The Python matplotlib scatter plot is a two dimensional graphical representation of the data. A Python scatter plot is useful to display the correlation between two numerical data values or two data sets. In general, we use this matplotlib scatter plot to analyze the relationship between two numerical data points by drawing a regression line.
The matplotlib pyplot module has a scatter function, which will draw or generate a scatter plot in Python. The basic syntax to draw matplotlib pyplot scatter plot is
matplotlib.pyplot.scatter(x, y)
- x: list of arguments that represents the X-axis.
- y: List of arguments represents Y-Axis.
Python matplotlib Scatter Plot Examples
This is a simple python scatter plot example where we declared two lists of random numeric values. Next, we used the pyplot scatter function to draw a scatter plot of x against y.
import matplotlib.pyplot as plt x = [1, 9, 5, 3, 8, 6, 2, 4, 7] y = [22, 4, 40, 27, 33, 15, 5, 20, 30] plt.scatter(x, y) plt.show()
OUTPUT
Here, we used Python randint function to generate 50 random integer values from 5 to 50 and 100 to 1000 for x and y. Next, we draw the Python scatter plot.
import matplotlib.pyplot as plt import numpy as np x = np.random.randint(5, 50, 50) y = np.random.randint(100, 1000, 50) print(x) print(y) plt.scatter(x, y) plt.show()
OUTPUT
matplotlib Scatter Chart using CSV
In this example, we were reading the CSV file and converted it into DataFarme. Next, we are drawing a Python matplotlib scatter plot by using Profit in X-Axis and Sales in Y-Axis.
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_excel('/Users/suresh/Downloads/Global_Superstore.xls')
sales_data = df.groupby('Order Date')[['Sales', 'Profit']].sum()
print(sales_data.sort_values(by = ['Profit']))
plt.scatter(sales_data['Profit'], sales_data['Sales'])
plt.show()
OUTPUT
matplotlib Scatter Chart titles
We already mentioned in previous charts about labeling the charts. In this matplotlib scatter plot example, we used the xlable, ylabel, and title functions to show X-Axis, Y-Axis labels, and chart titles.
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_excel('/Users/suresh/Downloads/Global_Superstore.xls')
sales_data = df.groupby('Order Date')[['Sales', 'Profit']].sum()
print(sales_data.sort_values(by = ['Profit']))
plt.scatter(sales_data['Profit'], sales_data['Sales'])
plt.title('Matplotlib Scatter Plot')
plt.xlabel('Profit')
plt.ylabel('Global Sales')
plt.show()
OUTPUT
Python Scatter plot color and Marker
In all our previous examples, you can see the default color of blue. However, you can change the marker colors using color argument, and the opacity by alpha argument. In this Python scatter plot example, we change the marker color to red and opacity to 0.3 (bit lite).
Apart from this, you can use markers argument to change the default marker shape. Here, we changed the shape of the marker to *. I suggest you refer matplotlib article to understand the list of available markers.
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_excel('/Users/suresh/Downloads/Global_Superstore.xls')
market_data = df.groupby('Order Date')[['Quantity', 'Profit']].sum()
plt.scatter(market_data['Quantity'], market_data['Profit'],
color = 'red',
marker = '*', alpha = 0.3)
plt.title('Matplotlib Scatter Plot')
plt.show()
OUTPUT
Here, we are trying to showcase three other available markers in Python scatter plot.
import matplotlib.pyplot as plt import numpy as np x = np.random.randint(5, 50, 50) y = np.random.randint(100, 1000, 50) fix, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize = (8, 4)) ax1.scatter(x, y, marker = '+', color = 'red') ax2.scatter(x, y, marker = '^', color = 'blue') ax3.scatter(x, y, marker = '$\clubsuit$', color = 'green', alpha = 0.5) plt.show()
OUTPUT
In the previous Python scatter plot examples, we used a single color for all the markers associated with the axis values. However, you can use multiple colors or individual colors to each marker using the color argument. Here, we defined two Radom integer arrays and a random array for colors. Next, we assigned that colors array to c to generate random colors to markers.
import matplotlib.pyplot as plt import numpy as np x = np.random.randint(10, 100, 30) y = np.random.randint(100, 10000, 30) colors = np.random.rand(30) plt.scatter(x, y, c = colors, alpha = 0.5, s = y/10) plt.show()
OUTPUT
It is another way of assigning different colors to the matplotlib scatter plot markers. Apart from the above, you can also define a gradient color to the markers (for example, rainbow colors) using the color and cmap arguments. To do this, first, you have to assign the list of values that define the marker color as a c argument. Second, you have to define the cmap color (gradient color that you want to use), as we defined below.
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_excel('/Users/suresh/Downloads/Global_Superstore.xls')
market_data = df.groupby('Order Date')[['Sales', 'Quantity', 'Profit']].sum()
plt.scatter(market_data['Quantity'], market_data['Profit'],
c = market_data['Quantity'],cmap = 'gist_rainbow_r',
marker = '*')
plt.title('Matplotlib Scatter Plot')
plt.show()
OUTPUT
Python Scatter plot size and edge colors
The matplotlib scatter function has an s argument that defines the size of a marker. It accepts a static one value for all the markers or array like values. Here, we assigned 150 as a marker size, which means all the markers will size to that value.
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_excel('/Users/suresh/Downloads/Global_Superstore.xls')
market_data = df.groupby('Order Date')[['Quantity', 'Profit']].sum()
plt.scatter(market_data['Quantity'], market_data['Profit'],
color = 'green', marker = '*', alpha = 0.5,
s = 150)
plt.title('Matplotlib Scatter Plot')
plt.show()
OUTPUT
In this Python scatter plot example, we assigned y/10 as the s values. It means each marker value will be different, and it entirely based on y value.
import matplotlib.pyplot as plt import matplotlib.patches as patches import numpy as np x = np.random.randint(10, 100, 30) y = np.random.randint(100, 10000, 30) colors = np.random.rand(30) plt.scatter(x, y, c = colors, alpha = 0.6, s = y/10) plt.show()
OUTPUT
Let me take a CSV file example. Here, we draw a scatter plot using Profit and Sales values. Next, we defined the size of the marker based on the profit. It means marker size will increase when the profit is more.
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_excel('/Users/suresh/Downloads/Global_Superstore.xls')
sales_data = df.groupby('Region')[['Sales', 'Profit']].sum()
print(sales_data.sort_values(by = ['Profit']))
plt.scatter(sales_data['Profit'], sales_data['Sales'], marker = 'o',
color = 'r', s = sales_data['Profit']/ 1000)
plt.show()
OUTPUT
The scatter function linewidths argument accepts a scalar value or array, and the default value is None. This linewidths argument defines the width of marker edges. The edgecolors argument allows choosing the line edge color of the markers. In this example, we assigned the line width as 1.1 and the edge color to green.
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_excel('/Users/suresh/Downloads/Global_Superstore.xls')
market_data = df.groupby('Order Date')[['Quantity', 'Profit']].sum()
plt.scatter(market_data['Quantity'], market_data['Profit'],
color = 'red', marker = '*', alpha = 0.6,
s = 100, linewidths = 1.1, edgecolors = 'g')
plt.title('Matplotlib Scatter Plot')
plt.show()
OUTPUT
Multiple scatter plots in Python
The matplotlib scatter function also allows you to plot multiple values. First, we are plotting y against x, and then we are plotting z against x. It will display z and y values agist x in one chart, and to differentiate them. We used red and blue colors.
import matplotlib.pyplot as plt x = [1, 9, 5, 3, 8, 6, 2, 4, 7] y = [22, 4, 40, 27, 33, 15, 5, 20, 30] z = [16, 35, 4, 19, 20, 40, 35, 7, 12] plt.scatter(x, y, color = 'blue') plt.scatter(x, z, color = 'red') plt.show()
OUTPUT
It is another example of drawing multiple scatter plots. However, this time, we are using the CSV file where we are comparing the Region and Market Sales against the Profits.
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_excel('/Users/suresh/Downloads/Global_Superstore.xls')
region_data = df.groupby('Region')[['Sales', 'Profit']].sum()
market_data = df.groupby('Market')[['Sales', 'Profit']].sum()
plt.scatter(region_data['Profit'], region_data['Sales'],
s= 100, marker = '*', color = 'yellow',
linewidths = 1.1, edgecolors = 'g')
plt.scatter(market_data['Profit'], market_data['Sales'],
s =100, marker = 'o', color = 'r')
plt.title('Matplotlib Scatter Plot')
plt.show()
OUTPUT
Matplotlib Scatter plot legend
As you can see from the above screenshot, you might not know or identify which markers represent the Region Sales and Market. To resolve this, you can use the legend function.
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_excel('/Users/suresh/Downloads/Global_Superstore.xls')
region_data = df.groupby('Region')[['Sales', 'Profit']].sum()
market_data = df.groupby('Market')[['Sales', 'Profit']].sum()
plt.scatter(region_data['Profit'], region_data['Sales'],
label = 'Region Sales',
s= 100, marker = '$\heartsuit$', color = 'b',
linewidths = 1.2, edgecolors = 'g')
plt.scatter(market_data['Profit'], market_data['Sales'],
label = 'Market Sales',
s =100, marker = '$\clubsuit$', color = 'r')
plt.legend()
plt.show()
OUTPUT
Highlight Area in a Python Scatter plot
In some situations, you might need to focus on a particular location or area within the Python scatter plot. So, you need to highlight that particular area for better focus. For this, all you need to do is add patches to an existing matplotlib scatter plot. In this example, we are adding a rectangle to highlight the area.
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
df = pd.read_excel('/Users/suresh/Downloads/Global_Superstore.xls')
market_data = df.groupby('Order Date')[['Quantity', 'Profit']].sum()
fig, ax = plt.subplots()
ax.scatter(market_data['Quantity'], market_data['Profit'],
color = '#A90303', marker = '*', alpha = 0.6,
s = 100, linewidths = 1.1, edgecolors = '#A4F5AF')
ax.add_patch(patches.Rectangle((50, -50), 100, 2000, alpha = 0.3))
plt.show()
OUTPUT
Similarly, we can add a circle to the matplotlib scatter plot area. Apart from this, we can format that circle to view it better. In this example, we add a circle to the scatter plot of random values and then format the color, line widths, etc.
import matplotlib.pyplot as plt import matplotlib.patches as patches import numpy as np x = np.random.randint(10, 100, 30) y = np.random.randint(10, 101, 30) colors = np.random.rand(30) fig, ax = plt.subplots() ax.scatter(x, y, c = colors, alpha = 0.5, s = y*10) ax.add_patch( patches.Circle((40, 60), 20, alpha = 0.3, edgecolor = 'red', facecolor = 'yellowgreen', linewidth = 2, linestyle = 'solid')) plt.show()
OUTPUT
By using the axvline function, you can add a vertical line inside a matplotlib scatter plot. Similarly, use the axhline to add a horizontal line.
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as patches
df = pd.read_excel('/Users/suresh/Downloads/Global_Superstore.xls')
market_data = df.groupby('Order Date')[['Quantity', 'Profit']].sum()
plt.scatter(market_data['Quantity'], market_data['Profit'],
color = '#A90303', marker = '*', alpha = 0.6,
s = 100, linewidths = 1.1, edgecolors = '#A4F5AF')
plt.axvline(150, color = 'b')
plt.axhline(1000, color = 'red')
plt.show()
OUTPUT
