Pandas Series Outline DRAFT

Goals:

• Develop good SEO content and authority on Pandas. For this we'll create several more articles on aspects of Pandas than we have now, and start grouping them in a category
• Develop a five day mini-course as an offering for email subscribers
• Improve the practice question list https://codesolid.com/pandas-practice-examples/. Offer the solution set as part of course.

Earlier "outline"

• Pandas introduction (John)

  • Core classes - DataFrames and Series.
  • See next topic, I think this is easier to understand with datasets, but many authors focus on creating from dictionaries of lists, etc.
  • Using the tools

• "Pandas DataSets" - perhaps one article covering the following:

  • Downloading and unzipping arbitrary file using urllib or requests, plus python zipfile
  • Seaborn load_dataset and get_dataset_names.
  • https://scikit-learn.org/stable/datasets/toy_dataset.html

• "Kaggle Datasets" (Full article). On using Kaggle API to download datasets: https://www.kaggle.com/docs/api#interacting-with-datasets

• Selecting data in pandas (Beginner to Expert)

  • Relation to indexing.
  • See Indexing and Selecting data
  • loc. iloc. Others? SQL Article I have in progress already.
  • Multi-indexing.

• Data cleaning (one article?)

  • Filling in / handling missing data
  • sklearn has tools for this too?
  • removing duplicates

• Data Visualization in Pandas (Bashir) 2000-3000

Data manipulation:

Data transformation: * Vectorized string methods / other string techniques

• Grouping data (already have GroupBy article. See below. Anything else?)

• Pivot tables and cross-tabulation A lot of this in McKinney's book under "Data Wrangling: Join / Combine / Reshape". So:

  • Dataframe.combine
  • Dataframe.merge
  • stack and unstack

• Time series data Pandas - A whole chapter in McKinney. Several articles possible here?

  • Time deltas
  • Windowing functions?
  • Other material, see for example Pandas Time Series / Date Functionality

• Pandas Data Formats.

  • Pandas loading dataframe from various types (this has been done a lot)

Other somewhat related articles:

JupyterHub + AWS

Existing Content:

• Large Datasets in Pandas
• Using SQL with Pandas (in progress)
• How to Use the Pandas GroupBy Method
• Pandas Practice Examples
• How to Work With Google Sheets in Python and Pandas
• Matplotlib vs. Seaborn (Other plotting articles -- relate to this)