# 44、pandas.crosstab函数

# 原始数据集:

# Date City Category Values

# 0 2023-01-01 New York A 100.0

# 1 2023-01-02 Los Angeles A 200.0

# 2 2023-01-03 New York B 150.0

# 3 2023-01-04 Los Angeles B 250.0

# 4 2023-01-05 New York A NaN

# 5 2023-01-06 Los Angeles B 300.0

# crosstab结果:

# Category A B All

# Date City

# 2023-01-01 00:00:00 New York 100.0 NaN 100.0

# 2023-01-02 00:00:00 Los Angeles 200.0 NaN 200.0

# 2023-01-03 00:00:00 New York NaN 150.0 150.0

# 2023-01-04 00:00:00 Los Angeles NaN 250.0 250.0

# 2023-01-05 00:00:00 New York 0.0 NaN NaN

# 2023-01-06 00:00:00 Los Angeles NaN 300.0 300.0

# All 300.0 700.0 1000.0

45、pandas.cut函数

45-1、语法

# 45、pandas.cut函数

pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False, duplicates='raise', ordered=True)code>

Bin values into discrete intervals.

Use cut when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable. For example, cut could convert ages to groups of age ranges. Supports binning into an equal number of bins, or a pre-specified array of bins.

Parameters:

x

array-like

The input array to be binned. Must be 1-dimensional.

bins

int, sequence of scalars, or IntervalIndex

The criteria to bin by.

int : Defines the number of equal-width bins in the range of x. The range of x is extended by .1% on each side to include the minimum and maximum values of x.

sequence of scalars : Defines the bin edges allowing for non-uniform width. No extension of the range of x is done.

IntervalIndex : Defines the exact bins to be used. Note that IntervalIndex for bins must be non-overlapping.

right

bool, default True

Indicates whether bins includes the rightmost edge or not. If right == True (the default), then the bins [1, 2, 3, 4] indicate (1,2], (2,3], (3,4]. This argument is ignored when bins is an IntervalIndex.

labels

array or False, default None

Specifies the labels for the returned bins. Must be the same length as the resulting bins. If False, returns only integer indicators of the bins. This affects the type of the output container (see below). This argument is ignored when bins is an IntervalIndex. If True, raises an error. When ordered=False, labels must be provided.

retbins

bool, default False

Whether to return the bins or not. Useful when bins is provided as a scalar.

precision

int, default 3

The precision at which to store and display the bins labels.

include_lowest

bool, default False

Whether the first interval should be left-inclusive or not.

duplicates

{default ‘raise’, ‘drop’}, optional

If bin edges are not unique, raise ValueError or drop non-uniques.

ordered

bool, default True

Whether the labels are ordered or not. Applies to returned types Categorical and Series (with Categorical dtype). If True, the resulting categorical will be ordered. If False, the resulting categorical will be unordered (labels must be provided).

Returns:

out

Categorical, Series, or ndarray

An array-like object representing the respective bin for each value of x. The type depends on the value of labels.

None (default) : returns a Series for Series x or a Categorical for all other inputs. The values stored within are Interval dtype.

sequence of scalars : returns a Series for Series x or a Categorical for all other inputs. The values stored within are whatever the type in the sequence is.

False : returns an ndarray of integers.

bins

numpy.ndarray or IntervalIndex.

The computed or specified bins. Only returned when retbins=True. For scalar or sequence bins, this is an ndarray with the computed bins. If set duplicates=drop, bins will drop non-unique bin. For an IntervalIndex bins, this is equal to bins.

45-2、参数