doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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pandas.plotting.table pandas.plotting.table(ax, data, rowLabels=None, colLabels=None, **kwargs)[source]
Helper function to convert DataFrame and Series to matplotlib.table. Parameters
ax:Matplotlib axes object
data:DataFrame or Series
Data for table contents. **kwargs
Keyword arguments to be passed to mat... | pandas.reference.api.pandas.plotting.table |
pandas.qcut pandas.qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise')[source]
Quantile-based discretization function. Discretize variable into equal-sized buckets based on rank or based on sample quantiles. For example 1000 values for 10 quantiles would produce a Categorical object indicating q... | pandas.reference.api.pandas.qcut |
pandas.RangeIndex classpandas.RangeIndex(start=None, stop=None, step=None, dtype=None, copy=False, name=None)[source]
Immutable Index implementing a monotonic integer range. RangeIndex is a memory-saving special case of Int64Index limited to representing monotonic ranges. Using RangeIndex may in some instances impr... | pandas.reference.api.pandas.rangeindex |
pandas.RangeIndex.from_range classmethodRangeIndex.from_range(data, name=None, dtype=None)[source]
Create RangeIndex from a range object. Returns
RangeIndex | pandas.reference.api.pandas.rangeindex.from_range |
pandas.RangeIndex.start propertyRangeIndex.start
The value of the start parameter (0 if this was not supplied). | pandas.reference.api.pandas.rangeindex.start |
pandas.RangeIndex.step propertyRangeIndex.step
The value of the step parameter (1 if this was not supplied). | pandas.reference.api.pandas.rangeindex.step |
pandas.RangeIndex.stop propertyRangeIndex.stop
The value of the stop parameter. | pandas.reference.api.pandas.rangeindex.stop |
pandas.read_clipboard pandas.read_clipboard(sep='\\s+', **kwargs)[source]
Read text from clipboard and pass to read_csv. Parameters
sep:str, default ‘s+’
A string or regex delimiter. The default of ‘s+’ denotes one or more whitespace characters. **kwargs
See read_csv for the full argument list. Returns
... | pandas.reference.api.pandas.read_clipboard |
pandas.read_csv pandas.read_csv(filepath_or_buffer, sep=NoDefault.no_default, delimiter=None, header='infer', names=NoDefault.no_default, index_col=None, usecols=None, squeeze=None, prefix=NoDefault.no_default, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipi... | pandas.reference.api.pandas.read_csv |
pandas.read_excel pandas.read_excel(io, sheet_name=0, header=0, names=None, index_col=None, usecols=None, squeeze=None, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, parse_dates=False, date... | pandas.reference.api.pandas.read_excel |
pandas.read_feather pandas.read_feather(path, columns=None, use_threads=True, storage_options=None)[source]
Load a feather-format object from the file path. Parameters
path:str, path object, or file-like object
String, path object (implementing os.PathLike[str]), or file-like object implementing a binary read... | pandas.reference.api.pandas.read_feather |
pandas.read_fwf pandas.read_fwf(filepath_or_buffer, colspecs='infer', widths=None, infer_nrows=100, **kwds)[source]
Read a table of fixed-width formatted lines into DataFrame. Also supports optionally iterating or breaking of the file into chunks. Additional help can be found in the online docs for IO Tools. Param... | pandas.reference.api.pandas.read_fwf |
pandas.read_gbq pandas.read_gbq(query, project_id=None, index_col=None, col_order=None, reauth=False, auth_local_webserver=False, dialect=None, location=None, configuration=None, credentials=None, use_bqstorage_api=None, max_results=None, progress_bar_type=None)[source]
Load data from Google BigQuery. This function... | pandas.reference.api.pandas.read_gbq |
pandas.read_hdf pandas.read_hdf(path_or_buf, key=None, mode='r', errors='strict', where=None, start=None, stop=None, columns=None, iterator=False, chunksize=None, **kwargs)[source]
Read from the store, close it if we opened it. Retrieve pandas object stored in file, optionally based on where criteria. Warning Pand... | pandas.reference.api.pandas.read_hdf |
pandas.read_html pandas.read_html(io, match='.+', flavor=None, header=None, index_col=None, skiprows=None, attrs=None, parse_dates=False, thousands=',', encoding=None, decimal='.', converters=None, na_values=None, keep_default_na=True, displayed_only=True)[source]
Read HTML tables into a list of DataFrame objects. ... | pandas.reference.api.pandas.read_html |
pandas.read_json pandas.read_json(path_or_buf=None, orient=None, typ='frame', dtype=None, convert_axes=None, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, encoding_errors='strict', lines=False, chunksize=None, compression='infer', nrows=None, storage_opt... | pandas.reference.api.pandas.read_json |
pandas.read_orc pandas.read_orc(path, columns=None, **kwargs)[source]
Load an ORC object from the file path, returning a DataFrame. New in version 1.0.0. Parameters
path:str, path object, or file-like object
String, path object (implementing os.PathLike[str]), or file-like object implementing a binary read(... | pandas.reference.api.pandas.read_orc |
pandas.read_parquet pandas.read_parquet(path, engine='auto', columns=None, storage_options=None, use_nullable_dtypes=False, **kwargs)[source]
Load a parquet object from the file path, returning a DataFrame. Parameters
path:str, path object or file-like object
String, path object (implementing os.PathLike[str]... | pandas.reference.api.pandas.read_parquet |
pandas.read_pickle pandas.read_pickle(filepath_or_buffer, compression='infer', storage_options=None)[source]
Load pickled pandas object (or any object) from file. Warning Loading pickled data received from untrusted sources can be unsafe. See here. Parameters
filepath_or_buffer:str, path object, or file-like ... | pandas.reference.api.pandas.read_pickle |
pandas.read_sas pandas.read_sas(filepath_or_buffer, format=None, index=None, encoding=None, chunksize=None, iterator=False)[source]
Read SAS files stored as either XPORT or SAS7BDAT format files. Parameters
filepath_or_buffer:str, path object, or file-like object
String, path object (implementing os.PathLike[... | pandas.reference.api.pandas.read_sas |
pandas.read_spss pandas.read_spss(path, usecols=None, convert_categoricals=True)[source]
Load an SPSS file from the file path, returning a DataFrame. New in version 0.25.0. Parameters
path:str or Path
File path.
usecols:list-like, optional
Return a subset of the columns. If None, return all columns.
c... | pandas.reference.api.pandas.read_spss |
pandas.read_sql pandas.read_sql(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, columns=None, chunksize=None)[source]
Read SQL query or database table into a DataFrame. This function is a convenience wrapper around read_sql_table and read_sql_query (for backward compatibility). It will d... | pandas.reference.api.pandas.read_sql |
pandas.read_sql_query pandas.read_sql_query(sql, con, index_col=None, coerce_float=True, params=None, parse_dates=None, chunksize=None, dtype=None)[source]
Read SQL query into a DataFrame. Returns a DataFrame corresponding to the result set of the query string. Optionally provide an index_col parameter to use one o... | pandas.reference.api.pandas.read_sql_query |
pandas.read_sql_table pandas.read_sql_table(table_name, con, schema=None, index_col=None, coerce_float=True, parse_dates=None, columns=None, chunksize=None)[source]
Read SQL database table into a DataFrame. Given a table name and a SQLAlchemy connectable, returns a DataFrame. This function does not support DBAPI co... | pandas.reference.api.pandas.read_sql_table |
pandas.read_stata pandas.read_stata(filepath_or_buffer, convert_dates=True, convert_categoricals=True, index_col=None, convert_missing=False, preserve_dtypes=True, columns=None, order_categoricals=True, chunksize=None, iterator=False, compression='infer', storage_options=None)[source]
Read Stata file into DataFrame... | pandas.reference.api.pandas.read_stata |
pandas.read_table pandas.read_table(filepath_or_buffer, sep=NoDefault.no_default, delimiter=None, header='infer', names=NoDefault.no_default, index_col=None, usecols=None, squeeze=None, prefix=NoDefault.no_default, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, s... | pandas.reference.api.pandas.read_table |
pandas.read_xml pandas.read_xml(path_or_buffer, xpath='./*', namespaces=None, elems_only=False, attrs_only=False, names=None, encoding='utf-8', parser='lxml', stylesheet=None, compression='infer', storage_options=None)[source]
Read XML document into a DataFrame object. New in version 1.3.0. Parameters
path_or... | pandas.reference.api.pandas.read_xml |
pandas.reset_option pandas.reset_option(pat)=<pandas._config.config.CallableDynamicDoc object>
Reset one or more options to their default value. Pass “all” as argument to reset all options. Available options: compute.[use_bottleneck, use_numba, use_numexpr] display.[chop_threshold, colheader_justify, column_space,... | pandas.reference.api.pandas.reset_option |
pandas.Series classpandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)[source]
One-dimensional ndarray with axis labels (including time series). Labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host o... | pandas.reference.api.pandas.series |
pandas.Series.__array__ Series.__array__(dtype=None)[source]
Return the values as a NumPy array. Users should not call this directly. Rather, it is invoked by numpy.array() and numpy.asarray(). Parameters
dtype:str or numpy.dtype, optional
The dtype to use for the resulting NumPy array. By default, the dtype ... | pandas.reference.api.pandas.series.__array__ |
pandas.Series.__iter__ Series.__iter__()[source]
Return an iterator of the values. These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period) Returns
iterator | pandas.reference.api.pandas.series.__iter__ |
pandas.Series.abs Series.abs()[source]
Return a Series/DataFrame with absolute numeric value of each element. This function only applies to elements that are all numeric. Returns
abs
Series/DataFrame containing the absolute value of each element. See also numpy.absolute
Calculate the absolute value eleme... | pandas.reference.api.pandas.series.abs |
pandas.Series.add Series.add(other, level=None, fill_value=None, axis=0)[source]
Return Addition of series and other, element-wise (binary operator add). Equivalent to series + other, but with support to substitute a fill_value for missing data in either one of the inputs. Parameters
other:Series or scalar valu... | pandas.reference.api.pandas.series.add |
pandas.Series.add_prefix Series.add_prefix(prefix)[source]
Prefix labels with string prefix. For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed. Parameters
prefix:str
The string to add before each label. Returns
Series or DataFrame
New Series or DataFrame with updated... | pandas.reference.api.pandas.series.add_prefix |
pandas.Series.add_suffix Series.add_suffix(suffix)[source]
Suffix labels with string suffix. For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed. Parameters
suffix:str
The string to add after each label. Returns
Series or DataFrame
New Series or DataFrame with updated ... | pandas.reference.api.pandas.series.add_suffix |
pandas.Series.agg Series.agg(func=None, axis=0, *args, **kwargs)[source]
Aggregate using one or more operations over the specified axis. Parameters
func:function, str, list or dict
Function to use for aggregating the data. If a function, must either work when passed a Series or when passed to Series.apply. Ac... | pandas.reference.api.pandas.series.agg |
pandas.Series.aggregate Series.aggregate(func=None, axis=0, *args, **kwargs)[source]
Aggregate using one or more operations over the specified axis. Parameters
func:function, str, list or dict
Function to use for aggregating the data. If a function, must either work when passed a Series or when passed to Seri... | pandas.reference.api.pandas.series.aggregate |
pandas.Series.align Series.align(other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)[source]
Align two objects on their axes with the specified join method. Join method is specified for each axis Index. Parameters
other:DataFrame or ... | pandas.reference.api.pandas.series.align |
pandas.Series.all Series.all(axis=0, bool_only=None, skipna=True, level=None, **kwargs)[source]
Return whether all elements are True, potentially over an axis. Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty). Parameters
ax... | pandas.reference.api.pandas.series.all |
pandas.Series.any Series.any(axis=0, bool_only=None, skipna=True, level=None, **kwargs)[source]
Return whether any element is True, potentially over an axis. Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty). Paramet... | pandas.reference.api.pandas.series.any |
pandas.Series.append Series.append(to_append, ignore_index=False, verify_integrity=False)[source]
Concatenate two or more Series. Parameters
to_append:Series or list/tuple of Series
Series to append with self.
ignore_index:bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
v... | pandas.reference.api.pandas.series.append |
pandas.Series.apply Series.apply(func, convert_dtype=True, args=(), **kwargs)[source]
Invoke function on values of Series. Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values. Parameters
func:function
Python function or NumPy ufunc to apply. ... | pandas.reference.api.pandas.series.apply |
pandas.Series.argmax Series.argmax(axis=None, skipna=True, *args, **kwargs)[source]
Return int position of the largest value in the Series. If the maximum is achieved in multiple locations, the first row position is returned. Parameters
axis:{None}
Dummy argument for consistency with Series.
skipna:bool, de... | pandas.reference.api.pandas.series.argmax |
pandas.Series.argmin Series.argmin(axis=None, skipna=True, *args, **kwargs)[source]
Return int position of the smallest value in the Series. If the minimum is achieved in multiple locations, the first row position is returned. Parameters
axis:{None}
Dummy argument for consistency with Series.
skipna:bool, d... | pandas.reference.api.pandas.series.argmin |
pandas.Series.argsort Series.argsort(axis=0, kind='quicksort', order=None)[source]
Return the integer indices that would sort the Series values. Override ndarray.argsort. Argsorts the value, omitting NA/null values, and places the result in the same locations as the non-NA values. Parameters
axis:{0 or “index”}... | pandas.reference.api.pandas.series.argsort |
pandas.Series.array propertySeries.array
The ExtensionArray of the data backing this Series or Index. Returns
ExtensionArray
An ExtensionArray of the values stored within. For extension types, this is the actual array. For NumPy native types, this is a thin (no copy) wrapper around numpy.ndarray. .array differs... | pandas.reference.api.pandas.series.array |
pandas.Series.asfreq Series.asfreq(freq, method=None, how=None, normalize=False, fill_value=None)[source]
Convert time series to specified frequency. Returns the original data conformed to a new index with the specified frequency. If the index of this Series is a PeriodIndex, the new index is the result of transfor... | pandas.reference.api.pandas.series.asfreq |
pandas.Series.asof Series.asof(where, subset=None)[source]
Return the last row(s) without any NaNs before where. The last row (for each element in where, if list) without any NaN is taken. In case of a DataFrame, the last row without NaN considering only the subset of columns (if not None) If there is no good value... | pandas.reference.api.pandas.series.asof |
pandas.Series.astype Series.astype(dtype, copy=True, errors='raise')[source]
Cast a pandas object to a specified dtype dtype. Parameters
dtype:data type, or dict of column name -> data type
Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, …}, wher... | pandas.reference.api.pandas.series.astype |
pandas.Series.at propertySeries.at
Access a single value for a row/column label pair. Similar to loc, in that both provide label-based lookups. Use at if you only need to get or set a single value in a DataFrame or Series. Raises
KeyError
If ‘label’ does not exist in DataFrame. See also DataFrame.iat
Acc... | pandas.reference.api.pandas.series.at |
pandas.Series.at_time Series.at_time(time, asof=False, axis=None)[source]
Select values at particular time of day (e.g., 9:30AM). Parameters
time:datetime.time or str
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
Returns
Series or DataFrame
Raises
TypeError
If the index is not a DatetimeIndex ... | pandas.reference.api.pandas.series.at_time |
pandas.Series.attrs propertySeries.attrs
Dictionary of global attributes of this dataset. Warning attrs is experimental and may change without warning. See also DataFrame.flags
Global flags applying to this object. | pandas.reference.api.pandas.series.attrs |
pandas.Series.autocorr Series.autocorr(lag=1)[source]
Compute the lag-N autocorrelation. This method computes the Pearson correlation between the Series and its shifted self. Parameters
lag:int, default 1
Number of lags to apply before performing autocorrelation. Returns
float
The Pearson correlation be... | pandas.reference.api.pandas.series.autocorr |
pandas.Series.axes propertySeries.axes
Return a list of the row axis labels. | pandas.reference.api.pandas.series.axes |
pandas.Series.backfill Series.backfill(axis=None, inplace=False, limit=None, downcast=None)[source]
Synonym for DataFrame.fillna() with method='bfill'. Returns
Series/DataFrame or None
Object with missing values filled or None if inplace=True. | pandas.reference.api.pandas.series.backfill |
pandas.Series.between Series.between(left, right, inclusive='both')[source]
Return boolean Series equivalent to left <= series <= right. This function returns a boolean vector containing True wherever the corresponding Series element is between the boundary values left and right. NA values are treated as False. Pa... | pandas.reference.api.pandas.series.between |
pandas.Series.between_time Series.between_time(start_time, end_time, include_start=NoDefault.no_default, include_end=NoDefault.no_default, inclusive=None, axis=None)[source]
Select values between particular times of the day (e.g., 9:00-9:30 AM). By setting start_time to be later than end_time, you can get the times... | pandas.reference.api.pandas.series.between_time |
pandas.Series.bfill Series.bfill(axis=None, inplace=False, limit=None, downcast=None)[source]
Synonym for DataFrame.fillna() with method='bfill'. Returns
Series/DataFrame or None
Object with missing values filled or None if inplace=True. | pandas.reference.api.pandas.series.bfill |
pandas.Series.bool Series.bool()[source]
Return the bool of a single element Series or DataFrame. This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also raise a... | pandas.reference.api.pandas.series.bool |
pandas.Series.cat Series.cat()[source]
Accessor object for categorical properties of the Series values. Be aware that assigning to categories is a inplace operation, while all methods return new categorical data per default (but can be called with inplace=True). Parameters
data:Series or CategoricalIndex
Ex... | pandas.reference.api.pandas.series.cat |
pandas.Series.cat.add_categories Series.cat.add_categories(*args, **kwargs)[source]
Add new categories. new_categories will be included at the last/highest place in the categories and will be unused directly after this call. Parameters
new_categories:category or list-like of category
The new categories to be ... | pandas.reference.api.pandas.series.cat.add_categories |
pandas.Series.cat.as_ordered Series.cat.as_ordered(*args, **kwargs)[source]
Set the Categorical to be ordered. Parameters
inplace:bool, default False
Whether or not to set the ordered attribute in-place or return a copy of this categorical with ordered set to True. Returns
Categorical or None
Ordered Ca... | pandas.reference.api.pandas.series.cat.as_ordered |
pandas.Series.cat.as_unordered Series.cat.as_unordered(*args, **kwargs)[source]
Set the Categorical to be unordered. Parameters
inplace:bool, default False
Whether or not to set the ordered attribute in-place or return a copy of this categorical with ordered set to False. Returns
Categorical or None
Uno... | pandas.reference.api.pandas.series.cat.as_unordered |
pandas.Series.cat.categories Series.cat.categories
The categories of this categorical. Setting assigns new values to each category (effectively a rename of each individual category). The assigned value has to be a list-like object. All items must be unique and the number of items in the new categories must be the s... | pandas.reference.api.pandas.series.cat.categories |
pandas.Series.cat.codes Series.cat.codes
Return Series of codes as well as the index. | pandas.reference.api.pandas.series.cat.codes |
pandas.Series.cat.ordered Series.cat.ordered
Whether the categories have an ordered relationship. | pandas.reference.api.pandas.series.cat.ordered |
pandas.Series.cat.remove_categories Series.cat.remove_categories(*args, **kwargs)[source]
Remove the specified categories. removals must be included in the old categories. Values which were in the removed categories will be set to NaN Parameters
removals:category or list of categories
The categories which sho... | pandas.reference.api.pandas.series.cat.remove_categories |
pandas.Series.cat.remove_unused_categories Series.cat.remove_unused_categories(*args, **kwargs)[source]
Remove categories which are not used. Parameters
inplace:bool, default False
Whether or not to drop unused categories inplace or return a copy of this categorical with unused categories dropped. Deprecated... | pandas.reference.api.pandas.series.cat.remove_unused_categories |
pandas.Series.cat.rename_categories Series.cat.rename_categories(*args, **kwargs)[source]
Rename categories. Parameters
new_categories:list-like, dict-like or callable
New categories which will replace old categories. list-like: all items must be unique and the number of items in the new categories must matc... | pandas.reference.api.pandas.series.cat.rename_categories |
pandas.Series.cat.reorder_categories Series.cat.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_categories:Index-like
The categories in new order.
ordered:bool, optional... | pandas.reference.api.pandas.series.cat.reorder_categories |
pandas.Series.cat.set_categories Series.cat.set_categories(*args, **kwargs)[source]
Set the categories to the specified new_categories. new_categories can include new categories (which will result in unused categories) or remove old categories (which results in values set to NaN). If rename==True, the categories wi... | pandas.reference.api.pandas.series.cat.set_categories |
pandas.Series.clip Series.clip(lower=None, upper=None, axis=None, inplace=False, *args, **kwargs)[source]
Trim values at input threshold(s). Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the speci... | pandas.reference.api.pandas.series.clip |
pandas.Series.combine Series.combine(other, func, fill_value=None)[source]
Combine the Series with a Series or scalar according to func. Combine the Series and other using func to perform elementwise selection for combined Series. fill_value is assumed when value is missing at some index from one of the two objects... | pandas.reference.api.pandas.series.combine |
pandas.Series.combine_first Series.combine_first(other)[source]
Update null elements with value in the same location in ‘other’. Combine two Series objects by filling null values in one Series with non-null values from the other Series. Result index will be the union of the two indexes. Parameters
other:Series
... | pandas.reference.api.pandas.series.combine_first |
pandas.Series.compare Series.compare(other, align_axis=1, keep_shape=False, keep_equal=False)[source]
Compare to another Series and show the differences. New in version 1.1.0. Parameters
other:Series
Object to compare with.
align_axis:{0 or ‘index’, 1 or ‘columns’}, default 1
Determine which axis to ali... | pandas.reference.api.pandas.series.compare |
pandas.Series.convert_dtypes Series.convert_dtypes(infer_objects=True, convert_string=True, convert_integer=True, convert_boolean=True, convert_floating=True)[source]
Convert columns to best possible dtypes using dtypes supporting pd.NA. New in version 1.0.0. Parameters
infer_objects:bool, default True
Whet... | pandas.reference.api.pandas.series.convert_dtypes |
pandas.Series.copy Series.copy(deep=True)[source]
Make a copy of this object’s indices and data. When deep=True (default), a new object will be created with a copy of the calling object’s data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below... | pandas.reference.api.pandas.series.copy |
pandas.Series.corr Series.corr(other, method='pearson', min_periods=None)[source]
Compute correlation with other Series, excluding missing values. Parameters
other:Series
Series with which to compute the correlation.
method:{‘pearson’, ‘kendall’, ‘spearman’} or callable
Method used to compute correlation:... | pandas.reference.api.pandas.series.corr |
pandas.Series.count Series.count(level=None)[source]
Return number of non-NA/null observations in the Series. Parameters
level:int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a smaller Series. Returns
int or Series (if level spec... | pandas.reference.api.pandas.series.count |
pandas.Series.cov Series.cov(other, min_periods=None, ddof=1)[source]
Compute covariance with Series, excluding missing values. Parameters
other:Series
Series with which to compute the covariance.
min_periods:int, optional
Minimum number of observations needed to have a valid result.
ddof:int, default 1... | pandas.reference.api.pandas.series.cov |
pandas.Series.cummax Series.cummax(axis=None, skipna=True, *args, **kwargs)[source]
Return cumulative maximum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative maximum. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The index or the name of th... | pandas.reference.api.pandas.series.cummax |
pandas.Series.cummin Series.cummin(axis=None, skipna=True, *args, **kwargs)[source]
Return cumulative minimum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative minimum. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The index or the name of th... | pandas.reference.api.pandas.series.cummin |
pandas.Series.cumprod Series.cumprod(axis=None, skipna=True, *args, **kwargs)[source]
Return cumulative product over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative product. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The index or the name of ... | pandas.reference.api.pandas.series.cumprod |
pandas.Series.cumsum 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 0
The index or the name of the axis. ... | pandas.reference.api.pandas.series.cumsum |
pandas.Series.describe Series.describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False)[source]
Generate descriptive statistics. Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. Analyzes both num... | pandas.reference.api.pandas.series.describe |
pandas.Series.diff Series.diff(periods=1)[source]
First discrete difference of element. Calculates the difference of a Series element compared with another element in the Series (default is element in previous row). Parameters
periods:int, default 1
Periods to shift for calculating difference, accepts negativ... | pandas.reference.api.pandas.series.diff |
pandas.Series.div Series.div(other, level=None, fill_value=None, axis=0)[source]
Return Floating division of series and other, element-wise (binary operator truediv). Equivalent to series / other, but with support to substitute a fill_value for missing data in either one of the inputs. Parameters
other:Series o... | pandas.reference.api.pandas.series.div |
pandas.Series.divide Series.divide(other, level=None, fill_value=None, axis=0)[source]
Return Floating division of series and other, element-wise (binary operator truediv). Equivalent to series / other, but with support to substitute a fill_value for missing data in either one of the inputs. Parameters
other:Se... | pandas.reference.api.pandas.series.divide |
pandas.Series.divmod Series.divmod(other, level=None, fill_value=None, axis=0)[source]
Return Integer division and modulo of series and other, element-wise (binary operator divmod). Equivalent to divmod(series, other), but with support to substitute a fill_value for missing data in either one of the inputs. Parame... | pandas.reference.api.pandas.series.divmod |
pandas.Series.dot Series.dot(other)[source]
Compute the dot product between the Series and the columns of other. This method computes the dot product between the Series and another one, or the Series and each columns of a DataFrame, or the Series and each columns of an array. It can also be called using self @ othe... | pandas.reference.api.pandas.series.dot |
pandas.Series.drop Series.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')[source]
Return Series with specified index labels removed. Remove elements of a Series based on specifying the index labels. When using a multi-index, labels on different levels can be removed by... | pandas.reference.api.pandas.series.drop |
pandas.Series.drop_duplicates Series.drop_duplicates(keep='first', inplace=False)[source]
Return Series with duplicate values removed. Parameters
keep:{‘first’, ‘last’, False}, default ‘first’
Method to handle dropping duplicates: ‘first’ : Drop duplicates except for the first occurrence. ‘last’ : Drop dupli... | pandas.reference.api.pandas.series.drop_duplicates |
pandas.Series.droplevel Series.droplevel(level, axis=0)[source]
Return Series/DataFrame with requested index / column level(s) removed. Parameters
level:int, str, or list-like
If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels.
axis:{0 or ‘... | pandas.reference.api.pandas.series.droplevel |
pandas.Series.dropna Series.dropna(axis=0, inplace=False, how=None)[source]
Return a new Series with missing values removed. See the User Guide for more on which values are considered missing, and how to work with missing data. Parameters
axis:{0 or ‘index’}, default 0
There is only one axis to drop values fr... | pandas.reference.api.pandas.series.dropna |
pandas.Series.dt Series.dt()[source]
Accessor object for datetimelike properties of the Series values. Examples
>>> seconds_series = pd.Series(pd.date_range("2000-01-01", periods=3, freq="s"))
>>> seconds_series
0 2000-01-01 00:00:00
1 2000-01-01 00:00:01
2 2000-01-01 00:00:02
dtype: datetime64[ns]
>>> secon... | pandas.reference.api.pandas.series.dt |
pandas.Series.dt.ceil Series.dt.ceil(*args, **kwargs)[source]
Perform ceil operation on the data to the specified freq. Parameters
freq:str or Offset
The frequency level to ceil the index to. Must be a fixed frequency like ‘S’ (second) not ‘ME’ (month end). See frequency aliases for a list of possible freq va... | pandas.reference.api.pandas.series.dt.ceil |
pandas.Series.dt.components Series.dt.components
Return a Dataframe of the components of the Timedeltas. Returns
DataFrame
Examples
>>> s = pd.Series(pd.to_timedelta(np.arange(5), unit='s'))
>>> s
0 0 days 00:00:00
1 0 days 00:00:01
2 0 days 00:00:02
3 0 days 00:00:03
4 0 days 00:00:04
dtype: timed... | pandas.reference.api.pandas.series.dt.components |
pandas.Series.dt.date Series.dt.date
Returns numpy array of python datetime.date objects. Namely, the date part of Timestamps without time and timezone information. | pandas.reference.api.pandas.series.dt.date |
pandas.Series.dt.day Series.dt.day
The day of the datetime. Examples
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="D")
... )
>>> datetime_series
0 2000-01-01
1 2000-01-02
2 2000-01-03
dtype: datetime64[ns]
>>> datetime_series.dt.day
0 1
1 2
2 3
dtype: int64 | pandas.reference.api.pandas.series.dt.day |
pandas.Series.dt.day_name Series.dt.day_name(*args, **kwargs)[source]
Return the day names of the DateTimeIndex with specified locale. Parameters
locale:str, optional
Locale determining the language in which to return the day name. Default is English locale. Returns
Index
Index of day names. Example... | pandas.reference.api.pandas.series.dt.day_name |
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