Code Snippet Repository

In this code snippet, we look at how to edit axis and legend labels in seaborn. While this is a very basic task, it is one I find myself often searching the internet for. To give you (and me) a shortcut to a code example, I created this little post.

Code Snippet Repository

In this short post, we are looking at how to implement plotly graphs into your own blog posts. For this, I am assuming that you use a site-generating framework that is based on either markdown or html files. For DS-Econ I am using Hexo.

Code Snippet Repository

I wrote a little Code Snippet that returns the “California Housing” dataset provided by sklearn as a named pandas dataframe. Ready for you to use in your applications.

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import numpy as np
import pandas as pd
from sklearn.datasets import fetch_california_housing

dict_data = fetch_california_housing()

list_names = dict_data.feature_names
list_names.extend(dict_data.target_names)

data = np.concatenate([dict_data.data, dict_data.target.reshape(-1,1)], axis=1)
df = pd.DataFrame(data=data, columns=list_names)

I need some Data - Now!

Often times, we come up with an idea for a visualization or simply want to try out a new model we have heard off. In short: We need data.

However, this can sometimes become a tedious problem as a real world dataset might need additional cleaning, which can be too much effort for a short test or playing around. Here, a toy dataset can come in handy. There are tons of toy-data sets out there, many good ones presented by the sklearn.dataset module. In the example above I remodel the arrays provided by fetch_california into a pd.DataFrame.

For more information on the “California Housing” dataset visit sklearn.datasets.fetch_california.

In classification problems, we often want to assess the quality of our model beyond a simple metric like the models accuracy, especially if we have many different classes or they are of different importance to us. In this short article, I show you a more intuitive way to present the quality of your classification model - a color coded Confusion Matrix.