Learn how to customize the way Pandas DataFrame look inside a Jupyter notebook. You can show some of the built-in styles and will also create your own. See how to apply style to only parts of a DataFrame. You can also see qgrid that adds interactive featu
- [Instructor] We saw how styling can help us…make charts look more visually interesting,…you can also use styles with data frames.…As we saw before, data frames are rendered…nicely in Jupyter notebooks.…Let's take a look at our Boston housing data again.…Let's start a new notebook.…New, Python 3,…and let's name this one pandasstyles.…And we are going to input from sklearn.datasets,…we'll import the load boston function,…and we'll import pandas as pd.…
So let's load the data.…Boston equal load boston.…And now we'll create the data frame.…df is pd.DataFrame,…where the data is boston data,…and the columns are the boston feature names.…We'll also add the price column to the data frames.…So df of price equals boston target.…And now let's take only the first 10 rows,…hdf equal df.head of 10,…and let's take a look.…
Okay so this is the different styling…of how data frames are rendered on the notebook.…Let's highlight the maximal values in each column.…This can be done with the highlight max style.…So, hdf.style.highlight max.…
- Working with Jupyter notebooks
- Using code cells
- Extensions to the Python language
- Markdown cells
- Editing notebooks
- NumPy basics
- Broadcasting, array operations, and ufuncs
- Folium and Geo
- Machine learning with scikit-learn
- Plotting with matplotlib and bokeh
- Branching into Numba, Cython, deep learning, and NLP
Skill Level Intermediate
1. Scientific Python Overview
2. The Jupyter Notebook
3. NumPy Basics
Manage environments5m 11s
6. Folium and Geo
7. NY Taxi Data
10. Other Packages
11. Development Process
Next steps1m 33s
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