doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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pandas.CategoricalIndex.as_unordered CategoricalIndex.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 ... | pandas.reference.api.pandas.categoricalindex.as_unordered |
pandas.CategoricalIndex.categories propertyCategoricalIndex.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 cate... | pandas.reference.api.pandas.categoricalindex.categories |
pandas.CategoricalIndex.codes propertyCategoricalIndex.codes
The category codes of this categorical. Codes are an array of integers which are the positions of the actual values in the categories array. There is no setter, use the other categorical methods and the normal item setter to change values in the categoric... | pandas.reference.api.pandas.categoricalindex.codes |
pandas.CategoricalIndex.equals CategoricalIndex.equals(other)[source]
Determine if two CategoricalIndex objects contain the same elements. Returns
bool
If two CategoricalIndex objects have equal elements True, otherwise False. | pandas.reference.api.pandas.categoricalindex.equals |
pandas.CategoricalIndex.map CategoricalIndex.map(mapper)[source]
Map values using input an input mapping or function. Maps the values (their categories, not the codes) of the index to new categories. If the mapping correspondence is one-to-one the result is a CategoricalIndex which has the same order property as th... | pandas.reference.api.pandas.categoricalindex.map |
pandas.CategoricalIndex.ordered propertyCategoricalIndex.ordered
Whether the categories have an ordered relationship. | pandas.reference.api.pandas.categoricalindex.ordered |
pandas.CategoricalIndex.remove_categories CategoricalIndex.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 categori... | pandas.reference.api.pandas.categoricalindex.remove_categories |
pandas.CategoricalIndex.remove_unused_categories CategoricalIndex.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.... | pandas.reference.api.pandas.categoricalindex.remove_unused_categories |
pandas.CategoricalIndex.rename_categories CategoricalIndex.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 categori... | pandas.reference.api.pandas.categoricalindex.rename_categories |
pandas.CategoricalIndex.reorder_categories 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_categories:Index-like
The categories in new order.
ordered:bo... | pandas.reference.api.pandas.categoricalindex.reorder_categories |
pandas.CategoricalIndex.set_categories CategoricalIndex.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 c... | pandas.reference.api.pandas.categoricalindex.set_categories |
pandas.concat pandas.concat(objs, axis=0, join='outer', ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=False, copy=True)[source]
Concatenate pandas objects along a particular axis with optional set logic along the other axes. Can also add a layer of hierarchical indexing on the... | pandas.reference.api.pandas.concat |
pandas.core.groupby.DataFrameGroupBy.aggregate DataFrameGroupBy.aggregate(func=None, *args, engine=None, engine_kwargs=None, **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, mu... | pandas.reference.api.pandas.core.groupby.dataframegroupby.aggregate |
pandas.core.groupby.DataFrameGroupBy.all DataFrameGroupBy.all(skipna=True)[source]
Return True if all values in the group are truthful, else False. Parameters
skipna:bool, default True
Flag to ignore nan values during truth testing. Returns
Series or DataFrame
DataFrame or Series of boolean values, wher... | pandas.reference.api.pandas.core.groupby.dataframegroupby.all |
pandas.core.groupby.DataFrameGroupBy.any DataFrameGroupBy.any(skipna=True)[source]
Return True if any value in the group is truthful, else False. Parameters
skipna:bool, default True
Flag to ignore nan values during truth testing. Returns
Series or DataFrame
DataFrame or Series of boolean values, where ... | pandas.reference.api.pandas.core.groupby.dataframegroupby.any |
pandas.core.groupby.DataFrameGroupBy.backfill DataFrameGroupBy.backfill(limit=None)[source]
Backward fill the values. Parameters
limit:int, optional
Limit of how many values to fill. Returns
Series or DataFrame
Object with missing values filled. See also Series.bfill
Backward fill the missing val... | pandas.reference.api.pandas.core.groupby.dataframegroupby.backfill |
pandas.core.groupby.DataFrameGroupBy.bfill DataFrameGroupBy.bfill(limit=None)[source]
Backward fill the values. Parameters
limit:int, optional
Limit of how many values to fill. Returns
Series or DataFrame
Object with missing values filled. See also Series.bfill
Backward fill the missing values in... | pandas.reference.api.pandas.core.groupby.dataframegroupby.bfill |
pandas.core.groupby.DataFrameGroupBy.boxplot DataFrameGroupBy.boxplot(subplots=True, column=None, fontsize=None, rot=0, grid=True, ax=None, figsize=None, layout=None, sharex=False, sharey=True, backend=None, **kwargs)[source]
Make box plots from DataFrameGroupBy data. Parameters
grouped:Grouped DataFrame
subp... | pandas.reference.api.pandas.core.groupby.dataframegroupby.boxplot |
pandas.core.groupby.DataFrameGroupBy.corr propertyDataFrameGroupBy.corr
Compute pairwise correlation of columns, excluding NA/null values. Parameters
method:{‘pearson’, ‘kendall’, ‘spearman’} or callable
Method of correlation: pearson : standard correlation coefficient kendall : Kendall Tau correlation coeff... | pandas.reference.api.pandas.core.groupby.dataframegroupby.corr |
pandas.core.groupby.DataFrameGroupBy.corrwith propertyDataFrameGroupBy.corrwith
Compute pairwise correlation. Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations. Parameters... | pandas.reference.api.pandas.core.groupby.dataframegroupby.corrwith |
pandas.core.groupby.DataFrameGroupBy.count DataFrameGroupBy.count()[source]
Compute count of group, excluding missing values. Returns
Series or DataFrame
Count of values within each group. See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a function groupby to each r... | pandas.reference.api.pandas.core.groupby.dataframegroupby.count |
pandas.core.groupby.DataFrameGroupBy.cov propertyDataFrameGroupBy.cov
Compute pairwise covariance of columns, excluding NA/null values. Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix of the columns of the DataFrame. Both NA and null values are autom... | pandas.reference.api.pandas.core.groupby.dataframegroupby.cov |
pandas.core.groupby.DataFrameGroupBy.cumcount DataFrameGroupBy.cumcount(ascending=True)[source]
Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to
self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
Parameters
ascending:bool, default True
If Fal... | pandas.reference.api.pandas.core.groupby.dataframegroupby.cumcount |
pandas.core.groupby.DataFrameGroupBy.cummax DataFrameGroupBy.cummax(axis=0, **kwargs)[source]
Cumulative max for each group. Returns
Series or DataFrame
See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a function groupby to each row or column of a DataFrame. | pandas.reference.api.pandas.core.groupby.dataframegroupby.cummax |
pandas.core.groupby.DataFrameGroupBy.cummin DataFrameGroupBy.cummin(axis=0, **kwargs)[source]
Cumulative min for each group. Returns
Series or DataFrame
See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a function groupby to each row or column of a DataFrame. | pandas.reference.api.pandas.core.groupby.dataframegroupby.cummin |
pandas.core.groupby.DataFrameGroupBy.cumprod DataFrameGroupBy.cumprod(axis=0, *args, **kwargs)[source]
Cumulative product for each group. Returns
Series or DataFrame
See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a function groupby to each row or column of a DataFra... | pandas.reference.api.pandas.core.groupby.dataframegroupby.cumprod |
pandas.core.groupby.DataFrameGroupBy.cumsum DataFrameGroupBy.cumsum(axis=0, *args, **kwargs)[source]
Cumulative sum for each group. Returns
Series or DataFrame
See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a function groupby to each row or column of a DataFrame. | pandas.reference.api.pandas.core.groupby.dataframegroupby.cumsum |
pandas.core.groupby.DataFrameGroupBy.describe DataFrameGroupBy.describe(**kwargs)[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 numeric and object series, as wel... | pandas.reference.api.pandas.core.groupby.dataframegroupby.describe |
pandas.core.groupby.DataFrameGroupBy.diff propertyDataFrameGroupBy.diff
First discrete difference of element. Calculates the difference of a Dataframe element compared with another element in the Dataframe (default is element in previous row). Parameters
periods:int, default 1
Periods to shift for calculating... | pandas.reference.api.pandas.core.groupby.dataframegroupby.diff |
pandas.core.groupby.DataFrameGroupBy.ffill DataFrameGroupBy.ffill(limit=None)[source]
Forward fill the values. Parameters
limit:int, optional
Limit of how many values to fill. Returns
Series or DataFrame
Object with missing values filled. See also Series.ffill
Returns Series with minimum number o... | pandas.reference.api.pandas.core.groupby.dataframegroupby.ffill |
pandas.core.groupby.DataFrameGroupBy.fillna propertyDataFrameGroupBy.fillna
Fill NA/NaN values using the specified method. Parameters
value:scalar, dict, Series, or DataFrame
Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a ... | pandas.reference.api.pandas.core.groupby.dataframegroupby.fillna |
pandas.core.groupby.DataFrameGroupBy.filter DataFrameGroupBy.filter(func, dropna=True, *args, **kwargs)[source]
Return a copy of a DataFrame excluding filtered elements. Elements from groups are filtered if they do not satisfy the boolean criterion specified by func. Parameters
func:function
Function to apply... | pandas.reference.api.pandas.core.groupby.dataframegroupby.filter |
pandas.core.groupby.DataFrameGroupBy.hist propertyDataFrameGroupBy.hist
Make a histogram of the DataFrame’s columns. A histogram is a representation of the distribution of data. This function calls matplotlib.pyplot.hist(), on each series in the DataFrame, resulting in one histogram per column. Parameters
data:... | pandas.reference.api.pandas.core.groupby.dataframegroupby.hist |
pandas.core.groupby.DataFrameGroupBy.idxmax DataFrameGroupBy.idxmax(axis=0, skipna=True)[source]
Return index of first occurrence of maximum over requested axis. NA/null values are excluded. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ fo... | pandas.reference.api.pandas.core.groupby.dataframegroupby.idxmax |
pandas.core.groupby.DataFrameGroupBy.idxmin DataFrameGroupBy.idxmin(axis=0, skipna=True)[source]
Return index of first occurrence of minimum over requested axis. NA/null values are excluded. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ fo... | pandas.reference.api.pandas.core.groupby.dataframegroupby.idxmin |
pandas.core.groupby.DataFrameGroupBy.mad propertyDataFrameGroupBy.mad
Return the mean absolute deviation of the values over the requested axis. Parameters
axis:{index (0), columns (1)}
Axis for the function to be applied on.
skipna:bool, default True
Exclude NA/null values when computing the result.
lev... | pandas.reference.api.pandas.core.groupby.dataframegroupby.mad |
pandas.core.groupby.DataFrameGroupBy.nunique DataFrameGroupBy.nunique(dropna=True)[source]
Return DataFrame with counts of unique elements in each position. Parameters
dropna:bool, default True
Don’t include NaN in the counts. Returns
nunique: DataFrame
Examples
>>> df = pd.DataFrame({'id': ['spam', ... | pandas.reference.api.pandas.core.groupby.dataframegroupby.nunique |
pandas.core.groupby.DataFrameGroupBy.pad DataFrameGroupBy.pad(limit=None)[source]
Forward fill the values. Parameters
limit:int, optional
Limit of how many values to fill. Returns
Series or DataFrame
Object with missing values filled. See also Series.ffill
Returns Series with minimum number of ch... | pandas.reference.api.pandas.core.groupby.dataframegroupby.pad |
pandas.core.groupby.DataFrameGroupBy.pct_change DataFrameGroupBy.pct_change(periods=1, fill_method='ffill', limit=None, freq=None, axis=0)[source]
Calculate pct_change of each value to previous entry in group. Returns
Series or DataFrame
Percentage changes within each group. See also Series.groupby
Apply... | pandas.reference.api.pandas.core.groupby.dataframegroupby.pct_change |
pandas.core.groupby.DataFrameGroupBy.plot propertyDataFrameGroupBy.plot
Class implementing the .plot attribute for groupby objects. | pandas.reference.api.pandas.core.groupby.dataframegroupby.plot |
pandas.core.groupby.DataFrameGroupBy.quantile DataFrameGroupBy.quantile(q=0.5, interpolation='linear')[source]
Return group values at the given quantile, a la numpy.percentile. Parameters
q:float or array-like, default 0.5 (50% quantile)
Value(s) between 0 and 1 providing the quantile(s) to compute.
interpo... | pandas.reference.api.pandas.core.groupby.dataframegroupby.quantile |
pandas.core.groupby.DataFrameGroupBy.rank DataFrameGroupBy.rank(method='average', ascending=True, na_option='keep', pct=False, axis=0)[source]
Provide the rank of values within each group. Parameters
method:{‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’
average: average rank of group. min: lo... | pandas.reference.api.pandas.core.groupby.dataframegroupby.rank |
pandas.core.groupby.DataFrameGroupBy.resample DataFrameGroupBy.resample(rule, *args, **kwargs)[source]
Provide resampling when using a TimeGrouper. Given a grouper, the function resamples it according to a string “string” -> “frequency”. See the frequency aliases documentation for more details. Parameters
rule:... | pandas.reference.api.pandas.core.groupby.dataframegroupby.resample |
pandas.core.groupby.DataFrameGroupBy.sample DataFrameGroupBy.sample(n=None, frac=None, replace=False, weights=None, random_state=None)[source]
Return a random sample of items from each group. You can use random_state for reproducibility. New in version 1.1.0. Parameters
n:int, optional
Number of items to re... | pandas.reference.api.pandas.core.groupby.dataframegroupby.sample |
pandas.core.groupby.DataFrameGroupBy.shift DataFrameGroupBy.shift(periods=1, freq=None, axis=0, fill_value=None)[source]
Shift each group by periods observations. If freq is passed, the index will be increased using the periods and the freq. Parameters
periods:int, default 1
Number of periods to shift.
freq... | pandas.reference.api.pandas.core.groupby.dataframegroupby.shift |
pandas.core.groupby.DataFrameGroupBy.size DataFrameGroupBy.size()[source]
Compute group sizes. Returns
DataFrame or Series
Number of rows in each group as a Series if as_index is True or a DataFrame if as_index is False. See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Ap... | pandas.reference.api.pandas.core.groupby.dataframegroupby.size |
pandas.core.groupby.DataFrameGroupBy.skew propertyDataFrameGroupBy.skew
Return unbiased skew over requested axis. Normalized by N-1. Parameters
axis:{index (0), columns (1)}
Axis for the function to be applied on.
skipna:bool, default True
Exclude NA/null values when computing the result.
level:int or l... | pandas.reference.api.pandas.core.groupby.dataframegroupby.skew |
pandas.core.groupby.DataFrameGroupBy.take propertyDataFrameGroupBy.take
Return the elements in the given positional indices along an axis. This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the objec... | pandas.reference.api.pandas.core.groupby.dataframegroupby.take |
pandas.core.groupby.DataFrameGroupBy.transform DataFrameGroupBy.transform(func, *args, engine=None, engine_kwargs=None, **kwargs)[source]
Call function producing a like-indexed DataFrame on each group and return a DataFrame having the same indexes as the original object filled with the transformed values. Paramete... | pandas.reference.api.pandas.core.groupby.dataframegroupby.transform |
pandas.core.groupby.DataFrameGroupBy.tshift propertyDataFrameGroupBy.tshift
Shift the time index, using the index’s frequency if available. Deprecated since version 1.1.0: Use shift instead. Parameters
periods:int
Number of periods to move, can be positive or negative.
freq:DateOffset, timedelta, or str, ... | pandas.reference.api.pandas.core.groupby.dataframegroupby.tshift |
pandas.core.groupby.DataFrameGroupBy.value_counts DataFrameGroupBy.value_counts(subset=None, normalize=False, sort=True, ascending=False, dropna=True)[source]
Return a Series or DataFrame containing counts of unique rows. New in version 1.4.0. Parameters
subset:list-like, optional
Columns to use when counti... | pandas.reference.api.pandas.core.groupby.dataframegroupby.value_counts |
pandas.core.groupby.GroupBy.__iter__ GroupBy.__iter__()[source]
Groupby iterator. Returns
Generator yielding sequence of (name, subsetted object)
for each group | pandas.reference.api.pandas.core.groupby.groupby.__iter__ |
pandas.core.groupby.GroupBy.agg GroupBy.agg(func, *args, **kwargs)[source] | pandas.reference.api.pandas.core.groupby.groupby.agg |
pandas.core.groupby.GroupBy.all finalGroupBy.all(skipna=True)[source]
Return True if all values in the group are truthful, else False. Parameters
skipna:bool, default True
Flag to ignore nan values during truth testing. Returns
Series or DataFrame
DataFrame or Series of boolean values, where a value is ... | pandas.reference.api.pandas.core.groupby.groupby.all |
pandas.core.groupby.GroupBy.any finalGroupBy.any(skipna=True)[source]
Return True if any value in the group is truthful, else False. Parameters
skipna:bool, default True
Flag to ignore nan values during truth testing. Returns
Series or DataFrame
DataFrame or Series of boolean values, where a value is Tr... | pandas.reference.api.pandas.core.groupby.groupby.any |
pandas.core.groupby.GroupBy.apply GroupBy.apply(func, *args, **kwargs)[source]
Apply function func group-wise and combine the results together. The function passed to apply must take a dataframe as its first argument and return a DataFrame, Series or scalar. apply will then take care of combining the results back t... | pandas.reference.api.pandas.core.groupby.groupby.apply |
pandas.core.groupby.GroupBy.backfill GroupBy.backfill(limit=None)[source]
Backward fill the values. Parameters
limit:int, optional
Limit of how many values to fill. Returns
Series or DataFrame
Object with missing values filled. See also Series.bfill
Backward fill the missing values in the dataset... | pandas.reference.api.pandas.core.groupby.groupby.backfill |
pandas.core.groupby.GroupBy.bfill finalGroupBy.bfill(limit=None)[source]
Backward fill the values. Parameters
limit:int, optional
Limit of how many values to fill. Returns
Series or DataFrame
Object with missing values filled. See also Series.bfill
Backward fill the missing values in the dataset.... | pandas.reference.api.pandas.core.groupby.groupby.bfill |
pandas.core.groupby.GroupBy.count finalGroupBy.count()[source]
Compute count of group, excluding missing values. Returns
Series or DataFrame
Count of values within each group. See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a function groupby to each row or column ... | pandas.reference.api.pandas.core.groupby.groupby.count |
pandas.core.groupby.GroupBy.cumcount finalGroupBy.cumcount(ascending=True)[source]
Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to
self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
Parameters
ascending:bool, default True
If False, number in... | pandas.reference.api.pandas.core.groupby.groupby.cumcount |
pandas.core.groupby.GroupBy.cummax finalGroupBy.cummax(axis=0, **kwargs)[source]
Cumulative max for each group. Returns
Series or DataFrame
See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a function groupby to each row or column of a DataFrame. | pandas.reference.api.pandas.core.groupby.groupby.cummax |
pandas.core.groupby.GroupBy.cummin finalGroupBy.cummin(axis=0, **kwargs)[source]
Cumulative min for each group. Returns
Series or DataFrame
See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a function groupby to each row or column of a DataFrame. | pandas.reference.api.pandas.core.groupby.groupby.cummin |
pandas.core.groupby.GroupBy.cumprod finalGroupBy.cumprod(axis=0, *args, **kwargs)[source]
Cumulative product for each group. Returns
Series or DataFrame
See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a function groupby to each row or column of a DataFrame. | pandas.reference.api.pandas.core.groupby.groupby.cumprod |
pandas.core.groupby.GroupBy.cumsum finalGroupBy.cumsum(axis=0, *args, **kwargs)[source]
Cumulative sum for each group. Returns
Series or DataFrame
See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a function groupby to each row or column of a DataFrame. | pandas.reference.api.pandas.core.groupby.groupby.cumsum |
pandas.core.groupby.GroupBy.ffill finalGroupBy.ffill(limit=None)[source]
Forward fill the values. Parameters
limit:int, optional
Limit of how many values to fill. Returns
Series or DataFrame
Object with missing values filled. See also Series.ffill
Returns Series with minimum number of char in obj... | pandas.reference.api.pandas.core.groupby.groupby.ffill |
pandas.core.groupby.GroupBy.first finalGroupBy.first(numeric_only=False, min_count=- 1)[source]
Compute first of group values. Parameters
numeric_only:bool, default False
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_count:int, default -1
... | pandas.reference.api.pandas.core.groupby.groupby.first |
pandas.core.groupby.GroupBy.get_group GroupBy.get_group(name, obj=None)[source]
Construct DataFrame from group with provided name. Parameters
name:object
The name of the group to get as a DataFrame.
obj:DataFrame, default None
The DataFrame to take the DataFrame out of. If it is None, the object groupby w... | pandas.reference.api.pandas.core.groupby.groupby.get_group |
pandas.core.groupby.GroupBy.groups propertyGroupBy.groups
Dict {group name -> group labels}. | pandas.reference.api.pandas.core.groupby.groupby.groups |
pandas.core.groupby.GroupBy.head finalGroupBy.head(n=5)[source]
Return first n rows of each group. Similar to .apply(lambda x: x.head(n)), but it returns a subset of rows from the original DataFrame with original index and order preserved (as_index flag is ignored). Parameters
n:int
If positive: number of ent... | pandas.reference.api.pandas.core.groupby.groupby.head |
pandas.core.groupby.GroupBy.indices propertyGroupBy.indices
Dict {group name -> group indices}. | pandas.reference.api.pandas.core.groupby.groupby.indices |
pandas.core.groupby.GroupBy.last finalGroupBy.last(numeric_only=False, min_count=- 1)[source]
Compute last of group values. Parameters
numeric_only:bool, default False
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_count:int, default -1
T... | pandas.reference.api.pandas.core.groupby.groupby.last |
pandas.core.groupby.GroupBy.max finalGroupBy.max(numeric_only=False, min_count=- 1)[source]
Compute max of group values. Parameters
numeric_only:bool, default False
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_count:int, default -1
The ... | pandas.reference.api.pandas.core.groupby.groupby.max |
pandas.core.groupby.GroupBy.mean finalGroupBy.mean(numeric_only=NoDefault.no_default, engine='cython', engine_kwargs=None)[source]
Compute mean of groups, excluding missing values. Parameters
numeric_only:bool, default True
Include only float, int, boolean columns. If None, will attempt to use everything, the... | pandas.reference.api.pandas.core.groupby.groupby.mean |
pandas.core.groupby.GroupBy.median finalGroupBy.median(numeric_only=NoDefault.no_default)[source]
Compute median of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex Parameters
numeric_only:bool, default True
Include only float, int, boolean columns. If None, will... | pandas.reference.api.pandas.core.groupby.groupby.median |
pandas.core.groupby.GroupBy.min finalGroupBy.min(numeric_only=False, min_count=- 1)[source]
Compute min of group values. Parameters
numeric_only:bool, default False
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_count:int, default -1
The ... | pandas.reference.api.pandas.core.groupby.groupby.min |
pandas.core.groupby.GroupBy.ngroup finalGroupBy.ngroup(ascending=True)[source]
Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not... | pandas.reference.api.pandas.core.groupby.groupby.ngroup |
pandas.core.groupby.GroupBy.nth finalGroupBy.nth(n, dropna=None)[source]
Take the nth row from each group if n is an int, otherwise a subset of rows. Can be either a call or an index. dropna is not available with index notation. Index notation accepts a comma separated list of integers and slices. If dropna, will t... | pandas.reference.api.pandas.core.groupby.groupby.nth |
pandas.core.groupby.GroupBy.ohlc finalGroupBy.ohlc()[source]
Compute open, high, low and close values of a group, excluding missing values. For multiple groupings, the result index will be a MultiIndex Returns
DataFrame
Open, high, low and close values within each group. See also Series.groupby
Apply a f... | pandas.reference.api.pandas.core.groupby.groupby.ohlc |
pandas.core.groupby.GroupBy.pad GroupBy.pad(limit=None)[source]
Forward fill the values. Parameters
limit:int, optional
Limit of how many values to fill. Returns
Series or DataFrame
Object with missing values filled. See also Series.ffill
Returns Series with minimum number of char in object. Dat... | pandas.reference.api.pandas.core.groupby.groupby.pad |
pandas.core.groupby.GroupBy.pct_change finalGroupBy.pct_change(periods=1, fill_method='ffill', limit=None, freq=None, axis=0)[source]
Calculate pct_change of each value to previous entry in group. Returns
Series or DataFrame
Percentage changes within each group. See also Series.groupby
Apply a function g... | pandas.reference.api.pandas.core.groupby.groupby.pct_change |
pandas.core.groupby.GroupBy.pipe GroupBy.pipe(func, *args, **kwargs)[source]
Apply a function func with arguments to this GroupBy object and return the function’s result. Use .pipe when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy or Resampler objects. Inste... | pandas.reference.api.pandas.core.groupby.groupby.pipe |
pandas.core.groupby.GroupBy.prod finalGroupBy.prod(numeric_only=NoDefault.no_default, min_count=0)[source]
Compute prod of group values. Parameters
numeric_only:bool, default True
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data.
min_count:int, de... | pandas.reference.api.pandas.core.groupby.groupby.prod |
pandas.core.groupby.GroupBy.rank finalGroupBy.rank(method='average', ascending=True, na_option='keep', pct=False, axis=0)[source]
Provide the rank of values within each group. Parameters
method:{‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’
average: average rank of group. min: lowest rank in ... | pandas.reference.api.pandas.core.groupby.groupby.rank |
pandas.core.groupby.GroupBy.sem finalGroupBy.sem(ddof=1)[source]
Compute standard error of the mean of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters
ddof:int, default 1
Degrees of freedom. Returns
Series or DataFrame
Standard error of the mea... | pandas.reference.api.pandas.core.groupby.groupby.sem |
pandas.core.groupby.GroupBy.size finalGroupBy.size()[source]
Compute group sizes. Returns
DataFrame or Series
Number of rows in each group as a Series if as_index is True or a DataFrame if as_index is False. See also Series.groupby
Apply a function groupby to a Series. DataFrame.groupby
Apply a functio... | pandas.reference.api.pandas.core.groupby.groupby.size |
pandas.core.groupby.GroupBy.std finalGroupBy.std(ddof=1, engine=None, engine_kwargs=None)[source]
Compute standard deviation of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters
ddof:int, default 1
Degrees of freedom.
engine:str, default None
'cyt... | pandas.reference.api.pandas.core.groupby.groupby.std |
pandas.core.groupby.GroupBy.sum finalGroupBy.sum(numeric_only=NoDefault.no_default, min_count=0, engine=None, engine_kwargs=None)[source]
Compute sum of group values. Parameters
numeric_only:bool, default True
Include only float, int, boolean columns. If None, will attempt to use everything, then use only num... | pandas.reference.api.pandas.core.groupby.groupby.sum |
pandas.core.groupby.GroupBy.tail finalGroupBy.tail(n=5)[source]
Return last n rows of each group. Similar to .apply(lambda x: x.tail(n)), but it returns a subset of rows from the original DataFrame with original index and order preserved (as_index flag is ignored). Parameters
n:int
If positive: number of entr... | pandas.reference.api.pandas.core.groupby.groupby.tail |
pandas.core.groupby.GroupBy.var finalGroupBy.var(ddof=1, engine=None, engine_kwargs=None)[source]
Compute variance of groups, excluding missing values. For multiple groupings, the result index will be a MultiIndex. Parameters
ddof:int, default 1
Degrees of freedom.
engine:str, default None
'cython' : Run... | pandas.reference.api.pandas.core.groupby.groupby.var |
pandas.core.groupby.SeriesGroupBy.aggregate SeriesGroupBy.aggregate(func=None, *args, engine=None, engine_kwargs=None, **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 eit... | pandas.reference.api.pandas.core.groupby.seriesgroupby.aggregate |
pandas.core.groupby.SeriesGroupBy.hist propertySeriesGroupBy.hist
Draw histogram of the input series using matplotlib. Parameters
by:object, optional
If passed, then used to form histograms for separate groups.
ax:matplotlib axis object
If not passed, uses gca().
grid:bool, default True
Whether to sho... | pandas.reference.api.pandas.core.groupby.seriesgroupby.hist |
pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing propertySeriesGroupBy.is_monotonic_decreasing
Return boolean if values in the object are monotonic_decreasing. Returns
bool | pandas.reference.api.pandas.core.groupby.seriesgroupby.is_monotonic_decreasing |
pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing propertySeriesGroupBy.is_monotonic_increasing
Alias for is_monotonic. | pandas.reference.api.pandas.core.groupby.seriesgroupby.is_monotonic_increasing |
pandas.core.groupby.SeriesGroupBy.nlargest SeriesGroupBy.nlargest(n=5, keep='first')[source]
Return the largest n elements. Parameters
n:int, default 5
Return this many descending sorted values.
keep:{‘first’, ‘last’, ‘all’}, default ‘first’
When there are duplicate values that cannot all fit in a Series ... | pandas.reference.api.pandas.core.groupby.seriesgroupby.nlargest |
pandas.core.groupby.SeriesGroupBy.nsmallest SeriesGroupBy.nsmallest(n=5, keep='first')[source]
Return the smallest n elements. Parameters
n:int, default 5
Return this many ascending sorted values.
keep:{‘first’, ‘last’, ‘all’}, default ‘first’
When there are duplicate values that cannot all fit in a Serie... | pandas.reference.api.pandas.core.groupby.seriesgroupby.nsmallest |
pandas.core.groupby.SeriesGroupBy.nunique SeriesGroupBy.nunique(dropna=True)[source]
Return number of unique elements in the group. Returns
Series
Number of unique values within each group. | pandas.reference.api.pandas.core.groupby.seriesgroupby.nunique |
pandas.core.groupby.SeriesGroupBy.transform SeriesGroupBy.transform(func, *args, engine=None, engine_kwargs=None, **kwargs)[source]
Call function producing a like-indexed Series on each group and return a Series having the same indexes as the original object filled with the transformed values. Parameters
f:func... | pandas.reference.api.pandas.core.groupby.seriesgroupby.transform |
pandas.core.groupby.SeriesGroupBy.unique propertySeriesGroupBy.unique
Return unique values of Series object. Uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort. Returns
ndarray or ExtensionArray
The unique values returned as a NumPy array. See Notes. See also uni... | pandas.reference.api.pandas.core.groupby.seriesgroupby.unique |
pandas.core.groupby.SeriesGroupBy.value_counts SeriesGroupBy.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)[source] | pandas.reference.api.pandas.core.groupby.seriesgroupby.value_counts |
pandas.core.resample.Resampler.__iter__ Resampler.__iter__()[source]
Groupby iterator. Returns
Generator yielding sequence of (name, subsetted object)
for each group | pandas.reference.api.pandas.core.resample.resampler.__iter__ |
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