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Python Pandas - Create a Count Plot and style the bars with Seaborn
Count Plot in Seaborn is used to display the counts of observations in each categorical bin using bars. The seaborn.countplot() is used for this. Style the bars using the facecolor, linewidth and edgecolor parameters.
Let’s say the following is our dataset in the form of a CSV file − Cricketers.csv
At first, import the required libraries −
import seaborn as sb import pandas as pd import matplotlib.pyplot as plt
Load data from a CSV file into a Pandas DataFrame −
dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers.csv")
Style and design the bars using the facecolor, linewidth and edgecolor parameters −
sb.countplot(dataFrame["Age"], facecolor=(0, 0.0, 0, 0),linewidth=3,edgecolor=sb.color_palette("dark", 2))
Example
Following is the code −
import seaborn as sb import pandas as pd import matplotlib.pyplot as plt # Load data from a CSV file into a Pandas DataFrame dataFrame = pd.read_csv("C:\Users\amit_\Desktop\Cricketers.csv") # plotting count plot with Age column # designing the bars sb.countplot(dataFrame["Age"], facecolor=(0, 0.0, 0, 0),linewidth=3,edgecolor=sb.color_palette("dark", 2)) # display plt.show()
Output
This will produce the following output −
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