@tn.3343: 🚗 SỐ TỰ ĐỘNG (AT): ✅ Dễ học – không cần côn ✅ Không lo tắt máy ✅ Phù hợp đi gia đình, người mới 🚗 SỐ SÀN (MT): ✅ Khó lúc đầu – nhưng quen là chạy mượt ✅ Lái được nhiều loại xe hơn (kể cả xe tải) ✅ Phù hợp ai muốn làm nghề, chạy dịch vụ #b #c1 #hoclaixe ##đaotaolaixethanhtan #botuctaylai

Thanh Tân Thực Hành Lái Xe
Thanh Tân Thực Hành Lái Xe
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Region: VN
Friday 01 May 2026 14:40:49 GMT
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tuananhh033
Tuan Anhh :
số này đi sao thầy
2026-05-02 02:41:41
18
dywpqtbp90m5
hiếu trần :
e thà học số sàn còn sướng hơn vừa đc lái cả AT lẫn MT
2026-05-02 00:12:53
2
hoanghuydepzai29
H2Tk :
bro ơi xóa thể đi xe tự động đc mà :)
2026-05-03 06:35:33
1
tm.c.car
Tâm Đức Car :
Số 5 chỉ đi 50 thôi à
2026-05-03 02:08:31
1
nguoidungsau27ngay
🚭 :
canh km thì tuỳ đường nữa thầy canh số bằng rpm tua máy chuẩn nhất😁
2026-05-04 02:49:41
1
phuc_la
Phúc :
Côn tự động là sao nhỉ🥺
2026-05-02 04:17:06
0
l.n.trg.vy1905
TVy :
Hỗ trợ dùm e cái thầy
2026-05-05 00:20:31
1
tuan_anh1155
Tuan Anh1202 :
Mời khầy đi thử số này😂
2026-05-02 10:30:01
1
cuonghg23pt
Cường Nguyễn :
Vẽ nhầm số rồi kìa
2026-05-02 02:25:05
0
tran.khang3012
khang🥺🇻🇳 :
số sàn
2026-05-02 13:11:41
0
h.bao209gmail.com
HB :
nhưng số sàn chạy cảm giác vẫn đã hơn thầy nhỉ🥰
2026-05-02 10:38:56
0
jetjetmienbac29
Jet jet miền bắc :)) :
Team số nút đâu:)))))
2026-05-02 11:00:19
0
nthpleiku_0904
Nguyen TanHai :
15 tuổi chạy xe 2 tấn được 2 năm roii giờ học bằng được hong thầy
2026-05-02 15:19:14
1
quyen316
anh nông dân 48 :
2026-05-03 13:04:10
1
minhgia9156
Lamine YaMinh⚽️🥇🏆 :
DcT
2026-05-04 13:36:22
1
bebe34_09
pắppii🐴 :
o
2026-05-03 06:04:17
1
an.an.l.mt.tri.nh761
VIN PHÉT😈😈 :
Mua đc ô tô chưa mà lướt v🥰☺️
2026-05-04 12:54:20
1
gia.dhhbhjzcf
Trần Thị Hoa :
Sao xe thầy tui R trên chỗ số 1 trời
2026-05-04 05:37:06
1
duccmanh09
duccmanh :
đam mê oto từ nhỏ mới 17 mà đã xem hết rồi kkk
2026-05-02 12:18:01
0
toannguyen2531998
🔱Lão Nhị🔱65🇻🇳✅ :
Con côn j
2026-06-08 01:52:36
1
thoc9_6
Thóc96🌾 :
Tui sao mà mấy cái lý thuyết như này chả hiểu gì đâu nhưng lên xe thì mượt nhe sun siu😂
2026-06-22 16:26:32
1
user889413244
@ Tus =) :
😁😁😁
2026-05-03 03:55:01
1
dakha3
Hà ma ma :
.@L̺͆O̺͆N̺͆E͜͡L̺͆Y̺͆❤️‍🩹
2026-05-03 22:52:31
1
nguyenhaihieu
Nguyễn Hải Hiếu :
😁😁😁
2026-05-04 04:36:11
1
quan.862
quan 86 :
😁😁😁
2026-05-02 12:29:18
0
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Other Videos

Time Series Date Time Formatting in Python Explore essential techniques for handling and formatting date-time data in Python for time series analysis. Learn about parsing, formatting, timezone handling, and working with fiscal years. Discover practical examples using built-in modules and popular libraries like pandas and pytz. #python #datascience #timeseriesanalysis #stem #coding #dataengineering you can find, for free, this and all others slideshow on the xbe.at website Suggestions to reinforce your understanding of time series date-time formatting: 1. Practice regularly with diverse datasets. Work with time series data from various domains to encounter different date-time formats and challenges. 2. Experiment with different time zones and DST transitions. Understanding these complexities is crucial for accurate global data analysis. 3. Build small projects that involve date-time manipulation. This hands-on experience will solidify your understanding of concepts like parsing, formatting, and time arithmetic. 4. Document your code thoroughly. Include comments explaining the purpose of each date-time operation, especially when dealing with complex transformations or timezone conversions. 5. Stay updated with Python's datetime module and relevant libraries. The ecosystem evolves, and new features or best practices may emerge. 6. Collaborate with others working on time series projects. Sharing knowledge and reviewing each other's code can uncover new techniques and prevent common pitfalls. 7. When in doubt, consult the official documentation. Python's datetime module and libraries like pandas have extensive documentation that can clarify nuanced behaviors. 8. Challenge yourself to optimize date-time operations for large datasets. Consider performance implications when working with millions of timestamps.
Time Series Date Time Formatting in Python Explore essential techniques for handling and formatting date-time data in Python for time series analysis. Learn about parsing, formatting, timezone handling, and working with fiscal years. Discover practical examples using built-in modules and popular libraries like pandas and pytz. #python #datascience #timeseriesanalysis #stem #coding #dataengineering you can find, for free, this and all others slideshow on the xbe.at website Suggestions to reinforce your understanding of time series date-time formatting: 1. Practice regularly with diverse datasets. Work with time series data from various domains to encounter different date-time formats and challenges. 2. Experiment with different time zones and DST transitions. Understanding these complexities is crucial for accurate global data analysis. 3. Build small projects that involve date-time manipulation. This hands-on experience will solidify your understanding of concepts like parsing, formatting, and time arithmetic. 4. Document your code thoroughly. Include comments explaining the purpose of each date-time operation, especially when dealing with complex transformations or timezone conversions. 5. Stay updated with Python's datetime module and relevant libraries. The ecosystem evolves, and new features or best practices may emerge. 6. Collaborate with others working on time series projects. Sharing knowledge and reviewing each other's code can uncover new techniques and prevent common pitfalls. 7. When in doubt, consult the official documentation. Python's datetime module and libraries like pandas have extensive documentation that can clarify nuanced behaviors. 8. Challenge yourself to optimize date-time operations for large datasets. Consider performance implications when working with millions of timestamps.

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