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QuantEcon DataScience

Introduction to Economic Modeling and Data Science

# Reshape¶

Prerequisites

Outcomes

• Understand and be able to apply the melt/stack/unstack/pivot methods
• Practice transformations of indices
• Understand tidy data
In [1]:
# Uncomment following line to install on colab
#! pip install qeds

In [2]:
import numpy as np
import pandas as pd

%matplotlib inline
# activate plot theme
import qeds
qeds.themes.mpl_style();


## Tidy Data¶

While pushed more generally in the R language, the concept of “tidy data” is helpful in understanding the objectives for reshaping data, which in turn makes advanced features like GroupBy more seamless.

Hadley Wickham gives a terminology slightly better-adapted for the experimental sciences, but nevertheless useful for the social sciences.

A dataset is a collection of values, usually either numbers (if quantitative) or strings (if qualitative). Values are organized in two ways. Every value belongs to a variable and an observation. A variable contains all values that measure the same underlying attribute (like height, temperature, duration) across units. An observation contains all values measured on the same unit (like a person, or a day, or a race) across attributes. – Tidy Data (Journal of Statistical Software 2013)

With this framing,

A dataset is messy or tidy depending on how rows, columns and tables are matched with observations, variables, and types. In tidy data:

1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit forms a table.

The “column” and “row” terms map directly to pandas columns and rows, while the “table” maps to a pandas DataFrame.

With this thinking and interpretation, it becomes essential to think through what uniquely identifies an “observation” in your data.

Is it a country? A year? A combination of country and year?

These will become the indices of your DataFrame.

For those with more of a database background, the “tidy” format matches the 3rd normal form in database theory, where the referential integrity of the database is maintained by the uniqueness of the index.

When considering how to map this to the social sciences, note that reshaping data can change what we consider to be the variable and observation in a way that doesn’t occur within the natural sciences.

For example, if the “observation” uniquely identified by a country and year and the “variable” is GDP, you may wish to reshape it so that the “observable” is a country, and the variables are a GDP for each year.

A word of caution: The tidy approach, where there is no redundancy and each type of observational unit forms a table, is a good approach for storing data, but you will frequently reshape/merge/etc. in order to make graphing or analysis easier. This doesn’t break the tidy format since those examples are ephemeral states used in analysis.

The data you receive is not always in a “shape” that makes it easy to analyze.

What do we mean by shape? The number of rows and columns in a DataFrame and how information is stored in the index and column names.

This lecture will teach you the basic concepts of reshaping data.

As with other topics, we recommend reviewing the pandas documentation on this subject for additional information.

We will keep our discussion here as brief and simple as possible because these tools will reappear in subsequent lectures.

In [3]:
url = "https://datascience.quantecon.org/assets/data/bball.csv"
bball.info()

bball

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9 entries, 0 to 8
Data columns (total 8 columns):
Year        9 non-null int64
Player      9 non-null object
Team        9 non-null object
TeamName    9 non-null object
Games       9 non-null int64
Pts         9 non-null float64
Assist      9 non-null float64
Rebound     9 non-null float64
dtypes: float64(3), int64(2), object(3)
memory usage: 656.0+ bytes

Out[3]:
Year Player Team TeamName Games Pts Assist Rebound
0 2015 Curry GSW Warriors 79 30.1 6.7 5.4
1 2016 Curry GSW Warriors 79 25.3 6.6 4.5
2 2017 Curry GSW Warriors 51 26.4 6.1 5.1
3 2015 Durant OKC Thunder 72 28.2 5.0 8.2
4 2016 Durant GSW Warriors 62 25.1 4.8 8.3
5 2017 Durant GSW Warriors 68 26.4 5.4 6.8
6 2015 Ibaka OKC Thunder 78 12.6 0.8 6.8
7 2016 Ibaka ORL Magic 56 15.1 1.1 6.8
8 2016 Ibaka TOR Raptors 23 14.2 0.7 6.8

## Long vs Wide¶

Many of these operations change between long and wide DataFrames.

What does it mean for a DataFrame to be long or wide?

Here is long possible long-form representation of our basketball data.

In [4]:
# Don't worry about what this command does -- We'll see it soon
bball_long = bball.melt(id_vars=["Year", "Player", "Team", "TeamName"])

bball_long

Out[4]:
Year Player Team TeamName variable value
0 2015 Curry GSW Warriors Games 79.0
1 2016 Curry GSW Warriors Games 79.0
2 2017 Curry GSW Warriors Games 51.0
3 2015 Durant OKC Thunder Games 72.0
4 2016 Durant GSW Warriors Games 62.0
5 2017 Durant GSW Warriors Games 68.0
6 2015 Ibaka OKC Thunder Games 78.0
7 2016 Ibaka ORL Magic Games 56.0
8 2016 Ibaka TOR Raptors Games 23.0
9 2015 Curry GSW Warriors Pts 30.1
10 2016 Curry GSW Warriors Pts 25.3
11 2017 Curry GSW Warriors Pts 26.4
12 2015 Durant OKC Thunder Pts 28.2
13 2016 Durant GSW Warriors Pts 25.1
14 2017 Durant GSW Warriors Pts 26.4
15 2015 Ibaka OKC Thunder Pts 12.6
16 2016 Ibaka ORL Magic Pts 15.1
17 2016 Ibaka TOR Raptors Pts 14.2
18 2015 Curry GSW Warriors Assist 6.7
19 2016 Curry GSW Warriors Assist 6.6
20 2017 Curry GSW Warriors Assist 6.1
21 2015 Durant OKC Thunder Assist 5.0
22 2016 Durant GSW Warriors Assist 4.8
23 2017 Durant GSW Warriors Assist 5.4
24 2015 Ibaka OKC Thunder Assist 0.8
25 2016 Ibaka ORL Magic Assist 1.1
26 2016 Ibaka TOR Raptors Assist 0.7
27 2015 Curry GSW Warriors Rebound 5.4
28 2016 Curry GSW Warriors Rebound 4.5
29 2017 Curry GSW Warriors Rebound 5.1
30 2015 Durant OKC Thunder Rebound 8.2
31 2016 Durant GSW Warriors Rebound 8.3
32 2017 Durant GSW Warriors Rebound 6.8
33 2015 Ibaka OKC Thunder Rebound 6.8
34 2016 Ibaka ORL Magic Rebound 6.8
35 2016 Ibaka TOR Raptors Rebound 6.8

And here is a wide-form version.

In [5]:
# Again, don't worry about this command... We'll see it soon too
bball_wide = bball_long.pivot_table(
index="Year",
columns=["Player", "variable", "Team"],
values="value"
)
bball_wide

Out[5]:
Player Curry Durant ... Ibaka
variable Assist Games Pts Rebound Assist Games Pts ... Assist Games Pts Rebound
Team GSW GSW GSW GSW GSW OKC GSW OKC GSW OKC ... TOR OKC ORL TOR OKC ORL TOR OKC ORL TOR
Year
2015 6.7 79.0 30.1 5.4 NaN 5.0 NaN 72.0 NaN 28.2 ... NaN 78.0 NaN NaN 12.6 NaN NaN 6.8 NaN NaN
2016 6.6 79.0 25.3 4.5 4.8 NaN 62.0 NaN 25.1 NaN ... 0.7 NaN 56.0 23.0 NaN 15.1 14.2 NaN 6.8 6.8
2017 6.1 51.0 26.4 5.1 5.4 NaN 68.0 NaN 26.4 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

3 rows × 24 columns

## set_index, reset_index, and Transpose¶

We have already seen a few basic methods for reshaping a DataFrame.

• set_index: Move one or more columns into the index.
• reset_index: Move one or more index levels out of the index and make them either columns or drop from DataFrame.
• T: Swap row and column labels.

Sometimes, the simplest approach is the right approach.

Let’s review them briefly.

In [6]:
bball2 = bball.set_index(["Player", "Year"])

Out[6]:
Team TeamName Games Pts Assist Rebound
Player Year
Curry 2015 GSW Warriors 79 30.1 6.7 5.4
2016 GSW Warriors 79 25.3 6.6 4.5
2017 GSW Warriors 51 26.4 6.1 5.1
Durant 2015 OKC Thunder 72 28.2 5.0 8.2
2016 GSW Warriors 62 25.1 4.8 8.3
In [7]:
bball3 = bball2.T

Out[7]:
Player Curry Durant Ibaka
Year 2015 2016 2017 2015 2016 2017 2015 2016 2016
Team GSW GSW GSW OKC GSW GSW OKC ORL TOR
TeamName Warriors Warriors Warriors Thunder Warriors Warriors Thunder Magic Raptors
Games 79 79 51 72 62 68 78 56 23
Pts 30.1 25.3 26.4 28.2 25.1 26.4 12.6 15.1 14.2
Assist 6.7 6.6 6.1 5 4.8 5.4 0.8 1.1 0.7

## stack and unstack¶

The stack and unstack methods operate directly on the index and/or column labels.

### stack¶

stack is used to move certain levels of the column labels into the index (i.e. moving from wide to long)

Let’s take ball_wide as an example.

In [8]:
bball_wide

Out[8]:
Player Curry Durant ... Ibaka
variable Assist Games Pts Rebound Assist Games Pts ... Assist Games Pts Rebound
Team GSW GSW GSW GSW GSW OKC GSW OKC GSW OKC ... TOR OKC ORL TOR OKC ORL TOR OKC ORL TOR
Year
2015 6.7 79.0 30.1 5.4 NaN 5.0 NaN 72.0 NaN 28.2 ... NaN 78.0 NaN NaN 12.6 NaN NaN 6.8 NaN NaN
2016 6.6 79.0 25.3 4.5 4.8 NaN 62.0 NaN 25.1 NaN ... 0.7 NaN 56.0 23.0 NaN 15.1 14.2 NaN 6.8 6.8
2017 6.1 51.0 26.4 5.1 5.4 NaN 68.0 NaN 26.4 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

3 rows × 24 columns

Suppose that we want to be able to use the mean method to compute the average value of each stat for each player, regardless of year or team.

To do that, we need two column levels: one for the player and one for the variable.

We can achieve this using the stack method.

In [9]:
bball_wide.stack()

Out[9]:
Player Curry Durant Ibaka
variable Assist Games Pts Rebound Assist Games Pts Rebound Assist Games Pts Rebound
Year Team
2015 GSW 6.7 79.0 30.1 5.4 NaN NaN NaN NaN NaN NaN NaN NaN
OKC NaN NaN NaN NaN 5.0 72.0 28.2 8.2 0.8 78.0 12.6 6.8
2016 GSW 6.6 79.0 25.3 4.5 4.8 62.0 25.1 8.3 NaN NaN NaN NaN
ORL NaN NaN NaN NaN NaN NaN NaN NaN 1.1 56.0 15.1 6.8
TOR NaN NaN NaN NaN NaN NaN NaN NaN 0.7 23.0 14.2 6.8
2017 GSW 6.1 51.0 26.4 5.1 5.4 68.0 26.4 6.8 NaN NaN NaN NaN

Now, we can compute the statistic we are after.

In [10]:
player_stats = bball_wide.stack().mean()
player_stats

Out[10]:
Player  variable
Curry   Assist       6.466667
Games       69.666667
Pts         27.266667
Rebound      5.000000
Durant  Assist       5.066667
Games       67.333333
Pts         26.566667
Rebound      7.766667
Ibaka   Assist       0.866667
Games       52.333333
Pts         13.966667
Rebound      6.800000
dtype: float64

Now suppose instead of that we wanted to compute the average for each team and stat, averaging over years and players.

We’d need to move the Player level down into the index so we are left with column levels for Team and variable.

We can ask pandas do this using the level keyword argument.

In [11]:
bball_wide.stack(level="Player")

Out[11]:
variable Assist Games Pts Rebound
Team GSW OKC ORL TOR GSW OKC ORL TOR GSW OKC ORL TOR GSW OKC ORL TOR
Year Player
2015 Curry 6.7 NaN NaN NaN 79.0 NaN NaN NaN 30.1 NaN NaN NaN 5.4 NaN NaN NaN
Durant NaN 5.0 NaN NaN NaN 72.0 NaN NaN NaN 28.2 NaN NaN NaN 8.2 NaN NaN
Ibaka NaN 0.8 NaN NaN NaN 78.0 NaN NaN NaN 12.6 NaN NaN NaN 6.8 NaN NaN
2016 Curry 6.6 NaN NaN NaN 79.0 NaN NaN NaN 25.3 NaN NaN NaN 4.5 NaN NaN NaN
Durant 4.8 NaN NaN NaN 62.0 NaN NaN NaN 25.1 NaN NaN NaN 8.3 NaN NaN NaN
Ibaka NaN NaN 1.1 0.7 NaN NaN 56.0 23.0 NaN NaN 15.1 14.2 NaN NaN 6.8 6.8
2017 Curry 6.1 NaN NaN NaN 51.0 NaN NaN NaN 26.4 NaN NaN NaN 5.1 NaN NaN NaN
Durant 5.4 NaN NaN NaN 68.0 NaN NaN NaN 26.4 NaN NaN NaN 6.8 NaN NaN NaN

Now we can compute the mean.

In [12]:
bball_wide.stack(level="Player").mean()

Out[12]:
variable  Team
Assist    GSW      5.92
OKC      2.90
ORL      1.10
TOR      0.70
Games     GSW     67.80
OKC     75.00
ORL     56.00
TOR     23.00
Pts       GSW     26.66
OKC     20.40
ORL     15.10
TOR     14.20
Rebound   GSW      6.02
OKC      7.50
ORL      6.80
TOR      6.80
dtype: float64

Notice a few features of the stack method:

• Without any arguments, the stack arguments move the level of column labels closest to the data (also called inner-most or bottom level of labels) to become the index level closest to the data (also called the inner-most or right-most level of the index). In our example, this moved Team down from columns to the index.
• When we do pass a level, that level of column labels is moved down to the right-most level of the index and all other column labels stay in their relative position.

Note that we can also move multiple levels at a time in one call to stack.

In [13]:
bball_wide.stack(level=["Player", "Team"])

Out[13]:
variable Assist Games Pts Rebound
Year Player Team
2015 Curry GSW 6.7 79.0 30.1 5.4
Durant OKC 5.0 72.0 28.2 8.2
Ibaka OKC 0.8 78.0 12.6 6.8
2016 Curry GSW 6.6 79.0 25.3 4.5
Durant GSW 4.8 62.0 25.1 8.3
Ibaka ORL 1.1 56.0 15.1 6.8
TOR 0.7 23.0 14.2 6.8
2017 Curry GSW 6.1 51.0 26.4 5.1
Durant GSW 5.4 68.0 26.4 6.8

In the example above, we started with one level on the index (just the year) and stacked two levels to end up with a three-level index.

Notice that the two new index levels went closer to the data than the existing level and that their order matched the order we passed in our list argument to level.

### unstack¶

Now suppose that we wanted to see a bar chart of each player’s stats.

This chart should have one “section” for each player and a different colored bar for each variable.

As we’ll learn in more detail in a later lecture, we will need to have the player’s name on the index and the variables as columns to do this.

Note

In general, for a DataFrame, calling the plot method will put the index on the horizontal (x) axis and make a new line/bar/etc. for each column.

Notice that we are close to that with the player_stats variable.

In [14]:
player_stats

Out[14]:
Player  variable
Curry   Assist       6.466667
Games       69.666667
Pts         27.266667
Rebound      5.000000
Durant  Assist       5.066667
Games       67.333333
Pts         26.566667
Rebound      7.766667
Ibaka   Assist       0.866667
Games       52.333333
Pts         13.966667
Rebound      6.800000
dtype: float64

We now need to rotate the variable level of the index up to be column layers.

We use the unstack method for this.

In [15]:
player_stats.unstack()

Out[15]:
variable Assist Games Pts Rebound
Player
Curry 6.466667 69.666667 27.266667 5.000000
Durant 5.066667 67.333333 26.566667 7.766667
Ibaka 0.866667 52.333333 13.966667 6.800000

And we can make our plot!

In [16]:
player_stats.unstack().plot.bar()

Out[16]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fe411a8b390>

This particular visualization would be helpful if we wanted to see which stats for which each player is strongest.

For example, we can see that Steph Curry scores far more points than he does rebound, but Serge Ibaka is a bit more balanced.

What if we wanted to be able to compare all players for each statistic?

This would be easier to do if the bars were grouped by variable, with a different bar for each player.

To plot this, we need to have the variables on the index and the player name as column names.

We can get this DataFrame by setting level="Player" when calling unstack.

In [17]:
player_stats.unstack(level="Player")

Out[17]:
Player Curry Durant Ibaka
variable
Assist 6.466667 5.066667 0.866667
Games 69.666667 67.333333 52.333333
Pts 27.266667 26.566667 13.966667
Rebound 5.000000 7.766667 6.800000
In [18]:
player_stats.unstack(level="Player").plot.bar()

Out[18]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fe4116cc7b8>

Now we can use the chart to make a number of statements about players:

• Ibaka does not get many assists, compared to Curry and Durant.
• Steph and Kevin Durant are both high scorers.

Based on the examples above, notice a few things about unstack:

• It is the inverse of stack; stack will move labels down from columns to index, while unstack moves them up from index to columns.
• By default, unstack will move the level of the index closest to the data and place it in the column labels closest to the data.

Note

Just as we can pass multiple levels to stack, we can also pass multiple levels to unstack.

We needed to use this in our solution to the exercise below.

See exercise 1 in the exercise list

### Summary¶

In some ways set_index, reset_index, stack, and unstack are the “most fundamental” reshaping operations…

The other operations we discuss can be formulated with these four operations (and, in fact, some of them are exactly written as these operations in pandas’s code base).

Pro tip: We remember stack vs unstack with a mnemonic: Unstack moves index levels Up

## melt¶

The melt method is used to move from wide to long form.

It can be used to move all of the “values” stored in your DataFrame to a single column with all other columns being used to contain identifying information.

Warning: When you use melt, any index that you currently have will be deleted.

We saw used melt above when we constructed bball_long:

In [19]:
bball

Out[19]:
Year Player Team TeamName Games Pts Assist Rebound
0 2015 Curry GSW Warriors 79 30.1 6.7 5.4
1 2016 Curry GSW Warriors 79 25.3 6.6 4.5
2 2017 Curry GSW Warriors 51 26.4 6.1 5.1
3 2015 Durant OKC Thunder 72 28.2 5.0 8.2
4 2016 Durant GSW Warriors 62 25.1 4.8 8.3
5 2017 Durant GSW Warriors 68 26.4 5.4 6.8
6 2015 Ibaka OKC Thunder 78 12.6 0.8 6.8
7 2016 Ibaka ORL Magic 56 15.1 1.1 6.8
8 2016 Ibaka TOR Raptors 23 14.2 0.7 6.8
In [20]:
# this is how we made bball_long
bball.melt(id_vars=["Year", "Player", "Team", "TeamName"])

Out[20]:
Year Player Team TeamName variable value
0 2015 Curry GSW Warriors Games 79.0
1 2016 Curry GSW Warriors Games 79.0
2 2017 Curry GSW Warriors Games 51.0
3 2015 Durant OKC Thunder Games 72.0
4 2016 Durant GSW Warriors Games 62.0
5 2017 Durant GSW Warriors Games 68.0
6 2015 Ibaka OKC Thunder Games 78.0
7 2016 Ibaka ORL Magic Games 56.0
8 2016 Ibaka TOR Raptors Games 23.0
9 2015 Curry GSW Warriors Pts 30.1
10 2016 Curry GSW Warriors Pts 25.3
11 2017 Curry GSW Warriors Pts 26.4
12 2015 Durant OKC Thunder Pts 28.2
13 2016 Durant GSW Warriors Pts 25.1
14 2017 Durant GSW Warriors Pts 26.4
15 2015 Ibaka OKC Thunder Pts 12.6
16 2016 Ibaka ORL Magic Pts 15.1
17 2016 Ibaka TOR Raptors Pts 14.2
18 2015 Curry GSW Warriors Assist 6.7
19 2016 Curry GSW Warriors Assist 6.6
20 2017 Curry GSW Warriors Assist 6.1
21 2015 Durant OKC Thunder Assist 5.0
22 2016 Durant GSW Warriors Assist 4.8
23 2017 Durant GSW Warriors Assist 5.4
24 2015 Ibaka OKC Thunder Assist 0.8
25 2016 Ibaka ORL Magic Assist 1.1
26 2016 Ibaka TOR Raptors Assist 0.7
27 2015 Curry GSW Warriors Rebound 5.4
28 2016 Curry GSW Warriors Rebound 4.5
29 2017 Curry GSW Warriors Rebound 5.1
30 2015 Durant OKC Thunder Rebound 8.2
31 2016 Durant GSW Warriors Rebound 8.3
32 2017 Durant GSW Warriors Rebound 6.8
33 2015 Ibaka OKC Thunder Rebound 6.8
34 2016 Ibaka ORL Magic Rebound 6.8
35 2016 Ibaka TOR Raptors Rebound 6.8

Notice that the columns we specified as id_vars remained columns, but all other columns were put into two new columns:

1. variable: This has dtype string and contains the former column names. as values
2. value: This has the former values.

Using this method is an effective way to get our data in tidy form as noted above.

See exercise 2 in the exercise list

## pivot and pivot_table¶

The next two reshaping methods that we will use are closely related.

Some of you might even already be familiar with these ideas because you have previously used pivot tables in Excel.

• If so, good news. We think this is even more powerful than Excel and easier to use!
• If not, good news. You are about to learn a very powerful and user-friendly tool.

We will begin with pivot.

The pivot method:

• Takes the unique values of one column and places them along the index.
• Takes the unique values of another column and places them along the columns.
• Takes the values that correspond to a third column and fills in the DataFrame values that correspond to that index/column pair.

We’ll illustrate with an example.

In [21]:
# .head 8 excludes Ibaka -- will discuss why later

Out[21]:
Player Curry Durant
Year
2015 30.1 28.2
2016 25.3 25.1
2017 26.4 26.4

We can replicate pivot using three of the fundamental operations from above:

1. Call set_index with the index and columns arguments
2. Extract the values column
3. unstack the columns level of the new index
In [22]:
#  1---------------------------------------  2---  3----------------------

Out[22]:
Player Curry Durant
Year
2015 30.1 28.2
2016 25.3 25.1
2017 26.4 26.4

One important thing to be aware of is that in order for pivot to work, the index/column pairs must be unique!

Below, we demonstrate the error that occurs when they are not unique.

# Ibaka shows up twice in 2016 because he was traded mid-season from
# the Orlando Magic to the Toronto Raptors
bball.pivot(index="Year", columns="Player", values="Pts")


### pivot_table¶

The pivot_table method is a generalization of pivot.

It overcomes two limitations of pivot:

1. It allows you to choose multiple columns for the index/columns/values arguments.
2. It allows you to deal with duplicate entries by having you choose how to combine them.
In [23]:
bball

Out[23]:
Year Player Team TeamName Games Pts Assist Rebound
0 2015 Curry GSW Warriors 79 30.1 6.7 5.4
1 2016 Curry GSW Warriors 79 25.3 6.6 4.5
2 2017 Curry GSW Warriors 51 26.4 6.1 5.1
3 2015 Durant OKC Thunder 72 28.2 5.0 8.2
4 2016 Durant GSW Warriors 62 25.1 4.8 8.3
5 2017 Durant GSW Warriors 68 26.4 5.4 6.8
6 2015 Ibaka OKC Thunder 78 12.6 0.8 6.8
7 2016 Ibaka ORL Magic 56 15.1 1.1 6.8
8 2016 Ibaka TOR Raptors 23 14.2 0.7 6.8

Notice that we can replicate the functionality of pivot if we pass the same arguments.

In [24]:
bball.head(6).pivot(index="Year", columns="Player", values="Pts")

Out[24]:
Player Curry Durant
Year
2015 30.1 28.2
2016 25.3 25.1
2017 26.4 26.4

But we can also choose multiple columns to be used in index/columns/values.

In [25]:
bball.pivot_table(index=["Year", "Team"], columns="Player", values="Pts")

Out[25]:
Player Curry Durant Ibaka
Year Team
2015 GSW 30.1 NaN NaN
OKC NaN 28.2 12.6
2016 GSW 25.3 25.1 NaN
ORL NaN NaN 15.1
TOR NaN NaN 14.2
2017 GSW 26.4 26.4 NaN
In [26]:
bball.pivot_table(index="Year", columns=["Player", "Team"], values="Pts")

Out[26]:
Player Curry Durant Ibaka
Team GSW GSW OKC OKC ORL TOR
Year
2015 30.1 NaN 28.2 12.6 NaN NaN
2016 25.3 25.1 NaN NaN 15.1 14.2
2017 26.4 26.4 NaN NaN NaN NaN

AND we can deal with duplicated index/column pairs.

In [27]:
# This produced an error
# bball.pivot(index="Year", columns="Player", values="Pts")

# This doesn't!
bball_pivoted = bball.pivot_table(index="Year", columns="Player", values="Pts")
bball_pivoted

Out[27]:
Player Curry Durant Ibaka
Year
2015 30.1 28.2 12.60
2016 25.3 25.1 14.65
2017 26.4 26.4 NaN

pivot_table handles duplicate index/column pairs using an aggregation.

By default, the aggregation is the mean.

For example, our duplicated index/column pair is ("x", 1) and had associated values of 2 and 5.

Notice that bball_pivoted.loc[2016, "Ibaka"] is (15.1 + 14.2)/2 = 14.65.

We can choose how pandas aggregates all of the values.

For example, here’s how we would keep the max.

In [28]:
bball.pivot_table(index="Year", columns="Player", values="Pts", aggfunc=max)

Out[28]:
Player Curry Durant Ibaka
Year
2015 30.1 28.2 12.6
2016 25.3 25.1 15.1
2017 26.4 26.4 NaN

Maybe we wanted to count how many values there were.

In [29]:
bball.pivot_table(index="Year", columns="Player", values="Pts", aggfunc=len)

Out[29]:
Player Curry Durant Ibaka
Year
2015 1.0 1.0 1.0
2016 1.0 1.0 2.0
2017 1.0 1.0 NaN

We can even pass multiple aggregation functions!

In [30]:
bball.pivot_table(index="Year", columns="Player", values="Pts", aggfunc=[max, len])

Out[30]:
max len
Player Curry Durant Ibaka Curry Durant Ibaka
Year
2015 30.1 28.2 12.6 1.0 1.0 1.0
2016 25.3 25.1 15.1 1.0 1.0 2.0
2017 26.4 26.4 NaN 1.0 1.0 NaN

See exercise 3 in the exercise list

## Visualizing Reshaping¶

Now that you have learned the basics and had a chance to experiment, we will use some generic data to provide a visualization of what the above reshape operations do.

The data we will use is:

In [31]:
# made up
# columns A and B are "identifiers" while C, D, and E are variables.
df = pd.DataFrame({
"A": [0, 0, 1, 1],
"B": "x y x z".split(),
"C": [1, 2, 1, 4],
"D": [10, 20, 30, 20,],
"E": [2, 1, 5, 4,]
})

df.info()
df

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4 entries, 0 to 3
Data columns (total 5 columns):
A    4 non-null int64
B    4 non-null object
C    4 non-null int64
D    4 non-null int64
E    4 non-null int64
dtypes: int64(4), object(1)
memory usage: 240.0+ bytes

Out[31]:
A B C D E
0 0 x 1 10 2
1 0 y 2 20 1
2 1 x 1 30 5
3 1 z 4 20 4
In [32]:
df2 = df.set_index(["A", "B"])

Out[32]:
C D E
A B
0 x 1 10 2
y 2 20 1
1 x 1 30 5
z 4 20 4
In [33]:
df3 = df2.T

Out[33]:
A 0 1
B x y x z
C 1 2 1 4
D 10 20 30 20
E 2 1 5 4

### stack and unstack¶

Below is an animation that shows how stacking works.

In [34]:
df2

Out[34]:
C D E
A B
0 x 1 10 2
y 2 20 1
1 x 1 30 5
z 4 20 4
In [35]:
df2_stack = df2.stack()
df2_stack

Out[35]:
A  B
0  x  C     1
D    10
E     2
y  C     2
D    20
E     1
1  x  C     1
D    30
E     5
z  C     4
D    20
E     4
dtype: int64

And here is an animation that shows how unstacking works.

In [36]:
df2

Out[36]:
C D E
A B
0 x 1 10 2
y 2 20 1
1 x 1 30 5
z 4 20 4
In [37]:
df2.unstack()

Out[37]:
C D E
B x y z x y z x y z
A
0 1.0 2.0 NaN 10.0 20.0 NaN 2.0 1.0 NaN
1 1.0 NaN 4.0 30.0 NaN 20.0 5.0 NaN 4.0

### melt¶

As noted above, the melt method transforms data from wide to long in form.

Here’s a visualization of that operation.

In [38]:
df

Out[38]:
A B C D E
0 0 x 1 10 2
1 0 y 2 20 1
2 1 x 1 30 5
3 1 z 4 20 4
In [39]:
df_melted = df.melt(id_vars=["A", "B"])
df_melted

Out[39]:
A B variable value
0 0 x C 1
1 0 y C 2
2 1 x C 1
3 1 z C 4
4 0 x D 10
5 0 y D 20
6 1 x D 30
7 1 z D 20
8 0 x E 2
9 0 y E 1
10 1 x E 5
11 1 z E 4

## Exercises¶

Exercise 1

(Warning: This one is challenging):

Recall the bball_wide DataFrame from above (repeated below to jog your memory).

In this task, you will start from ball and re-recreate bball_wide by combining the operations we just learned about.

There are many ways to do this, so be creative.

Our solution used set_index, T, stack, and unstack in that order.

Here are a few hints:

• Think about what columns you will need to call set_index on so that their data ends up as labels (either in index or columns).
• Leave other columns (e.g. the actual game stats) as actual columns so their data can stay data during your reshaping.

Don't spend too much time on this... if you get stuck, open up this markdown cell, and you will see our answer hidden.

Hint: You might need to add .sort_index(axis=1) after you are finished to get the columns in the same order.

Hint: You may not end up with a variable header on the second level of column labels. This is ok.

bball.drop("TeamName", axis=1).set_index(["Year", "Player", "Team"]).stack().unstack(level=[1, 3, 2]).sort_index(axis=1)
In [40]:
bball_wide

Out[40]:
Player Curry Durant ... Ibaka
variable Assist Games Pts Rebound Assist Games Pts ... Assist Games Pts Rebound
Team GSW GSW GSW GSW GSW OKC GSW OKC GSW OKC ... TOR OKC ORL TOR OKC ORL TOR OKC ORL TOR
Year
2015 6.7 79.0 30.1 5.4 NaN 5.0 NaN 72.0 NaN 28.2 ... NaN 78.0 NaN NaN 12.6 NaN NaN 6.8 NaN NaN
2016 6.6 79.0 25.3 4.5 4.8 NaN 62.0 NaN 25.1 NaN ... 0.7 NaN 56.0 23.0 NaN 15.1 14.2 NaN 6.8 6.8
2017 6.1 51.0 26.4 5.1 5.4 NaN 68.0 NaN 26.4 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

3 rows × 24 columns

Exercise 2

• What do you think would happen if we wrote bball.melt(id_vars=["Year", "Player"]) rather than bball.melt(id_vars=["Year", "Player", "Team", "TeamName"])? Were you right? Write your thoughts.
• Read the documentation and focus on the argument value_vars. How does bball.melt(id_vars=["Year", "Player"], value_vars=["Pts", "Rebound"]) differ from bball.melt(id_vars=["Year", "Player"])?
• Consider the differences between bball.stack and bball.melt. Is there a way to make them generate the same output? (Hint: you might need to use both stack and another method from above)? Write your thoughts.

Exercise 3

• First, take a breath... That was a lot to take in.
• Can you think of a reason to ever use pivot rather than pivot_table? Write your thoughts.
• Create a pivot table with column Player as the index, TeamName as the columns, and [Rebound, Assist] as the values. What happens when you use aggfunc=[np.max, np.min, len]? Describe how Python produced each of the values in the resultant pivot table.