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Compute a simple cross tabulation of two (or more) factors.
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By default, computes a frequency table of the factors unless an
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array of values and an aggregation function are passed.
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|
Parameters
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----------
|
index : array-like, Series, or list of arrays/Series
|
Values to group by in the rows.
|
columns : array-like, Series, or list of arrays/Series
|
Values to group by in the columns.
|
values : array-like, optional
|
Array of values to aggregate according to the factors.
|
Requires `aggfunc` be specified.
|
rownames : sequence, default None
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If passed, must match number of row arrays passed.
|
colnames : sequence, default None
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If passed, must match number of column arrays passed.
|
aggfunc : function, optional
|
If specified, requires `values` be specified as well.
|
margins : bool, default False
|
Add row/column margins (subtotals).
|
margins_name : str, default 'All'
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Name of the row/column that will contain the totals
|
when margins is True.
|
dropna : bool, default True
|
Do not include columns whose entries are all NaN.
|
normalize : bool, {'all', 'index', 'columns'}, or {0,1}, default False
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Normalize by dividing all values by the sum of values.
|
|
- If passed 'all' or `True`, will normalize over all values.
|
- If passed 'index' will normalize over each row.
|
- If passed 'columns' will normalize over each column.
|
- If margins is `True`, will also normalize margin values.
|
|
Returns
|
-------
|
DataFrame
|
Cross tabulation of the data.
|
|
See Also
|
--------
|
DataFrame.pivot : Reshape data based on column values.
|
pivot_table : Create a pivot table as a DataFrame.
|
|
Notes
|
-----
|
Any Series passed will have their name attributes used unless row or column
|
names for the cross-tabulation are specified.
|
|
Any input passed containing Categorical data will have **all** of its
|
categories included in the cross-tabulation, even if the actual data does
|
not contain any instances of a particular category.
|
|
In the event that there aren't overlapping indexes an empty DataFrame will
|
be returned.
|
|
Reference :ref:`the user guide <reshaping.crosstabulations>` for more examples.
|
|
Examples
|
--------
|
>>> a = np.array(["foo", "foo", "foo", "foo", "bar", "bar",
|
... "bar", "bar", "foo", "foo", "foo"], dtype=object)
|
>>> b = np.array(["one", "one", "one", "two", "one", "one",
|
... "one", "two", "two", "two", "one"], dtype=object)
|
>>> c = np.array(["dull", "dull", "shiny", "dull", "dull", "shiny",
|
... "shiny", "dull", "shiny", "shiny", "shiny"],
|
... dtype=object)
|
>>> pd.crosstab(a, [b, c], rownames=['a'], colnames=['b', 'c'])
|
b one two
|
c dull shiny dull shiny
|
a
|
bar 1 2 1 0
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foo 2 2 1 2
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|
Here 'c' and 'f' are not represented in the data and will not be
|
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|
dropna=False to preserve categories with no data.
|
|
>>> foo = pd.Categorical(['a', 'b'], categories=['a', 'b', 'c'])
|
>>> bar = pd.Categorical(['d', 'e'], categories=['d', 'e', 'f'])
|
>>> pd.crosstab(foo, bar)
|
col_0 d e
|
row_0
|
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|
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|
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Given the names of a DataFrame's rows and columns, returns a set of unique row
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A row or column name is replaced if it is duplicate among the rows of the inputs,
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Parameters
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----------
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rownames: list[str]
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colnames: list[str]
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Returns
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-------
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Tuple(Dict[str, str], List[str], Dict[str, str], List[str])
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rownames_mapper: dict[str, str]
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a dictionary with new row names as keys and original rownames as values
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unique_rownames: list[str]
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a list of rownames with duplicate names replaced by dummy names
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colnames_mapper: dict[str, str]
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a dictionary with new column names as keys and original column names as values
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unique_colnames: list[str]
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a list of column names with duplicate names replaced by dummy names
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