Pivot Tables

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Do curso por University of Michigan

Introduction to Data Science in Python

6610 ratings

University of Michigan

Introduction to Data Science in Python

6610 ratings

Curso 1 de 5 em Specialization Applied Data Science with Python

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

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Week 3

In this week you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.

Merging Dataframes7:42

Pandas Idioms6:33

Pivot Tables2:25

Date Functionality5:08

Conheça os instrutores

Christopher Brooks

A pivot table is a way of summarizing data in a data frame for a particular purpose.

It makes heavy use of the aggregation function.

A pivot table is itself a data frame, where the rows represent one variable that

you're interested in, the columns another, and the cell's some aggregate value.

A pivot table also tends to includes marginal values as well,

which are the sums for each column and row.

This allows you to be able to see the relationship between two variables at just

a glance.

Let's take a look at an example.

Here we'll load a new data set, cars.csv.

This data set comes from the Open Data Initiative of the Canadian government.

And has information about the efficiency of different electric cars which

are available for purchase.

When we look at the head of the data frame,

we'll see that there are model years, vendors, sizes of cars, and statistics,

like how big the battery is in kilowatt hours.

A pivot table allows us to pivot out one of these columns into a new column headers

and compare it against another column as row indices.

For instance,

let's say we wanted to compare the makes of electric vehicles versus the years and

that we wanted to do this comparison in terms of battery capacity.

To do this, we tell pandas we want the values to be kilowatts,

the index to be the year and the columns to be the make.

Then we specify that the aggregation function,

and here we'll use the NumPy mean.

Here's the results.

We see there are NaN values for

vendors who didn't have an entry in a given year like Ford in 2012.

And we see that most vendors don't have a change in battery capacity over the years,

except for Tesla, as they've introduced several new models.

Now, pivot tables aren't limited to one function that you might want to apply.

You can pass aggfunc, a list of the different functions to apply,

and pandas will provide you with the result using hierarchical column names.

Here, I’ll also pass margins equals true.

And that you can see for each of the functions there's now an all category,

which shows the overall mean and the minimum values for a given year and

a given vendor.

So that's pivot tables.

This has been a pretty short description, but

they're incredibly useful when dealing with numeric data.

And of course, you can pass any function you want to the aggregate function,

including those that you define yourself.

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