Lecture Note
University
University of California San DiegoCourse
DSC 207R | Python for Data SciencePages
2
Academic year
2023
anon
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13
Pandas, Data Visualization Now We Will Review the Plotting Functions in Pandas Large datasets can be easily manipulated and analyzed with Pandas, a robust data manipulation tool. The capability of pandas to produce high-quality representations using itsintegrated plotting functions is one of the aspects that make it such a popular option for datascientists and analysts. In this article, we will take a detailed look at the plotting functions in pandas and explore how to use them to create meaningful visualizations that can help us gain valuable insightsfrom our data. Bar Plots One of the simplest and most popular types of plots used in pandas to show data are bar graphs. They are a wonderful technique to display categorical data or other data withdiscrete values. Bar plots can be created in pandas by using the plot.bar() function. The plot.bar() function creates a bar chart for each column in the DataFrame. Each bar is represented by a different color and goes up to the value in that column. This makes it easyto compare the values across different columns. Box Plots Box plots are a great way to visualize the distribution of data in a pandas DataFrame. They are particularly useful for identifying outliers and understanding the spread of the data. Inpandas, box plots can be generated using the plot.box() function. The plot.box() function creates a box for each column in the DataFrame. The box represents the middle 50% of the data, with the median line drawn in the center. Thewhiskers extend to the minimum and maximum values within 1.5 times the interquartilerange (IQR) from the edge of the box. Any points outside this range are considered outliersand are plotted as individual points. Histograms Histograms are a great way to visualize the distribution of a continuous variable in a pandas DataFrame. They allow us to see how the data is spread out across a range of
values and identify any patterns or outliers. In pandas, histograms can be generated usingthe plot.hist() function. For each column in the DataFrame, a histogram is produced via the plot.hist() function. By default, it will produce separate histograms for each column; however, by providing in a listof column names, it is possible to plot many columns on a single histogram. The resultingplot, with each bin denoting a range of values, displays the frequency of values within eachbin. Line Plots Line plots are a great way to visualize trends in a pandas DataFrame over time or across a range of values. They allow us to see how the data changes over time or as the values ofone variable change. In pandas, line plots can be generated using the plot.line() function. The plot.line() function creates a line plot for each column in the DataFrame. Each line is represented by a different color and shows how the values of that column change over timeor across a range of values. By default, the plot will connect the points in each line withstraight lines, but it is possible to use other types of lines, such as dashed lines or dottedlines. Conclusion In conclusion, pandas offers a large variety of plotting tools that let us rapidly and simply produce high-quality visuals of our data. We can learn important things about our data andspot patterns and anomalies that we might not have noticed otherwise by using thesefunctions. In this article, we have covered some of the most common types of plots that can be generated using pandas, including bar plots, box plots, histograms, and line plots. However,this is just the tip of the iceberg. Pandas offers many other types of plots, including scatterplots, area plots, and heatmaps. So, pandas is an excellent place to start if you're trying to build high-quality visualizations of your data. We advise taking some time.
Pandas, Data Visualization
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