Line Chart in Matplotlib

A line chart is used to show how the value of a variable changes over time. It is very useful for showing changes and trends over time. The x-axis of a line chart represents the time like months or years. The y-axis represents the value that we are interested in measuring like revenue or number of visitors to a website. We can also create multiple line charts to compare related groups over time like the population of India, China, and US in the past 20 years or traffic to a website from search, social media, or referral. The continuous lines in the line chart make it easy to see how the value increases, decreases or remains stable during a time frame. It is also helpful in showing future forecasts like the stock prices of a company in the next 3 months.

Read Data

For data manipulation and analysis we will use the Pandas library. Let’s import the Pandas library and read a dataset using the read_csv() function. We will also convert the Date column to datetime using the parse_dates parameter. You can get the dataset used in this post here - matplotlib-python-book

# silence warnings
import warnings
warnings.filterwarnings('ignore')

# import pandas
import pandas as pd

# read the data into pandas DataFrame
nvidia = pd.read_csv("../data/NVDA.csv", parse_dates=['Date'])
nvidia.head()

Create a Line Chart

To create a Line Chart in matplotlib, we use the plt.plot() function. And we use the plt.show() function to display the figure.

Before we create any kind of charts in Matplotlib, we need to first import the Matplotlib library. Then create the plot.

Let’s create a Line chart in matplotlib. we will plot the Date column on the x-axis and Adj Close column on the y-axis.

# create a line chart
import matplotlib.pyplot as plt

plt.plot(nvidia['Date'], nvidia['Adj Close'])
plt.show()

We can see that the share prices of Nvidia were constantly going up from 2019-22 and then it started to go down between 2022-23 and then started to go up again and went very high at the beginning of 2024. The reason for such a hike in Nvidia stock prices is the result of the explosion of Artificial Intelligence and their revenue from selling GPUs as Nvidia controls more than 95% of the market.

Add Title and Axis Labels

Let’s add a title to the plot using the plt.title() and x and y axis labels using plt.xlabel() and plt.ylabel() respectively.

# create a line chart with title and axis labels
plt.plot(nvidia['Date'], nvidia['Adj Close'])
plt.title("Nvidia Stock Prices (2019-2024)")
plt.xlabel("Year")
plt.ylabel("Adj Close Price")
plt.show()

Multiple Line Charts

To Create multiple line charts on the same figure, we just need to add another plt.plot() function. This will cause matplotlib to create another line chart that shares the same x and y axis as the first graph.

Let’s read some more data for this.

# read data
netflix = pd.read_csv("../data/NFLX.csv", parse_dates=['Date'])
tesla = pd.read_csv("../data/TSLA.csv", parse_dates=['Date'])

# create multiple line charts
plt.plot(nvidia['Date'], nvidia['Adj Close'])     # Nvidia 
plt.plot(netflix['Date'], netflix['Adj Close'])   # Netflix
plt.plot(tesla['Date'], tesla['Adj Close'])       # Tesla
plt.title("Stock Prices (2019-2024)")
plt.xlabel("Year")
plt.ylabel("Adj Close Price")
plt.show()

Instead of writing multiple plot statements as we did before, we can make it a little bit cleaner like this.

dfs = [nvidia, netflix, tesla]

for df in dfs:
    plt.plot(df['Date'], df['Adj Close'])  
plt.title("Stock Prices (2019-2024)")
plt.xlabel("Year")
plt.ylabel("Adj Close Price")
plt.show()

Add a Legend

Although we plotted all three lines on the graph, it is not clear which line corresponds to which company. To solve this problem, we need to add a legend to the plot. First, we will add labels to the plt.plot() function and then use the plt.legend() function to add a legend.

# create multiple line charts
dfs = [nvidia, netflix, tesla]
labels = ["Nvidia", "Netflix", "Tesla"]

for df, label in zip(dfs, labels):
    plt.plot(df['Date'], df['Adj Close'], label=label)
plt.title("Stock Prices (2019-2024)")
plt.xlabel("Year")
plt.ylabel("Adj Close Price")
plt.legend() 
plt.show()

Line style and color

Instead of using the default color and line style, we can apply our styles. To change the line style use the linestyle and color to change the color.

# create multiple line charts
plt.plot(tesla['Date'], tesla['Adj Close'], label="Tesla", 
         linestyle=":", color="blue")

plt.plot(nvidia['Date'], nvidia['Adj Close'], label="Nvidia", 
         linestyle="--", color="seagreen")

plt.plot(netflix['Date'], netflix['Adj Close'], label="Netflix",
        linestyle="-.", color='crimson')

plt.title("Stock Prices (2019-2024)")
plt.xlabel("Year")
plt.ylabel("Adj Close Price")
plt.legend() 
plt.show()

I think the plot with solid lines looked much better than this version. There are many other parameters you can use to change the appearance of the line chart, please check the plt.plot() document.

Style Sheets

There are also various style sheets available to change the appearance of your plots. You can list all the styles available using matplotlib.style.available attribute. You can also see the examples of them here - stylesheet reference 

# uncomment and run the below line to see
# print(plt.style.available)

To add a style use the plt.style.use(“stylesheet_name“)

# add a style sheet
plt.style.use("seaborn-v0_8-dark")

Figure size

To change the size of the figure, we can use the figsize parameter of plt.figure() function. The figsize parameter takes a tuple of (width, height) in inches.

# Change figure size
plt.figure(figsize=(8, 6))

# then rest of the code

Save a Figure

To save a figure in matplotlib use the plt.savefig(“image.png“) function before calling the plt.show(). When saving a figure you have to specify the name of the file with the extension like png, pdf, svg, etc. You can also include a path plt.savefig(“filepath/image.png“) to save it in any other directory.

plt.figure(figsize=(8, 6))

plt.plot(tesla['Date'], tesla['Adj Close'], 
         label="Tesla", color="blue")

plt.plot(nvidia['Date'], nvidia['Adj Close'], 
         label="Nvidia", color="seagreen")

plt.plot(netflix['Date'], netflix['Adj Close'], 
         label="Netflix", color='crimson')

plt.title("Stock Prices (2019-2024)")
plt.xlabel("Year")
plt.ylabel("Adj Close Price")
plt.legend() 
# save it in the current directory
plt.savefig("stock_prices_2019_2024.png")
plt.show()

Exercise 1.1

1. Read the WorldExpenditure.csv data into a Pandas dataframe.

2. Select only the data for United States of America, China, Germany and Australia.

3. Select only the Total function in the sector column that contains aggregated data.

4. You can perform both operations 2 and 3 together if you want to.

5. Plot Year on the x-axis and Expenditure (million USD) on the y-axis.

6. Add a Title and Labels for the x and y axis.

7. Add a Legend to the plot.

Summary

• To Create a Line Chart in matplotlib use the plt.plot() function.

• To add a title to the plot use plt.title()

• For the x and y-axis labels use the plt.xlabel() and plt.ylabel() respectively.

• Multiple line charts can be added to the same figure by adding another plt.plot() function.

• To add a legend, first specify label for each plot and then use plt.legend()

• We can change the line style and color using linestyle and color parameters of plt.plot()

• To change the style sheet use plt.style.use(“stylesheet_name“)

• To change the figure size use the figsize=(width, height) parameter of plt.figure()

• To save a figure use the plt.savefig(“filepath/image.png“)

Solution

Exercise 1.1

import warnings
warnings.filterwarnings('ignore')

# import pandas and matplotlib
import pandas as pd
import matplotlib.pyplot as plt

# read data
df = pd.read_csv("../data/WorldExpenditures.csv")

# Filter the data 
selected_countries = ['United States of America', 'China', 'Germany', 'Australia']
total_function = df[(df['Sector'] == 'Total function') & (df['Country'].isin(selected_countries))]

# Plot multiple line charts
for country in selected_countries:
    country_data = total_function[total_function['Country'] == country]
    plt.plot(country_data['Year'], country_data['Expenditure(million USD)'], label=country)

plt.title('Total Government Expenditure Trend (2000-2020)')
plt.xlabel('Year')
plt.ylabel('Expenditure (million USD)')
plt.legend()
plt.show()

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