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Customize Time Series Plots

Customizing your time series plots by highlighting important events in time is a great way to draw attention to key insights and communicate them efficiently.

# Load packages
import pandas as pd
import matplotlib.pyplot as plt

# Upload your data as CSV and load as data frame
df = pd.read_csv(
    "data.csv",
    parse_dates=["datestamp"],  # Tell pandas which column(s) to parse as dates
    index_col="datestamp", # Use a date column as your index
)  

# Specify a particular subset to analyze
sub = df["2006-01-01":"2010-01-01"]
sub.head()

# See available styles plt.style.available
plt.style.use("ggplot")

ax = sub.plot(y=["Agriculture", "Finance", "Information"], figsize=(12, 7))

# Customize title and labels
ax.set_title("Unemployment rate over time")
ax.set_ylabel("Unemployment rate in %")
ax.set_xlabel("Date")

# Add a vertical red shaded region
ax.axvspan(
    "2007-01-01",  # From
    "2008-01-12",  # To
    color="yellow",  # Set color of region
    alpha=0.3,  # Set Transparency
)
# Add a vertical line
ax.axvline("2008-09-01", color="red", linestyle="--")

# Add a horizontal green shaded region
ax.axhspan(
    5.5,  # From
    6.5,  # To
    color="green",  # Set color of region
    alpha=0.3,  # Set Transparency
)
# Add a horizontal line
ax.axhline(13, color="orange", linestyle="--")


# Annotate your figure
plt.annotate(
    "Healthy unemployment rate",  # Annotation text
    xy=("2010-01-01", 5.7),  # Annotation position
    xycoords="data",  # The coordinate system that xy is given in
    color="black",  # Text Color
)

plt.annotate(
    "Financial Crisis",  # Annotation text
    xy=("2007-04-01", 17.5),  # Annotation position
    xycoords="data",  # The coordinate system that xy is given in
    color="black",  # Text Color
)
plt.show()