How to uncover and investigate NaN values.
How to convert objects and string data types to numbers.
Creating donut and bar charts with plotly.
Create a rolling average to smooth out time-series data and show a trend.
How to use .value_counts(), .groupby(), .merge(), .sort_values() and .agg().
In addition, we learned many new things too. We looked at how to:
Create a Choropleth to display data on a map.
Create bar charts showing different segments of the data with plotly.
Create Sunburst charts with plotly.
Use Seaborn's .lmplot() and show best-fit lines across multiple categories using the row, hue, and lowess parameters.
Understand how a different picture emerges when looking at the same data in different ways (e.g., box plots vs a time series analysis).
See the distribution of our data and visualise descriptive statistics with the help of a histogram in Seaborn.