Missing Values

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From the course by University of Michigan

Introduction to Data Science in Python

6954 ratings

University of Michigan

Introduction to Data Science in Python

6954 ratings

Course 1 of 5 in the 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.

From the lesson

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

Meet the Instructors

Christopher Brooks

We're going to end this week of lecture with a quick discussion of missing values.

We've seen a preview of how Pandas handles missing values using the None type

and NumPy NaN values.

Missing values are pretty common in data cleaning activities.

There are couple of caveats and discussion points which we should address.

First, the built in loading from delimited files provides control for

missing values in a few ways.

The most germane of these, is the na_values list,

to indicate other strings which could refer to missing values.

Some of my sociologist colleagues for instance, regularly

use the value of 99 in binary categories to indicate that there's no value.

So this comes in handy.

You can also use the na_filter option to turn off white space filtering,

if white space is an actual value of interest.

But in practice, this is pretty rare.

In addition to rules controlling how missing values might be loaded,

it's sometimes useful to consider missing values as actually having information.

I'll give an example from my own research.

I often deal with logs from online learning systems.

In particular,

I've done a couple of projects looking at video use in lecture capture systems.

In these systems it's common for the player for have a heartbeat functionality where

playback statistics are sent to the server every so often, maybe every 30 seconds.

These heartbeats can get big as they can carry the whole state of the playback

system, such as where the video play head is at, where the video size is,

which video is being rendered to the screen, how loud the volume is, etc.

If we load the data file log.txt,

we can see an example of what this might look like.

In this data the first column is a timestamp in the Unix epoch format.

The next column is the user name followed by a web page they're visiting and

the video that they're playing.

Each row of the DataFrame has a playback position.

And we can see that as the playback position increases by one,

the time stamp increases by about 30 seconds.

Except for user Bob.

It turns out that Bob has paused his playback so

as time increases the playback position doesn't change.

Note too how difficult it is for us to try and derive this knowledge from the data,

because it's not sorted by time stamp as one might expect.

This is actually not uncommon on systems which have a high degree of parallelism.

There are a lot of missing values in the paused and volume columns.

It's not efficient to send this information across the network if it

hasn't changed.

So this particular system just inserts null values into the database if

there's no changes.

One of the handy functions that Pandas has for

working with missing values is the filling function, fillna.

This function takes a number or parameters, for

instance, you could pass in a single value which is called a scalar value

to change all of the missing data to one value.

This isn't really applicable in this case, but it's a pretty common use case.

Next up though is the method parameter.

The two common fill values are ffill and bfill.

ffill is for forward filling and it updates an na value for

a particular cell with the value from the previous row.

It's important to note that your data needs to be sorted in order for

this to have the effect you might want.

Data that comes from traditional database management systems usually has no

order guarantee, just like this data.

So be careful.

In Pandas we can sort either by index or by values.

Here we'll just promote the time stamp to an index then sort on the index.

If we look closely at the output though we'll notice that the index

isn't really unique.

Two users seem to be able to use the system at the same time.

Again, a very common case.

Let's reset the index, and use some multi-level indexing instead, and

promote the user name to a second level of the index to deal with that issue.

Now that we have the data indexed and

sorted appropriately, we can fill the missing datas using ffill.

It's good to remember when dealing with missing values so

you can deal with individual columns or

sets of columns by projecting them just as we spoke about earlier.

So you don't have to fix all missing values in one command.

It's sometimes useful to use forward filling, sometimes backwards filling, and

sometimes useful to just use a single number.

More recently, the Pandas team introduced a method of filling missing values with

a series which is the same length as your DataFrame.

This makes it easy to derive values which are missing if you have

the underlying to do so.

For instance, if you're dealing with receipts and you have a column for

final price and a column for discount but are missing information from the original

price column, you can fill this automatically using fillna.

One last note on missing values.

When you use statistical functions on DataFrames,

these functions typically ignore missing values.

For instance if you try and calculate the mean value of a DataFrame,

the underlying NumPy function will ignore missing values.

This is usually what you want but

you should be aware that values are being excluded.

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