With these commands, we increase the limits and we can visualize the whole data. The first thing we must do is visualize a few examples to see what columns there are, what information they contain, how the values are coded… import pandas as pd df = pd.read_csv('temporal.csv') df.head(10) #View first 10 data rows We will simply use pandas to take a look at the data and get an idea of how it is distributed.
Pandasīefore we move on to more complex methods, let’s start with the most basic way of visualizing data. We will use it in the last section of the article when working with maps. The file mapa.csv includes popularity data separated by country. In addition, I have added a categorical variable (ones and zeros) to demonstrate the functionality of charts with categorical variables. The first one we will use in the vast majority of the tutorial includes popularity data of the three terms over time (from 2004 to the present, 2020). There are two files temporal.csv and mapa.csv. They have been extracted from a famous search engine. They are data on the popularity of searches on the Internet for three terms related to artificial intelligence (data science, machine learning and deep learning). We will work with two datasets that will adapt to the visualizations we show in the article, the datasets can be downloaded here. We will start with the most basic visualization that is looking at the data directly, then we will move on to plotting charts and finally, we will make interactive charts. We will also see which library is recommended to use on each occasion and the unique capabilities of each library. Let’s see the main libraries for data visualization with Python and all the types of charts that can be done with them. Complete Guide to Data Visualization with Python
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