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Intro to Seaborn

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Exploring the Data

3:10 with AJ Tran

Use Pandas to import the dataset and call .head() to get a preview of the data.

Importing the dataset:

pokemon = pd.read_csv('pokemon_40.csv')
pokemon.head()

Questions about the data:

  • What is the relationship between Attack and HP?

  • What is the distribution of Attack?

  • What is the relationship between Attack and Type?

  • What is the distribution of Attack for each Type?

  • What is the average (mean) Attack for each Type?

  • What is the count of Pokemon for each Type?

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    Now we will import our data set. 0:00
    Use Pandas to import the data set and call head to get a preview of the data. 0:04
    In cell 3, we'll say pokemon 0:09
    = pd.read_csv('pokemon_40.csv'). 0:14
    Next line, pokemon.head(), run the cell. 0:26
    Let's examine together the data given to us by the head function 0:35
    to become familiar with it. 0:40
    Each row represents a Pokemon. 0:42
    Pokemon is a game where players battle monsters. 0:49
    Every monster or Pokemon has a name, a categorical type, 0:53
    like water, grass or electric, and some numerical statistics. 0:58
    These numerical statistics include HP, 1:05
    which stands for health points, attack, and defense. 1:09
    There are other statistics, too, but this data set is simplified so 1:14
    that even if you are not familiar with the game, 1:18
    you will be able to perform some statistical analysis. 1:21
    If you'd like to manually examine more of the data set, 1:25
    you can open pokemon_40.csv in a new tab to look at all 40 observations. 1:30
    Let's ask some questions about our data set. 1:41
    In this stage of the course, 1:45
    I will be asking questions about the attack statistics of these Pokemon. 1:46
    Then I will perform exploratory data analysis with different 1:51
    kinds of plots in order to find answers to these questions. 1:56
    After each plot, I'll challenge you to explore the data for 2:00
    the Pokemon's defense statistics. 2:04
    Here are my initial questions. 2:07
    What is the relationship between Attack and HP? 2:11
    What is the distribution of Attack? 2:16
    What is the relationship between Attack and Type? 2:19
    What is the distribution of Attack for each Type? 2:23
    What is the average, or mean, Attack for each Type? 2:28
    And what is the count of Pokemon for each Type? 2:33
    Notice that a lot of these questions ask about relationships between numerical and 2:38
    categorical data. 2:44
    Categorical data means data that is words instead of numbers. 2:45
    The categorical data for these questions is the type of Pokemon. 2:49
    This is one of the main strengths of Seaborn. 2:55
    Unlike Matplotlib, which is optimized for 2:58
    creating plots with strictly numerical data, 3:01
    we can use Seaborn to analyze data that has both categorical and numerical data. 3:04

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