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The Basics of Tabular Data

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Content Summary

The basics of tabular data consist of understanding:

• the structure of a table and how it represents a real-world phenomenon,

• the basic operations that can be performed on a table and how they reflect the real-world phenomenon it represents,

• the computational foundations for tabular data structures in Pandas.

Datasets

The primary dataset in this chapter consists of player statistics from the US Women’s National Team in Soccer between 1991 and 2019. The data is taken from Football Reference.

Summary of Library References

In the lists below, assume that the usual imports have been executed:

import pandas as pd
import numpy as np

Creating Tabular Structures:

Function or Method Name	Description
pd.Series	Series constructor
pd.DataFrame	DataFrame constructor
pd.read_csv	Reading CSV from file

Series/DataFrame attributes and methods:

Function or Method Name	Description
shape	Number of rows/columns
head	Returns first few lines
tail	Returns the last few lines
nunique	Returns the number of unique values
dtypes	Returns the type of the column(s)
astype	Returns column(s) coerced to a given type
sort_values	sorts Series/DataFrame according to its values
drop_duplicates	drops duplicates indices/columns
Series.apply and DataFrame.apply	apply a function to the entries of a Series / slices of a DataFrame
Series.agg and DataFrame.agg	apply a collection of functions to a Series/DataFrame

Methods for computing descriptive statistics on Series/DataFrames:

Function or Method Name	Description
describe	Returns descriptive statistice of column(s)
count	Returns the number of non-null entries
sum	Returns the sum
median	Returns the median
mean	Returns the median
std	Returns the sample standard deviation
var	Returns the sample variance

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An Example Investigation

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2.1. Introduction to Tabular Data