CampusX Data Science Mentorship Program 2022-23 - Session 22 - Vectorized String Operations | DateTime in Pandas | Pivot Table | DSMP 2022-23

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365.3 hours of video
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Data Science Mentorship Program (DSMP) 2022-23 Enroll in this Programme from our Website - https://learnwith.campusx.in/ Course Website Link - https://campusx1040.graphy.com/s/preview/courses/CampusX-Data-Science-Mentorship-Program#6376dbd8e4b065a4cadac4db YouTube Playlist - https://www.youtube.com/playlist?list=PLKnIA16_RmvbAlyx4_rdtR66B7EHX5k3z ------------------------------------------------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------------------------------------------------------------- Datasets used in the session - https://drive.google.com/drive/folders/1Vy1LilxgmyBiDg-UAnrBnJ5R1XBvwHGx?usp=share_link Notebook Links -------------------------- Pivot Table - https://colab.research.google.com/drive/17l8EddlrS2Ed35frmeS6cHAvdf5Fbw-g?usp=sharing Pandas String - https://colab.research.google.com/drive/1IbvN3BABXN2sgxNr3EckyB_WO4tP0EpE?usp=sharing Date Time- https://colab.research.google.com/drive/1zkfBGu48iLfJWNzAosD_qbCjzi7dYf8-?usp=sharing ------------------------------------------------------------------------------------------------------------------------------------------------------- ----------------------- | Time stamp | ----------------------- 0:00 Start 6:26 #pivot_table 15:58 #aggfunc 19:27 #multidimensional pivot_table 23:29 #pivot_table margin 25:31 #plotting_graph 36:50 #pandas_string 37:53 # What are vectorized string operations 40:04 # problem in vectorized opertions in vanilla python 42:52 # How pandas solves this issue? # Common Functions 46:21 lower/upper/capitalize/title 47:54 #len/strip 51:03 #split -- get 1:00:27 #replace 1:02:58 # filtering - # startswith/endswith isdigit/isalpha... 1:05:21 # applying regex 1:07:06 # find last names with start and end char vowel 1:10:47 # slicing 1:14:56 #pandas date_time 1:17:00 Creating Timestamp objects 1:23:24 # using datetime.datetime object 1:25:31 # fetching attributes - year/month/day/ 1:26:41 #why separate objects to handle data and time when python already has datetime functionality? 1:31:34 #DatetimeIndex Object 1:37:45 #date_range function 1:44:55 #to_datetime function 1:52:33 #date time accessor 1:53:41 #plotting 2:00:12 Doubt