * other, but with support to substitute a fill_value for missing data in either one of the inputs. Parameters otherSeries or scalar value levelint or nameBroadcast across a level, matching Index values on the passed MultiIndex level. fill_valueNone or float value, default None (NaN)Fill existing missing (NaN) values, and any new element needed for successful Series alignment, with this value before computation. If data in both corresponding Series locations is missing the result of filling (at that location) will be missing. axis{0 or ‘index’}Unused. Parameter needed for compatibility with DataFrame. Returns SeriesThe result of the operation. See also Series.rmulReverse of the Multiplication operator, see Python documentation for more details. Examples >>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd']) >>> a a 1.0 b 1.0 c 1.0 d NaN dtype: float64 >>> b = pd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e']) >>> b a 1.0 b NaN d 1.0 e NaN dtype: float64 >>> a.multiply(b, fill_value=0) a 1.0 b 0.0 c 0.0 d 0.0 e NaN dtype: float64
reference/api/pandas.Series.mul.html
pandas.tseries.offsets.FY5253Quarter.is_quarter_start
`pandas.tseries.offsets.FY5253Quarter.is_quarter_start` Return boolean whether a timestamp occurs on the quarter start. Examples ``` >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_quarter_start(ts) True ```
FY5253Quarter.is_quarter_start()# Return boolean whether a timestamp occurs on the quarter start. Examples >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_quarter_start(ts) True
reference/api/pandas.tseries.offsets.FY5253Quarter.is_quarter_start.html
pandas.Series.str.title
`pandas.Series.str.title` Convert strings in the Series/Index to titlecase. Equivalent to str.title(). ``` >>> s = pd.Series(['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']) >>> s 0 lower 1 CAPITALS 2 this is a sentence 3 SwApCaSe dtype: object ```
Series.str.title()[source]# Convert strings in the Series/Index to titlecase. Equivalent to str.title(). Returns Series or Index of object See also Series.str.lowerConverts all characters to lowercase. Series.str.upperConverts all characters to uppercase. Series.str.titleConverts first character of each word to uppercase and remaining to lowercase. Series.str.capitalizeConverts first character to uppercase and remaining to lowercase. Series.str.swapcaseConverts uppercase to lowercase and lowercase to uppercase. Series.str.casefoldRemoves all case distinctions in the string. Examples >>> s = pd.Series(['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']) >>> s 0 lower 1 CAPITALS 2 this is a sentence 3 SwApCaSe dtype: object >>> s.str.lower() 0 lower 1 capitals 2 this is a sentence 3 swapcase dtype: object >>> s.str.upper() 0 LOWER 1 CAPITALS 2 THIS IS A SENTENCE 3 SWAPCASE dtype: object >>> s.str.title() 0 Lower 1 Capitals 2 This Is A Sentence 3 Swapcase dtype: object >>> s.str.capitalize() 0 Lower 1 Capitals 2 This is a sentence 3 Swapcase dtype: object >>> s.str.swapcase() 0 LOWER 1 capitals 2 THIS IS A SENTENCE 3 sWaPcAsE dtype: object
reference/api/pandas.Series.str.title.html
pandas.tseries.offsets.FY5253Quarter.nanos
pandas.tseries.offsets.FY5253Quarter.nanos
FY5253Quarter.nanos#
reference/api/pandas.tseries.offsets.FY5253Quarter.nanos.html
pandas.tseries.offsets.LastWeekOfMonth.name
`pandas.tseries.offsets.LastWeekOfMonth.name` Return a string representing the base frequency. ``` >>> pd.offsets.Hour().name 'H' ```
LastWeekOfMonth.name# Return a string representing the base frequency. Examples >>> pd.offsets.Hour().name 'H' >>> pd.offsets.Hour(5).name 'H'
reference/api/pandas.tseries.offsets.LastWeekOfMonth.name.html
pandas.tseries.offsets.BQuarterBegin.nanos
pandas.tseries.offsets.BQuarterBegin.nanos
BQuarterBegin.nanos#
reference/api/pandas.tseries.offsets.BQuarterBegin.nanos.html
pandas.DataFrame.first_valid_index
`pandas.DataFrame.first_valid_index` Return index for first non-NA value or None, if no non-NA value is found. Notes
DataFrame.first_valid_index()[source]# Return index for first non-NA value or None, if no non-NA value is found. Returns scalartype of index Notes If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame.
reference/api/pandas.DataFrame.first_valid_index.html
pandas.io.formats.style.Styler.set_na_rep
`pandas.io.formats.style.Styler.set_na_rep` Set the missing data representation on a Styler.
Styler.set_na_rep(na_rep)[source]# Set the missing data representation on a Styler. New in version 1.0.0. Deprecated since version 1.3.0. Parameters na_repstr Returns selfStyler Notes This method is deprecated. See Styler.format()
reference/api/pandas.io.formats.style.Styler.set_na_rep.html
pandas.DataFrame.to_clipboard
`pandas.DataFrame.to_clipboard` Copy object to the system clipboard. ``` >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C']) ```
DataFrame.to_clipboard(excel=True, sep=None, **kwargs)[source]# Copy object to the system clipboard. Write a text representation of object to the system clipboard. This can be pasted into Excel, for example. Parameters excelbool, default TrueProduce output in a csv format for easy pasting into excel. True, use the provided separator for csv pasting. False, write a string representation of the object to the clipboard. sepstr, default '\t'Field delimiter. **kwargsThese parameters will be passed to DataFrame.to_csv. See also DataFrame.to_csvWrite a DataFrame to a comma-separated values (csv) file. read_clipboardRead text from clipboard and pass to read_csv. Notes Requirements for your platform. Linux : xclip, or xsel (with PyQt4 modules) Windows : none macOS : none This method uses the processes developed for the package pyperclip. A solution to render any output string format is given in the examples. Examples Copy the contents of a DataFrame to the clipboard. >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C']) >>> df.to_clipboard(sep=',') ... # Wrote the following to the system clipboard: ... # ,A,B,C ... # 0,1,2,3 ... # 1,4,5,6 We can omit the index by passing the keyword index and setting it to false. >>> df.to_clipboard(sep=',', index=False) ... # Wrote the following to the system clipboard: ... # A,B,C ... # 1,2,3 ... # 4,5,6 Using the original pyperclip package for any string output format. import pyperclip html = df.style.to_html() pyperclip.copy(html)
reference/api/pandas.DataFrame.to_clipboard.html
pandas.tseries.offsets.WeekOfMonth
`pandas.tseries.offsets.WeekOfMonth` Describes monthly dates like “the Tuesday of the 2nd week of each month”. A specific integer for the week of the month. e.g. 0 is 1st week of month, 1 is the 2nd week, etc. ``` >>> ts = pd.Timestamp(2022, 1, 1) >>> ts + pd.offsets.WeekOfMonth() Timestamp('2022-01-03 00:00:00') ```
class pandas.tseries.offsets.WeekOfMonth# Describes monthly dates like “the Tuesday of the 2nd week of each month”. Parameters nint weekint {0, 1, 2, 3, …}, default 0A specific integer for the week of the month. e.g. 0 is 1st week of month, 1 is the 2nd week, etc. weekdayint {0, 1, …, 6}, default 0A specific integer for the day of the week. 0 is Monday 1 is Tuesday 2 is Wednesday 3 is Thursday 4 is Friday 5 is Saturday 6 is Sunday. Examples >>> ts = pd.Timestamp(2022, 1, 1) >>> ts + pd.offsets.WeekOfMonth() Timestamp('2022-01-03 00:00:00') Attributes base Returns a copy of the calling offset object with n=1 and all other attributes equal. freqstr Return a string representing the frequency. kwds Return a dict of extra parameters for the offset. name Return a string representing the base frequency. n nanos normalize rule_code week weekday Methods __call__(*args, **kwargs) Call self as a function. apply_index (DEPRECATED) Vectorized apply of DateOffset to DatetimeIndex. copy Return a copy of the frequency. is_anchored Return boolean whether the frequency is a unit frequency (n=1). is_month_end Return boolean whether a timestamp occurs on the month end. is_month_start Return boolean whether a timestamp occurs on the month start. is_on_offset Return boolean whether a timestamp intersects with this frequency. is_quarter_end Return boolean whether a timestamp occurs on the quarter end. is_quarter_start Return boolean whether a timestamp occurs on the quarter start. is_year_end Return boolean whether a timestamp occurs on the year end. is_year_start Return boolean whether a timestamp occurs on the year start. rollback Roll provided date backward to next offset only if not on offset. rollforward Roll provided date forward to next offset only if not on offset. apply isAnchored onOffset
reference/api/pandas.tseries.offsets.WeekOfMonth.html
pandas.Index.is_monotonic_decreasing
`pandas.Index.is_monotonic_decreasing` Return a boolean if the values are equal or decreasing. ``` >>> Index([3, 2, 1]).is_monotonic_decreasing True >>> Index([3, 2, 2]).is_monotonic_decreasing True >>> Index([3, 1, 2]).is_monotonic_decreasing False ```
property Index.is_monotonic_decreasing[source]# Return a boolean if the values are equal or decreasing. Examples >>> Index([3, 2, 1]).is_monotonic_decreasing True >>> Index([3, 2, 2]).is_monotonic_decreasing True >>> Index([3, 1, 2]).is_monotonic_decreasing False
reference/api/pandas.Index.is_monotonic_decreasing.html
pandas.errors.PerformanceWarning
`pandas.errors.PerformanceWarning` Warning raised when there is a possible performance impact.
exception pandas.errors.PerformanceWarning[source]# Warning raised when there is a possible performance impact.
reference/api/pandas.errors.PerformanceWarning.html
pandas.Index.unique
`pandas.Index.unique` Return unique values in the index.
Index.unique(level=None)[source]# Return unique values in the index. Unique values are returned in order of appearance, this does NOT sort. Parameters levelint or hashable, optionalOnly return values from specified level (for MultiIndex). If int, gets the level by integer position, else by level name. Returns Index See also uniqueNumpy array of unique values in that column. Series.uniqueReturn unique values of Series object.
reference/api/pandas.Index.unique.html
Comparison with Stata
Comparison with Stata For potential users coming from Stata this page is meant to demonstrate how different Stata operations would be performed in pandas. If you’re new to pandas, you might want to first read through 10 Minutes to pandas to familiarize yourself with the library. As is customary, we import pandas and NumPy as follows: pandas Stata
For potential users coming from Stata this page is meant to demonstrate how different Stata operations would be performed in pandas. If you’re new to pandas, you might want to first read through 10 Minutes to pandas to familiarize yourself with the library. As is customary, we import pandas and NumPy as follows: In [1]: import pandas as pd In [2]: import numpy as np Data structures# General terminology translation# pandas Stata DataFrame data set column variable row observation groupby bysort NaN . DataFrame# A DataFrame in pandas is analogous to a Stata data set – a two-dimensional data source with labeled columns that can be of different types. As will be shown in this document, almost any operation that can be applied to a data set in Stata can also be accomplished in pandas. Series# A Series is the data structure that represents one column of a DataFrame. Stata doesn’t have a separate data structure for a single column, but in general, working with a Series is analogous to referencing a column of a data set in Stata. Index# Every DataFrame and Series has an Index – labels on the rows of the data. Stata does not have an exactly analogous concept. In Stata, a data set’s rows are essentially unlabeled, other than an implicit integer index that can be accessed with _n. In pandas, if no index is specified, an integer index is also used by default (first row = 0, second row = 1, and so on). While using a labeled Index or MultiIndex can enable sophisticated analyses and is ultimately an important part of pandas to understand, for this comparison we will essentially ignore the Index and just treat the DataFrame as a collection of columns. Please see the indexing documentation for much more on how to use an Index effectively. Copies vs. in place operations# Most pandas operations return copies of the Series/DataFrame. To make the changes “stick”, you’ll need to either assign to a new variable: sorted_df = df.sort_values("col1") or overwrite the original one: df = df.sort_values("col1") Note You will see an inplace=True keyword argument available for some methods: df.sort_values("col1", inplace=True) Its use is discouraged. More information. Data input / output# Constructing a DataFrame from values# A Stata data set can be built from specified values by placing the data after an input statement and specifying the column names. input x y 1 2 3 4 5 6 end A pandas DataFrame can be constructed in many different ways, but for a small number of values, it is often convenient to specify it as a Python dictionary, where the keys are the column names and the values are the data. In [3]: df = pd.DataFrame({"x": [1, 3, 5], "y": [2, 4, 6]}) In [4]: df Out[4]: x y 0 1 2 1 3 4 2 5 6 Reading external data# Like Stata, pandas provides utilities for reading in data from many formats. The tips data set, found within the pandas tests (csv) will be used in many of the following examples. Stata provides import delimited to read csv data into a data set in memory. If the tips.csv file is in the current working directory, we can import it as follows. import delimited tips.csv The pandas method is read_csv(), which works similarly. Additionally, it will automatically download the data set if presented with a url. In [5]: url = ( ...: "https://raw.githubusercontent.com/pandas-dev" ...: "/pandas/main/pandas/tests/io/data/csv/tips.csv" ...: ) ...: In [6]: tips = pd.read_csv(url) In [7]: tips Out[7]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 .. ... ... ... ... ... ... ... 239 29.03 5.92 Male No Sat Dinner 3 240 27.18 2.00 Female Yes Sat Dinner 2 241 22.67 2.00 Male Yes Sat Dinner 2 242 17.82 1.75 Male No Sat Dinner 2 243 18.78 3.00 Female No Thur Dinner 2 [244 rows x 7 columns] Like import delimited, read_csv() can take a number of parameters to specify how the data should be parsed. For example, if the data were instead tab delimited, did not have column names, and existed in the current working directory, the pandas command would be: tips = pd.read_csv("tips.csv", sep="\t", header=None) # alternatively, read_table is an alias to read_csv with tab delimiter tips = pd.read_table("tips.csv", header=None) pandas can also read Stata data sets in .dta format with the read_stata() function. df = pd.read_stata("data.dta") In addition to text/csv and Stata files, pandas supports a variety of other data formats such as Excel, SAS, HDF5, Parquet, and SQL databases. These are all read via a pd.read_* function. See the IO documentation for more details. Limiting output# By default, pandas will truncate output of large DataFrames to show the first and last rows. This can be overridden by changing the pandas options, or using DataFrame.head() or DataFrame.tail(). In [8]: tips.head(5) Out[8]: total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 The equivalent in Stata would be: list in 1/5 Exporting data# The inverse of import delimited in Stata is export delimited export delimited tips2.csv Similarly in pandas, the opposite of read_csv is DataFrame.to_csv(). tips.to_csv("tips2.csv") pandas can also export to Stata file format with the DataFrame.to_stata() method. tips.to_stata("tips2.dta") Data operations# Operations on columns# In Stata, arbitrary math expressions can be used with the generate and replace commands on new or existing columns. The drop command drops the column from the data set. replace total_bill = total_bill - 2 generate new_bill = total_bill / 2 drop new_bill pandas provides vectorized operations by specifying the individual Series in the DataFrame. New columns can be assigned in the same way. The DataFrame.drop() method drops a column from the DataFrame. In [9]: tips["total_bill"] = tips["total_bill"] - 2 In [10]: tips["new_bill"] = tips["total_bill"] / 2 In [11]: tips Out[11]: total_bill tip sex smoker day time size new_bill 0 14.99 1.01 Female No Sun Dinner 2 7.495 1 8.34 1.66 Male No Sun Dinner 3 4.170 2 19.01 3.50 Male No Sun Dinner 3 9.505 3 21.68 3.31 Male No Sun Dinner 2 10.840 4 22.59 3.61 Female No Sun Dinner 4 11.295 .. ... ... ... ... ... ... ... ... 239 27.03 5.92 Male No Sat Dinner 3 13.515 240 25.18 2.00 Female Yes Sat Dinner 2 12.590 241 20.67 2.00 Male Yes Sat Dinner 2 10.335 242 15.82 1.75 Male No Sat Dinner 2 7.910 243 16.78 3.00 Female No Thur Dinner 2 8.390 [244 rows x 8 columns] In [12]: tips = tips.drop("new_bill", axis=1) Filtering# Filtering in Stata is done with an if clause on one or more columns. list if total_bill > 10 DataFrames can be filtered in multiple ways; the most intuitive of which is using boolean indexing. In [13]: tips[tips["total_bill"] > 10] Out[13]: total_bill tip sex smoker day time size 0 14.99 1.01 Female No Sun Dinner 2 2 19.01 3.50 Male No Sun Dinner 3 3 21.68 3.31 Male No Sun Dinner 2 4 22.59 3.61 Female No Sun Dinner 4 5 23.29 4.71 Male No Sun Dinner 4 .. ... ... ... ... ... ... ... 239 27.03 5.92 Male No Sat Dinner 3 240 25.18 2.00 Female Yes Sat Dinner 2 241 20.67 2.00 Male Yes Sat Dinner 2 242 15.82 1.75 Male No Sat Dinner 2 243 16.78 3.00 Female No Thur Dinner 2 [204 rows x 7 columns] The above statement is simply passing a Series of True/False objects to the DataFrame, returning all rows with True. In [14]: is_dinner = tips["time"] == "Dinner" In [15]: is_dinner Out[15]: 0 True 1 True 2 True 3 True 4 True ... 239 True 240 True 241 True 242 True 243 True Name: time, Length: 244, dtype: bool In [16]: is_dinner.value_counts() Out[16]: True 176 False 68 Name: time, dtype: int64 In [17]: tips[is_dinner] Out[17]: total_bill tip sex smoker day time size 0 14.99 1.01 Female No Sun Dinner 2 1 8.34 1.66 Male No Sun Dinner 3 2 19.01 3.50 Male No Sun Dinner 3 3 21.68 3.31 Male No Sun Dinner 2 4 22.59 3.61 Female No Sun Dinner 4 .. ... ... ... ... ... ... ... 239 27.03 5.92 Male No Sat Dinner 3 240 25.18 2.00 Female Yes Sat Dinner 2 241 20.67 2.00 Male Yes Sat Dinner 2 242 15.82 1.75 Male No Sat Dinner 2 243 16.78 3.00 Female No Thur Dinner 2 [176 rows x 7 columns] If/then logic# In Stata, an if clause can also be used to create new columns. generate bucket = "low" if total_bill < 10 replace bucket = "high" if total_bill >= 10 The same operation in pandas can be accomplished using the where method from numpy. In [18]: tips["bucket"] = np.where(tips["total_bill"] < 10, "low", "high") In [19]: tips Out[19]: total_bill tip sex smoker day time size bucket 0 14.99 1.01 Female No Sun Dinner 2 high 1 8.34 1.66 Male No Sun Dinner 3 low 2 19.01 3.50 Male No Sun Dinner 3 high 3 21.68 3.31 Male No Sun Dinner 2 high 4 22.59 3.61 Female No Sun Dinner 4 high .. ... ... ... ... ... ... ... ... 239 27.03 5.92 Male No Sat Dinner 3 high 240 25.18 2.00 Female Yes Sat Dinner 2 high 241 20.67 2.00 Male Yes Sat Dinner 2 high 242 15.82 1.75 Male No Sat Dinner 2 high 243 16.78 3.00 Female No Thur Dinner 2 high [244 rows x 8 columns] Date functionality# Stata provides a variety of functions to do operations on date/datetime columns. generate date1 = mdy(1, 15, 2013) generate date2 = date("Feb152015", "MDY") generate date1_year = year(date1) generate date2_month = month(date2) * shift date to beginning of next month generate date1_next = mdy(month(date1) + 1, 1, year(date1)) if month(date1) != 12 replace date1_next = mdy(1, 1, year(date1) + 1) if month(date1) == 12 generate months_between = mofd(date2) - mofd(date1) list date1 date2 date1_year date2_month date1_next months_between The equivalent pandas operations are shown below. In addition to these functions, pandas supports other Time Series features not available in Stata (such as time zone handling and custom offsets) – see the timeseries documentation for more details. In [20]: tips["date1"] = pd.Timestamp("2013-01-15") In [21]: tips["date2"] = pd.Timestamp("2015-02-15") In [22]: tips["date1_year"] = tips["date1"].dt.year In [23]: tips["date2_month"] = tips["date2"].dt.month In [24]: tips["date1_next"] = tips["date1"] + pd.offsets.MonthBegin() In [25]: tips["months_between"] = tips["date2"].dt.to_period("M") - tips[ ....: "date1" ....: ].dt.to_period("M") ....: In [26]: tips[ ....: ["date1", "date2", "date1_year", "date2_month", "date1_next", "months_between"] ....: ] ....: Out[26]: date1 date2 date1_year date2_month date1_next months_between 0 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 1 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 2 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 3 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 4 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> .. ... ... ... ... ... ... 239 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 240 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 241 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 242 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> 243 2013-01-15 2015-02-15 2013 2 2013-02-01 <25 * MonthEnds> [244 rows x 6 columns] Selection of columns# Stata provides keywords to select, drop, and rename columns. keep sex total_bill tip drop sex rename total_bill total_bill_2 The same operations are expressed in pandas below. Keep certain columns# In [27]: tips[["sex", "total_bill", "tip"]] Out[27]: sex total_bill tip 0 Female 14.99 1.01 1 Male 8.34 1.66 2 Male 19.01 3.50 3 Male 21.68 3.31 4 Female 22.59 3.61 .. ... ... ... 239 Male 27.03 5.92 240 Female 25.18 2.00 241 Male 20.67 2.00 242 Male 15.82 1.75 243 Female 16.78 3.00 [244 rows x 3 columns] Drop a column# In [28]: tips.drop("sex", axis=1) Out[28]: total_bill tip smoker day time size 0 14.99 1.01 No Sun Dinner 2 1 8.34 1.66 No Sun Dinner 3 2 19.01 3.50 No Sun Dinner 3 3 21.68 3.31 No Sun Dinner 2 4 22.59 3.61 No Sun Dinner 4 .. ... ... ... ... ... ... 239 27.03 5.92 No Sat Dinner 3 240 25.18 2.00 Yes Sat Dinner 2 241 20.67 2.00 Yes Sat Dinner 2 242 15.82 1.75 No Sat Dinner 2 243 16.78 3.00 No Thur Dinner 2 [244 rows x 6 columns] Rename a column# In [29]: tips.rename(columns={"total_bill": "total_bill_2"}) Out[29]: total_bill_2 tip sex smoker day time size 0 14.99 1.01 Female No Sun Dinner 2 1 8.34 1.66 Male No Sun Dinner 3 2 19.01 3.50 Male No Sun Dinner 3 3 21.68 3.31 Male No Sun Dinner 2 4 22.59 3.61 Female No Sun Dinner 4 .. ... ... ... ... ... ... ... 239 27.03 5.92 Male No Sat Dinner 3 240 25.18 2.00 Female Yes Sat Dinner 2 241 20.67 2.00 Male Yes Sat Dinner 2 242 15.82 1.75 Male No Sat Dinner 2 243 16.78 3.00 Female No Thur Dinner 2 [244 rows x 7 columns] Sorting by values# Sorting in Stata is accomplished via sort sort sex total_bill pandas has a DataFrame.sort_values() method, which takes a list of columns to sort by. In [30]: tips = tips.sort_values(["sex", "total_bill"]) In [31]: tips Out[31]: total_bill tip sex smoker day time size 67 1.07 1.00 Female Yes Sat Dinner 1 92 3.75 1.00 Female Yes Fri Dinner 2 111 5.25 1.00 Female No Sat Dinner 1 145 6.35 1.50 Female No Thur Lunch 2 135 6.51 1.25 Female No Thur Lunch 2 .. ... ... ... ... ... ... ... 182 43.35 3.50 Male Yes Sun Dinner 3 156 46.17 5.00 Male No Sun Dinner 6 59 46.27 6.73 Male No Sat Dinner 4 212 46.33 9.00 Male No Sat Dinner 4 170 48.81 10.00 Male Yes Sat Dinner 3 [244 rows x 7 columns] String processing# Finding length of string# Stata determines the length of a character string with the strlen() and ustrlen() functions for ASCII and Unicode strings, respectively. generate strlen_time = strlen(time) generate ustrlen_time = ustrlen(time) You can find the length of a character string with Series.str.len(). In Python 3, all strings are Unicode strings. len includes trailing blanks. Use len and rstrip to exclude trailing blanks. In [32]: tips["time"].str.len() Out[32]: 67 6 92 6 111 6 145 5 135 5 .. 182 6 156 6 59 6 212 6 170 6 Name: time, Length: 244, dtype: int64 In [33]: tips["time"].str.rstrip().str.len() Out[33]: 67 6 92 6 111 6 145 5 135 5 .. 182 6 156 6 59 6 212 6 170 6 Name: time, Length: 244, dtype: int64 Finding position of substring# Stata determines the position of a character in a string with the strpos() function. This takes the string defined by the first argument and searches for the first position of the substring you supply as the second argument. generate str_position = strpos(sex, "ale") You can find the position of a character in a column of strings with the Series.str.find() method. find searches for the first position of the substring. If the substring is found, the method returns its position. If not found, it returns -1. Keep in mind that Python indexes are zero-based. In [34]: tips["sex"].str.find("ale") Out[34]: 67 3 92 3 111 3 145 3 135 3 .. 182 1 156 1 59 1 212 1 170 1 Name: sex, Length: 244, dtype: int64 Extracting substring by position# Stata extracts a substring from a string based on its position with the substr() function. generate short_sex = substr(sex, 1, 1) With pandas you can use [] notation to extract a substring from a string by position locations. Keep in mind that Python indexes are zero-based. In [35]: tips["sex"].str[0:1] Out[35]: 67 F 92 F 111 F 145 F 135 F .. 182 M 156 M 59 M 212 M 170 M Name: sex, Length: 244, dtype: object Extracting nth word# The Stata word() function returns the nth word from a string. The first argument is the string you want to parse and the second argument specifies which word you want to extract. clear input str20 string "John Smith" "Jane Cook" end generate first_name = word(name, 1) generate last_name = word(name, -1) The simplest way to extract words in pandas is to split the strings by spaces, then reference the word by index. Note there are more powerful approaches should you need them. In [36]: firstlast = pd.DataFrame({"String": ["John Smith", "Jane Cook"]}) In [37]: firstlast["First_Name"] = firstlast["String"].str.split(" ", expand=True)[0] In [38]: firstlast["Last_Name"] = firstlast["String"].str.rsplit(" ", expand=True)[1] In [39]: firstlast Out[39]: String First_Name Last_Name 0 John Smith John Smith 1 Jane Cook Jane Cook Changing case# The Stata strupper(), strlower(), strproper(), ustrupper(), ustrlower(), and ustrtitle() functions change the case of ASCII and Unicode strings, respectively. clear input str20 string "John Smith" "Jane Cook" end generate upper = strupper(string) generate lower = strlower(string) generate title = strproper(string) list The equivalent pandas methods are Series.str.upper(), Series.str.lower(), and Series.str.title(). In [40]: firstlast = pd.DataFrame({"string": ["John Smith", "Jane Cook"]}) In [41]: firstlast["upper"] = firstlast["string"].str.upper() In [42]: firstlast["lower"] = firstlast["string"].str.lower() In [43]: firstlast["title"] = firstlast["string"].str.title() In [44]: firstlast Out[44]: string upper lower title 0 John Smith JOHN SMITH john smith John Smith 1 Jane Cook JANE COOK jane cook Jane Cook Merging# The following tables will be used in the merge examples: In [45]: df1 = pd.DataFrame({"key": ["A", "B", "C", "D"], "value": np.random.randn(4)}) In [46]: df1 Out[46]: key value 0 A 0.469112 1 B -0.282863 2 C -1.509059 3 D -1.135632 In [47]: df2 = pd.DataFrame({"key": ["B", "D", "D", "E"], "value": np.random.randn(4)}) In [48]: df2 Out[48]: key value 0 B 1.212112 1 D -0.173215 2 D 0.119209 3 E -1.044236 In Stata, to perform a merge, one data set must be in memory and the other must be referenced as a file name on disk. In contrast, Python must have both DataFrames already in memory. By default, Stata performs an outer join, where all observations from both data sets are left in memory after the merge. One can keep only observations from the initial data set, the merged data set, or the intersection of the two by using the values created in the _merge variable. * First create df2 and save to disk clear input str1 key B D D E end generate value = rnormal() save df2.dta * Now create df1 in memory clear input str1 key A B C D end generate value = rnormal() preserve * Left join merge 1:n key using df2.dta keep if _merge == 1 * Right join restore, preserve merge 1:n key using df2.dta keep if _merge == 2 * Inner join restore, preserve merge 1:n key using df2.dta keep if _merge == 3 * Outer join restore merge 1:n key using df2.dta pandas DataFrames have a merge() method, which provides similar functionality. The data does not have to be sorted ahead of time, and different join types are accomplished via the how keyword. In [49]: inner_join = df1.merge(df2, on=["key"], how="inner") In [50]: inner_join Out[50]: key value_x value_y 0 B -0.282863 1.212112 1 D -1.135632 -0.173215 2 D -1.135632 0.119209 In [51]: left_join = df1.merge(df2, on=["key"], how="left") In [52]: left_join Out[52]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 In [53]: right_join = df1.merge(df2, on=["key"], how="right") In [54]: right_join Out[54]: key value_x value_y 0 B -0.282863 1.212112 1 D -1.135632 -0.173215 2 D -1.135632 0.119209 3 E NaN -1.044236 In [55]: outer_join = df1.merge(df2, on=["key"], how="outer") In [56]: outer_join Out[56]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 5 E NaN -1.044236 Missing data# Both pandas and Stata have a representation for missing data. pandas represents missing data with the special float value NaN (not a number). Many of the semantics are the same; for example missing data propagates through numeric operations, and is ignored by default for aggregations. In [57]: outer_join Out[57]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 5 E NaN -1.044236 In [58]: outer_join["value_x"] + outer_join["value_y"] Out[58]: 0 NaN 1 0.929249 2 NaN 3 -1.308847 4 -1.016424 5 NaN dtype: float64 In [59]: outer_join["value_x"].sum() Out[59]: -3.5940742896293765 One difference is that missing data cannot be compared to its sentinel value. For example, in Stata you could do this to filter missing values. * Keep missing values list if value_x == . * Keep non-missing values list if value_x != . In pandas, Series.isna() and Series.notna() can be used to filter the rows. In [60]: outer_join[outer_join["value_x"].isna()] Out[60]: key value_x value_y 5 E NaN -1.044236 In [61]: outer_join[outer_join["value_x"].notna()] Out[61]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 NaN 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 pandas provides a variety of methods to work with missing data. Here are some examples: Drop rows with missing values# In [62]: outer_join.dropna() Out[62]: key value_x value_y 1 B -0.282863 1.212112 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 Forward fill from previous rows# In [63]: outer_join.fillna(method="ffill") Out[63]: key value_x value_y 0 A 0.469112 NaN 1 B -0.282863 1.212112 2 C -1.509059 1.212112 3 D -1.135632 -0.173215 4 D -1.135632 0.119209 5 E -1.135632 -1.044236 Replace missing values with a specified value# Using the mean: In [64]: outer_join["value_x"].fillna(outer_join["value_x"].mean()) Out[64]: 0 0.469112 1 -0.282863 2 -1.509059 3 -1.135632 4 -1.135632 5 -0.718815 Name: value_x, dtype: float64 GroupBy# Aggregation# Stata’s collapse can be used to group by one or more key variables and compute aggregations on numeric columns. collapse (sum) total_bill tip, by(sex smoker) pandas provides a flexible groupby mechanism that allows similar aggregations. See the groupby documentation for more details and examples. In [65]: tips_summed = tips.groupby(["sex", "smoker"])[["total_bill", "tip"]].sum() In [66]: tips_summed Out[66]: total_bill tip sex smoker Female No 869.68 149.77 Yes 527.27 96.74 Male No 1725.75 302.00 Yes 1217.07 183.07 Transformation# In Stata, if the group aggregations need to be used with the original data set, one would usually use bysort with egen(). For example, to subtract the mean for each observation by smoker group. bysort sex smoker: egen group_bill = mean(total_bill) generate adj_total_bill = total_bill - group_bill pandas provides a Transformation mechanism that allows these type of operations to be succinctly expressed in one operation. In [67]: gb = tips.groupby("smoker")["total_bill"] In [68]: tips["adj_total_bill"] = tips["total_bill"] - gb.transform("mean") In [69]: tips Out[69]: total_bill tip sex smoker day time size adj_total_bill 67 1.07 1.00 Female Yes Sat Dinner 1 -17.686344 92 3.75 1.00 Female Yes Fri Dinner 2 -15.006344 111 5.25 1.00 Female No Sat Dinner 1 -11.938278 145 6.35 1.50 Female No Thur Lunch 2 -10.838278 135 6.51 1.25 Female No Thur Lunch 2 -10.678278 .. ... ... ... ... ... ... ... ... 182 43.35 3.50 Male Yes Sun Dinner 3 24.593656 156 46.17 5.00 Male No Sun Dinner 6 28.981722 59 46.27 6.73 Male No Sat Dinner 4 29.081722 212 46.33 9.00 Male No Sat Dinner 4 29.141722 170 48.81 10.00 Male Yes Sat Dinner 3 30.053656 [244 rows x 8 columns] By group processing# In addition to aggregation, pandas groupby can be used to replicate most other bysort processing from Stata. For example, the following example lists the first observation in the current sort order by sex/smoker group. bysort sex smoker: list if _n == 1 In pandas this would be written as: In [70]: tips.groupby(["sex", "smoker"]).first() Out[70]: total_bill tip day time size adj_total_bill sex smoker Female No 5.25 1.00 Sat Dinner 1 -11.938278 Yes 1.07 1.00 Sat Dinner 1 -17.686344 Male No 5.51 2.00 Thur Lunch 2 -11.678278 Yes 5.25 5.15 Sun Dinner 2 -13.506344 Other considerations# Disk vs memory# pandas and Stata both operate exclusively in memory. This means that the size of data able to be loaded in pandas is limited by your machine’s memory. If out of core processing is needed, one possibility is the dask.dataframe library, which provides a subset of pandas functionality for an on-disk DataFrame.
getting_started/comparison/comparison_with_stata.html
pandas.melt
`pandas.melt` Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’. ``` >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'}, ... 'B': {0: 1, 1: 3, 2: 5}, ... 'C': {0: 2, 1: 4, 2: 6}}) >>> df A B C 0 a 1 2 1 b 3 4 2 c 5 6 ```
pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)[source]# Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’. Parameters id_varstuple, list, or ndarray, optionalColumn(s) to use as identifier variables. value_varstuple, list, or ndarray, optionalColumn(s) to unpivot. If not specified, uses all columns that are not set as id_vars. var_namescalarName to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’. value_namescalar, default ‘value’Name to use for the ‘value’ column. col_levelint or str, optionalIf columns are a MultiIndex then use this level to melt. ignore_indexbool, default TrueIf True, original index is ignored. If False, the original index is retained. Index labels will be repeated as necessary. New in version 1.1.0. Returns DataFrameUnpivoted DataFrame. See also DataFrame.meltIdentical method. pivot_tableCreate a spreadsheet-style pivot table as a DataFrame. DataFrame.pivotReturn reshaped DataFrame organized by given index / column values. DataFrame.explodeExplode a DataFrame from list-like columns to long format. Notes Reference the user guide for more examples. Examples >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'}, ... 'B': {0: 1, 1: 3, 2: 5}, ... 'C': {0: 2, 1: 4, 2: 6}}) >>> df A B C 0 a 1 2 1 b 3 4 2 c 5 6 >>> pd.melt(df, id_vars=['A'], value_vars=['B']) A variable value 0 a B 1 1 b B 3 2 c B 5 >>> pd.melt(df, id_vars=['A'], value_vars=['B', 'C']) A variable value 0 a B 1 1 b B 3 2 c B 5 3 a C 2 4 b C 4 5 c C 6 The names of ‘variable’ and ‘value’ columns can be customized: >>> pd.melt(df, id_vars=['A'], value_vars=['B'], ... var_name='myVarname', value_name='myValname') A myVarname myValname 0 a B 1 1 b B 3 2 c B 5 Original index values can be kept around: >>> pd.melt(df, id_vars=['A'], value_vars=['B', 'C'], ignore_index=False) A variable value 0 a B 1 1 b B 3 2 c B 5 0 a C 2 1 b C 4 2 c C 6 If you have multi-index columns: >>> df.columns = [list('ABC'), list('DEF')] >>> df A B C D E F 0 a 1 2 1 b 3 4 2 c 5 6 >>> pd.melt(df, col_level=0, id_vars=['A'], value_vars=['B']) A variable value 0 a B 1 1 b B 3 2 c B 5 >>> pd.melt(df, id_vars=[('A', 'D')], value_vars=[('B', 'E')]) (A, D) variable_0 variable_1 value 0 a B E 1 1 b B E 3 2 c B E 5
reference/api/pandas.melt.html
pandas.Index.is_
`pandas.Index.is_` More flexible, faster check like is but that works through views.
final Index.is_(other)[source]# More flexible, faster check like is but that works through views. Note: this is not the same as Index.identical(), which checks that metadata is also the same. Parameters otherobjectOther object to compare against. Returns boolTrue if both have same underlying data, False otherwise. See also Index.identicalWorks like Index.is_ but also checks metadata.
reference/api/pandas.Index.is_.html
pandas.tseries.offsets.CustomBusinessMonthBegin.calendar
pandas.tseries.offsets.CustomBusinessMonthBegin.calendar
CustomBusinessMonthBegin.calendar#
reference/api/pandas.tseries.offsets.CustomBusinessMonthBegin.calendar.html
pandas.tseries.offsets.Easter.__call__
`pandas.tseries.offsets.Easter.__call__` Call self as a function.
Easter.__call__(*args, **kwargs)# Call self as a function.
reference/api/pandas.tseries.offsets.Easter.__call__.html
pandas.Series.dt.freq
pandas.Series.dt.freq
Series.dt.freq[source]#
reference/api/pandas.Series.dt.freq.html
pandas.core.window.rolling.Rolling.var
`pandas.core.window.rolling.Rolling.var` Calculate the rolling variance. ``` >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5]) >>> s.rolling(3).var() 0 NaN 1 NaN 2 0.333333 3 1.000000 4 1.000000 5 1.333333 6 0.000000 dtype: float64 ```
Rolling.var(ddof=1, numeric_only=False, *args, engine=None, engine_kwargs=None, **kwargs)[source]# Calculate the rolling variance. Parameters ddofint, default 1Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. numeric_onlybool, default FalseInclude only float, int, boolean columns. New in version 1.5.0. *argsFor NumPy compatibility and will not have an effect on the result. Deprecated since version 1.5.0. enginestr, default None 'cython' : Runs the operation through C-extensions from cython. 'numba' : Runs the operation through JIT compiled code from numba. None : Defaults to 'cython' or globally setting compute.use_numba New in version 1.4.0. engine_kwargsdict, default None For 'cython' engine, there are no accepted engine_kwargs For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {'nopython': True, 'nogil': False, 'parallel': False} New in version 1.4.0. **kwargsFor NumPy compatibility and will not have an effect on the result. Deprecated since version 1.5.0. Returns Series or DataFrameReturn type is the same as the original object with np.float64 dtype. See also numpy.varEquivalent method for NumPy array. pandas.Series.rollingCalling rolling with Series data. pandas.DataFrame.rollingCalling rolling with DataFrames. pandas.Series.varAggregating var for Series. pandas.DataFrame.varAggregating var for DataFrame. Notes The default ddof of 1 used in Series.var() is different than the default ddof of 0 in numpy.var(). A minimum of one period is required for the rolling calculation. Examples >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5]) >>> s.rolling(3).var() 0 NaN 1 NaN 2 0.333333 3 1.000000 4 1.000000 5 1.333333 6 0.000000 dtype: float64
reference/api/pandas.core.window.rolling.Rolling.var.html
pandas.tseries.offsets.QuarterEnd.n
pandas.tseries.offsets.QuarterEnd.n
QuarterEnd.n#
reference/api/pandas.tseries.offsets.QuarterEnd.n.html
pandas.tseries.offsets.BusinessMonthEnd.is_quarter_start
`pandas.tseries.offsets.BusinessMonthEnd.is_quarter_start` Return boolean whether a timestamp occurs on the quarter start. ``` >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_quarter_start(ts) True ```
BusinessMonthEnd.is_quarter_start()# Return boolean whether a timestamp occurs on the quarter start. Examples >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_quarter_start(ts) True
reference/api/pandas.tseries.offsets.BusinessMonthEnd.is_quarter_start.html
pandas.tseries.offsets.Day.rule_code
pandas.tseries.offsets.Day.rule_code
Day.rule_code#
reference/api/pandas.tseries.offsets.Day.rule_code.html
pandas.tseries.offsets.CustomBusinessHour.rollforward
`pandas.tseries.offsets.CustomBusinessHour.rollforward` Roll provided date forward to next offset only if not on offset.
CustomBusinessHour.rollforward(other)# Roll provided date forward to next offset only if not on offset.
reference/api/pandas.tseries.offsets.CustomBusinessHour.rollforward.html
pandas.Timestamp.fromordinal
`pandas.Timestamp.fromordinal` Construct a timestamp from a a proleptic Gregorian ordinal. Date corresponding to a proleptic Gregorian ordinal. ``` >>> pd.Timestamp.fromordinal(737425) Timestamp('2020-01-01 00:00:00') ```
classmethod Timestamp.fromordinal(ordinal, freq=None, tz=None)# Construct a timestamp from a a proleptic Gregorian ordinal. Parameters ordinalintDate corresponding to a proleptic Gregorian ordinal. freqstr, DateOffsetOffset to apply to the Timestamp. tzstr, pytz.timezone, dateutil.tz.tzfile or NoneTime zone for the Timestamp. Notes By definition there cannot be any tz info on the ordinal itself. Examples >>> pd.Timestamp.fromordinal(737425) Timestamp('2020-01-01 00:00:00')
reference/api/pandas.Timestamp.fromordinal.html
pandas.tseries.offsets.SemiMonthBegin.is_month_end
`pandas.tseries.offsets.SemiMonthBegin.is_month_end` Return boolean whether a timestamp occurs on the month end. Examples ``` >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_month_end(ts) False ```
SemiMonthBegin.is_month_end()# Return boolean whether a timestamp occurs on the month end. Examples >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_month_end(ts) False
reference/api/pandas.tseries.offsets.SemiMonthBegin.is_month_end.html
pandas.DataFrame.sparse.to_dense
`pandas.DataFrame.sparse.to_dense` Convert a DataFrame with sparse values to dense. ``` >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])}) >>> df.sparse.to_dense() A 0 0 1 1 2 0 ```
DataFrame.sparse.to_dense()[source]# Convert a DataFrame with sparse values to dense. New in version 0.25.0. Returns DataFrameA DataFrame with the same values stored as dense arrays. Examples >>> df = pd.DataFrame({"A": pd.arrays.SparseArray([0, 1, 0])}) >>> df.sparse.to_dense() A 0 0 1 1 2 0
reference/api/pandas.DataFrame.sparse.to_dense.html
pandas.ExcelWriter.if_sheet_exists
`pandas.ExcelWriter.if_sheet_exists` How to behave when writing to a sheet that already exists in append mode.
property ExcelWriter.if_sheet_exists[source]# How to behave when writing to a sheet that already exists in append mode.
reference/api/pandas.ExcelWriter.if_sheet_exists.html
pandas.tseries.offsets.SemiMonthEnd.rule_code
pandas.tseries.offsets.SemiMonthEnd.rule_code
SemiMonthEnd.rule_code#
reference/api/pandas.tseries.offsets.SemiMonthEnd.rule_code.html
pandas maintenance
pandas maintenance This guide is for pandas’ maintainers. It may also be interesting to contributors looking to understand the pandas development process and what steps are necessary to become a maintainer. The main contributing guide is available at Contributing to pandas. pandas uses two levels of permissions: triage and core team members. Triage members can label and close issues and pull requests. Core team members can label and close issues and pull request, and can merge pull requests.
This guide is for pandas’ maintainers. It may also be interesting to contributors looking to understand the pandas development process and what steps are necessary to become a maintainer. The main contributing guide is available at Contributing to pandas. Roles# pandas uses two levels of permissions: triage and core team members. Triage members can label and close issues and pull requests. Core team members can label and close issues and pull request, and can merge pull requests. GitHub publishes the full list of permissions. Tasks# pandas is largely a volunteer project, so these tasks shouldn’t be read as “expectations” of triage and maintainers. Rather, they’re general descriptions of what it means to be a maintainer. Triage newly filed issues (see Issue triage) Review newly opened pull requests Respond to updates on existing issues and pull requests Drive discussion and decisions on stalled issues and pull requests Provide experience / wisdom on API design questions to ensure consistency and maintainability Project organization (run / attend developer meetings, represent pandas) https://matthewrocklin.com/blog/2019/05/18/maintainer may be interesting background reading. Issue triage# Here’s a typical workflow for triaging a newly opened issue. Thank the reporter for opening an issue The issue tracker is many people’s first interaction with the pandas project itself, beyond just using the library. As such, we want it to be a welcoming, pleasant experience. Is the necessary information provided? Ideally reporters would fill out the issue template, but many don’t. If crucial information (like the version of pandas they used), is missing feel free to ask for that and label the issue with “Needs info”. The report should follow the guidelines in Bug reports and enhancement requests. You may want to link to that if they didn’t follow the template. Make sure that the title accurately reflects the issue. Edit it yourself if it’s not clear. Is this a duplicate issue? We have many open issues. If a new issue is clearly a duplicate, label the new issue as “Duplicate” assign the milestone “No Action”, and close the issue with a link to the original issue. Make sure to still thank the reporter, and encourage them to chime in on the original issue, and perhaps try to fix it. If the new issue provides relevant information, such as a better or slightly different example, add it to the original issue as a comment or an edit to the original post. Is the issue minimal and reproducible? For bug reports, we ask that the reporter provide a minimal reproducible example. See https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports for a good explanation. If the example is not reproducible, or if it’s clearly not minimal, feel free to ask the reporter if they can provide and example or simplify the provided one. Do acknowledge that writing minimal reproducible examples is hard work. If the reporter is struggling, you can try to write one yourself and we’ll edit the original post to include it. If a reproducible example can’t be provided, add the “Needs info” label. If a reproducible example is provided, but you see a simplification, edit the original post with your simpler reproducible example. Is this a clearly defined feature request? Generally, pandas prefers to discuss and design new features in issues, before a pull request is made. Encourage the submitter to include a proposed API for the new feature. Having them write a full docstring is a good way to pin down specifics. We’ll need a discussion from several pandas maintainers before deciding whether the proposal is in scope for pandas. Is this a usage question? We prefer that usage questions are asked on StackOverflow with the pandas tag. https://stackoverflow.com/questions/tagged/pandas If it’s easy to answer, feel free to link to the relevant documentation section, let them know that in the future this kind of question should be on StackOverflow, and close the issue. What labels and milestones should I add? Apply the relevant labels. This is a bit of an art, and comes with experience. Look at similar issues to get a feel for how things are labeled. If the issue is clearly defined and the fix seems relatively straightforward, label the issue as “Good first issue”. Typically, new issues will be assigned the “Contributions welcome” milestone, unless it’s know that this issue should be addressed in a specific release (say because it’s a large regression). Closing issues# Be delicate here: many people interpret closing an issue as us saying that the conversation is over. It’s typically best to give the reporter some time to respond or self-close their issue if it’s determined that the behavior is not a bug, or the feature is out of scope. Sometimes reporters just go away though, and we’ll close the issue after the conversation has died. Reviewing pull requests# Anybody can review a pull request: regular contributors, triagers, or core-team members. But only core-team members can merge pull requests when they’re ready. Here are some things to check when reviewing a pull request. Tests should be in a sensible location: in the same file as closely related tests. New public APIs should be included somewhere in doc/source/reference/. New / changed API should use the versionadded or versionchanged directives in the docstring. User-facing changes should have a whatsnew in the appropriate file. Regression tests should reference the original GitHub issue number like # GH-1234. The pull request should be labeled and assigned the appropriate milestone (the next patch release for regression fixes and small bug fixes, the next minor milestone otherwise) Changes should comply with our Version policy. Backporting# pandas supports point releases (e.g. 1.4.3) that aim to: Fix bugs in new features introduced in the first minor version release. e.g. If a new feature was added in 1.4 and contains a bug, a fix can be applied in 1.4.3 Fix bugs that used to work in a few minor releases prior. There should be agreement between core team members that a backport is appropriate. e.g. If a feature worked in 1.2 and stopped working since 1.3, a fix can be applied in 1.4.3. Since pandas minor releases are based on Github branches (e.g. point release of 1.4 are based off the 1.4.x branch), “backporting” means merging a pull request fix to the main branch and correct minor branch associated with the next point release. By default, if a pull request is assigned to the next point release milestone within the Github interface, the backporting process should happen automatically by the @meeseeksdev bot once the pull request is merged. A new pull request will be made backporting the pull request to the correct version branch. Sometimes due to merge conflicts, a manual pull request will need to be made addressing the code conflict. If the bot does not automatically start the backporting process, you can also write a Github comment in the merged pull request to trigger the backport: @meeseeksdev backport version-branch This will trigger a workflow which will backport a given change to a branch (e.g. @meeseeksdev backport 1.4.x) Cleaning up old issues# Every open issue in pandas has a cost. Open issues make finding duplicates harder, and can make it harder to know what needs to be done in pandas. That said, closing issues isn’t a goal on its own. Our goal is to make pandas the best it can be, and that’s best done by ensuring that the quality of our open issues is high. Occasionally, bugs are fixed but the issue isn’t linked to in the Pull Request. In these cases, comment that “This has been fixed, but could use a test.” and label the issue as “Good First Issue” and “Needs Test”. If an older issue doesn’t follow our issue template, edit the original post to include a minimal example, the actual output, and the expected output. Uniformity in issue reports is valuable. If an older issue lacks a reproducible example, label it as “Needs Info” and ask them to provide one (or write one yourself if possible). If one isn’t provide reasonably soon, close it according to the policies in Closing issues. Cleaning up old pull requests# Occasionally, contributors are unable to finish off a pull request. If some time has passed (two weeks, say) since the last review requesting changes, gently ask if they’re still interested in working on this. If another two weeks or so passes with no response, thank them for their work and close the pull request. Comment on the original issue that “There’s a stalled PR at #1234 that may be helpful.”, and perhaps label the issue as “Good first issue” if the PR was relatively close to being accepted. Additionally, core-team members can push to contributors branches. This can be helpful for pushing an important PR across the line, or for fixing a small merge conflict. Becoming a pandas maintainer# The full process is outlined in our governance documents. In summary, we’re happy to give triage permissions to anyone who shows interest by being helpful on the issue tracker. The required steps for adding a maintainer are: Contact the contributor and ask their interest to join. Add the contributor to the appropriate Github Team if accepted the invitation. pandas-core is for core team members pandas-triage is for pandas triage members Add the contributor to the pandas Google group. Create a pull request to add the contributor’s Github handle to pandas-dev/pandas/web/pandas/config.yml. Create a pull request to add the contributor’s name/Github handle to the governance document. The current list of core-team members is at https://github.com/pandas-dev/pandas-governance/blob/master/people.md Merging pull requests# Only core team members can merge pull requests. We have a few guidelines. You should typically not self-merge your own pull requests. Exceptions include things like small changes to fix CI (e.g. pinning a package version). You should not merge pull requests that have an active discussion, or pull requests that has any -1 votes from a core maintainer. pandas operates by consensus. For larger changes, it’s good to have a +1 from at least two core team members. In addition to the items listed in Closing issues, you should verify that the pull request is assigned the correct milestone. Pull requests merged with a patch-release milestone will typically be backported by our bot. Verify that the bot noticed the merge (it will leave a comment within a minute typically). If a manual backport is needed please do that, and remove the “Needs backport” label once you’ve done it manually. If you forget to assign a milestone before tagging, you can request the bot to backport it with: @Meeseeksdev backport <branch> Benchmark machine# The team currently owns dedicated hardware for hosting a website for pandas’ ASV performance benchmark. The results are published to http://pandas.pydata.org/speed/pandas/ Configuration# The machine can be configured with the Ansible playbook in https://github.com/tomaugspurger/asv-runner. Publishing# The results are published to another Github repository, https://github.com/tomaugspurger/asv-collection. Finally, we have a cron job on our docs server to pull from https://github.com/tomaugspurger/asv-collection, to serve them from /speed. Ask Tom or Joris for access to the webserver. Debugging# The benchmarks are scheduled by Airflow. It has a dashboard for viewing and debugging the results. You’ll need to setup an SSH tunnel to view them ssh -L 8080:localhost:8080 pandas@panda.likescandy.com Release process# The process for releasing a new version of pandas can be found at https://github.com/pandas-dev/pandas-release
development/maintaining.html
Extending pandas
Extending pandas While pandas provides a rich set of methods, containers, and data types, your needs may not be fully satisfied. pandas offers a few options for extending pandas. Libraries can use the decorators pandas.api.extensions.register_dataframe_accessor(), pandas.api.extensions.register_series_accessor(), and pandas.api.extensions.register_index_accessor(), to add additional “namespaces” to pandas objects. All of these follow a similar convention: you decorate a class, providing the name of attribute to add. The class’s __init__ method gets the object being decorated. For example: Now users can access your methods using the geo namespace: This can be a convenient way to extend pandas objects without subclassing them. If you write a custom accessor, make a pull request adding it to our pandas ecosystem page. We highly recommend validating the data in your accessor’s __init__. In our GeoAccessor, we validate that the data contains the expected columns, raising an AttributeError when the validation fails. For a Series accessor, you should validate the dtype if the accessor applies only to certain dtypes.
While pandas provides a rich set of methods, containers, and data types, your needs may not be fully satisfied. pandas offers a few options for extending pandas. Registering custom accessors# Libraries can use the decorators pandas.api.extensions.register_dataframe_accessor(), pandas.api.extensions.register_series_accessor(), and pandas.api.extensions.register_index_accessor(), to add additional “namespaces” to pandas objects. All of these follow a similar convention: you decorate a class, providing the name of attribute to add. The class’s __init__ method gets the object being decorated. For example: @pd.api.extensions.register_dataframe_accessor("geo") class GeoAccessor: def __init__(self, pandas_obj): self._validate(pandas_obj) self._obj = pandas_obj @staticmethod def _validate(obj): # verify there is a column latitude and a column longitude if "latitude" not in obj.columns or "longitude" not in obj.columns: raise AttributeError("Must have 'latitude' and 'longitude'.") @property def center(self): # return the geographic center point of this DataFrame lat = self._obj.latitude lon = self._obj.longitude return (float(lon.mean()), float(lat.mean())) def plot(self): # plot this array's data on a map, e.g., using Cartopy pass Now users can access your methods using the geo namespace: >>> ds = pd.DataFrame( ... {"longitude": np.linspace(0, 10), "latitude": np.linspace(0, 20)} ... ) >>> ds.geo.center (5.0, 10.0) >>> ds.geo.plot() # plots data on a map This can be a convenient way to extend pandas objects without subclassing them. If you write a custom accessor, make a pull request adding it to our pandas ecosystem page. We highly recommend validating the data in your accessor’s __init__. In our GeoAccessor, we validate that the data contains the expected columns, raising an AttributeError when the validation fails. For a Series accessor, you should validate the dtype if the accessor applies only to certain dtypes. Extension types# Note The pandas.api.extensions.ExtensionDtype and pandas.api.extensions.ExtensionArray APIs were experimental prior to pandas 1.5. Starting with version 1.5, future changes will follow the pandas deprecation policy. pandas defines an interface for implementing data types and arrays that extend NumPy’s type system. pandas itself uses the extension system for some types that aren’t built into NumPy (categorical, period, interval, datetime with timezone). Libraries can define a custom array and data type. When pandas encounters these objects, they will be handled properly (i.e. not converted to an ndarray of objects). Many methods like pandas.isna() will dispatch to the extension type’s implementation. If you’re building a library that implements the interface, please publicize it on Extension data types. The interface consists of two classes. ExtensionDtype# A pandas.api.extensions.ExtensionDtype is similar to a numpy.dtype object. It describes the data type. Implementors are responsible for a few unique items like the name. One particularly important item is the type property. This should be the class that is the scalar type for your data. For example, if you were writing an extension array for IP Address data, this might be ipaddress.IPv4Address. See the extension dtype source for interface definition. pandas.api.extensions.ExtensionDtype can be registered to pandas to allow creation via a string dtype name. This allows one to instantiate Series and .astype() with a registered string name, for example 'category' is a registered string accessor for the CategoricalDtype. See the extension dtype dtypes for more on how to register dtypes. ExtensionArray# This class provides all the array-like functionality. ExtensionArrays are limited to 1 dimension. An ExtensionArray is linked to an ExtensionDtype via the dtype attribute. pandas makes no restrictions on how an extension array is created via its __new__ or __init__, and puts no restrictions on how you store your data. We do require that your array be convertible to a NumPy array, even if this is relatively expensive (as it is for Categorical). They may be backed by none, one, or many NumPy arrays. For example, pandas.Categorical is an extension array backed by two arrays, one for codes and one for categories. An array of IPv6 addresses may be backed by a NumPy structured array with two fields, one for the lower 64 bits and one for the upper 64 bits. Or they may be backed by some other storage type, like Python lists. See the extension array source for the interface definition. The docstrings and comments contain guidance for properly implementing the interface. ExtensionArray operator support# By default, there are no operators defined for the class ExtensionArray. There are two approaches for providing operator support for your ExtensionArray: Define each of the operators on your ExtensionArray subclass. Use an operator implementation from pandas that depends on operators that are already defined on the underlying elements (scalars) of the ExtensionArray. Note Regardless of the approach, you may want to set __array_priority__ if you want your implementation to be called when involved in binary operations with NumPy arrays. For the first approach, you define selected operators, e.g., __add__, __le__, etc. that you want your ExtensionArray subclass to support. The second approach assumes that the underlying elements (i.e., scalar type) of the ExtensionArray have the individual operators already defined. In other words, if your ExtensionArray named MyExtensionArray is implemented so that each element is an instance of the class MyExtensionElement, then if the operators are defined for MyExtensionElement, the second approach will automatically define the operators for MyExtensionArray. A mixin class, ExtensionScalarOpsMixin supports this second approach. If developing an ExtensionArray subclass, for example MyExtensionArray, can simply include ExtensionScalarOpsMixin as a parent class of MyExtensionArray, and then call the methods _add_arithmetic_ops() and/or _add_comparison_ops() to hook the operators into your MyExtensionArray class, as follows: from pandas.api.extensions import ExtensionArray, ExtensionScalarOpsMixin class MyExtensionArray(ExtensionArray, ExtensionScalarOpsMixin): pass MyExtensionArray._add_arithmetic_ops() MyExtensionArray._add_comparison_ops() Note Since pandas automatically calls the underlying operator on each element one-by-one, this might not be as performant as implementing your own version of the associated operators directly on the ExtensionArray. For arithmetic operations, this implementation will try to reconstruct a new ExtensionArray with the result of the element-wise operation. Whether or not that succeeds depends on whether the operation returns a result that’s valid for the ExtensionArray. If an ExtensionArray cannot be reconstructed, an ndarray containing the scalars returned instead. For ease of implementation and consistency with operations between pandas and NumPy ndarrays, we recommend not handling Series and Indexes in your binary ops. Instead, you should detect these cases and return NotImplemented. When pandas encounters an operation like op(Series, ExtensionArray), pandas will unbox the array from the Series (Series.array) call result = op(values, ExtensionArray) re-box the result in a Series NumPy universal functions# Series implements __array_ufunc__. As part of the implementation, pandas unboxes the ExtensionArray from the Series, applies the ufunc, and re-boxes it if necessary. If applicable, we highly recommend that you implement __array_ufunc__ in your extension array to avoid coercion to an ndarray. See the NumPy documentation for an example. As part of your implementation, we require that you defer to pandas when a pandas container (Series, DataFrame, Index) is detected in inputs. If any of those is present, you should return NotImplemented. pandas will take care of unboxing the array from the container and re-calling the ufunc with the unwrapped input. Testing extension arrays# We provide a test suite for ensuring that your extension arrays satisfy the expected behavior. To use the test suite, you must provide several pytest fixtures and inherit from the base test class. The required fixtures are found in https://github.com/pandas-dev/pandas/blob/main/pandas/tests/extension/conftest.py. To use a test, subclass it: from pandas.tests.extension import base class TestConstructors(base.BaseConstructorsTests): pass See https://github.com/pandas-dev/pandas/blob/main/pandas/tests/extension/base/__init__.py for a list of all the tests available. Compatibility with Apache Arrow# An ExtensionArray can support conversion to / from pyarrow arrays (and thus support for example serialization to the Parquet file format) by implementing two methods: ExtensionArray.__arrow_array__ and ExtensionDtype.__from_arrow__. The ExtensionArray.__arrow_array__ ensures that pyarrow knowns how to convert the specific extension array into a pyarrow.Array (also when included as a column in a pandas DataFrame): class MyExtensionArray(ExtensionArray): ... def __arrow_array__(self, type=None): # convert the underlying array values to a pyarrow Array import pyarrow return pyarrow.array(..., type=type) The ExtensionDtype.__from_arrow__ method then controls the conversion back from pyarrow to a pandas ExtensionArray. This method receives a pyarrow Array or ChunkedArray as only argument and is expected to return the appropriate pandas ExtensionArray for this dtype and the passed values: class ExtensionDtype: ... def __from_arrow__(self, array: pyarrow.Array/ChunkedArray) -> ExtensionArray: ... See more in the Arrow documentation. Those methods have been implemented for the nullable integer and string extension dtypes included in pandas, and ensure roundtrip to pyarrow and the Parquet file format. Subclassing pandas data structures# Warning There are some easier alternatives before considering subclassing pandas data structures. Extensible method chains with pipe Use composition. See here. Extending by registering an accessor Extending by extension type This section describes how to subclass pandas data structures to meet more specific needs. There are two points that need attention: Override constructor properties. Define original properties Note You can find a nice example in geopandas project. Override constructor properties# Each data structure has several constructor properties for returning a new data structure as the result of an operation. By overriding these properties, you can retain subclasses through pandas data manipulations. There are 3 possible constructor properties to be defined on a subclass: DataFrame/Series._constructor: Used when a manipulation result has the same dimension as the original. DataFrame._constructor_sliced: Used when a DataFrame (sub-)class manipulation result should be a Series (sub-)class. Series._constructor_expanddim: Used when a Series (sub-)class manipulation result should be a DataFrame (sub-)class, e.g. Series.to_frame(). Below example shows how to define SubclassedSeries and SubclassedDataFrame overriding constructor properties. class SubclassedSeries(pd.Series): @property def _constructor(self): return SubclassedSeries @property def _constructor_expanddim(self): return SubclassedDataFrame class SubclassedDataFrame(pd.DataFrame): @property def _constructor(self): return SubclassedDataFrame @property def _constructor_sliced(self): return SubclassedSeries >>> s = SubclassedSeries([1, 2, 3]) >>> type(s) <class '__main__.SubclassedSeries'> >>> to_framed = s.to_frame() >>> type(to_framed) <class '__main__.SubclassedDataFrame'> >>> df = SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) >>> df A B C 0 1 4 7 1 2 5 8 2 3 6 9 >>> type(df) <class '__main__.SubclassedDataFrame'> >>> sliced1 = df[["A", "B"]] >>> sliced1 A B 0 1 4 1 2 5 2 3 6 >>> type(sliced1) <class '__main__.SubclassedDataFrame'> >>> sliced2 = df["A"] >>> sliced2 0 1 1 2 2 3 Name: A, dtype: int64 >>> type(sliced2) <class '__main__.SubclassedSeries'> Define original properties# To let original data structures have additional properties, you should let pandas know what properties are added. pandas maps unknown properties to data names overriding __getattribute__. Defining original properties can be done in one of 2 ways: Define _internal_names and _internal_names_set for temporary properties which WILL NOT be passed to manipulation results. Define _metadata for normal properties which will be passed to manipulation results. Below is an example to define two original properties, “internal_cache” as a temporary property and “added_property” as a normal property class SubclassedDataFrame2(pd.DataFrame): # temporary properties _internal_names = pd.DataFrame._internal_names + ["internal_cache"] _internal_names_set = set(_internal_names) # normal properties _metadata = ["added_property"] @property def _constructor(self): return SubclassedDataFrame2 >>> df = SubclassedDataFrame2({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) >>> df A B C 0 1 4 7 1 2 5 8 2 3 6 9 >>> df.internal_cache = "cached" >>> df.added_property = "property" >>> df.internal_cache cached >>> df.added_property property # properties defined in _internal_names is reset after manipulation >>> df[["A", "B"]].internal_cache AttributeError: 'SubclassedDataFrame2' object has no attribute 'internal_cache' # properties defined in _metadata are retained >>> df[["A", "B"]].added_property property Plotting backends# Starting in 0.25 pandas can be extended with third-party plotting backends. The main idea is letting users select a plotting backend different than the provided one based on Matplotlib. For example: >>> pd.set_option("plotting.backend", "backend.module") >>> pd.Series([1, 2, 3]).plot() This would be more or less equivalent to: >>> import backend.module >>> backend.module.plot(pd.Series([1, 2, 3])) The backend module can then use other visualization tools (Bokeh, Altair,…) to generate the plots. Libraries implementing the plotting backend should use entry points to make their backend discoverable to pandas. The key is "pandas_plotting_backends". For example, pandas registers the default “matplotlib” backend as follows. # in setup.py setup( # noqa: F821 ..., entry_points={ "pandas_plotting_backends": [ "matplotlib = pandas:plotting._matplotlib", ], }, ) More information on how to implement a third-party plotting backend can be found at https://github.com/pandas-dev/pandas/blob/main/pandas/plotting/__init__.py#L1.
development/extending.html
pandas.tseries.offsets.BYearEnd.apply
pandas.tseries.offsets.BYearEnd.apply
BYearEnd.apply()#
reference/api/pandas.tseries.offsets.BYearEnd.apply.html
pandas.Series.dt.timetz
`pandas.Series.dt.timetz` Returns numpy array of datetime.time objects with timezones.
Series.dt.timetz[source]# Returns numpy array of datetime.time objects with timezones. The time part of the Timestamps.
reference/api/pandas.Series.dt.timetz.html
pandas.tseries.offsets.Hour.n
pandas.tseries.offsets.Hour.n
Hour.n#
reference/api/pandas.tseries.offsets.Hour.n.html
pandas.Series.iloc
`pandas.Series.iloc` Purely integer-location based indexing for selection by position. .iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. ``` >>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4}, ... {'a': 100, 'b': 200, 'c': 300, 'd': 400}, ... {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 }] >>> df = pd.DataFrame(mydict) >>> df a b c d 0 1 2 3 4 1 100 200 300 400 2 1000 2000 3000 4000 ```
property Series.iloc[source]# Purely integer-location based indexing for selection by position. .iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. Allowed inputs are: An integer, e.g. 5. A list or array of integers, e.g. [4, 3, 0]. A slice object with ints, e.g. 1:7. A boolean array. A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference to the calling object, but would like to base your selection on some value. A tuple of row and column indexes. The tuple elements consist of one of the above inputs, e.g. (0, 1). .iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics). See more at Selection by Position. See also DataFrame.iatFast integer location scalar accessor. DataFrame.locPurely label-location based indexer for selection by label. Series.ilocPurely integer-location based indexing for selection by position. Examples >>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4}, ... {'a': 100, 'b': 200, 'c': 300, 'd': 400}, ... {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000 }] >>> df = pd.DataFrame(mydict) >>> df a b c d 0 1 2 3 4 1 100 200 300 400 2 1000 2000 3000 4000 Indexing just the rows With a scalar integer. >>> type(df.iloc[0]) <class 'pandas.core.series.Series'> >>> df.iloc[0] a 1 b 2 c 3 d 4 Name: 0, dtype: int64 With a list of integers. >>> df.iloc[[0]] a b c d 0 1 2 3 4 >>> type(df.iloc[[0]]) <class 'pandas.core.frame.DataFrame'> >>> df.iloc[[0, 1]] a b c d 0 1 2 3 4 1 100 200 300 400 With a slice object. >>> df.iloc[:3] a b c d 0 1 2 3 4 1 100 200 300 400 2 1000 2000 3000 4000 With a boolean mask the same length as the index. >>> df.iloc[[True, False, True]] a b c d 0 1 2 3 4 2 1000 2000 3000 4000 With a callable, useful in method chains. The x passed to the lambda is the DataFrame being sliced. This selects the rows whose index label even. >>> df.iloc[lambda x: x.index % 2 == 0] a b c d 0 1 2 3 4 2 1000 2000 3000 4000 Indexing both axes You can mix the indexer types for the index and columns. Use : to select the entire axis. With scalar integers. >>> df.iloc[0, 1] 2 With lists of integers. >>> df.iloc[[0, 2], [1, 3]] b d 0 2 4 2 2000 4000 With slice objects. >>> df.iloc[1:3, 0:3] a b c 1 100 200 300 2 1000 2000 3000 With a boolean array whose length matches the columns. >>> df.iloc[:, [True, False, True, False]] a c 0 1 3 1 100 300 2 1000 3000 With a callable function that expects the Series or DataFrame. >>> df.iloc[:, lambda df: [0, 2]] a c 0 1 3 1 100 300 2 1000 3000
reference/api/pandas.Series.iloc.html
pandas.tseries.offsets.FY5253Quarter.is_year_end
`pandas.tseries.offsets.FY5253Quarter.is_year_end` Return boolean whether a timestamp occurs on the year end. Examples ``` >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_year_end(ts) False ```
FY5253Quarter.is_year_end()# Return boolean whether a timestamp occurs on the year end. Examples >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_year_end(ts) False
reference/api/pandas.tseries.offsets.FY5253Quarter.is_year_end.html
pandas.tseries.offsets.CustomBusinessDay.is_year_end
`pandas.tseries.offsets.CustomBusinessDay.is_year_end` Return boolean whether a timestamp occurs on the year end. Examples ``` >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_year_end(ts) False ```
CustomBusinessDay.is_year_end()# Return boolean whether a timestamp occurs on the year end. Examples >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_year_end(ts) False
reference/api/pandas.tseries.offsets.CustomBusinessDay.is_year_end.html
pandas.Timedelta.to_numpy
`pandas.Timedelta.to_numpy` Convert the Timedelta to a NumPy timedelta64.
Timedelta.to_numpy()# Convert the Timedelta to a NumPy timedelta64. New in version 0.25.0. This is an alias method for Timedelta.to_timedelta64(). The dtype and copy parameters are available here only for compatibility. Their values will not affect the return value. Returns numpy.timedelta64 See also Series.to_numpySimilar method for Series.
reference/api/pandas.Timedelta.to_numpy.html
pandas.core.window.expanding.Expanding.min
`pandas.core.window.expanding.Expanding.min` Calculate the expanding minimum. Include only float, int, boolean columns.
Expanding.min(numeric_only=False, *args, engine=None, engine_kwargs=None, **kwargs)[source]# Calculate the expanding minimum. Parameters numeric_onlybool, default FalseInclude only float, int, boolean columns. New in version 1.5.0. *argsFor NumPy compatibility and will not have an effect on the result. Deprecated since version 1.5.0. enginestr, default None 'cython' : Runs the operation through C-extensions from cython. 'numba' : Runs the operation through JIT compiled code from numba. None : Defaults to 'cython' or globally setting compute.use_numba New in version 1.3.0. engine_kwargsdict, default None For 'cython' engine, there are no accepted engine_kwargs For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {'nopython': True, 'nogil': False, 'parallel': False} New in version 1.3.0. **kwargsFor NumPy compatibility and will not have an effect on the result. Deprecated since version 1.5.0. Returns Series or DataFrameReturn type is the same as the original object with np.float64 dtype. See also pandas.Series.expandingCalling expanding with Series data. pandas.DataFrame.expandingCalling expanding with DataFrames. pandas.Series.minAggregating min for Series. pandas.DataFrame.minAggregating min for DataFrame. Notes See Numba engine and Numba (JIT compilation) for extended documentation and performance considerations for the Numba engine.
reference/api/pandas.core.window.expanding.Expanding.min.html
pandas.DataFrame.last
`pandas.DataFrame.last` Select final periods of time series data based on a date offset. ``` >>> i = pd.date_range('2018-04-09', periods=4, freq='2D') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 1 2018-04-11 2 2018-04-13 3 2018-04-15 4 ```
DataFrame.last(offset)[source]# Select final periods of time series data based on a date offset. For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset. Parameters offsetstr, DateOffset, dateutil.relativedeltaThe offset length of the data that will be selected. For instance, ‘3D’ will display all the rows having their index within the last 3 days. Returns Series or DataFrameA subset of the caller. Raises TypeErrorIf the index is not a DatetimeIndex See also firstSelect initial periods of time series based on a date offset. at_timeSelect values at a particular time of the day. between_timeSelect values between particular times of the day. Examples >>> i = pd.date_range('2018-04-09', periods=4, freq='2D') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 1 2018-04-11 2 2018-04-13 3 2018-04-15 4 Get the rows for the last 3 days: >>> ts.last('3D') A 2018-04-13 3 2018-04-15 4 Notice the data for 3 last calendar days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned.
reference/api/pandas.DataFrame.last.html
pandas.DataFrame.boxplot
`pandas.DataFrame.boxplot` Make a box plot from DataFrame columns. ``` >>> np.random.seed(1234) >>> df = pd.DataFrame(np.random.randn(10, 4), ... columns=['Col1', 'Col2', 'Col3', 'Col4']) >>> boxplot = df.boxplot(column=['Col1', 'Col2', 'Col3']) ```
DataFrame.boxplot(column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, figsize=None, layout=None, return_type=None, backend=None, **kwargs)[source]# Make a box plot from DataFrame columns. Make a box-and-whisker plot from DataFrame columns, optionally grouped by some other columns. A box plot is a method for graphically depicting groups of numerical data through their quartiles. The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2). The whiskers extend from the edges of box to show the range of the data. By default, they extend no more than 1.5 * IQR (IQR = Q3 - Q1) from the edges of the box, ending at the farthest data point within that interval. Outliers are plotted as separate dots. For further details see Wikipedia’s entry for boxplot. Parameters columnstr or list of str, optionalColumn name or list of names, or vector. Can be any valid input to pandas.DataFrame.groupby(). bystr or array-like, optionalColumn in the DataFrame to pandas.DataFrame.groupby(). One box-plot will be done per value of columns in by. axobject of class matplotlib.axes.Axes, optionalThe matplotlib axes to be used by boxplot. fontsizefloat or strTick label font size in points or as a string (e.g., large). rotint or float, default 0The rotation angle of labels (in degrees) with respect to the screen coordinate system. gridbool, default TrueSetting this to True will show the grid. figsizeA tuple (width, height) in inchesThe size of the figure to create in matplotlib. layouttuple (rows, columns), optionalFor example, (3, 5) will display the subplots using 3 columns and 5 rows, starting from the top-left. return_type{‘axes’, ‘dict’, ‘both’} or None, default ‘axes’The kind of object to return. The default is axes. ‘axes’ returns the matplotlib axes the boxplot is drawn on. ‘dict’ returns a dictionary whose values are the matplotlib Lines of the boxplot. ‘both’ returns a namedtuple with the axes and dict. when grouping with by, a Series mapping columns to return_type is returned. If return_type is None, a NumPy array of axes with the same shape as layout is returned. backendstr, default NoneBackend to use instead of the backend specified in the option plotting.backend. For instance, ‘matplotlib’. Alternatively, to specify the plotting.backend for the whole session, set pd.options.plotting.backend. New in version 1.0.0. **kwargsAll other plotting keyword arguments to be passed to matplotlib.pyplot.boxplot(). Returns resultSee Notes. See also Series.plot.histMake a histogram. matplotlib.pyplot.boxplotMatplotlib equivalent plot. Notes The return type depends on the return_type parameter: ‘axes’ : object of class matplotlib.axes.Axes ‘dict’ : dict of matplotlib.lines.Line2D objects ‘both’ : a namedtuple with structure (ax, lines) For data grouped with by, return a Series of the above or a numpy array: Series array (for return_type = None) Use return_type='dict' when you want to tweak the appearance of the lines after plotting. In this case a dict containing the Lines making up the boxes, caps, fliers, medians, and whiskers is returned. Examples Boxplots can be created for every column in the dataframe by df.boxplot() or indicating the columns to be used: >>> np.random.seed(1234) >>> df = pd.DataFrame(np.random.randn(10, 4), ... columns=['Col1', 'Col2', 'Col3', 'Col4']) >>> boxplot = df.boxplot(column=['Col1', 'Col2', 'Col3']) Boxplots of variables distributions grouped by the values of a third variable can be created using the option by. For instance: >>> df = pd.DataFrame(np.random.randn(10, 2), ... columns=['Col1', 'Col2']) >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', ... 'B', 'B', 'B', 'B', 'B']) >>> boxplot = df.boxplot(by='X') A list of strings (i.e. ['X', 'Y']) can be passed to boxplot in order to group the data by combination of the variables in the x-axis: >>> df = pd.DataFrame(np.random.randn(10, 3), ... columns=['Col1', 'Col2', 'Col3']) >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', ... 'B', 'B', 'B', 'B', 'B']) >>> df['Y'] = pd.Series(['A', 'B', 'A', 'B', 'A', ... 'B', 'A', 'B', 'A', 'B']) >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y']) The layout of boxplot can be adjusted giving a tuple to layout: >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X', ... layout=(2, 1)) Additional formatting can be done to the boxplot, like suppressing the grid (grid=False), rotating the labels in the x-axis (i.e. rot=45) or changing the fontsize (i.e. fontsize=15): >>> boxplot = df.boxplot(grid=False, rot=45, fontsize=15) The parameter return_type can be used to select the type of element returned by boxplot. When return_type='axes' is selected, the matplotlib axes on which the boxplot is drawn are returned: >>> boxplot = df.boxplot(column=['Col1', 'Col2'], return_type='axes') >>> type(boxplot) <class 'matplotlib.axes._subplots.AxesSubplot'> When grouping with by, a Series mapping columns to return_type is returned: >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X', ... return_type='axes') >>> type(boxplot) <class 'pandas.core.series.Series'> If return_type is None, a NumPy array of axes with the same shape as layout is returned: >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X', ... return_type=None) >>> type(boxplot) <class 'numpy.ndarray'>
reference/api/pandas.DataFrame.boxplot.html
pandas.Series.unique
`pandas.Series.unique` Return unique values of Series object. Uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort. ``` >>> pd.Series([2, 1, 3, 3], name='A').unique() array([2, 1, 3]) ```
Series.unique()[source]# Return unique values of Series object. Uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort. Returns ndarray or ExtensionArrayThe unique values returned as a NumPy array. See Notes. See also Series.drop_duplicatesReturn Series with duplicate values removed. uniqueTop-level unique method for any 1-d array-like object. Index.uniqueReturn Index with unique values from an Index object. Notes Returns the unique values as a NumPy array. In case of an extension-array backed Series, a new ExtensionArray of that type with just the unique values is returned. This includes Categorical Period Datetime with Timezone Interval Sparse IntegerNA See Examples section. Examples >>> pd.Series([2, 1, 3, 3], name='A').unique() array([2, 1, 3]) >>> pd.Series([pd.Timestamp('2016-01-01') for _ in range(3)]).unique() array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]') >>> pd.Series([pd.Timestamp('2016-01-01', tz='US/Eastern') ... for _ in range(3)]).unique() <DatetimeArray> ['2016-01-01 00:00:00-05:00'] Length: 1, dtype: datetime64[ns, US/Eastern] An Categorical will return categories in the order of appearance and with the same dtype. >>> pd.Series(pd.Categorical(list('baabc'))).unique() ['b', 'a', 'c'] Categories (3, object): ['a', 'b', 'c'] >>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'), ... ordered=True)).unique() ['b', 'a', 'c'] Categories (3, object): ['a' < 'b' < 'c']
reference/api/pandas.Series.unique.html
pandas.Series.ndim
`pandas.Series.ndim` Number of dimensions of the underlying data, by definition 1.
property Series.ndim[source]# Number of dimensions of the underlying data, by definition 1.
reference/api/pandas.Series.ndim.html
pandas.tseries.offsets.CustomBusinessDay.name
`pandas.tseries.offsets.CustomBusinessDay.name` Return a string representing the base frequency. ``` >>> pd.offsets.Hour().name 'H' ```
CustomBusinessDay.name# Return a string representing the base frequency. Examples >>> pd.offsets.Hour().name 'H' >>> pd.offsets.Hour(5).name 'H'
reference/api/pandas.tseries.offsets.CustomBusinessDay.name.html
pandas.tseries.offsets.WeekOfMonth.is_month_start
`pandas.tseries.offsets.WeekOfMonth.is_month_start` Return boolean whether a timestamp occurs on the month start. Examples ``` >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_month_start(ts) True ```
WeekOfMonth.is_month_start()# Return boolean whether a timestamp occurs on the month start. Examples >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_month_start(ts) True
reference/api/pandas.tseries.offsets.WeekOfMonth.is_month_start.html
pandas.Period.freq
pandas.Period.freq
Period.freq#
reference/api/pandas.Period.freq.html
pandas.tseries.offsets.SemiMonthBegin.apply_index
`pandas.tseries.offsets.SemiMonthBegin.apply_index` Vectorized apply of DateOffset to DatetimeIndex.
SemiMonthBegin.apply_index()# Vectorized apply of DateOffset to DatetimeIndex. Deprecated since version 1.1.0: Use offset + dtindex instead. Parameters indexDatetimeIndex Returns DatetimeIndex Raises NotImplementedErrorWhen the specific offset subclass does not have a vectorized implementation.
reference/api/pandas.tseries.offsets.SemiMonthBegin.apply_index.html
pandas.api.types.is_dict_like
`pandas.api.types.is_dict_like` Check if the object is dict-like. Whether obj has dict-like properties. ``` >>> is_dict_like({1: 2}) True >>> is_dict_like([1, 2, 3]) False >>> is_dict_like(dict) False >>> is_dict_like(dict()) True ```
pandas.api.types.is_dict_like(obj)[source]# Check if the object is dict-like. Parameters objThe object to check Returns is_dict_likeboolWhether obj has dict-like properties. Examples >>> is_dict_like({1: 2}) True >>> is_dict_like([1, 2, 3]) False >>> is_dict_like(dict) False >>> is_dict_like(dict()) True
reference/api/pandas.api.types.is_dict_like.html
pandas.Series.idxmin
`pandas.Series.idxmin` Return the row label of the minimum value. ``` >>> s = pd.Series(data=[1, None, 4, 1], ... index=['A', 'B', 'C', 'D']) >>> s A 1.0 B NaN C 4.0 D 1.0 dtype: float64 ```
Series.idxmin(axis=0, skipna=True, *args, **kwargs)[source]# Return the row label of the minimum value. If multiple values equal the minimum, the first row label with that value is returned. Parameters axis{0 or ‘index’}Unused. Parameter needed for compatibility with DataFrame. skipnabool, default TrueExclude NA/null values. If the entire Series is NA, the result will be NA. *args, **kwargsAdditional arguments and keywords have no effect but might be accepted for compatibility with NumPy. Returns IndexLabel of the minimum value. Raises ValueErrorIf the Series is empty. See also numpy.argminReturn indices of the minimum values along the given axis. DataFrame.idxminReturn index of first occurrence of minimum over requested axis. Series.idxmaxReturn index label of the first occurrence of maximum of values. Notes This method is the Series version of ndarray.argmin. This method returns the label of the minimum, while ndarray.argmin returns the position. To get the position, use series.values.argmin(). Examples >>> s = pd.Series(data=[1, None, 4, 1], ... index=['A', 'B', 'C', 'D']) >>> s A 1.0 B NaN C 4.0 D 1.0 dtype: float64 >>> s.idxmin() 'A' If skipna is False and there is an NA value in the data, the function returns nan. >>> s.idxmin(skipna=False) nan
reference/api/pandas.Series.idxmin.html
pandas.api.extensions.ExtensionArray.view
`pandas.api.extensions.ExtensionArray.view` Return a view on the array.
ExtensionArray.view(dtype=None)[source]# Return a view on the array. Parameters dtypestr, np.dtype, or ExtensionDtype, optionalDefault None. Returns ExtensionArray or np.ndarrayA view on the ExtensionArray’s data.
reference/api/pandas.api.extensions.ExtensionArray.view.html
pandas.MultiIndex.get_level_values
`pandas.MultiIndex.get_level_values` Return vector of label values for requested level. ``` >>> mi = pd.MultiIndex.from_arrays((list('abc'), list('def'))) >>> mi.names = ['level_1', 'level_2'] ```
MultiIndex.get_level_values(level)[source]# Return vector of label values for requested level. Length of returned vector is equal to the length of the index. Parameters levelint or strlevel is either the integer position of the level in the MultiIndex, or the name of the level. Returns valuesIndexValues is a level of this MultiIndex converted to a single Index (or subclass thereof). Notes If the level contains missing values, the result may be casted to float with missing values specified as NaN. This is because the level is converted to a regular Index. Examples Create a MultiIndex: >>> mi = pd.MultiIndex.from_arrays((list('abc'), list('def'))) >>> mi.names = ['level_1', 'level_2'] Get level values by supplying level as either integer or name: >>> mi.get_level_values(0) Index(['a', 'b', 'c'], dtype='object', name='level_1') >>> mi.get_level_values('level_2') Index(['d', 'e', 'f'], dtype='object', name='level_2') If a level contains missing values, the return type of the level maybe casted to float. >>> pd.MultiIndex.from_arrays([[1, None, 2], [3, 4, 5]]).dtypes level_0 int64 level_1 int64 dtype: object >>> pd.MultiIndex.from_arrays([[1, None, 2], [3, 4, 5]]).get_level_values(0) Float64Index([1.0, nan, 2.0], dtype='float64')
reference/api/pandas.MultiIndex.get_level_values.html
pandas.tseries.offsets.SemiMonthBegin.isAnchored
pandas.tseries.offsets.SemiMonthBegin.isAnchored
SemiMonthBegin.isAnchored()#
reference/api/pandas.tseries.offsets.SemiMonthBegin.isAnchored.html
pandas ecosystem
Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. This is encouraging because it means pandas is not only helping users to handle their data tasks but also that it provides a better starting point for developers to build powerful and more focused data tools. The creation of libraries that complement pandas’ functionality also allows pandas development to remain focused around it’s original requirements. This is an inexhaustive list of projects that build on pandas in order to provide tools in the PyData space. For a list of projects that depend on pandas, see the Github network dependents for pandas or search pypi for pandas. We’d like to make it easier for users to find these projects, if you know of other substantial projects that you feel should be on this list, please let us know. Data cleaning and validation# Pyjanitor# Pyjanitor provides a clean API for cleaning data, using method chaining. Pandera# Pandera provides a flexible and expressive API for performing data validation on dataframes to make data processing pipelines more readable and robust. Dataframes contain information that pandera explicitly validates at runtime. This is useful in production-critical data pipelines or reproducible research settings. pandas-path# Since Python 3.4, pathlib has been included in the Python standard library. Path objects provide a simple and delightful way to interact with the file system. The pandas-path package enables the Path API for pandas through a custom accessor .path. Getting just the filenames from a series of full file paths is as simple as my_files.path.name. Other convenient operations like joining paths, replacing file extensions, and checking if files exist are also available. Statistics and machine learning# pandas-tfrecords# Easy saving pandas dataframe to tensorflow tfrecords format and reading tfrecords to pandas. Statsmodels# Statsmodels is the prominent Python “statistics and econometrics library” and it has a long-standing special relationship with pandas. Statsmodels provides powerful statistics, econometrics, analysis and modeling functionality that is out of pandas’ scope. Statsmodels leverages pandas objects as the underlying data container for computation. sklearn-pandas# Use pandas DataFrames in your scikit-learn ML pipeline. Featuretools# Featuretools is a Python library for automated feature engineering built on top of pandas. It excels at transforming temporal and relational datasets into feature matrices for machine learning using reusable feature engineering “primitives”. Users can contribute their own primitives in Python and share them with the rest of the community. Compose# Compose is a machine learning tool for labeling data and prediction engineering. It allows you to structure the labeling process by parameterizing prediction problems and transforming time-driven relational data into target values with cutoff times that can be used for supervised learning. STUMPY# STUMPY is a powerful and scalable Python library for modern time series analysis. At its core, STUMPY efficiently computes something called a matrix profile, which can be used for a wide variety of time series data mining tasks. Visualization# Pandas has its own Styler class for table visualization, and while pandas also has built-in support for data visualization through charts with matplotlib, there are a number of other pandas-compatible libraries. Altair# Altair is a declarative statistical visualization library for Python. With Altair, you can spend more time understanding your data and its meaning. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite JSON specification. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. Altair works with pandas DataFrames. Bokeh# Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients. Pandas-Bokeh provides a high level API for Bokeh that can be loaded as a native pandas plotting backend via pd.set_option("plotting.backend", "pandas_bokeh") It is very similar to the matplotlib plotting backend, but provides interactive web-based charts and maps. Seaborn# Seaborn is a Python visualization library based on matplotlib. It provides a high-level, dataset-oriented interface for creating attractive statistical graphics. The plotting functions in seaborn understand pandas objects and leverage pandas grouping operations internally to support concise specification of complex visualizations. Seaborn also goes beyond matplotlib and pandas with the option to perform statistical estimation while plotting, aggregating across observations and visualizing the fit of statistical models to emphasize patterns in a dataset. plotnine# Hadley Wickham’s ggplot2 is a foundational exploratory visualization package for the R language. Based on “The Grammar of Graphics” it provides a powerful, declarative and extremely general way to generate bespoke plots of any kind of data. Various implementations to other languages are available. A good implementation for Python users is has2k1/plotnine. IPython vega# IPython Vega leverages Vega to create plots within Jupyter Notebook. Plotly# Plotly’s Python API enables interactive figures and web shareability. Maps, 2D, 3D, and live-streaming graphs are rendered with WebGL and D3.js. The library supports plotting directly from a pandas DataFrame and cloud-based collaboration. Users of matplotlib, ggplot for Python, and Seaborn can convert figures into interactive web-based plots. Plots can be drawn in IPython Notebooks , edited with R or MATLAB, modified in a GUI, or embedded in apps and dashboards. Plotly is free for unlimited sharing, and has offline, or on-premise accounts for private use. Lux# Lux is a Python library that facilitates fast and easy experimentation with data by automating the visual data exploration process. To use Lux, simply add an extra import alongside pandas: import lux import pandas as pd df = pd.read_csv("data.csv") df # discover interesting insights! By printing out a dataframe, Lux automatically recommends a set of visualizations that highlights interesting trends and patterns in the dataframe. Users can leverage any existing pandas commands without modifying their code, while being able to visualize their pandas data structures (e.g., DataFrame, Series, Index) at the same time. Lux also offers a powerful, intuitive language that allow users to create Altair, matplotlib, or Vega-Lite visualizations without having to think at the level of code. Qtpandas# Spun off from the main pandas library, the qtpandas library enables DataFrame visualization and manipulation in PyQt4 and PySide applications. D-Tale# D-Tale is a lightweight web client for visualizing pandas data structures. It provides a rich spreadsheet-style grid which acts as a wrapper for a lot of pandas functionality (query, sort, describe, corr…) so users can quickly manipulate their data. There is also an interactive chart-builder using Plotly Dash allowing users to build nice portable visualizations. D-Tale can be invoked with the following command import dtale dtale.show(df) D-Tale integrates seamlessly with Jupyter notebooks, Python terminals, Kaggle & Google Colab. Here are some demos of the grid. hvplot# hvPlot is a high-level plotting API for the PyData ecosystem built on HoloViews. It can be loaded as a native pandas plotting backend via pd.set_option("plotting.backend", "hvplot") IDE# IPython# IPython is an interactive command shell and distributed computing environment. IPython tab completion works with pandas methods and also attributes like DataFrame columns. Jupyter Notebook / Jupyter Lab# Jupyter Notebook is a web application for creating Jupyter notebooks. A Jupyter notebook is a JSON document containing an ordered list of input/output cells which can contain code, text, mathematics, plots and rich media. Jupyter notebooks can be converted to a number of open standard output formats (HTML, HTML presentation slides, LaTeX, PDF, ReStructuredText, Markdown, Python) through ‘Download As’ in the web interface and jupyter convert in a shell. pandas DataFrames implement _repr_html_ and _repr_latex methods which are utilized by Jupyter Notebook for displaying (abbreviated) HTML or LaTeX tables. LaTeX output is properly escaped. (Note: HTML tables may or may not be compatible with non-HTML Jupyter output formats.) See Options and Settings and Available Options for pandas display. settings. Quantopian/qgrid# qgrid is “an interactive grid for sorting and filtering DataFrames in IPython Notebook” built with SlickGrid. Spyder# Spyder is a cross-platform PyQt-based IDE combining the editing, analysis, debugging and profiling functionality of a software development tool with the data exploration, interactive execution, deep inspection and rich visualization capabilities of a scientific environment like MATLAB or Rstudio. Its Variable Explorer allows users to view, manipulate and edit pandas Index, Series, and DataFrame objects like a “spreadsheet”, including copying and modifying values, sorting, displaying a “heatmap”, converting data types and more. pandas objects can also be renamed, duplicated, new columns added, copied/pasted to/from the clipboard (as TSV), and saved/loaded to/from a file. Spyder can also import data from a variety of plain text and binary files or the clipboard into a new pandas DataFrame via a sophisticated import wizard. Most pandas classes, methods and data attributes can be autocompleted in Spyder’s Editor and IPython Console, and Spyder’s Help pane can retrieve and render Numpydoc documentation on pandas objects in rich text with Sphinx both automatically and on-demand. API# pandas-datareader# pandas-datareader is a remote data access library for pandas (PyPI:pandas-datareader). It is based on functionality that was located in pandas.io.data and pandas.io.wb but was split off in v0.19. See more in the pandas-datareader docs: The following data feeds are available: Google Finance Tiingo Morningstar IEX Robinhood Enigma Quandl FRED Fama/French World Bank OECD Eurostat TSP Fund Data Nasdaq Trader Symbol Definitions Stooq Index Data MOEX Data Quandl/Python# Quandl API for Python wraps the Quandl REST API to return pandas DataFrames with timeseries indexes. Pydatastream# PyDatastream is a Python interface to the Refinitiv Datastream (DWS) REST API to return indexed pandas DataFrames with financial data. This package requires valid credentials for this API (non free). pandaSDMX# pandaSDMX is a library to retrieve and acquire statistical data and metadata disseminated in SDMX 2.1, an ISO-standard widely used by institutions such as statistics offices, central banks, and international organisations. pandaSDMX can expose datasets and related structural metadata including data flows, code-lists, and data structure definitions as pandas Series or MultiIndexed DataFrames. fredapi# fredapi is a Python interface to the Federal Reserve Economic Data (FRED) provided by the Federal Reserve Bank of St. Louis. It works with both the FRED database and ALFRED database that contains point-in-time data (i.e. historic data revisions). fredapi provides a wrapper in Python to the FRED HTTP API, and also provides several convenient methods for parsing and analyzing point-in-time data from ALFRED. fredapi makes use of pandas and returns data in a Series or DataFrame. This module requires a FRED API key that you can obtain for free on the FRED website. dataframe_sql# dataframe_sql is a Python package that translates SQL syntax directly into operations on pandas DataFrames. This is useful when migrating from a database to using pandas or for users more comfortable with SQL looking for a way to interface with pandas. Domain specific# Geopandas# Geopandas extends pandas data objects to include geographic information which support geometric operations. If your work entails maps and geographical coordinates, and you love pandas, you should take a close look at Geopandas. staircase# staircase is a data analysis package, built upon pandas and numpy, for modelling and manipulation of mathematical step functions. It provides a rich variety of arithmetic operations, relational operations, logical operations, statistical operations and aggregations for step functions defined over real numbers, datetime and timedelta domains. xarray# xarray brings the labeled data power of pandas to the physical sciences by providing N-dimensional variants of the core pandas data structures. It aims to provide a pandas-like and pandas-compatible toolkit for analytics on multi- dimensional arrays, rather than the tabular data for which pandas excels. IO# BCPandas# BCPandas provides high performance writes from pandas to Microsoft SQL Server, far exceeding the performance of the native df.to_sql method. Internally, it uses Microsoft’s BCP utility, but the complexity is fully abstracted away from the end user. Rigorously tested, it is a complete replacement for df.to_sql. Deltalake# Deltalake python package lets you access tables stored in Delta Lake natively in Python without the need to use Spark or JVM. It provides the delta_table.to_pyarrow_table().to_pandas() method to convert any Delta table into Pandas dataframe. Out-of-core# Blaze# Blaze provides a standard API for doing computations with various in-memory and on-disk backends: NumPy, pandas, SQLAlchemy, MongoDB, PyTables, PySpark. Cylon# Cylon is a fast, scalable, distributed memory parallel runtime with a pandas like Python DataFrame API. ”Core Cylon” is implemented with C++ using Apache Arrow format to represent the data in-memory. Cylon DataFrame API implements most of the core operators of pandas such as merge, filter, join, concat, group-by, drop_duplicates, etc. These operators are designed to work across thousands of cores to scale applications. It can interoperate with pandas DataFrame by reading data from pandas or converting data to pandas so users can selectively scale parts of their pandas DataFrame applications. from pycylon import read_csv, DataFrame, CylonEnv from pycylon.net import MPIConfig # Initialize Cylon distributed environment config: MPIConfig = MPIConfig() env: CylonEnv = CylonEnv(config=config, distributed=True) df1: DataFrame = read_csv('/tmp/csv1.csv') df2: DataFrame = read_csv('/tmp/csv2.csv') # Using 1000s of cores across the cluster to compute the join df3: Table = df1.join(other=df2, on=[0], algorithm="hash", env=env) print(df3) Dask# Dask is a flexible parallel computing library for analytics. Dask provides a familiar DataFrame interface for out-of-core, parallel and distributed computing. Dask-ML# Dask-ML enables parallel and distributed machine learning using Dask alongside existing machine learning libraries like Scikit-Learn, XGBoost, and TensorFlow. Ibis# Ibis offers a standard way to write analytics code, that can be run in multiple engines. It helps in bridging the gap between local Python environments (like pandas) and remote storage and execution systems like Hadoop components (like HDFS, Impala, Hive, Spark) and SQL databases (Postgres, etc.). Koalas# Koalas provides a familiar pandas DataFrame interface on top of Apache Spark. It enables users to leverage multi-cores on one machine or a cluster of machines to speed up or scale their DataFrame code. Modin# The modin.pandas DataFrame is a parallel and distributed drop-in replacement for pandas. This means that you can use Modin with existing pandas code or write new code with the existing pandas API. Modin can leverage your entire machine or cluster to speed up and scale your pandas workloads, including traditionally time-consuming tasks like ingesting data (read_csv, read_excel, read_parquet, etc.). # import pandas as pd import modin.pandas as pd df = pd.read_csv("big.csv") # use all your cores! Odo# Odo provides a uniform API for moving data between different formats. It uses pandas own read_csv for CSV IO and leverages many existing packages such as PyTables, h5py, and pymongo to move data between non pandas formats. Its graph based approach is also extensible by end users for custom formats that may be too specific for the core of odo. Pandarallel# Pandarallel provides a simple way to parallelize your pandas operations on all your CPUs by changing only one line of code. If also displays progress bars. from pandarallel import pandarallel pandarallel.initialize(progress_bar=True) # df.apply(func) df.parallel_apply(func) Vaex# Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. Vaex is a Python library for Out-of-Core DataFrames (similar to pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (109) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted). vaex.from_pandas vaex.to_pandas_df Extension data types# pandas provides an interface for defining extension types to extend NumPy’s type system. The following libraries implement that interface to provide types not found in NumPy or pandas, which work well with pandas’ data containers. Cyberpandas# Cyberpandas provides an extension type for storing arrays of IP Addresses. These arrays can be stored inside pandas’ Series and DataFrame. Pandas-Genomics# Pandas-Genomics provides extension types, extension arrays, and extension accessors for working with genomics data Pint-Pandas# Pint-Pandas provides an extension type for storing numeric arrays with units. These arrays can be stored inside pandas’ Series and DataFrame. Operations between Series and DataFrame columns which use pint’s extension array are then units aware. Text Extensions for Pandas# Text Extensions for Pandas provides extension types to cover common data structures for representing natural language data, plus library integrations that convert the outputs of popular natural language processing libraries into Pandas DataFrames. Accessors# A directory of projects providing extension accessors. This is for users to discover new accessors and for library authors to coordinate on the namespace. Library Accessor Classes Description cyberpandas ip Series Provides common operations for working with IP addresses. pdvega vgplot Series, DataFrame Provides plotting functions from the Altair library. pandas-genomics genomics Series, DataFrame Provides common operations for quality control and analysis of genomics data. pandas_path path Index, Series Provides pathlib.Path functions for Series. pint-pandas pint Series, DataFrame Provides units support for numeric Series and DataFrames. composeml slice DataFrame Provides a generator for enhanced data slicing. datatest validate Series, DataFrame, Index Provides validation, differences, and acceptance managers. woodwork ww Series, DataFrame Provides physical, logical, and semantic data typing information for Series and DataFrames. staircase sc Series Provides methods for querying, aggregating and plotting step functions Development tools# pandas-stubs# While pandas repository is partially typed, the package itself doesn’t expose this information for external use. Install pandas-stubs to enable basic type coverage of pandas API. Learn more by reading through GH14468, GH26766, GH28142. See installation and usage instructions on the github page.
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pandas.tseries.offsets.CustomBusinessDay.is_month_start
`pandas.tseries.offsets.CustomBusinessDay.is_month_start` Return boolean whether a timestamp occurs on the month start. ``` >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_month_start(ts) True ```
CustomBusinessDay.is_month_start()# Return boolean whether a timestamp occurs on the month start. Examples >>> ts = pd.Timestamp(2022, 1, 1) >>> freq = pd.offsets.Hour(5) >>> freq.is_month_start(ts) True
reference/api/pandas.tseries.offsets.CustomBusinessDay.is_month_start.html
pandas.api.types.is_datetime64_dtype
`pandas.api.types.is_datetime64_dtype` Check whether an array-like or dtype is of the datetime64 dtype. ``` >>> is_datetime64_dtype(object) False >>> is_datetime64_dtype(np.datetime64) True >>> is_datetime64_dtype(np.array([], dtype=int)) False >>> is_datetime64_dtype(np.array([], dtype=np.datetime64)) True >>> is_datetime64_dtype([1, 2, 3]) False ```
pandas.api.types.is_datetime64_dtype(arr_or_dtype)[source]# Check whether an array-like or dtype is of the datetime64 dtype. Parameters arr_or_dtypearray-like or dtypeThe array-like or dtype to check. Returns booleanWhether or not the array-like or dtype is of the datetime64 dtype. Examples >>> is_datetime64_dtype(object) False >>> is_datetime64_dtype(np.datetime64) True >>> is_datetime64_dtype(np.array([], dtype=int)) False >>> is_datetime64_dtype(np.array([], dtype=np.datetime64)) True >>> is_datetime64_dtype([1, 2, 3]) False
reference/api/pandas.api.types.is_datetime64_dtype.html
pandas.DataFrame.drop
`pandas.DataFrame.drop` Drop specified labels from rows or columns. ``` >>> df = pd.DataFrame(np.arange(12).reshape(3, 4), ... columns=['A', 'B', 'C', 'D']) >>> df A B C D 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 ```
DataFrame.drop(labels=None, *, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')[source]# Drop specified labels from rows or columns. Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a multi-index, labels on different levels can be removed by specifying the level. See the user guide <advanced.shown_levels> for more information about the now unused levels. Parameters labelssingle label or list-likeIndex or column labels to drop. A tuple will be used as a single label and not treated as a list-like. axis{0 or ‘index’, 1 or ‘columns’}, default 0Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’). indexsingle label or list-likeAlternative to specifying axis (labels, axis=0 is equivalent to index=labels). columnssingle label or list-likeAlternative to specifying axis (labels, axis=1 is equivalent to columns=labels). levelint or level name, optionalFor MultiIndex, level from which the labels will be removed. inplacebool, default FalseIf False, return a copy. Otherwise, do operation inplace and return None. errors{‘ignore’, ‘raise’}, default ‘raise’If ‘ignore’, suppress error and only existing labels are dropped. Returns DataFrame or NoneDataFrame without the removed index or column labels or None if inplace=True. Raises KeyErrorIf any of the labels is not found in the selected axis. See also DataFrame.locLabel-location based indexer for selection by label. DataFrame.dropnaReturn DataFrame with labels on given axis omitted where (all or any) data are missing. DataFrame.drop_duplicatesReturn DataFrame with duplicate rows removed, optionally only considering certain columns. Series.dropReturn Series with specified index labels removed. Examples >>> df = pd.DataFrame(np.arange(12).reshape(3, 4), ... columns=['A', 'B', 'C', 'D']) >>> df A B C D 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 Drop columns >>> df.drop(['B', 'C'], axis=1) A D 0 0 3 1 4 7 2 8 11 >>> df.drop(columns=['B', 'C']) A D 0 0 3 1 4 7 2 8 11 Drop a row by index >>> df.drop([0, 1]) A B C D 2 8 9 10 11 Drop columns and/or rows of MultiIndex DataFrame >>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'], ... ['speed', 'weight', 'length']], ... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2], ... [0, 1, 2, 0, 1, 2, 0, 1, 2]]) >>> df = pd.DataFrame(index=midx, columns=['big', 'small'], ... data=[[45, 30], [200, 100], [1.5, 1], [30, 20], ... [250, 150], [1.5, 0.8], [320, 250], ... [1, 0.8], [0.3, 0.2]]) >>> df big small lama speed 45.0 30.0 weight 200.0 100.0 length 1.5 1.0 cow speed 30.0 20.0 weight 250.0 150.0 length 1.5 0.8 falcon speed 320.0 250.0 weight 1.0 0.8 length 0.3 0.2 Drop a specific index combination from the MultiIndex DataFrame, i.e., drop the combination 'falcon' and 'weight', which deletes only the corresponding row >>> df.drop(index=('falcon', 'weight')) big small lama speed 45.0 30.0 weight 200.0 100.0 length 1.5 1.0 cow speed 30.0 20.0 weight 250.0 150.0 length 1.5 0.8 falcon speed 320.0 250.0 length 0.3 0.2 >>> df.drop(index='cow', columns='small') big lama speed 45.0 weight 200.0 length 1.5 falcon speed 320.0 weight 1.0 length 0.3 >>> df.drop(index='length', level=1) big small lama speed 45.0 30.0 weight 200.0 100.0 cow speed 30.0 20.0 weight 250.0 150.0 falcon speed 320.0 250.0 weight 1.0 0.8
reference/api/pandas.DataFrame.drop.html
pandas.tseries.offsets.Minute.copy
`pandas.tseries.offsets.Minute.copy` Return a copy of the frequency. ``` >>> freq = pd.DateOffset(1) >>> freq_copy = freq.copy() >>> freq is freq_copy False ```
Minute.copy()# Return a copy of the frequency. Examples >>> freq = pd.DateOffset(1) >>> freq_copy = freq.copy() >>> freq is freq_copy False
reference/api/pandas.tseries.offsets.Minute.copy.html
pandas.DataFrame.sort_index
`pandas.DataFrame.sort_index` Sort object by labels (along an axis). ``` >>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150], ... columns=['A']) >>> df.sort_index() A 1 4 29 2 100 1 150 5 234 3 ```
DataFrame.sort_index(*, axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, ignore_index=False, key=None)[source]# Sort object by labels (along an axis). Returns a new DataFrame sorted by label if inplace argument is False, otherwise updates the original DataFrame and returns None. Parameters axis{0 or ‘index’, 1 or ‘columns’}, default 0The axis along which to sort. The value 0 identifies the rows, and 1 identifies the columns. levelint or level name or list of ints or list of level namesIf not None, sort on values in specified index level(s). ascendingbool or list-like of bools, default TrueSort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually. inplacebool, default FalseWhether to modify the DataFrame rather than creating a new one. kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, default ‘quicksort’Choice of sorting algorithm. See also numpy.sort() for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label. na_position{‘first’, ‘last’}, default ‘last’Puts NaNs at the beginning if first; last puts NaNs at the end. Not implemented for MultiIndex. sort_remainingbool, default TrueIf True and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level. ignore_indexbool, default FalseIf True, the resulting axis will be labeled 0, 1, …, n - 1. New in version 1.0.0. keycallable, optionalIf not None, apply the key function to the index values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level. New in version 1.1.0. Returns DataFrame or NoneThe original DataFrame sorted by the labels or None if inplace=True. See also Series.sort_indexSort Series by the index. DataFrame.sort_valuesSort DataFrame by the value. Series.sort_valuesSort Series by the value. Examples >>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150], ... columns=['A']) >>> df.sort_index() A 1 4 29 2 100 1 150 5 234 3 By default, it sorts in ascending order, to sort in descending order, use ascending=False >>> df.sort_index(ascending=False) A 234 3 150 5 100 1 29 2 1 4 A key function can be specified which is applied to the index before sorting. For a MultiIndex this is applied to each level separately. >>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd']) >>> df.sort_index(key=lambda x: x.str.lower()) a A 1 b 2 C 3 d 4
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