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Data Analysis with pandas: A Comprehensive Guide to DataFrame and Series Classes, Slides of Programming Languages

King Mongkut's University of Technology LadkrabangProgramming Languages

python programming language for finance fields

Typology: Slides

2022/2023

Uploaded on 11/07/2023

emelia-goh
emelia-goh 🇹🇭

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Data Analysis with pandas
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•This session is about pandas library àit is popularly used for data analysis, especially
tabular data
•Provides numerous classes and functions for data manipulation
•Wraps functionality from other packages for enhanced capabilities
•Offers a user-friendly interface for efficient data analysis
•Ideal for financial analysis and other data-driven tasks
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Download Data Analysis with pandas: A Comprehensive Guide to DataFrame and Series Classes and more Slides Programming Languages in PDF only on Docsity!

Data Analysis with pandas

  • This session is about pandas library à it is popularly used for data analysis, especially tabular data
  • Provides numerous classes and functions for data manipulation
  • Wraps functionality from other packages for enhanced capabilities
  • Offers a user-friendly interface for efficient data analysis
  • Ideal for financial analysis and other data-driven tasks

The DataFrame Class

  • Pandas DataFrame is the core class for handling tabular data efficiently
  • DataFrames feature columnar organization, column labeling, and flexible row indexing
  • Comparable to a table in a relational database or an Excel spreadsheet
  • This section covers fundamental aspects of the pandas DataFrame class
  • The class is complex and powerful with many capabilities
  • Subsequent chapters provide more examples and explore different aspects

First Steos with the DataFrame Class

  • The example shows some major features of the DataFrame class when it comes to storing data: o Data itself can be provided in different shapes and types (list, tuple, ndarray, and dict objects are candidates) o Data is organized in columns, which can have custom names (labels) o There is an index that can take on different formats (e.g. number, strings, time information)
  • DataFrame objects provide convenient and efficient handling compared to ndarray objects.
  • ndarray objects are more specialized and restrictive, especially when enlarging existing objects.

First Steos with the DataFrame Class

  • DataFrame objects provide convenient and efficient handling compared to ndarray objects.
  • ndarray objects are more specialized and restrictive, especially when enlarging existing objects.
  • Both DataFrame and ndarray objects offer comparable computational efficiency.

First Steos with the DataFrame Class

  • Cont.

First Steos with the DataFrame Class

  • Contrary to NumPy ndarray objects, enlarging the DataFrame object in both dimensions is possible

First Steos with the DataFrame Class

  • Appending data works similarly à however, in the following example, the index gets replaced by a simple range index:

First Steos with the DataFrame Class

  • Cont.

First Steos with the DataFrame Class

  • Although there are now missing values à the majority of methods calls will still work

Second Steps with the DataFrame Class

  • Example based on ndarray object containing standard normally distributed random numbers
  • Demonstrates additional features, including DatetimeIndex
  • DatetimeIndex used for managing time series data

Second Steps with the DataFrame Class

  • The following table lists the parameters that the DataFrame() function takes

Second Steps with the DataFrame Class

  • DataFrame objects have column names, similar to structured arrays.
  • Column names can be defined by assigning a list object with the appropriate number of elements.
  • This demonstrates the ease of defining or modifying DataFrame object attributes.

Second Steps with the DataFrame Class

  • The following table lists the parameters that the data_range() function takes

Second Steps with the DataFrame Class

  • We can read DatetimeIndex object as the relevant index object, making a time series of the original data set