༆༒💞Muskan❤️🔥༒༆
Region: NP
Thursday 08 October 2026 08:55:39 GMT
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sunil kumar :
hi🥰👌❤️
2026-10-08 23:26:24
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2026-10-08 22:51:22
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2026-10-08 18:52:03
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karmdev14754010 :
2026-10-08 10:54:00
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AhmadIbrahim :
❤️
2026-10-09 08:55:31
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Mr🇳🇵Seraj 🇳🇵✊ :
2026-10-08 09:24:38
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Mayalu_333 :
2026-10-08 11:16:04
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꧁༺MDHASiM༻꧂ :
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2026-10-08 17:58:01
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siraj :
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2026-10-08 09:38:24
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🇳🇵New🖕🇳🇵Nepal🇳🇵 🖕🇳🇵 :
2026-10-08 10:26:09
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Raja Raja :
@
2026-10-08 15:48:00
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sajan :
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2026-10-09 04:16:00
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2026-10-08 10:36:09
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2026-10-08 18:27:50
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꧁𓊈𒆜🇳🇵Muslim⚔️bo¥🇲🇾𒆜𓊉꧂ :
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2026-10-08 14:05:55
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2026-10-08 15:29:11
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Rajesh Kumar Yadav 930 :
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2026-10-08 13:40:53
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2026-10-08 14:08:07
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2026-10-08 11:52:31
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🌹Purushottam Yadav 510🇳🇵🌻 :
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2026-10-08 11:40:19
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Chand babu Ansari :
[Heartwarming][Heartwarming][Heartwarming]
2026-10-08 11:33:26
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ẞmårt bōy🤗 :
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2026-10-08 11:19:49
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Ashamat ❤️🌹Raja🇳🇵🤝💋💋❤️ :
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2026-10-08 13:58:54
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ALONE BOY 🔥😎🤍🔥 :
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2026-10-08 10:54:51
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Mr🇳🇵Seraj 🇳🇵✊ :
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2026-10-08 09:24:06
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To see more videos from user @alisha_1234565, please go to the Tikwm
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![If you're working with data in Python, Pandas is a must-know library. It makes data manipulation easy and efficient. These are 9 essential Pandas operations you should know: 1. Read data – Load CSV, Excel, or JSON files using pd.read_csv() or pd.read_excel(). 2. View data – Use .head() and .tail() to quickly check the first and last rows. 3. Filter rows – Apply conditions with .loc[] and .query() to extract relevant data. 4. Select columns – Pick specific columns using df[['column1', 'column2']]. 5. Handle missing values – Fill or drop missing data with .fillna() and .dropna(). 6. Group and aggregate – Summarize data using .groupby() and .agg(). 7. Sort data – Arrange values in order with .sort_values(by='column'). 8. Merge datasets – Combine multiple DataFrames with .merge() or .concat(). 9. Apply functions – Use .apply() to transform data efficiently. Mastering these operations will make working with data faster and smoother. #dataanalyst #dataanalytics #python](/video/cover/7504113882189860126.webp)
