The first thing to start with is reading the data. When working with time series, the rules of the game do not change - you can still use pandas to get data from csv files.

In the files, let's say you have a Date column that contains dates in str type. For further time series analysis, you must turn the str type into a datetime. This is implemented using the pandas function to_datetime()

Let's take the dataset air_quality_no2_long.csv as an example:

dataset = pd.read_csv("daily-total-female-births.csv")

Next, we convert the data type in the Date column from str to datetime:

dataset["Date"] = pd.to_datetime(dataset["Date"])

You can also do this immediately when reading the dataset:

dataset = pd.read_csv("daily-total-female-births.csv", parse_dates=["Date"])

Now we can plot our dataset:

fig, ax = plt.subplots(figsize=(11, 9))
ax.plot(dataset["Date"], dataset["Births"])
ax.set_xlabel("Datetime")
ax.set_ylabel("Births")
plt.show()