Plot Multiple Columns of Pandas Dataframe on Bar Chart with Matplotlib



Plot Multiple Columns of Pandas Dataframe on Bar Chart with Matplotlib

 

In this article, we will learn how to plot multiple columns on bar chart using Matplotlib. Bar Plot is used to represent categories of data using rectangular bars. We can plot these bars with overlapping edges or on same axes. Different ways of plotting bar graph in the same chart are using matplotlib and pandas are discussed below.

Method 1: Providing multiple columns in y parameter

The trick here is to pass all the data that has to be plotted together as a value to ‘y’ parameter of plot function.

Syntax:

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

Approach:

  • Import module
  • Create or load data
  • Pass data to plot()
  • Plot graph

Example:

# importing pandas library
import pandas as pd
# import matplotlib library
import matplotlib.pyplot as plt
 
# creating dataframe
df = pd.DataFrame({
    'Name': ['John', 'Sammy', 'Joe'],
    'Age': [45, 38, 90],
    'Height(in cm)': [150, 180, 160]
})
 
# plotting graph
df.plot(x="Name", y=["Age", "Height(in cm)"], kind="bar")

Output:

Method 2: By plotting on the same axis

Plotting all separate graph on the same axes, differentiated by color can be one alternative. Here again plot() function is employed.

 

Approach:

  • Import module
  • Create or load data
  • Plot first graph
  • Plot all other graphs on the same axes

Example:

# importing pandas library
import pandas as pd
# import matplotlib library
import matplotlib.pyplot as plt
 
# creating dataframe
df = pd.DataFrame({
    'Name': ['John', 'Sammy', 'Joe'],
    'Age': [45, 38, 90],
    'Height(in cm)': [150, 180, 160]
})
 
# plotting Height
ax = df.plot(x="Name", y="Height(in cm)", kind="bar")
# plotting age on the same axis
df.plot(x="Name", y="Age", kind="bar", ax=ax, color="maroon")

Output:

Method 3: By creating subplots

Another way of creating such a functionality can be plotting multiple subplots and displaying them as one. This can be done using subplot() function.

Syntax:

subplot(nrows, ncols, index, **kwargs)

Approach:

  • Import module
  • Create or load data
  • Create multiple subplots
  • Plot on single axes

Example:

# importing pandas library
import pandas as pd
# import matplotlib library
import matplotlib.pyplot as plt
 
# creating dataframe
df = pd.DataFrame({
    'Name': ['John', 'Sammy', 'Joe'],
    'Age': [45, 38, 90],
    'Height(in cm)': [150, 180, 160]
})
 
# creating subplots and plotting them together
ax = plt.subplot()
ax.bar(df["Name"], df["Height(in cm)"])
ax.bar(df["Name"], df["Age"], color="maroon")

Output:

Last Updated on October 19, 2021 by admin

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