Plot the Target Distribution

Plotting bar charts, also known as bar plots, is a useful way to visualize data and gain insights. These charts provide a simple and effective method for comparing the relative sizes of different groups or categories of data. By employing various colors or patterns, one can easily compare each group and understand data distribution.

Bar charts also facilitate the identification of patterns or trends. For example, sorting the bars in a specific order can reveal correlations between variables. Their straightforward and readable format makes them accessible, even to those with little experience in data analysis.

Furthermore, bar charts excel in displaying comparative data. They allow for easy comparisons across different groups and over time.

Overall, bar charts are a powerful visualization tool. They enable quick identification of patterns, trends, and outliers, enhancing the decision-making process based on the analyzed data.

Завдання

Import the pyplot module of matplotlib with the plt alias.
Import seaborn with the sns alias.
Create a count plot based on the 'label' column from the df DataFrame.

Завдання

Import the pyplot module of matplotlib with the plt alias.
Import seaborn with the sns alias.
Create a count plot based on the 'label' column from the df DataFrame.

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Furthermore, bar charts excel in displaying comparative data. They allow for easy comparisons across different groups and over time.

Overall, bar charts are a powerful visualization tool. They enable quick identification of patterns, trends, and outliers, enhancing the decision-making process based on the analyzed data.

Завдання

Import the pyplot module of matplotlib with the plt alias.
Import seaborn with the sns alias.
Create a count plot based on the 'label' column from the df DataFrame.

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