Public account: You and the cabin by: Peter Editor: Peter

Hello, I’m Peter

I recently condensed my introductory articles for Pandas into a simple POWERPOINT presentation. Pandas is a panda library. It is a panda library. It is a panda library.

  1. Pandas’ two data structures
  2. 11 ways to create a DataFrame
  3. DataFrame number fetching technique
  4. Data processing cornerstone: Pandas Data Exploration
  5. Pandas Data type
  6. Pandas Focuses on the groupby, rank, and sort_values mechanisms
  7. Pandas Handles the missing and repeated values
  8. Pandas Merges data: Merge and concat
  9. Axis rotation operations: unstack and stack
  10. Pandas pivot table

It is very helpful to learn Pandas. At the end of the article, there are specific ways to obtain PPT

Content of the PPT

Two big data structures

Pandas has two data structures: Series and DataFrame.

  • Series data: consists of name, index, and values
  • DataFrame data: Can be viewed as multiple Series data

Pandas’ data processing is more often associated with dataframes. The following chart highlights 11 different ways to generate DataFrame type data.

After we get a copy of the data and read it as a DataFrame, we need to find the data we need. How do we view or find the data we want? The following is a variety of methods to take the number, which can be roughly divided into:

  • Expression fetch
  • Index/attribute fetch
  • Slice access
  • Use functions to take numbers and so on

With a piece of data, before processing, we must check some basic information of the data: data size, dimension, field type, missing value, etc., we call this work: data exploration

Pandas, Python native, and NUMPY are compared in the following:

The following pages describe the functions commonly used by Pandas: groupby, rank, sort_VALUES, DROP_DUPLICATED, merge, and concat. They are mainly used in data pretreatment, data combination, data deduplication, PivotTable production and other aspects

access

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