In this lesson we looked at how to:

• How to use .describe() to quickly see some descriptive statistics at a glance.

• How to use .resample() to make a time-series data comparable to another by changing the periodicity.

• How to work with matplotlib.dates Locators to better style a timeline (e.g., an axis on a chart).

• How to find the number of NaN values with .isna().values.sum()

• How to change the resolution of a chart using the figure's dpi

• How to create dashed '--' and dotted '-.' lines using linestyles

• How to use different kinds of markers (e.g., 'o' or '^') on charts.

• Fine-tuning the styling of Matplotlib charts by using limits, labels, linewidth and colours (both in the form of named colours and HEX codes).

• Using .grid() to help visually identify seasonality in a time series.

  
  

You can download the completed code for today in this lesson.

Well done for completing another challenging data science day! I hope working with Pandas is starting to feel more and more second nature at this point.