Regular Aggregate Mode Examples for describe() - Teradata Package for Python

Example 1: Generate statistics for DataFrame "sales"

The following example computes count, mean, std, min, percentiles, and max for numeric columns

>>> df = DataFrame('sales')
>>> df
              Feb   Jan   Mar   Apr    datetime
accounts                                      
Alpha Co    210.0   200   215   250  04/01/2017
Red Inc     200.0   150   140  None  04/01/2017
Orange Inc  210.0  None  None   250  04/01/2017
Jones LLC   200.0   150   140   180  04/01/2017
Yellow Inc   90.0  None  None  None  04/01/2017
Blue Inc     90.0    50    95   101  04/01/2017

>>> 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

Example 2: Compute mean, min and max for numeric columns

>>> df.describe(pivot=True, statistics = ['mean', 'min', 'max'], columns= ['Jan', 'Feb', 'Mar', 'Apr'])
func Feb     Jan   Mar   Apr
max  210.000 200.0 215.0 250.00
min  90.000  50.0  95.0  101.00
mean 166.667 137.5 147.5 195.25

Example 3: Use percentiles to compute the 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

Example 4: Group by to compute statistics for specific groups

>>> 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
>>>

Example 5: Compute count, mean, std, min, percentiles, and max for numeric columns with default arguments and pivot set to False

>>> df.describe(pivot=False)
ATTRIBUTE StatName StatValue