@kiranshahzadi192: Nomi discount store location 6 no HBL bank k Opposite side 😍❤️

Kiran09⚡️

Open In TikTok:

Region: PK

Saturday 05 September 2026 11:12:57 GMT

126741

5022

91

1328

Music

Download

No Watermark .mp4 (9.28MB) No Watermark(HD) .mp4 (9.28MB) Watermark .mp4 (0MB) Music .mp3

Comments

🦁 Dr Khaki⛎ 🦁 :

Kal tum mare sath wali table pa the 2 girls ky sath mujhy Laga Tha koi tiktoker ho ap 😂

2026-09-05 19:39:37

0

ملک 😎 ✨ :

peechy dekho peechy 😂

2026-09-05 11:45:36

48

🌷 :

location?

2026-09-05 14:51:46

10

🇯 🇺 🇹 🇹 :

Didi ka promotion ka treeqa thora casual tha 😂😂

2026-09-05 18:08:57

0

ateeq rohan :

Background kabi Acha ha [Tears of joy][Tears of joy][Tears of joy]

2026-09-05 15:22:25

0

ꪖᦔỉỉ💫💥 :

chlo ain gy subh phr

2026-09-05 17:23:30

6

𝓑𝓪𝓭𝓪𝓻 𝓢𝓪𝓵𝓮𝓮𝓶 ✍️ :

wo to pechy Nazar araha ha😁

2026-09-05 16:55:34

7

Rao multani Right :

madam lases ki b bando video ak

2026-09-05 18:14:51

1

usama raj 786 :

Amazing 🥰🥰🥰🥰

2026-09-05 11:50:15

4

🍷𓆩𝗡𝗼𝗧ʸᵒᵘʳ᭄YARM彡: :

pachaaa wale multan 😂😁😁

2026-09-05 19:55:56

0

Saifal.lqbal :

😂😂

2026-09-05 20:59:11

0

Ahmad Rath :

🌹🌹

2026-09-05 13:20:00

1

it's Haider Ali ❤️ :

yaqeen ni ayaa

2026-09-05 19:28:39

0

HAمza ABID ❣️ :

Sadqy KIRAN ❣️❣️

2026-09-05 11:46:25

2

Malik-kamran-Nazir :

so Nice

2026-09-05 14:32:42

1

Sajid Prince✨ :

mundyaaa liey ve koi cheez Hy ya ni 😁😁😁😁

2026-09-05 11:48:13

2

CH Abdul Qadeer Engr :

good 👍

2026-09-05 18:11:21

0

khawar :

nomi nam hy itmad ka

2026-09-05 16:10:58

0

M jahangir :

listen

2026-09-05 19:49:53

0

🔱 :

boys ka lia bi koi shop dhund do

2026-09-05 13:58:47

1

Hammad :

bro

2026-09-05 15:34:26

0

🧸عبداللہ🕊️ :

first like

2026-09-05 11:16:31

3

Arzoo khan :

or braa b😂

2026-09-05 13:21:22

4

🧸عبداللہ🕊️ :

2026-09-05 11:16:20

6

To see more videos from user @kiranshahzadi192, please go to the Tikwm homepage.

Other Videos

Pops is so tuff | Pops Edit | Song: BREGA DO FRUTIGER || #regularshow #regularshowedit #regularshowthelosttapes #pops #popsedit

Pops is so tuff | Pops Edit | Song: BREGA DO FRUTIGER || #regularshow #regularshowedit #regularshowthelosttapes #pops #popsedit

#daenerystargaryen #daenerystargaryenedit #gameofthrones #gameofthronesedit #emiliaclarke #emiliaclarkeedit #fyp #fypシ #edit #viral #CapCut

#daenerystargaryen #daenerystargaryenedit #gameofthrones #gameofthronesedit #emiliaclarke #emiliaclarkeedit #fyp #fypシ #edit #viral #CapCut

Porque en el trabajo moderno de datos, la cuestión no es qué herramienta usar. Es qué tan fluidamente puedes cambiar entre ellas, dependiendo de la escala, la pila y la velocidad que necesites. Aquí tienes la hoja 👇 definitiva de trucos SQL vs PySpark ✅ Select Columns ↳ SQL: SELECT name, salary FROM employees; ↳ PySpark: df.select(

80000; ↳ PySpark: df.filter(df.salary > 80000) ✅ Sort Data ↳ SQL: SELECT * FROM employees ORDER BY salary DESC; ↳ PySpark: df.orderBy(df.salary.desc()) ✅ Group & Count ↳ SQL: SELECT department, COUNT(*) FROM employees GROUP BY department; ↳ PySpark: df.groupBy("department").count() ✅ Group & Sum ↳ SQL: SELECT department, SUM(salary) FROM employees GROUP BY department; ↳ PySpark: df.groupBy("department").sum("salary") ✅ Join Tables ↳ SQL: SELECT e.name, d.department_name FROM employees e JOIN departments d ON e.dept_id = d.id; ↳ PySpark: employees.join(departments, employees.dept_id == departments.id) ✅ Add New Column ↳ SQL: SELECT name, salary, salary * 0.10 AS bonus FROM employees; ↳ PySpark: df.withColumn("bonus", df.salary * 0.10) La verdad es que SQL te ayuda a conseguir un trabajo. Ser bilingüe en SQL y PySpark te ayuda a ascender. #sql #pyspark #python #DataEngineering #BigData " width="135" height="240">
Porque en el trabajo moderno de datos, la cuestión no es qué herramienta usar. Es qué tan fluidamente puedes cambiar entre ellas, dependiendo de la escala, la pila y la velocidad que necesites. Aquí tienes la hoja 👇 definitiva de trucos SQL vs PySpark ✅ Select Columns ↳ SQL: SELECT name, salary FROM employees; ↳ PySpark: df.select("name", "salary") ✅ Filter Rows ↳ SQL: SELECT * FROM employees WHERE salary > 80000; ↳ PySpark: df.filter(df.salary > 80000) ✅ Sort Data ↳ SQL: SELECT * FROM employees ORDER BY salary DESC; ↳ PySpark: df.orderBy(df.salary.desc()) ✅ Group & Count ↳ SQL: SELECT department, COUNT(*) FROM employees GROUP BY department; ↳ PySpark: df.groupBy("department").count() ✅ Group & Sum ↳ SQL: SELECT department, SUM(salary) FROM employees GROUP BY department; ↳ PySpark: df.groupBy("department").sum("salary") ✅ Join Tables ↳ SQL: SELECT e.name, d.department_name FROM employees e JOIN departments d ON e.dept_id = d.id; ↳ PySpark: employees.join(departments, employees.dept_id == departments.id) ✅ Add New Column ↳ SQL: SELECT name, salary, salary * 0.10 AS bonus FROM employees; ↳ PySpark: df.withColumn("bonus", df.salary * 0.10) La verdad es que SQL te ayuda a conseguir un trabajo. Ser bilingüe en SQL y PySpark te ayuda a ascender. #sql #pyspark #python #DataEngineering #BigData

✦ 𝙃𝙖𝙥𝙥𝙮 𝘽𝙞𝙧𝙩𝙝𝙙𝙖𝙮 𝙔𝙪𝙩𝙖 𝙏𝙖𝙢𝙖𝙢𝙤𝙧𝙞 ✦ 最高の1年になりますように💛 #玉森裕太 #KisMyFt2 #キスマイ #KisMyFt2_HB2U #happybirthday

✦ 𝙃𝙖𝙥𝙥𝙮 𝘽𝙞𝙧𝙩𝙝𝙙𝙖𝙮 𝙔𝙪𝙩𝙖 𝙏𝙖𝙢𝙖𝙢𝙤𝙧𝙞 ✦ 最高の1年になりますように💛 #玉森裕太 #KisMyFt2 #キスマイ #KisMyFt2_HB2U #happybirthday

Lagta Hai Jase Koi Kami Rah Gai 🥲 Movie name [Wish dragon] #anime #animeedit #itachi #sadedit #wishdragon

Lagta Hai Jase Koi Kami Rah Gai 🥲 Movie name [Wish dragon] #anime #animeedit #itachi #sadedit #wishdragon

About

Robot
API

Legal

Privacy Policy