teradataml.dataframe.dataframe.DataFrame.describe = describe(self, percentiles=[0.25, 0.5, 0.75], verbose=False, distinct=False, statistics=None, columns=None, pivot=False)

DESCRIPTION:
    Generates statistics for numeric columns. This function can be used in two modes:
        1. Regular Aggregate Mode.
            It computes the count, mean, std, min, percentiles, and max for numeric columns.
            Default statistics include:
                "count", "mean", "std", "min", "percentile", "max"
        2. Time Series Aggregate Mode.
            It computes max, mean, min, std, median, mode, and percentiles for numeric columns.
            Default statistics include:
                'max', 'mean', 'min', 'std'
 
    Notes:
        * Regular Aggregate Mode: If describe() is used on the output of any DataFrame API or groupby(),
                                then describe() is used as regular aggregation.
        * Time Series Aggregate Mode: If describe() is used on the output of groupby_time(), then describe()
                                    is a time series aggregate, where time series aggregates are used
                                    to calculate the statistics.
        * This method does not support operations on array columns.
 
PARAMETERS:
    percentiles:
        Optional Argument.
        A list of values between 0 and 1. Applicable for both modes.
        By default, percentiles are calculated for statistics for 'Regular Aggregate Mode', whereas
        for 'Time Series Aggregate Mode', percentiles are calculated when verbose is set to True.
        Default Values: [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.
        Types: float or List of floats
 
    verbose:
        Optional Argument.
        Specifies a boolean value to be used for time series aggregation, stating whether to get
        verbose output or not.
        When this argument is set to 'True', function calculates median, mode, and percentile values
        on top of its default statistics.
        Note:
            verbose as 'True' is not applicable for 'Regular Aggregate Mode'.
        Default Values: False
        Types: bool
 
    distinct:
        Optional Argument.
        Specifies a boolean value to decide whether to consider duplicate rows in statistic
        calculation or not. By default, duplicate values are considered for statistic calculation.
        When this is set to True, only distinct rows are considered for statistic calculation.
        Default Values: False
        Types: bool
 
    statistics:
        Optional Argument.
        Specifies the aggregate operation to be performed.
        Computes count, mean, std, min, percentiles, and max for numeric columns.
        Computes count and unique for non-numeric columns.
        Notes:
            1. statistics is not applicable for 'Time Series Aggregate Mode'.
        Permitted Values: count, mean, min, max, unique, std, describe, percentile
        Default Values: None
        Types: str or List of str
 
    columns:
        Optional Argument.
        Specifies the name(s) of the columns we are collecting statistics for.
        Default Values: None
        Types: str or List of str
    
    pivot:
        Optional Argument.
        Specifies a boolean value to pivot the output.
        Note:
            * "pivot" is not supported for PTI tables.
        Default Values: 'False'
        Types: bool
 
RETURNS:
    teradataml DataFrame
 
RAISE:
    TeradataMlException
 
EXAMPLES:
    >>> load_example_data("dataframe","sales")
    >>> df = DataFrame('sales')
    >>> print(df)
                  Feb   Jan   Mar   Apr    datetime
    accounts
    Blue Inc     90.0    50    95   101  04/01/2017
    Alpha Co    210.0   200   215   250  04/01/2017
    Jones LLC   200.0   150   140   180  04/01/2017
    Yellow Inc   90.0  None  None  None  04/01/2017
    Red Inc     200.0   150   140  None  04/01/2017
    Orange Inc  210.0  None  None   250  04/01/2017
 
    # Computes count, mean, std, min, percentiles, and max for numeric columns.
    >>> df.describe(pivot=True)
              Apr      Feb     Mar     Jan
    func
    count       4        6       4       4
    mean   195.25  166.667   147.5   137.5
    std    70.971   59.554  49.749  62.915
    min       101       90      95      50
    25%    160.25    117.5  128.75     125
    50%       215      200     140     150
    75%       250    207.5  158.75   162.5
    max       250      210     215     200
 
    # Computes count, mean, std, min, percentiles, and max for numeric columns with 
    # default arugments.
    >>> df.describe()
    ATTRIBUTE   StatName            StatValue
    Jan         MAXIMUM             200.0
    Jan         STANDARD DEVIATION      62.91528696058958
    Jan         PERCENTILES(25)     125.0
    Jan         PERCENTILES(50)     150.0
    Mar         COUNT               4.0
    Mar         MINIMUM             95.0
    Mar         MAXIMUM             215.0
    Mar         MEAN                147.5
    Mar         STANDARD DEVIATION      49.749371855331
    Mar         PERCENTILES(25)     128.75
    Mar         PERCENTILES(50)     140.0
    Apr         COUNT               4.0
    Apr         MINIMUM             101.0
    Apr         MAXIMUM             250.0
    Apr         MEAN                195.25
    Apr         STANDARD DEVIATION      70.97123830585646
    Apr         PERCENTILES(25)     160.25
    Apr         PERCENTILES(50)     215.0
    Apr         PERCENTILES(75)     250.0
    Feb         COUNT               6.0
    Feb         MINIMUM             90.0
    Feb         MAXIMUM             210.0
    Feb         MEAN                166.66666666666666
    Feb         STANDARD DEVIATION      59.553897157672786
    Feb         PERCENTILES(25)     117.5
    Feb         PERCENTILES(50)     200.0
    Feb         PERCENTILES(75)     207.5
    Mar         PERCENTILES(75)     158.75
    Jan         PERCENTILES(75)     162.5
    Jan         MEAN                137.5
    Jan         MINIMUM             50.0
    Jan         COUNT               4.0
 
    # Computes count, mean, std, min, percentiles, and max for numeric columns with 30th and 60th percentiles.
    >>> df.describe(percentiles=[.3, .6], pivot=True)
              Apr      Feb     Mar     Jan
    func
    count       4        6       4       4
    mean   195.25  166.667   147.5   137.5
    std    70.971   59.554  49.749  62.915
    min       101       90      95      50
    30%     172.1      145   135.5     140
    60%       236      200     140     150
    max       250      210     215     200
 
    # Computes count, mean, std, min, percentiles, and max for numeric columns group by "datetime" and "Feb".
    >>> df1 = df.groupby(["datetime", "Feb"])
    >>> df1.describe(pivot=True)
                             Jan   Mar   Apr
    datetime   Feb   func
    04/01/2017 90.0  25%      50    95   101
                     50%      50    95   101
                     75%      50    95   101
                     count     1     1     1
                     max      50    95   101
                     mean     50    95   101
                     min      50    95   101
                     std    None  None  None
               200.0 25%     150   140   180
                     50%     150   140   180
                     75%     150   140   180
                     count     2     2     1
                     max     150   140   180
                     mean    150   140   180
                     min     150   140   180
                     std       0     0  None
               210.0 25%     200   215   250
                     50%     200   215   250
                     75%     200   215   250
                     count     1     1     2
                     max     200   215   250
                     mean    200   215   250
                     min     200   215   250
                     std    None  None     0
 
    # Examples for describe() function as Time Series Aggregate.
    #
    >>> # Load the example datasets.
    ... load_example_data("dataframe", ["ocean_buoys"])
    >>>
 
    >>> # Create the required DataFrames.
    ... # DataFrame on non-sequenced PTI table
    ... ocean_buoys = DataFrame("ocean_buoys")
    >>> # Check DataFrame columns and let's peek at the data
    ... ocean_buoys.columns
    ['buoyid', 'TD_TIMECODE', 'temperature', 'salinity']
    >>> ocean_buoys.head()
                           TD_TIMECODE  temperature  salinity
    buoyid
    0       2014-01-06 08:10:00.000000        100.0        55
    0       2014-01-06 08:08:59.999999          NaN        55
    1       2014-01-06 09:01:25.122200         77.0        55
    1       2014-01-06 09:03:25.122200         79.0        55
    1       2014-01-06 09:01:25.122200         70.0        55
    1       2014-01-06 09:02:25.122200         71.0        55
    1       2014-01-06 09:03:25.122200         72.0        55
    0       2014-01-06 08:09:59.999999         99.0        55
    0       2014-01-06 08:00:00.000000         10.0        55
    0       2014-01-06 08:10:00.000000         10.0        55
 
    >>> ocean_buoys_grpby = ocean_buoys.groupby_time(timebucket_duration="2cy", value_expression="buoyid", fill="NULLS")
    >>>
 
    #
    # Example 1: Get the basic statistics for time series aggregation for all the numeric columns.
    #            This returns max, mean, min and std values.
    #
    >>> ocean_buoys_grpby.describe()
                                                                                               temperature salinity
    TIMECODE_RANGE                                     GROUP BY TIME(CAL_YEARS(2)) buoyid func
    ('2014-01-01 00:00:00.000000-00:00', '2016-01-0... 2                           0      max          100       55
                                                                                          mean       54.75       55
                                                                                          min           10       55
                                                                                          std       51.674        0
                                                                                   1      max           79       55
                                                                                          mean        74.5       55
                                                                                          min           70       55
                                                                                          std        3.937        0
                                                                                   2      max           82       55
                                                                                          mean          81       55
                                                                                          min           80       55
                                                                                          std            1        0
                                                                                   44     max           56       55
                                                                                          mean      48.077       55
                                                                                          min           43       55
                                                                                          std        5.766        0
    >>>
 
    #
    # Example 2: Get the verbose statistics for time series aggregation for all the numeric columns.
    #            This returns max, mean, min, std, median, mode, 25th, 50th and 75th percentile.
    #
    >>> ocean_buoys_grpby.describe(verbose=True)
                                                                                                 temperature salinity
    TIMECODE_RANGE                                     GROUP BY TIME(CAL_YEARS(2)) buoyid func
    ('2014-01-01 00:00:00.000000-00:00', '2016-01-0... 2                           0      25%             10       55
                                                                                          50%           54.5       55
                                                                                          75%          99.25       55
                                                                                          max            100       55
                                                                                          mean         54.75       55
                                                                                          median        54.5       55
                                                                                          min             10       55
                                                                                          mode            10       55
                                                                                          std         51.674        0
                                                                                   1      25%          71.25       55
                                                                                          50%           74.5       55
                                                                                          75%          77.75       55
                                                                                          max             79       55
                                                                                          mean          74.5       55
                                                                                          median        74.5       55
                                                                                          min             70       55
                                                                                          mode            71       55
                                                                                          mode            72       55
                                                                                          mode            77       55
                                                                                          mode            78       55
                                                                                          mode            79       55
                                                                                          mode            70       55
                                                                                          std          3.937        0
                                                                                   2      25%           80.5       55
                                                                                          50%             81       55
                                                                                          75%           81.5       55
                                                                                          max             82       55
                                                                                          mean            81       55
                                                                                          median          81       55
                                                                                          min             80       55
                                                                                          mode            80       55
                                                                                          mode            81       55
                                                                                          mode            82       55
                                                                                          std              1        0
                                                                                   44     25%             43       55
                                                                                          50%             43       55
                                                                                          75%             53       55
                                                                                          max             56       55
                                                                                          mean        48.077       55
                                                                                          median          43       55
                                                                                          min             43       55
                                                                                          mode            43       55
                                                                                          std          5.766        0
    >>>
 
    #
    # Example 3: Get the basic statistics for time series aggregation for all the numeric columns,
    #            consider only unique values.
    #            This returns max, mean, min and std values.
    #
    >>> ocean_buoys_grpby.describe(distinct=True)
                                                                                               temperature salinity
    TIMECODE_RANGE                                     GROUP BY TIME(CAL_YEARS(2)) buoyid func
    ('2014-01-01 00:00:00.000000-00:00', '2016-01-0... 2                           0      max          100       55
                                                                                          mean      69.667       55
                                                                                          min           10       55
                                                                                          std       51.675     None
                                                                                   1      max           79       55
                                                                                          mean        74.5       55
                                                                                          min           70       55
                                                                                          std        3.937     None
                                                                                   2      max           82       55
                                                                                          mean          81       55
                                                                                          min           80       55
                                                                                          std            1     None
                                                                                   44     max           56       55
                                                                                          mean        52.2       55
                                                                                          min           43       55
                                                                                          std        5.263     None
    >>>
 
    #
    # Example 4: Get the verbose statistics for time series aggregation for all the numeric columns.
    #            This select non-default percentiles 33rd and 66th.
    #            This returns max, mean, min, std, median, mode, 33rd, and 66th percentile.
    #
    >>> ocean_buoys_grpby.describe(verbose=True, percentiles=[0.33, 0.66])
                                                                                                 temperature salinity
    TIMECODE_RANGE                                     GROUP BY TIME(CAL_YEARS(2)) buoyid func
    ('2014-01-01 00:00:00.000000-00:00', '2016-01-0... 2                           0      33%             10       55
                                                                                          66%          97.22       55
                                                                                          max            100       55
                                                                                          mean         54.75       55
                                                                                          median        54.5       55
                                                                                          min             10       55
                                                                                          mode            10       55
                                                                                          std         51.674        0
                                                                                   1      33%          71.65       55
                                                                                          66%           77.3       55
                                                                                          max             79       55
                                                                                          mean          74.5       55
                                                                                          median        74.5       55
                                                                                          min             70       55
                                                                                          mode            70       55
                                                                                          mode            71       55
                                                                                          mode            77       55
                                                                                          mode            78       55
                                                                                          mode            79       55
                                                                                          mode            72       55
                                                                                          std          3.937        0
                                                                                   2      33%          80.66       55
                                                                                          66%          81.32       55
                                                                                          max             82       55
                                                                                          mean            81       55
                                                                                          median          81       55
                                                                                          min             80       55
                                                                                          mode            80       55
                                                                                          mode            81       55
                                                                                          mode            82       55
                                                                                          std              1        0
                                                                                   44     33%             43       55
                                                                                          66%             53       55
                                                                                          max             56       55
                                                                                          mean        48.077       55
                                                                                          median          43       55
                                                                                          min             43       55
                                                                                          mode            43       55
                                                                                          std          5.766        0
    >>>