Plt.plot python for mac
![plt.plot python for mac plt.plot python for mac](https://people.duke.edu/~ccc14/sta-663/_images/UsingNumpySolutions_117_1.png)
However, I’m leaving the transparency level of the percentages at 100% because this is the information we want to convey (that there is a big difference between the two bars).Īdd the following lines of code to the previous code: # Increase x ticks label size, rotate them by 90 degrees, and remove tick lines In other words, the audience should, first of all, see the difference between the heights of the two bars and only then pay attention to the surrounding information. I also believe that we need to increase the transparency of x tick labels because these labels shouldn’t be the focus of attention. Additionally, we also need to rotate them by 90 degrees to improve readability. However, we can barely see the x tick labels (“Movie” and “TV Show”). # Increase the size of the plotĪx = netflix_movies.value_counts(normalize=True).mul(100).plot.bar(figsize=(12, 7)) The next step is to increase the size of the plot using figsize parameter.
![plt.plot python for mac plt.plot python for mac](https://i.stack.imgur.com/Qty0c.png)
Now it looks much cleaner: we increased the signal-to-noise ratio by removing useless clutter. The code above is pretty advanced, so spend some time in the documentation to understand how it works. We should plot the numbers directly above the bars! # Label bars with percentagesĪx = netflix_movies.value_counts(normalize=True).mul(100).plot.bar() But what if we want to determine the exact proportion of movies and shows? We can go back and forth from each bar to the y axis and make a guess, but it’s not an easy task. netflix_movies.value_counts(normalize=True).mul(100).plot.bar() I believe that the best way to show something interesting in this data is to show relative percentages. I already see the first problem: we still have pure numbers.
#Plt.plot python for mac tv
We now can easily see the difference in the number of movies and TV shows. This is the most basic plot we can make using pandas. A nice way to represent categorical data is to use simple bar plots. Plain numbers are more abstract than visualizations. However, it’s not a good idea to present your results simply as numbers (unless it’s something like “we have grown by 30% this year!”, then this number can be very effective). We have roughly 2.5 times more movies than TV series.
#Plt.plot python for mac series
One of our questions could be What is the number of movies and TV series on Netflix? netflix_movies.value_counts() We have plenty of columns we can easily visualize. Netflix_movies = pd.read_csv("./data/netflix_titles.csv") # From
![plt.plot python for mac plt.plot python for mac](https://i.stack.imgur.com/Ufv5q.png)
We’ll be using a dataset that contains data on Netflix movies and TV shows ( ).įirst of all, let’s look at the dataset.
![plt.plot python for mac plt.plot python for mac](https://i.stack.imgur.com/8XERU.png)
However, depending on your audience, some of these aspects may be more relevant than others, so choose wisely when determining what to present. In this article, I will talk about the technical aspects of improving data visualizations in Python. Whatever your final goal, it’s critical to present the data clearly. You may do this for multiple reasons: to convince investors to finance your project, to highlight the importance of changes at your company, or just to present the results in the annual report and emphasize the most valuable achievements. JanuHow to Make Your Plots Appealing in Pythonĭata visualization is arguably the most important step in a data science project because it’s how you communicate your findings to the audience.