Pandas has special Pandas string methods, too. Remember all the special things that you can do with Python strings aka string methods? The maximum value in the “age” column is 2013! That seems like an error. sample() method.ĭo you notice any outliers, anomalies, or potential problems here? To look at a random sample of rows, we can use the. To look at the first n rows in a DataFrame, we can use a method called. Pandas reports how many rows and columns are in this dataset at the bottom of the output (23,052 x 10 columns). To display all the rows, we would need to alter Pandas’ default display settings yet again. Anything more than 100 rows will be truncated. The DataFrame is truncated because we set our default display settings to 100 rows. The DataFrame is truncated, signaled by the ellipses in the middle. However, you can change the Index to something else, such as one of the columns in your dataset. You can select rows based on the Index.īy default, the Index is a sequence of numbers starting with zero. The bolded ascending numbers in the very left-hand column of the DataFrame is called the Pandas Index. There are a few important things to note about the DataFrame displayed here: To use the Pandas library, we first need to import it. How does such a binary understanding of gender, gleaned from the IMDB pages of actors, influence our later results and conclusions? What do we gain by using such an approach, and what do we lose? How else might we have encoded or determined gender for the same data? We’re aware that this means some of the data is wrong, AND we’re still fine with the methodology and approach.”Īs we work with this data, we want to be critical and cognizant of this approach to gender. Bart Simpson, for example, is voiced by a woman. To determine character gender, they used actors’ IMDB information, which they acknowledge is an imperfect approach: “Sometimes, women voice male characters. is a boy!” The short answer is that they don’t. How do you know the monster in Monsters Inc. They claim, in fact, that one of the most frequently asked questions about the piece is about gender: “Wait, but let’s talk about gender. Yet transforming complex social constructs like gender into quantifiable data is tricky and historically fraught.
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They included character gender information because they wanted to contribute data to a broader conversation about how “white men dominate movie roles.” The dataset provides information about 2,000 films from 1925 to 2015, including characters’ names, genders, ages, how many words each character spoke in each film, the release year of each film, and how much money the film grossed. The dataset that we’re working with in this lesson is taken from Hannah Andersen and Matt Daniels’s Pudding essay, “Film Dialogue from 2,000 screenplays, Broken Down by Gender and Age”. Hannah Anderson and Matt Daniels, “Film Dialogue from 2,000 screenplays, Broken Down by Gender and Age” How many movies are actually about men? What changes by genre, era, or box-office revenue? What circumstances generate more diversity? The prevailing theme: white men dominate movie roles.īut it’s all rhetoric and no data, which gets us nowhere in terms of having an informed discussion. Lately, Hollywood has been taking so much shit for rampant sexism and racism. Make an Interactive Network Visualization with Bokeh Tomotopy & Text Files (NYT Articles) - No Java required Term-Frequency Inverse Document Frequency Users’ Data: Legal & Ethical ConsiderationsĪpplication Programming Interfaces (APIs) Data Collection (Web Scraping, APIs, Social Media)
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