Python Tutorial: Visual exploratory data analysis

Want to learn more? Take the full course at https://learn.datacamp.com/courses/cleaning-data-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Visualizing our data is a great way to spot outliers and obvious errors in our data. Data visualization is more than just looking at patterns and reporting data. They can also be an effective way to spot outliers, and plan your data cleaning pipeline. Here's the tabular form of summary statistics for a clean European Environment Agency dataset. You can imagine how overwhelming the results can be on a dataset with many more columns. This is why it is important to visualize our data because abnormal data points, such as the max population, would immediately stand out. We use bar plots to count discrete data, and histograms to count continuous data. These plots will give us the ability to look at frequencies of our data, which can be used to look for potential errors. Here's an example of a histogram. We take the dataframe and column of interest, and call the plot method. We pass 'hist' into the method to have pandas create a histogram. We have to make sure matplotlib is loaded before we can show the plot. Here is our histogram. The x-axis shows the range of values that are counted, and the y-axis is how many observations in our data are in a particular range of values. Our histogram shows 2 observations between 1 and 1-point-5 billion, and 1 observation above 2 billion. This dataset comes from 2012. At the time it was impossible for a country to have over 2 billion people. We can slice our data to look for all data points where the population is greater than 1 billion people. We use bracket notation to slice our data, and inside we specify the condition we are interested in: countries with populations greater than 1 billion people. Our dataset reports Australia having 2-point-3 billion people. This is a data error. China and India are also