Histograms

 


Welcome to Histograms! In this video, you'll learn about histograms, their definition, and how to create them using Matplotlib.

So, what exactly is a histogram? A histogram is a graphical representation of the frequency distribution of a numeric dataset. It partitions the spread of the data into bins, counts the number of data points falling into each bin, and displays this count on the vertical axis. Essentially, the histogram shows how data is distributed across different intervals.

Let's illustrate this with an example: Suppose we have a numeric dataset with a range of values from 0 to 34,129. We divide this range into, say, 10 bins of equal width. Then, we count how many data points fall into each bin and represent this count with the height of bars on the histogram.

Now, let's see how to create a histogram using Matplotlib. First, we'll process our DataFrame so that the country names become the index, and we'll add an extra column representing the cumulative sum of annual immigration from each country from 1980 to 2013. Let's name our DataFrame df_canada.

Suppose we want to visualize the distribution of immigrants to Canada in the year 2013. We can achieve this by generating a histogram of the data in the column representing 2013. Here's how we do it with Matplotlib:

  1. Import Matplotlib as mpl and its scripting interface as plt.
  2. Call the plot function on the data in column 2013 with kind='hist' to generate the histogram.
  3. Add a title and label both axes appropriately.
  4. Finally, use the show function to display the figure.

However, sometimes the bins may not align perfectly with the tick marks on the horizontal axis, making the histogram hard to read. To address this, we can use the NumPy library to create bins with precisely aligned edges. Here's how:

  1. Import Matplotlib and NumPy libraries.
  2. Call the NumPy histogram function on the data in column 2013 to compute bin frequencies and edges.
  3. Pass the bin edges as an additional parameter in the plot function to generate the histogram.

By using NumPy to create bins, we ensure that the histogram's bins and tick marks are clearly aligned on the horizontal axis, making the visualization more effective.

In summary, histograms provide a visual representation of the frequency distribution of numeric data, and Matplotlib makes it easy to create them. By understanding histograms, you can effectively analyze and communicate insights from your data.

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