Pandas offers built-in functionalities for creating basic visualizations directly from your DataFrame, leveraging the power of Matplotlib under the hood. Here's a breakdown of some common visualizations:
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Imports and Data Preparation: Import libraries: import pandas as pd and import matplotlib.pyplot as plt (usually shortened to plt). Load your data into a Pandas DataFrame.
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Visualizing Distributions: Histogram: Use df['column_name'].plot.hist(). Adjust bin sizes with the bins parameter. Box Plot: Create box plots using df.boxplot()
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Exploring Relationships: Scatter Plot: Visualize relationships between two columns using df.plot.scatter(x='column1', y='column2').
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Categorical Data Analysis: Bar Charts: Represent data grouped by categories using df['column_name'].value_counts().plot.bar(). Pie Charts: Show proportional distribution of categories using df['column_name'].value_counts().plot.pie().
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Customization and Saving: Customize plot elements like labels, titles, and legend using Matplotlib functions within the plot method. Save visualizations as images using plt.savefig("plot.png").