In this section, we are going to use Open Power System Data to understand TSA. We'll look at the time series data structures, time-based indexing, and several ways to visualize time series data. 

We will start by importing the dataset. Look at the code snippet given here:

# load time series dataset
df_power = pd.read_csv("https://raw.githubusercontent.com/jenfly/opsd/master/opsd_germany_daily.csv")
df_power.columns

The output of the preceding code is given here:

Index(['Consumption', 'Wind', 'Solar', 'Wind+Solar'], dtype='object')

The columns of the dataframe are described here:

• Date: The date is in the format yyyy-mm-dd. 
• Consumption: This indicates electricity consumption in GWh. 
• Solar: This indicates solar power production in GWh. 
• Wind+Solar: This represents the sum of solar and wind power production in GWh.

Note the date column, which contains the time series dataset. We can use this dataset to discover how electricity consumption and production varies over time in Germany.