Data Analysis with Polars in Python
- CategoryOther
- TypeTutorials
- LanguageEnglish
- Total size1.1 GB
- Uploaded Byfreecoursewb
- Downloads84
- Last checkedJun. 01st '26
- Date uploadedMay. 31st '26
- Seeders 6
- Leechers8
Data Analysis with Polars in Python
https://WebToolTip.com
Last updated 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 4h 2m | Size: 1.04 GB
Master Data Manipulation with Polars - The High Performance DataFrame Library
What you'll learn
How to Read CSV Files into Polars DataFrames
How to Push Data from Polars into a Database
How to Read Excel Files into Polars DataFrames
How to Aggregate Data
How to Join DataFrames
How to Take Advantage of Polars' Superior Processing Speed
Requirements
No Prior Experience Required
Files:
[ WebToolTip.com ] Data Analysis with Polars in Python- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1 - Getting Started
- 1. Welcome to the Course!.en_US.srt (1.4 KB)
- 1. Welcome to the Course!.mp4 (1.8 MB)
- 2. Installing Python.en_US.srt (4.3 KB)
- 2. Installing Python.mp4 (31.9 MB)
- 3. Installing Visual Studio Code.en_US.srt (2.8 KB)
- 3. Installing Visual Studio Code.mp4 (10.0 MB)
- 4. Extensions for Visual Studio Code.en_US.srt (2.9 KB)
- 4. Extensions for Visual Studio Code.mp4 (13.0 MB)
- 5. Download Course Materials.html (7.2 KB) Course Materials 02 - DataFrame Inputs & Outputs Notebooks
- 01 - Installing Polars.ipynb (1.2 KB)
- 02- Read CSV.ipynb (41.3 KB)
- 03 - Write CSV Part 1.ipynb (12.9 KB)
- 04 - Write CSV Part 2.ipynb (12.9 KB)
- 05 - Write Database.ipynb (8.5 KB)
- 06 - Read Database.ipynb (22.5 KB)
- 07- Read Excel.ipynb (8.6 KB)
- 08 - from_pandas.ipynb (1.5 KB)
- 09 - to_pandas.ipynb (17.1 KB)
- 10 - Read ODS.ipynb (40.4 KB)
- 11 - json_normalize.ipynb (55.3 KB)
- 12 - Scan CSV.ipynb (4.1 KB)
- 13 - Reading Multiple CSV Files.ipynb (6.7 KB)
- 14 - Write Database (SQL Server).ipynb (5.7 KB)
- 15 - Schemas.ipynb (23.5 KB)
- 16 - LazyFrame Query Optimization.ipynb (26.7 KB)
- 01 - Select.ipynb (23.8 KB)
- 02 - Filter.ipynb (17.8 KB)
- 03 -Slicing & Sampling.ipynb (44.2 KB)
- 04 - Frame SQL.ipynb (8.5 KB)
- 01 - Inner Join.ipynb (14.7 KB)
- 02 - Anti Joins.ipynb (10.8 KB)
- 03 - Left Join.ipynb (15.4 KB)
- 01 - Min & Max.ipynb (11.4 KB)
- 02 - Mean, Median & Mode.ipynb (12.8 KB)
- 03 - Sum.ipynb (8.0 KB)
- 04 - Quantiles.ipynb (14.1 KB)
- 01 - business_day_count.ipynb (1.3 KB)
- 02 - add_business_days.ipynb (2.3 KB)
- 03 - Handling Time Zones.ipynb (102.5 KB)
- 04 - Handing Epoch Timestamps.ipynb (13.3 KB)
- 05 - Year Expressions.ipynb (9.6 KB)
- 06 - Month Expressions.ipynb (20.4 KB)
- 01 - Regular Expressions (regex).ipynb (37.4 KB)
- 02 - Find & Replace.ipynb (11.5 KB)
- 03 - Date & Time Conversions.ipynb (49.1 KB)
- 01 - Hashing Sensitive Data.ipynb (23.9 KB)
- 02 - Rank.ipynb (35.5 KB)
- 01 - Data Types.ipynb (4.4 KB)
- 02 - Data Structures.ipynb (2.4 KB)
- 03 - Casting Methods.ipynb (3.8 KB)
- 04 - Mathematical Operators.ipynb (4.5 KB)
- 05 - Comparison Operators.ipynb (3.8 KB)
- 06 - Logical Operators.ipynb (3.2 KB)
- 07 - Membership Operators.ipynb (2.4 KB)
- 08 - If Statements.ipynb (1.0 KB)
- Accounting.xlsx (19.1 KB)
- Electric_Vehicles.json (78.0 MB) Marketing Cost Data
- marketing_cost_01.csv (0.6 KB)
- marketing_cost_02.csv (0.5 KB)
- marketing_cost_03.csv (0.5 KB)
- marketing_cost_04.csv (0.5 KB)
- marketing_cost_05.csv (0.6 KB)
- marketing_cost_06.csv (0.5 KB)
- marketing_cost_07.csv (0.5 KB)
- marketing_cost_08.csv (0.5 KB)
- marketing_cost_09.csv (0.5 KB)
- marketing_cost_10.csv (0.6 KB)
- marketing_cost_11.csv (0.5 KB)
- marketing_cost_12.csv (0.5 KB)
- marketing_cost_all.csv (6.3 KB)
- application_feedback.csv (0.5 KB)
- call_center_data.csv (18.8 KB)
- credit_card_transactions.csv (1.5 MB)
- customer_reviews.csv (20.6 KB)
- daily_aggregate_sales_2024.csv (38.0 KB)
- dates.csv (4.3 KB)
- employees.csv (2.8 KB)
- employees.ods (39.1 KB)
- employees.tsv (2.8 KB)
- fake_PII.csv (0.7 KB)
- long_form_dates.csv (0.9 KB)
- performance_data.csv (0.4 KB)
- properties.csv (22.8 KB)
- random_dates.csv (0.6 KB)
- social_media_posts.csv (24.3 KB)
- start_end_dates.csv (2.2 KB)
- underwriting.csv (0.5 KB)
- unix_timestamps.csv (11.7 KB) 2 - Inputs & Outputs
- 10. Write Database.en_US.srt (6.5 KB)
- 10. Write Database.mp4 (23.5 MB)
- 11. Read Database.en_US.srt (3.9 KB)
- 11. Read Database.mp4 (20.6 MB)
- 12. Read Excel.en_US.srt (3.0 KB)
- 12. Read Excel.mp4 (7.5 MB)
- 13. From Pandas.en_US.srt (1.6 KB)
- 13. From Pandas.mp4 (4.1 MB)
- 14. To Pandas.en_US.srt (2.4 KB)
- 14. To Pandas.mp4 (9.8 MB)
- 15. Read ODS.en_US.srt (8.1 KB)
- 15. Read ODS.mp4 (35.8 MB)
- 16. JSON Normalize.en_US.srt (13.0 KB)
- 16. JSON Normalize.mp4
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