Python Data Types for SQL Server Integration

• First identify the Python value and type.
• Convert user/input text before calculations or SQL integration.
• Match SQL range, length, precision, scale and nullability—not only the general type name.

1. Architecture: Python Value → SQL Server Data Type

The important idea is that a data value crosses several layers. A valid Python value can still fail in SQL Server if the target column has insufficient length, range, precision or nullability.

2. Value vs Variable vs Data Type

temperature = 850.25 print(temperature)
print(type(temperature))

3. Check Types with type() and isinstance()

sqf_no = 2
temperature = 850.25
charge_no = "CHG-001" print(type(sqf_no))
print(type(temperature))
print(type(charge_no)) print(isinstance(sqf_no, int))
print(isinstance(temperature, float))
print(isinstance(charge_no, str))

4. Important Python Data Types for Automation Learners

5. Type Conversion and input()

input() always returns text (str). If an operator enters a furnace number, temperature or setpoint, convert the text before numerical calculation or database integration.

from decimal import Decimal sqf_text = "2"
temp_text = "850.25"
cp_text = "0.810" sqf_no = int(sqf_text)
temperature = float(temp_text)
cp_set = Decimal(cp_text) print(type(sqf_no))
print(type(temperature))
print(type(cp_set))

6. Python → SQL Server Data Type Mapping

7. Strings and Unicode Text

charge_no = "CHG-2026-001"
event_to = "Soaking"
operator_note = "भट्ठी जाँच पूर्ण" print(type(charge_no))
print(type(operator_note))

Use SQL Server nvarchar when multilingual text must be preserved. For identifiers or English-only plant text, varchar may be appropriate depending on the database design. Always define a realistic maximum length.

8. Integers, Boolean Values and SQL Ranges

Python integers can grow beyond normal machine-word sizes, but SQL Server integer columns have fixed ranges. A Python int can therefore be valid in Python and still overflow a SQL int.

sqf_no = 2
fan_running = True if sqf_no not in {1, 2, 3}: raise ValueError("SQF number must be 1, 2 or 3.") print(type(sqf_no))
print(type(fan_running))

9. Python float vs Decimal

temperature = 850.25

from decimal import Decimal
cp_set = Decimal("0.810")

10. Date, Time and Datetime Values

from datetime import date, datetime, time event_time = datetime.now()
production_date = date.today()
shift_start = time(6, 0, 0) print(event_time, type(event_time))
print(production_date, type(production_date))
print(shift_start, type(shift_start))

For new SQL Server event timestamps, datetime2 is generally the better target than legacy datetime because it provides a wider range and configurable fractional-second precision. Decide whether plant timestamps are stored as local plant time or UTC; datetime2 does not store a timezone offset.

11. None and SQL NULL

operator_note = None print(operator_note)
print(type(operator_note)) if operator_note is None: print("No operator note is available.")

None represents the absence of a Python value. Through pyodbc, it normally maps to SQL NULL when the column allows NULL. Do not confuse NULL with 0, an empty string "", or False.

12. Lists, Tuples, Dictionaries and JSON

import json temperatures = [850.2, 851.0, 849.8]
event = { "SQF_No": 2, "ChargeNo": "CHG-001", "Temp_Act": 850.2
} event_json = json.dumps(event) print(type(temperatures))
print(type(event))
print(type(event_json))

For relational SQL Server design, normally store individual fields in their own columns. If the design intentionally stores structured JSON, serialize the dictionary to JSON text and use a suitable SQL text column.

13. pandas dtypes and SQL Data

When SQL rows are loaded into a pandas DataFrame, pandas has its own dtype system. Always inspect df.dtypes before calculations, filtering or Excel export.

# Later, after SQL data is loaded into a DataFrame:
print(df.dtypes)
print(df.head())

14. pyodbc Parameter Binding Preview

This lesson does not create the database yet. This short preview only shows how Python values will later be passed safely to SQL Server.

from datetime import datetime
from decimal import Decimal charge_no = "CHG-001" # str
sqf_no = 2 # int
temp_act = 850.25 # float
cp_set = Decimal("0.810") # Decimal
event_time = datetime.now() # datetime
operator_note = None # None → SQL NULL # Later, after a SQL Server connection/cursor is created:
sql = """
INSERT INTO dbo.tblEvent (DT, SQF_No, ChargeNo, Temp_Act, Cp_Set, Event_To)
VALUES (?, ?, ?, ?, ?, ?)
""" values = ( event_time, sqf_no, charge_no, temp_act, cp_set, operator_note,
) # cursor.execute(sql, values)

15. Copy-Paste Practice: Check All Important Types

This is the main practical. It requires only Python and the Standard Library; it does not connect to SQL Server.

from datetime import date, datetime, time
from decimal import Decimal # ============================================================
# PYTHON DATA TYPES FOR SQL SERVER INTEGRATION
# ============================================================ charge_no = "CHG-001" # str
sqf_no = 2 # int
temperature = 850.25 # float
fan_running = True # bool
cp_set = Decimal("0.810") # Decimal
event_time = datetime.now() # datetime
production_date = date.today() # date
shift_start = time(6, 0, 0) # time
operator_note = None # NoneType
payload = b"SQF" # bytes samples = [850.2, 851.0, 849.8] # list
event_key = (sqf_no, charge_no) # tuple
event = { # dict "SQF_No": sqf_no, "ChargeNo": charge_no, "Temp_Act": temperature,
} values = { "charge_no": charge_no, "sqf_no": sqf_no, "temperature": temperature, "fan_running": fan_running, "cp_set": cp_set, "event_time": event_time, "production_date": production_date, "shift_start": shift_start, "operator_note": operator_note, "payload": payload, "samples": samples, "event_key": event_key, "event": event,
} print("PYTHON DATA TYPE CHECK")
print("-" * 60) for name, value in values.items(): print(f"{name:18} = {str(value):28} {type(value).__name__}") print("-" * 60) # Type validation examples
print("sqf_no is int :", isinstance(sqf_no, int))
print("temperature is float:", isinstance(temperature, float))
print("charge_no is str :", isinstance(charge_no, str)) # Conversion examples
sqf_text = "3"
temp_text = "875.50"
cp_text = "0.825" sqf_from_text = int(sqf_text)
temp_from_text = float(temp_text)
cp_from_text = Decimal(cp_text) print("-" * 60)
print("CONVERSION CHECK")
print("sqf_from_text :", sqf_from_text, type(sqf_from_text).__name__)
print("temp_from_text:", temp_from_text, type(temp_from_text).__name__)
print("cp_from_text :", cp_from_text, type(cp_from_text).__name__) print("-" * 60)
print("Type Practice: COMPLETE")

What students should identify from the output

16. Learning Checklist and Next Step