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- Histogram(data=None, object=None, target_columns=None, method_type=None, nbins=1, inclusion='LEFT', **generic_arguments)
- DESCRIPTION:
Function calculates the frequency distribution of a data set using any of these methods:
* Sturges
* Scott
* Variable-width
* Equal-width
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
object:
Optional Argument.
Specifies the bin data, needed when "method_type" is set to 'EQUAL-WIDTH' or 'VARIABLE-WIDTH'.
Types: teradataml DataFrame
target_columns:
Required Argument.
Specifies the name(s) of the column(s) in "data" to perform computations on.
Types: str OR list of Strings (str)
method_type:
Required Argument.
Specifies the method type to be used for histogram computation.
Permitted Values:
* STURGES -
Algorithm for calculating bin width, w:
w = r/(1 + log₂ n)
where:
w = bin width
r = data value range
n = number of elements in data set
Sturges algorithm performs best if data is normally distributed
and n is at least 30.
* SCOTT -
Algorithm for calculating bin width, w:
w = 3.49s/(n^1/3)
where:
w = bin width
s = standard deviation of data values
n = number of elements in data set
r = data value range
Number of bins: r/w
Scott algorithm performs best on normally distributed data.
* EQUAL-WIDTH -
Requires "object" argument, which specifies the minimum value and the maximum
value of the bin in column1 and column2 respectively, and the label of the bin
in column3. Maximum number of bins cannot exceed 3500.
* VARIABLE-WIDTH -
Requires "object" argument, which specifies the minimum value of the bins
in column1 and the maximum value of the bins in column2.
Algorithm for calculating bin width, w:
w = (max - min)/k
where:
min = minimum value of the bins
max = maximum value of the bins
k = number of intervals into which algorithm divides data set
Interval boundaries: min+w, min+2w, …, min+(k-1)w
Types: str
nbins:
Optional Argument, Required when "method_type" is 'Variable-Width' and 'Equal-Width'.
Specifies the number of bins.
Default Value: 1
Types: int
inclusion:
Optional Argument.
Specifies whether points on bin boundaries should be included in the
bin on the left or the right.
Default Value: "LEFT"
Permitted Values: LEFT, RIGHT
Types: str
**generic_arguments:
Specifies the generic keyword arguments SQLE functions accept.
Below are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the function in table or not.
When set to True, results are persisted in table; otherwise, results
are garbage collected at the end of the session.
Default Value: False
Types: boolean
volatile:
Optional Argument.
Specifies whether to put the results of the function in volatile table or not.
When set to True, results are stored in volatile table, otherwise not.
Default Value: False
Types: boolean
Function allows the user to partition, hash, order or local order the input
data. These generic arguments are available for each argument that accepts
teradataml DataFrame as input and can be accessed as:
* "<input_data_arg_name>_partition_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_hash_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_order_column" accepts str or list of str (Strings)
* "local_order_<input_data_arg_name>" accepts boolean
Note:
These generic arguments are supported by teradataml if the underlying
SQLE Engine function supports, else an exception is raised.
RETURNS:
Instance of Histogram.
Output teradataml DataFrames can be accessed using attribute
references, such as HistogramObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage, before importing the function in user space.
# 2. User can import the function, if it is available on the Vantage user is connected to.
# 3. To check the list of analytic functions available on the Vantage user connected to,
# use "display_analytic_functions()".
# Load the example data.
load_example_data("teradataml", ["titanic", "min_max_titanic"])
# Create teradataml DataFrame object.
titanic_data = DataFrame.from_table("titanic")
# Create teradataml DataFrame object for minimum and maximum value of bins
# "Young age", "Middle Age" and, "Old Age".
min_max_object = DataFrame.from_table("min_max_titanic")
# Check the list of available analytic functions.
display_analytic_functions()
# Import function Histogram.
from teradataml import Histogram
# Example 1: Get the frequency distribution of a data set using 'sturges'
# method type for the values in column 'age'.
obj = Histogram(data=titanic_data,
target_columns="age",
method_type="Sturges")
# Print the result DataFrame.
print(obj.result)
# Example 2: Get the frequency distribution of a data set using 'variable-width'
# method type for the values in column 'age' with 3 number of bins.
obj = Histogram(data=titanic_data,
object=min_max_object,
target_columns="age",
method_type="variable-width",
nbins=3)
# Print the result DataFrame.
print(obj.result)
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