Adventures in Machine Learning

Mastering Histograms: Creating and Customizing with Pandas DataFrame

Creating Histograms with Pandas DataFrame

As data scientists, one of our primary responsibilities is to be able to visualize data. One tool we can use for this is the histogram.

A histogram is a graphical representation of the distribution of data. It shows the frequency of occurrence of a variable in an array of intervals.

In this article, we will explore how to create histograms using pandas DataFrame and customize the plots.

Single Histogram

To create a single histogram in pandas, we can use the hist() method. Simply pass the column name of the data we want to plot as the argument for the method.

For example, if we have a DataFrame named “df” and we want to plot the distribution of the “height” column, we can use the following command:

“`

df.hist(column=’height’)

“`

This creates a histogram with default settings, such as the number of bins and the color of the bars.

Customizing Histograms

We can customize the plot using various options available. The parameters we can use include bins, grid, rwidth, and color.

Bins: We can change the number of bins used in the histogram using the bins parameter. By default, pandas will use 10 bins.

We can adjust the granularity of the plot by increasing or decreasing the number of bins. “`

df.hist(column=’height’, bins=20)

“`

Grid: We can also add gridlines to the plot using the grid parameter.

By default, gridlines are disabled. “`

df.hist(column=’height’, grid=True)

“`

Rwidth: We can change the width of the bars in the histogram using the rwidth parameter.

The default value is 0.9.

“`

df.hist(column=’height’, rwidth=0.8)

“`

Color: We can change the color of the bars using the color parameter. The default color is blue.

“`

df.hist(column=’height’, color=’green’)

“`

Multiple Histograms

Sometimes, we want to compare the distribution of multiple variables in a single plot. We can use the by parameter to create multiple histograms in a single plot.

For example, if we have a DataFrame containing the height and weight of individuals, and we want to create separate histograms for each variable, we can use the following command:

“`

df.hist(column=[‘height’, ‘weight’], by=None, sharex=True, sharey=True)

“`

This creates two histograms, side-by-side, where the height data is plotted on the left and the weight data is plotted on the right. The sharex and sharey parameters are set to True, which means the x- and y-axes are shared between the plots.

Additional Resources

While histograms are useful tools for visualizing data, they are not the only ones available. There are several libraries in Python that provide many different data visualization techniques.

Two popular libraries are Matplotlib and Seaborn. Matplotlib: Matplotlib is a comprehensive data visualization library in Python.

It provides many plotting functions, including line plots, scatter plots, and histograms. The library is highly customizable and can create publication-ready plots.

Seaborn: Seaborn is a powerful library for creating statistical visualizations in Python. It includes many built-in functions for creating plots such as box plots, heatmaps, and violin plots.

Seaborn is based on Matplotlib and can also be integrated with pandas.

Conclusion

In this article, we have discussed how to create histograms using pandas DataFrame and customize them for your needs. We have seen how we can change the number of bins, add gridlines, adjust the width of the bars, and change the color scheme of the plot.

We have also explored how to create multiple histograms in a single plot. Finally, we have introduced two popular data visualization libraries in Python: Matplotlib and Seaborn.

With these tools at our disposal, we can effectively communicate our data to others. In this article, we’ve learned how to create histograms using pandas DataFrames and customize them for our needs.

We’ve seen how to adjust the number of bins, add gridlines, change the width of the bars, and modify the color scheme of the histogram. We also explored how to create multiple histograms and introduced two popular data visualization libraries in Python: Matplotlib and Seaborn.

Histograms are a crucial tool for visualizing data and communicating insights. By mastering histograms and other data visualization techniques, we can make better decisions and effectively communicate our findings to others.

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