Explore
For Enterprise
Join for Free
Log In

Artes e Humanas
Negócios
Ciência da Computação
Ciência de Dados
Tecnologia da informação
da saúdesaúde
Matemática e Lógica
Desenvolvimento Pessoal
Ciência e Engenharia Física
Ciências Sociais
Línguas
Notas
Certificados

Explore todo o Coursera

Navegar
Pesquisar
For Enterprise
Entrar
Inscreva-se gratuitamente

The DataFrame Data Structure

Loading...

Introduction to Data Science in Python

Universidade de Michigan

4.5 (11,673 classificações) | 270K alunos inscritos

Curso 1 de 5 no Ciência de dados aplicada com Python Programa de cursos integrados

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.

Visualizar o programa do curso

Habilidades que você aprenderá

Python Programming, Numpy, Pandas, Data Cleansing

Avaliações

4.5 (11,673 classificações)

5 stars
7,872 ratings
4 stars
2,817 ratings
3 stars
594 ratings
2 stars
197 ratings
1 star
193 ratings

SI

Mar 16, 2018

overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .

GS

Feb 20, 2017

This course was fast paced but the material was interesting and not to complex. I can only recommend this course to anyone interested in Data Science and who already has a basic knowledge of Python.

Na lição

Week 2

In this week of the course you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed. The module ends with a programming assignment and a discussion question.

Introduction2:55

The Series Data Structure4:46

Querying a Series8:54

The DataFrame Data Structure7:16

DataFrame Indexing and Loading5:19

Querying a DataFrame5:16

Indexing Dataframes5:24

Missing Values4:54

Ministrado por

Christopher Brooks

Transcrição

The DataFrame data structure is the heart of the Panda's library.

It's a primary object that you'll be working with in data analysis

and cleaning tasks.

The DataFrame is conceptually a two-dimensional series object, where

there's an index and multiple columns of content, with each column having a label.

In fact, the distinction between a column and

a row is really only a conceptual distinction.

And you can think of the DataFrame itself as simply a two-axes labeled array.

You can create a DataFrame in many different ways,

some of which you might expect.

For instance, you can use a group of series,

where each series represents a row of data.

Or you could use a group of dictionaries,

where each dictionary represents a row of data.

Let's take a look at an example.

I'm going to create three purchase order records as series objects for

a sort of fictional store.

Each series has a name of a customer,

the string which describes the item being purchased, and the cost of the items.

I like dogs, so I'll purchase a bunch of dog food.

Kevin Cullens Thompson, the instructor for the third course in the series,

he seems more like a cat man to me, so I'll have him purchase some kitty litter.

And I think Vinod, who teaches the fourth course in this series,

is more of a bird man, so I'll add some bird seed there.

Then we'll feed this into the DataFrame as the first argument and

set index values indicating which store where each purchase was done.

You'll see that when we print out a DataFrame, the Jupiter notebook's trying

to pretty it up a bit, and print it out as a table, which is nice.

Similar to the series, we can extract data using the iLoc and Loc attributes.

Because the DataFrame is two-dimensional, passing a single value to the loc

indexing operator will return series if there's only one row to return.

In this example, if we wanted to select data associated with Store 2,

we would just query the loc attribute with one parameter.

You'll note that the name of the series is returned as the row index value,

while the column name is included in the output as well.

We can check the data type of the return using the python type function.

It's important to remember that the indices and

column names along either axes, horizontal or vertical, could be non-unique.

For instance, in this example, we see two purchase records for

Store 1 as different rows.

If we use a single value with the DataFrame loc attribute, multiple rows of

the DataFrame will return, not as a new series, but as a new DataFrame.

For instance, if we query for Store 1 records,

we see that Chris and Kevin both shop at the same pets supply store.

One of the powers of the Panda's DataFrame is that you can quickly select data based

on multiple axes.

For instance, if you wanted to just list the costs for Store 1, you would supply

two parameters to .log, one being the row index and the other being the column name.

If we're only interested in Store 1 costs, we could write this as df.loc('Store 1',

'Cost').

What if we just wanted to do column selection and

just get a list of all of the costs?

Well, there's a couple of options.

First, you can get a transpose of the DataFrame,

using the capital T attribute, which swaps all of the columns and rows.

This essentially turns your column names into indices.

And we can then use the .loc method.

This works, but it's pretty ugly.

Since iloc and loc are used for row selection, the Panda's developers

reserved indexing operator directly on the DataFrame for column selection.

In a Panda's DataFrame, columns always have a name.

So this selection is always label based, not as confusing as it was when

using the square bracket operator on the series objects.

For those familiar with relational databases,

this operator is analogous to column projection.

Finally, since the result of using the indexing operators,

the DataFrame or series, you can chain operations together.

For instance, we could have rewritten the query for

all Store 1 costs as df.loc('Store 1', 'Cost').

This looks pretty reasonable and gets us the result we wanted.

But chaining can come with some costs and

is best avoided if you can use another approach.

In particular, chaining tends to cause Pandas to return a copy of the DataFrame

instead of a view on the DataFrame.

For selecting a data,

this is not a big deal, though it might be slower than necessary.

If you are changing data though, this is an important distinction and

can be a source of error.

Here's another method.

As we saw, .loc does row selection, and it can take two parameters,

the row index and the list of column names.

.loc also supports slicing.

If we wanted to select all rows,

we can use a column to indicate a full slice from beginning to end.

And then add the column name as the second parameter as a string.

In fact, if we wanted to include multiply columns, we could do so in a list.

And Pandas will bring back only the columns we have asked for.

Here's an example, where we ask for

all of the name and cost values for all stores using the .loc operator.

So that's selecting and projecting data from a DataFrame based on row and

column labels.

The key concepts to remember are that the rows and columns are really just for

our benefit.

Underneath this is just a two axes labeled array, and

transposing the columns is easy.

Also, consider the issue of chaining carefully, and

try to avoid it, it can cause unpredictable results.

Where your intent was to obtain a view of the data, but

instead Pandas returns to you a copy.

In the Panda's world, friends don't let friends chain calls.

So if you see it, point it out, and share a less ambiguous solution.

Now, before we leave the discussion of accessing data in DataFrames,

let's talk about dropping data.

It's easy to delete data in series and DataFrames, and

we can use the drop function to do so.

This function takes a single parameter, which is the index or roll label, to drop.

This is another tricky place for new users to pad this.

The drop function doesn't change the DataFrame by default.

And instead, returns to you a copy of the DataFrame with the given rows removed.

We can see that our original DataFrame is still intact.

Let's make a copy with the copy method and do a drop on it instead.

This is a very typical pattern in Pandas, where in place changes to a DataFrame

are only done if need be, usually on changes involving indices.

So it's important to be aware of.

Drop has two interesting optional parameters.

The first is called in place, and if it's set to true,

the DataFrame will be updated in place, instead of a copy being returned.

The second parameter is the axes, which should be dropped.

By default, this value is 0, indicating the row axes.

But you could change it to 1 if you want to drop a column.

There is a second way to drop a column, however.

And that's directly through the use of the indexing operator, using the del keyword.

This way of dropping data,

however, takes immediate effect on the DataFrame and does not return a view.

Finally, adding a new column to the DataFrame is

as easy as assigning it to some value.

For instance,

if we wanted to add a new location as a column with default value of none,

we could do so by using the assignment operator after the square brackets.

This broadcasts the default value to the new column immediately.

Explore nosso catálogo

Registre-se gratuitamente e obtenha recomendações, atualizações e ofertas personalizadas.

O Coursera proporciona acesso universal à melhor educação do mundo fazendo parcerias com as melhores universidades e organizações para oferecer cursos on-line.

© 2019 Coursera Inc. Todos os direitos reservados.

Baixar na App Store

Baixar no Google Play

Coursera
Sobre
Liderança
Carreiras
Lista de cursos
Certificados
Certificados MasterTrack™
Notas
For Enterprise
For Government

Comunidade
Learners
Partners
Developers
Beta Testers
Translators

Conectar
Blog
Facebook
LinkedIn
Twitter
Instagram
YouTube
Tech Blog

Mais
Termos
Privacidade
Ajuda
Acessibilidade
Imprensa
Contato
Diretório
Afiliados