Python for Data Analysis by Wes McKinney :: cares with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. This is a book about the parts of the Python language and libraries you’ll got to effectively solve a broad set of data analysis problems. This book isn’t an exposition on analytical methods using Python as the implementation language.
When I say “data”, what am I pertaining to exactly? The primary focus is on structured data, a deliberately vague term that encompasses many various common sorts of data, such as
- Multidimensional arrays (matrices)
- Tabular or spreadsheet-like data during which each column could also be a special type (string, numeric, date, or otherwise). This includes most kinds of data commonly
- Multiple tables of knowledge interrelated by key columns (what would be primary or foreign keys for a SQL user)
- Evenly or unevenly spaced time series
This is by no means a complete list. Even though it’s going to not always be obvious, a large percentage of knowledge sets are often transformed into a structured form that’s more suitable for analysis and modeling. If not, it’s going to be possible to extract features from a knowledge set into a structured form.
As an example, a set of stories articles might be processed into a word frequency table which could then be wont to perform sentiment analysis. Most users of spreadsheet programs like Microsoft Excel, perhaps the foremost widely used data analysis tool within the world, won’t be strangers to those sorts of data.
Writer of Python for Data Analysis pdf is Wes McKinney.
