Pandas plot()

import pandas as pd
import matplotlib.pyplot as plt

# create a DataFrame
data = {'x': [1, 2, 3, 4, 5], 'y': [10, 14, 18, 24, 30]}
df = pd.DataFrame(data)

# create a line plot
df.plot()

plt.show()

plot() Syntax

The syntax of the plot() method in Pandas is:

df.plot(x=None, y=None, kind='line', figsize=None, title=None, xlabel=None, ylabel=None, legend=True, xlim=None, ylim=None, style=None, color=None)

plot() Arguments

The plot() method takes following arguments:

• x and y (optional) - specifies the columns from the DataFrame to use as the x and y axes for the plot
• kind (optional) - specifies the type of plot to create
• figsize (optional) - sets the size of the figure
• title (optional) - sets the title for the plot
• xlabel and ylabel (optional) - sets labels for the x and y axes
• legend (optional) - controls the display of the legend
• xlim and ylim (optional) - sets custom limits for the x and y axes
• style (optional) - allows us to specify a style for the plot
• color (optional) - sets the color for the plot elements

plot() Return Value

The plot() method in Pandas returns a plot of our data.

Example1: Plot a Line

import pandas as pd
import matplotlib.pyplot as plt

# create a DataFrame 
data = {'Months': ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
        'Temperature (°C)': [5, 8, 15, 20, 25]}
df = pd.DataFrame(data)

# create a line plot with xlabel, ylabel, and title
result = df.plot(x='Months', y='Temperature (°C)', kind='line', xlabel='Months', ylabel='Temperature (°C)', title='Monthly Temperature Variation')

plt.show()

Output

In the above example, we have used the plot() method to create a line plot of temperature.

We have passed multiple arguments inside plot(),

• x='Months': specifies 'Months' column to be used for the x-axis values in the plot
• y='Temperature (°C)': specifies 'Temperature (°C)' column to be used for the y-axis values in the plot
• kind='line': specifies to create a line plot
• xlabel='Months': sets the label for the x-axis to Months
• ylabel='Temperature (°C)': sets the label for the y-axis to Temperature (°C)
• title='Monthly Temperature Variation': sets the title of the plot to Monthly Temperature Variation

If we don't pass any argument inside plot() as:

# create a line plot without passing any arguments
result = df.plot()

The output would be:

It's better to use meaningful arguments to enhance the clarity and purpose of our plot.

Example2: Create a Bar Plot With Custom Figure Size

import pandas as pd
import matplotlib.pyplot as plt

# create a DataFrame 
data = {'Cities': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix'],
        'Population (millions)': [8.4, 3.9, 2.7, 2.3, 1.8]}
df = pd.DataFrame(data)

# create a bar plot 
# also set a custom size for the figure using the figsize parameter
ax = df.plot(x='Cities', 
             y='Population (millions)', 
             kind='bar', 
             figsize=(8, 10),
             xlabel='Cities', 
             ylabel='Population (millions)', 
             title='Population of Major U.S. Cities')

# show the plot
plt.show()

Output

Here, we have used kind='bar' inside the plot() method to create a bar plot of the given data.

We have also used the figsize=(8,10) parameter inside plot() to set the custom size of the figure to be 8 inches in width and 10 inches in height.

Example 3: Create a Histogram

import pandas as pd
import matplotlib.pyplot as plt

# create a DataFrame 
data = {'Age': [25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75]}
df = pd.DataFrame(data)

# create a histogram 
ax = df.plot(y='Age', 
          kind='hist', 
          bins=5, 
          rwidth=0.9, 
          legend=True,
          ylabel='Frequency', 
          title='Age Distribution')

# show the plot
plt.show()

Output

Here, we use the plot() method to create a histogram by specifying:

• y='Age' - the column to be plotted on the y-axis
• kind='hist' - the type of plot to be a histogram
• bins=5 - the number of bins or intervals in the histogram to be 5
• rwidth=0.9 - the width of the bars in the histogram
• legend=False - displaying the legend is set to False
• ylabel='Frequency' - setting the label for the y-axis
• title='Age Distribution' - setting the title of the plot

Note: The bins and rwidth parameters are specific to the 'hist' kind of plot when using plot() for creating histograms. They are not applicable to other types of plots. To learn more, visit Pandas Histogram.

Example 4: Create a Scatter Plot

import pandas as pd
import matplotlib.pyplot as plt

# create a DataFrame 
data = {'X': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
        'Y': [10, 12, 8, 14, 18, 24, 22, 30, 28, 35]}
df = pd.DataFrame(data)

# create a scatter plot 
result = df.plot(x='X', 
                     y='Y', 
                     kind='scatter', 
                     xlabel='X-axis', 
                     ylabel='Y-axis', 
                     title='Scatter Plot',
                     marker='o', 
                     color='blue')

# show the plot
plt.show()

Output

In the above example, we have used kind='scatter' inside plot() to create a scatter plot.

Here,

1. The marker='o' parameter specifies that circular markers (dots) should be used in the scatter plot.
2. The color='blue' parameter sets the color of the markers to blue.

We can use different marker styles like 's', '*', 'x', etc. and also specify colors using hexadecimal codes like #FF5733. For example,

# create a scatter plot with a custom marker 'x' and hex color '#FF5733'
result = df.plot(x='X', y='Y', kind='scatter', xlabel='X-axis', ylabel='Y-axis', title='Scatter Plot', marker='x', color='#FF5733')

The output would be:

Example 5: Create a Pie Chart

import pandas as pd
import matplotlib.pyplot as plt

# create a DataFrame 
data = {'Categories': ['Category A', 'Category B', 'Category C', 'Category D'],
        'Values': [30, 45, 15, 20]}
df = pd.DataFrame(data)

# create a pie chart
ax = df.plot(y='Values', 
             kind='pie', 
             labels=df['Categories'], 
             autopct='%1.1f%%', 
             legend=False, 
             title='Pie Chart Example')

# show the plot
plt.axis('equal')  
plt.show()

Output

In the above example, we used the plot() method with kind='pie' to create a pie chart. We specified the Values column as the data to be plotted and set the labels using labels=df['Categories'].

Here, the autopct='%1.1f%%' parameter formats the percentages displayed on each wedge of the pie chart. And the plt.axis('equal') ensures that the pie chart is drawn as a circle.