How to plot a Pandas Dataframe with Matplotlib?

Prerequisites: 

• Pandas
• Matplotlib

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: