diff --git a/README.md b/README.md index f71ec50..166c5b9 100644 --- a/README.md +++ b/README.md @@ -124,4 +124,4 @@ Depending on server availability, you can use [this](https://mybinder.org/v2/gh/ Stefanie Molin ([@stefmolin](https://github.com/stefmolin)) is a software engineer and data scientist at Bloomberg in New York City, where she tackles tough problems in information security, particularly those revolving around data wrangling/visualization, building tools for gathering data, and knowledge sharing. She is also the author of [Hands-On Data Analysis with Pandas](https://www.amazon.com/dp/1800563450/), which is currently in its second edition and has been translated into Korean. She holds a bachelor’s of science degree in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, as well as a master’s degree in computer science, with a specialization in machine learning, from Georgia Tech. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers. ## Related Content -All examples herein were developed exclusively for this workshop. [Hands-On Data Analysis with Pandas](https://www.amazon.com/dp/1800563450/) contains additional examples and exercises, as does [this](https://medium.com/@stefaniemolin/how-to-pivot-and-plot-data-with-pandas-9450939fcf8) blog post. For a deeper dive into data visualization in Python, check out my [Beyond the Basics: Data Visualization in Python](https://github.com/stefmolin/python-data-viz-workshop) workshop. +All examples herein were developed exclusively for this workshop. [Hands-On Data Analysis with Pandas](https://www.amazon.com/dp/1800563450/) contains additional examples and exercises, as does [this article](https://stefaniemolin.com/articles/data-science/how-to-pivot-and-plot-data-with-pandas/). For a deeper dive into data visualization in Python, check out my [Beyond the Basics: Data Visualization in Python](https://stefaniemolin.com/workshops/python-data-viz-workshop/) workshop. diff --git a/asynchronous_lab/solutions.ipynb b/asynchronous_lab/solutions.ipynb index 25c28f8..64e9df9 100644 --- a/asynchronous_lab/solutions.ipynb +++ b/asynchronous_lab/solutions.ipynb @@ -1491,7 +1491,7 @@ "id": "2f860976-5d4e-497d-a6c5-d0bf19b53aee", "metadata": {}, "source": [ - "*Be sure to consult [this](https://medium.com/@stefaniemolin/how-to-pivot-and-plot-data-with-pandas-9450939fcf8) blog post for additional examples of creating pivot tables using this dataset.*" + "*Be sure to consult [this article](https://stefaniemolin.com/articles/data-science/how-to-pivot-and-plot-data-with-pandas/) for additional examples of creating pivot tables using this dataset.*" ] } ], diff --git a/notebooks/3-data_visualization.ipynb b/notebooks/3-data_visualization.ipynb index 44f0790..4327315 100644 --- a/notebooks/3-data_visualization.ipynb +++ b/notebooks/3-data_visualization.ipynb @@ -48,7 +48,7 @@ "\n", "
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So far, we have focused a lot on summarizing the data using statistics. However, summary statistics are not enough to understand the distribution – there are many possible distributions for a given set of summary statistics. Data visualization is necessary to truly understand the distribution:
Some additional things to keep in mind:
ticker
module provides functionality for customizing both the tick labels and locations – check out the documentation for more information.plot()
method takes a lot of parameters, many of which get passed down to Matplotlib; however, sometimes we need to use Matplotlib calls directly.Tip: If you're new to the zip()
function, check out this article.
Tip: If you're new to the zip()
function, check out this article.
For more on data visualization in Python, including animations and interactive plots, check out my Beyond the Basics: Data Visualization in Python workshop.
+For more on data visualization in Python, including animations and interactive plots, check out my Beyond the Basics: Data Visualization in Python workshop.