Introduction to Pandas
summary
Pandas provides Series and DataFrame primitives for labeled arrays, fast tabular joins and merges, and built‑in handling of missing values; the demo loads CSVs with read_csv, converts strings to datetime, sets the datetime index, resamples five‑minute windows to days, computes cumulative sums and means, and builds pivot_table views and SQL‑style joins (via SQLAlchemy or DataFrame.join). A real problem ingests ~100 vendor formats of spam feedback, pivots and fills NaNs, exports with to_csv, and visualizes results using vincent charts. It works but uses in‑memory frames and can exhaust memory.
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chapters (21)
- 0:00Introduction
- 0:16What is Panda
- 2:24Getting Started
- 3:56Series
- 4:36Values
- 5:19Index
- 9:09DataFrame
- 11:57Grouping
- 12:46Grouping Multiples
- 13:35Naming Columns
- 14:11Pivot Tables
- 15:39Join Pivot Tables
- 17:34Real World Problem
- 21:12Indexing Date Time
- 22:44Missing Points
- 25:34Resampling
- 26:26Sorting
- 27:01Describe
- 28:47Vincent
- 30:43Questions
- 41:18Integrate
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