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How to plot a Pandas Dataframe with Matplotlib?

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  • Last Updated : 24 Jan, 2021

Prerequisites: 

Data visualization is the most important part of any analysis. Matplotlib is an amazing python library which can be used to plot pandas dataframe. There are various ways in which a plot can be generated depending upon the requirement. 

Comparison between categorical data

Bar Plot is one such example. To plot a bar graph using plot() function will be used.

Syntax:

matplotlib.pyplot.plot(\*args, scalex=True, scaley=True, data=None, \*\*kwargs)

Example:

Python3




# importing pandas library
import pandas as pd
# importing matplotlib library
import matplotlib.pyplot as plt
  
# creating dataframe
df = pd.DataFrame({
    'Name': ['John', 'Sammy', 'Joe'],
    'Age': [45, 38, 90]
})
  
# plotting a bar graph
df.plot(x="Name", y="Age", kind="bar")


Output:

Visualizing continuous data

 Histogram is an example of representing data as which is divided into closely related intervals. For this hist() function will be employed.

Syntax:

matplotlib.pyplot.hist(x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, histtype=’bar’, align=’mid’, orientation=’vertical’, rwidth=None, log=False, color=None, label=None, stacked=False, \*, data=None, \*\*kwargs)

Example:

Python3




# importing pandas library
import pandas as pd
# importing matplotlib library
import matplotlib.pyplot as plt
  
# creating dataframe
df = pd.DataFrame({
    'Age': [45, 38, 90, 60, 40, 50, 2, 32, 8, 15, 27, 69, 73, 55]
})
  
# plotting a histogram
plt.hist(df["Age"])
plt.show()


Output:

For data distribution

Pie Chart is a great way of representing data which is a part of a whole. To plot a pie chart pie() function will be used.

Syntax:

matplotlib.pyplot.pie(data, explode=None, labels=None, colors=None, autopct=None, shadow=False)

Example:

Python3




# importing pandas library
import pandas as pd
# importing matplotlib library
import matplotlib.pyplot as plt
  
# creating dataframe
df = pd.DataFrame({
    'Object': ['Bulb', 'Lamp', 'Table', 'Pen', 'Notebook'],
    'Price': [45, 38, 90, 60, 40]
})
  
# plotting a pie chart
plt.pie(df["Price"], labels=df["Object"])
plt.show()


Output:

Dependency of data

In situations, where data is to be interpreted depending on dependent and non-dependent parameters, graphs like Line chart or Scatter plot, are used. To plot a line graph plot() function is sufficient but to visualize a scatter plot scatter() is used.

Syntax:

matplotlib.pyplot.scatter(x_axis_data, y_axis_data, s=None, c=None, marker=None, cmap=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors=None)

Example:

Python3




# importing pandas library
import pandas as pd
# importing matplotlib library
import matplotlib.pyplot as plt
  
# creating dataframe
df = pd.DataFrame({
    'X': [1, 2, 3, 4, 5],
    'Y': [2, 4, 6, 10, 15]
})
  
# plotting a line graph
print("Line graph: ")
plt.plot(df["X"], df["Y"])
plt.show()
  
# plotting a scatter plot
print("Scatter Plot:  ")
plt.scatter(df["X"], df["Y"])
plt.show()


Output:


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