Yagni Taggart catslovedata

## MyScript.sql
select top 10 * from Person.Person

## 20CatsSample.csv

          
            ID
            Name
            Gender
            Weight
            DateOfBirth

            
              1
              Leo
              Male
              5.1
              2015-06-12

            
              2
              Max
              Male
              4.8
              2017-08-18

            
              3
              Oliver
              Male
              5.4
              2016-08-28

            
              4
              Sooty
              Male
              5.3
              2014-10-23

            
              5
              George
              Male
              5.2
              2014-05-16

            
              6
              Felix
              Male
              5.3
              2018-08-25

            
              7
              Alfie
              Male
              4.9
              2014-08-19

            
              8
              Jasper
              Male
              5.1
              2018-07-29

            
              9
              Tigger
              Male
              5.0
              2018-09-07

## 20CatsSample.sql
CREATE TABLE [dbo].[CatDetails](
	[ID] [int] NOT NULL,
	[Name] [nvarchar](50) NOT NULL,
	[Gender] [nvarchar](50) NOT NULL,
	[Weight] [decimal](3, 1) NULL,
	[DateOfBirth] [date] NULL
)
GO
INSERT [dbo].[CatDetails] ([ID], [Name], [Gender], [Weight], [DateOfBirth])
VALUES

## test1
hello

## new1
something new I created as a test

## 20230102A.py
import numpy as np
from scipy.stats import norm, skewnorm
from matplotlib import pyplot as plt

RANDOM_SEED = 128       # Set to None if it should differ each time. RANDOM_SEED = None
NUM_VALUES = 10000      # Number of values to generate
MEAN, ST_DEV = 12, 2    # Mean and std dev to use in the normal distribution
NUM_BINS = 20           # Number of bins to group the data into

## 20230102B.py
# Normal distribution
data1 = norm.rvs(loc=MEAN, scale=ST_DEV, size=NUM_VALUES, random_state=RANDOM_SEED)
data_binned1 = np.histogram(data1, bins=NUM_BINS)
x_values1 = data_binned1[1][:-1]  # Boundary values - omit the last so that we have just the left boundaries.
y_values1 = data_binned1[0]       # Count of items in this bin.

data1_min, data1_q1, data1_median, data1_q3, data1_max, data1_mean = np.min(data1), np.quantile(data1, 0.25), np.quantile(data1, 0.5), np.quantile(data1, 0.75), np.max(data1), np.mean(data1)

# Positive skew (alpha = 5)
data2 = skewnorm.rvs(5, loc=MEAN, scale=ST_DEV, size=NUM_VALUES, random_state=RANDOM_SEED)

## 20230102C.ipynb

      
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## 20230102D.ipynb

      
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              January 2, 2023 21:03
            
          
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## 20230102E.ipynb

      
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                catslovedata
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              January 2, 2023 21:04
            
          
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ID	Name	Gender	Weight	DateOfBirth
1	Leo	Male	5.1	2015-06-12
2	Max	Male	4.8	2017-08-18
3	Oliver	Male	5.4	2016-08-28
4	Sooty	Male	5.3	2014-10-23
5	George	Male	5.2	2014-05-16
6	Felix	Male	5.3	2018-08-25
7	Alfie	Male	4.9	2014-08-19
8	Jasper	Male	5.1	2018-07-29
9	Tigger	Male	5.0	2018-09-07
	CREATE TABLE [dbo].[CatDetails](
	[ID] [int] NOT NULL,
	[Name] [nvarchar](50) NOT NULL,
	[Gender] [nvarchar](50) NOT NULL,
	[Weight] [decimal](3, 1) NULL,
	[DateOfBirth] [date] NULL
	)
	GO
	INSERT [dbo].[CatDetails] ([ID], [Name], [Gender], [Weight], [DateOfBirth])
	VALUES
	import numpy as np
	from scipy.stats import norm, skewnorm
	from matplotlib import pyplot as plt

	RANDOM_SEED = 128 # Set to None if it should differ each time. RANDOM_SEED = None
	NUM_VALUES = 10000 # Number of values to generate
	MEAN, ST_DEV = 12, 2 # Mean and std dev to use in the normal distribution
	NUM_BINS = 20 # Number of bins to group the data into
	# Normal distribution
	data1 = norm.rvs(loc=MEAN, scale=ST_DEV, size=NUM_VALUES, random_state=RANDOM_SEED)
	data_binned1 = np.histogram(data1, bins=NUM_BINS)
	x_values1 = data_binned1[1][:-1] # Boundary values - omit the last so that we have just the left boundaries.
	y_values1 = data_binned1[0] # Count of items in this bin.

	data1_min, data1_q1, data1_median, data1_q3, data1_max, data1_mean = np.min(data1), np.quantile(data1, 0.25), np.quantile(data1, 0.5), np.quantile(data1, 0.75), np.max(data1), np.mean(data1)

	# Positive skew (alpha = 5)
	data2 = skewnorm.rvs(5, loc=MEAN, scale=ST_DEV, size=NUM_VALUES, random_state=RANDOM_SEED)