nchelaru

SELECT name,
       type_desc,
       create_date,
       modify_date
FROM sys.objects

nchelaru

SELECT c.name AS 'ColumnName',
	     t.name AS 'TableName'
FROM sys.columns c
JOIN sys.tables  t
ON c.object_id = t.object_id
WHERE c.name LIKE '%sci_thin_credit_file_co_app%'
ORDER BY TableName,ColumnName

nchelaru

SELECT *
FROM tempdb.sys.objects
ORDER BY create_date DESC

nchelaru

SELECT Name
FROM sys.procedures
WHERE OBJECT_DEFINITION(OBJECT_ID) LIKE '%TableNameOrWhatever%'

nchelaru

import pandas as pd
import pandera as pa

from pandera import Check, Column, DataFrameSchema

schema = pa.DataFrameSchema(
    columns={
        "int_column": Column(int),
        "float_column": Column(float, Check.greater_than(0)),
        "str_column": Column(str, Check.equal_to("a")),

nchelaru

{{ config(store_failures = true) }}

-- Test
SELECT *
FROM <table>
WHERE <num_col> > 9

UNION 

SELECT *

nchelaru

select * from INFORMATION_SCHEMA.COLUMNS 
where COLUMN_NAME like '%clientid%' 
order by TABLE_NAME

nchelaru

from functools import partial, reduce

dfs = [df1, df2, df3]

merge = partial(pd.merge, on=['depth', 'profile'], how='outer')

reduce(merge, dfs)

nchelaru

## Function
def get_first_non_null(dfrow, columns_to_search):
    for c in columns_to_search:
        if pd.notnull(dfrow[c]):
            return dfrow[c]
    return None
  
## Sample usage:
cols_to_search = ['a1', 'a2', 'a3']
df['A'] = df.apply(lambda x: get_first_non_null(x, cols_to_search), axis=1)

nchelaru

df.groupby(pd.Grouper(key='date', freq='M'))['col2'].sum()