4  Pandas III

Important Note

This course note was developed in Fall 2023. If you are taking this class in a future semester, please keep in mind that this note may not be up to date with course content for that semester.

Learning Outcomes

• Perform advanced aggregation using .groupby()
• Use the pd.pivot_table method to construct a pivot table
• Perform simple merges between DataFrames using pd.merge()

Last time, we introduced the concept of aggregating data – we familiarized ourselves with GroupBy objects and used them as tools to consolidate and summarize a DataFrame. In this lecture, we will explore working with the different aggregation functions and dive into some advanced .groupby methods to show just how powerful of a resource they can be for understanding our data. We will also introduce other techniques for data aggregation to provide flexibility in how we manipulate our tables.

4.1 Revisiting the .agg() Function

We’ll start by loading the babynames dataset. Note that this dataset is filtered to only contain data from California.

Code

# This code pulls census data and loads it into a DataFrame
# We won't cover it explicitly in this class, but you are welcome to explore it on your own
import pandas as pd
import numpy as np
import urllib.request
import os.path
import zipfile

data_url = "https://www.ssa.gov/oact/babynames/state/namesbystate.zip"
local_filename = "data/babynamesbystate.zip"
if not os.path.exists(local_filename): # If the data exists don't download again
    with urllib.request.urlopen(data_url) as resp, open(local_filename, 'wb') as f:
        f.write(resp.read())

zf = zipfile.ZipFile(local_filename, 'r')

ca_name = 'STATE.CA.TXT'
field_names = ['State', 'Sex', 'Year', 'Name', 'Count']
with zf.open(ca_name) as fh:
    babynames = pd.read_csv(fh, header=None, names=field_names)

babynames.tail(10)

	State	Sex	Year	Name	Count
407418	CA	M	2022	Zach	5
407419	CA	M	2022	Zadkiel	5
407420	CA	M	2022	Zae	5
407421	CA	M	2022	Zai	5
407422	CA	M	2022	Zay	5
407423	CA	M	2022	Zayvier	5
407424	CA	M	2022	Zia	5
407425	CA	M	2022	Zora	5
407426	CA	M	2022	Zuriel	5
407427	CA	M	2022	Zylo	5

Let’s begin by using .agg to find the total number of babies born in each year. Recall that using .agg with .groupby() follows the format: df.groupby(column_name).agg(aggregation_function). The line of code below gives us the total number of babies born in each year.

babynames.groupby("Year")[["Count"]].agg(sum).head(5)

	Count
Year
1910	9163
1911	9983
1912	17946
1913	22094
1914	26926

Here’s an illustration of the process:

Let’s now dive deeper into groupby. As we learned last lecture, a groupby operation involves some combination of splitting a DataFrame into grouped subframes, applying a function, and combining the results.

For some arbitrary DataFrame df below, the code df.groupby("year").agg(sum) does the following:

• Splits the DataFrame into sub-DataFrames with rows belonging to the same year.
• Applies the sum function to each column of each sub-DataFrame.
• Combines the results of sum into a single DataFrame, indexed by year.

4.1.1 Aggregation Functions

There are many different aggregations that can be applied to the grouped data. .agg() can take in any function that aggregates several values into one summary value.

Because of this fairly broad requirement, pandas offers many ways of computing an aggregation.

In-built Python operations are automatically recognized by pandas. For example:

• .agg(sum)
• .agg(max)
• .agg(min)

NumPy functions are also fair game in pandas:

• .agg(np.sum)
• .agg(np.max)
• .agg(np.min)
• .agg("mean")

pandas also offers a number of in-built functions, including:

• .agg("sum")
• .agg("max")
• .agg("min")
• .agg("mean")
• .agg("first")
• .agg("last")

Some commonly-used aggregation functions can even be called directly, without explicit use of .agg(). For example, we can call .mean() on .groupby():

babynames.groupby("Year").mean().head()

We can now put this all into practice. Say we want to find the baby name with sex “F” that has fallen in popularity the most in California. To calculate this, we can first create a metric: “Ratio to Peak” (RTP). The RTP is the ratio of babies born with a given name in 2022 to the maximum number of babies born with the name in any year.

Let’s start with calculating this for one baby, “Jennifer”.

# We filter by babies with sex "F" and sort by "Year"
f_babynames = babynames[babynames["Sex"] == "F"]
f_babynames = f_babynames.sort_values(["Year"])

# Determine how many Jennifers were born in CA per year
jenn_counts_series = f_babynames[f_babynames["Name"] == "Jennifer"]["Count"]

# Determine the max number of Jennifers born in a year and the number born in 2022 
# to calculate RTP
max_jenn = max(f_babynames[f_babynames["Name"] == "Jennifer"]["Count"])
curr_jenn = f_babynames[f_babynames["Name"] == "Jennifer"]["Count"].iloc[-1]
rtp = curr_jenn / max_jenn
rtp

0.018796372629843364

By creating a function to calculate RTP and applying it to our DataFrame by using .groupby(), we can easily compute the RTP for all names at once!

def ratio_to_peak(series):
    return series.iloc[-1] / max(series)

#Using .groupby() to apply the function
rtp_table = f_babynames.groupby("Name")[["Year", "Count"]].agg(ratio_to_peak)
rtp_table.head()

	Year	Count
Name
Aadhini	1.0	1.000000
Aadhira	1.0	0.500000
Aadhya	1.0	0.660000
Aadya	1.0	0.586207
Aahana	1.0	0.269231

In the rows shown above, we can see that every row shown has a Year value of 1.0.

This is the “pandas-ification” of logic you saw in Data 8. Much of the logic you’ve learned in Data 8 will serve you well in Data 100.

4.1.2 Nuisance Columns

Note that you must be careful with which columns you apply the .agg() function to. If we were to apply our function to the table as a whole by doing f_babynames.groupby("Name").agg(ratio_to_peak), executing our .agg() call would result in a TypeError.

We can avoid this issue (and prevent unintentional loss of data) by explicitly selecting column(s) we want to apply our aggregation function to BEFORE calling .agg(),

4.1.3 Renaming Columns After Grouping

By default, .groupby will not rename any aggregated columns. As we can see in the table above, the aggregated column is still named Count even though it now represents the RTP. For better readability, we can rename Count to Count RTP

rtp_table = rtp_table.rename(columns = {"Count": "Count RTP"})
rtp_table

	Year	Count RTP
Name
Aadhini	1.0	1.000000
Aadhira	1.0	0.500000
Aadhya	1.0	0.660000
Aadya	1.0	0.586207
Aahana	1.0	0.269231
...	...	...
Zyanya	1.0	0.466667
Zyla	1.0	1.000000
Zylah	1.0	1.000000
Zyra	1.0	1.000000
Zyrah	1.0	0.833333

13782 rows × 2 columns

4.1.4 Some Data Science Payoff

By sorting rtp_table, we can see the names whose popularity has decreased the most.

rtp_table = rtp_table.rename(columns = {"Count": "Count RTP"})
rtp_table.sort_values("Count RTP").head()

	Year	Count RTP
Name
Debra	1.0	0.001260
Debbie	1.0	0.002815
Carol	1.0	0.003180
Tammy	1.0	0.003249
Susan	1.0	0.003305

To visualize the above Dataframe, let’s look at the line plot below:

Code

import plotly.express as px
px.line(f_babynames[f_babynames["Name"] == "Debra"], x = "Year", y = "Count")

We can get the list of the top 10 names and then plot popularity with the following code:

top10 = rtp_table.sort_values("Count RTP").head(10).index
px.line(
    f_babynames[f_babynames["Name"].isin(top10)], 
    x = "Year", 
    y = "Count", 
    color = "Name"
)

As a quick exercise, consider what code would compute the total number of babies with each name.

Code

babynames.groupby("Name")[["Count"]].agg(sum).head()
# alternative solution: 
# babynames.groupby("Name")[["Count"]].sum()

	Count
Name
Aadan	18
Aadarsh	6
Aaden	647
Aadhav	27
Aadhini	6

Now, let’s think about the code to compute the total number of babies born each year. You’ll see that there are multiple ways to achieve this, some of which are listed below.

Code

babynames.groupby("Year")[["Count"]].agg(sum).head()
# Alternative 1
# babynames.groupby("Year")[["Count"]].sum()
# Alternative 2
# babynames.groupby("Year").sum(numeric_only=True)

	Count
Year
1910	9163
1911	9983
1912	17946
1913	22094
1914	26926

For the second alternative, note how we can avoid the error we faced earlier with aggregating non-numeric colums by passing in the numeric_only=True argument to groupby.

4.1.5 Plotting Birth Counts

Plotting the Dataframe we obtain tells an interesting story.

Code

import plotly.express as px
puzzle2 = babynames.groupby("Year")[["Count"]].agg(sum)
px.line(puzzle2, y = "Count")

A word of warning: we made an enormous assumption when we decided to use this dataset to estimate birth rate. According to this article from the Legistlative Analyst Office, the true number of babies born in California in 2020 was 421,275. However, our plot shows 362,882 babies – what happened?

4.2 GroupBy(), Continued

We’ll work with the elections DataFrame again.

Code

import pandas as pd
import numpy as np

elections = pd.read_csv("data/elections.csv")
elections.head(5)

	Year	Candidate	Party	Popular vote	Result	%
0	1824	Andrew Jackson	Democratic-Republican	151271	loss	57.210122
1	1824	John Quincy Adams	Democratic-Republican	113142	win	42.789878
2	1828	Andrew Jackson	Democratic	642806	win	56.203927
3	1828	John Quincy Adams	National Republican	500897	loss	43.796073
4	1832	Andrew Jackson	Democratic	702735	win	54.574789

4.2.1 Raw GroupBy Objects

The result of groupby applied to a DataFrame is a DataFrameGroupBy object, not a DataFrame.

grouped_by_year = elections.groupby("Year")
type(grouped_by_year)

pandas.core.groupby.generic.DataFrameGroupBy

There are several ways to look into DataFrameGroupBy objects:

grouped_by_party = elections.groupby("Party")
grouped_by_party.groups

{'American': [22, 126], 'American Independent': [115, 119, 124], 'Anti-Masonic': [6], 'Anti-Monopoly': [38], 'Citizens': [127], 'Communist': [89], 'Constitution': [160, 164, 172], 'Constitutional Union': [24], 'Democratic': [2, 4, 8, 10, 13, 14, 17, 20, 28, 29, 34, 37, 39, 45, 47, 52, 55, 57, 64, 70, 74, 77, 81, 83, 86, 91, 94, 97, 100, 105, 108, 111, 114, 116, 118, 123, 129, 134, 137, 140, 144, 151, 158, 162, 168, 176, 178], 'Democratic-Republican': [0, 1], 'Dixiecrat': [103], 'Farmer–Labor': [78], 'Free Soil': [15, 18], 'Green': [149, 155, 156, 165, 170, 177, 181], 'Greenback': [35], 'Independent': [121, 130, 143, 161, 167, 174], 'Liberal Republican': [31], 'Libertarian': [125, 128, 132, 138, 139, 146, 153, 159, 163, 169, 175, 180], 'National Democratic': [50], 'National Republican': [3, 5], 'National Union': [27], 'Natural Law': [148], 'New Alliance': [136], 'Northern Democratic': [26], 'Populist': [48, 61, 141], 'Progressive': [68, 82, 101, 107], 'Prohibition': [41, 44, 49, 51, 54, 59, 63, 67, 73, 75, 99], 'Reform': [150, 154], 'Republican': [21, 23, 30, 32, 33, 36, 40, 43, 46, 53, 56, 60, 65, 69, 72, 79, 80, 84, 87, 90, 96, 98, 104, 106, 109, 112, 113, 117, 120, 122, 131, 133, 135, 142, 145, 152, 157, 166, 171, 173, 179], 'Socialist': [58, 62, 66, 71, 76, 85, 88, 92, 95, 102], 'Southern Democratic': [25], 'States' Rights': [110], 'Taxpayers': [147], 'Union': [93], 'Union Labor': [42], 'Whig': [7, 9, 11, 12, 16, 19]}

grouped_by_party.get_group("Socialist")

	Year	Candidate	Party	Popular vote	Result	%
58	1904	Eugene V. Debs	Socialist	402810	loss	2.985897
62	1908	Eugene V. Debs	Socialist	420852	loss	2.850866
66	1912	Eugene V. Debs	Socialist	901551	loss	6.004354
71	1916	Allan L. Benson	Socialist	590524	loss	3.194193
76	1920	Eugene V. Debs	Socialist	913693	loss	3.428282
85	1928	Norman Thomas	Socialist	267478	loss	0.728623
88	1932	Norman Thomas	Socialist	884885	loss	2.236211
92	1936	Norman Thomas	Socialist	187910	loss	0.412876
95	1940	Norman Thomas	Socialist	116599	loss	0.234237
102	1948	Norman Thomas	Socialist	139569	loss	0.286312

4.2.2 Other GroupBy Methods

There are many aggregation methods we can use with .agg. Some useful options are:

• .mean: creates a new DataFrame with the mean value of each group
• .sum: creates a new DataFrame with the sum of each group
• .max and .min: creates a new DataFrame with the maximum/minimum value of each group
• .first and .last: creates a new DataFrame with the first/last row in each group
• .size: creates a new Series with the number of entries in each group
• .count: creates a new DataFrame with the number of entries, excluding missing values.

Let’s illustrate some examples by creating a DataFrame called df.

df = pd.DataFrame({'letter':['A','A','B','C','C','C'], 
                   'num':[1,2,3,4,np.NaN,4], 
                   'state':[np.NaN, 'tx', 'fl', 'hi', np.NaN, 'ak']})
df

	letter	num	state
0	A	1.0	NaN
1	A	2.0	tx
2	B	3.0	fl
3	C	4.0	hi
4	C	NaN	NaN
5	C	4.0	ak

Note the slight difference between .size() and .count(): while .size() returns a Series and counts the number of entries including the missing values, .count() returns a DataFrame and counts the number of entries in each column excluding missing values.

df.groupby("letter").size()

letter
A    2
B    1
C    3
dtype: int64

df.groupby("letter").count()

	num	state
letter
A	2	1
B	1	1
C	2	2

You might recall that the value_counts() function in the previous note does something similar. It turns out value_counts() and groupby.size() are the same, except value_counts() sorts the resulting Series in descending order automatically.

df["letter"].value_counts()

C    3
A    2
B    1
Name: letter, dtype: int64

These (and other) aggregation functions are so common that pandas allows for writing shorthand. Instead of explicitly stating the use of .agg, we can call the function directly on the GroupBy object.

For example, the following are equivalent:

• elections.groupby("Candidate").agg(mean)
• elections.groupby("Candidate").mean()

There are many other methods that pandas supports. You can check them out on the pandas documentation.

4.2.3 Filtering by Group

Another common use for GroupBy objects is to filter data by group.

groupby.filter takes an argument func, where func is a function that:

• Takes a DataFrame object as input
• Returns a single True or False for the each sub-DataFrame

Sub-DataFrames that correspond to True are returned in the final result, whereas those with a False value are not. Importantly, groupby.filter is different from groupby.agg in that an entire sub-DataFrame is returned in the final DataFrame, not just a single row. As a result, groupby.filter preserves the original indices.

To illustrate how this happens, let’s go back to the elections dataset. Say we want to identify “tight” election years – that is, we want to find all rows that correspond to elections years where all candidates in that year won a similar portion of the total vote. Specifically, let’s find all rows corresponding to a year where no candidate won more than 45% of the total vote.

In other words, we want to:

• Find the years where the maximum % in that year is less than 45%
• Return all DataFrame rows that correspond to these years

For each year, we need to find the maximum % among all rows for that year. If this maximum % is lower than 45%, we will tell pandas to keep all rows corresponding to that year.

elections.groupby("Year").filter(lambda sf: sf["%"].max() < 45).head(9)

	Year	Candidate	Party	Popular vote	Result	%
23	1860	Abraham Lincoln	Republican	1855993	win	39.699408
24	1860	John Bell	Constitutional Union	590901	loss	12.639283
25	1860	John C. Breckinridge	Southern Democratic	848019	loss	18.138998
26	1860	Stephen A. Douglas	Northern Democratic	1380202	loss	29.522311
66	1912	Eugene V. Debs	Socialist	901551	loss	6.004354
67	1912	Eugene W. Chafin	Prohibition	208156	loss	1.386325
68	1912	Theodore Roosevelt	Progressive	4122721	loss	27.457433
69	1912	William Taft	Republican	3486242	loss	23.218466
70	1912	Woodrow Wilson	Democratic	6296284	win	41.933422

What’s going on here? In this example, we’ve defined our filtering function, func, to be lambda sf: sf["%"].max() < 45. This filtering function will find the maximum "%" value among all entries in the grouped sub-DataFrame, which we call sf. If the maximum value is less than 45, then the filter function will return True and all rows in that grouped sub-DataFrame will appear in the final output DataFrame.

Examine the DataFrame above. Notice how, in this preview of the first 9 rows, all entries from the years 1860 and 1912 appear. This means that in 1860 and 1912, no candidate in that year won more than 45% of the total vote.

You may ask: how is the groupby.filter procedure different to the boolean filtering we’ve seen previously? Boolean filtering considers individual rows when applying a boolean condition. For example, the code elections[elections["%"] < 45] will check the "%" value of every single row in elections; if it is less than 45, then that row will be kept in the output. groupby.filter, in contrast, applies a boolean condition across all rows in a group. If not all rows in that group satisfy the condition specified by the filter, the entire group will be discarded in the output.

4.2.4 Aggregation with lambda Functions

What if we wish to aggregate our DataFrame using a non-standard function – for example, a function of our own design? We can do so by combining .agg with lambda expressions.

Let’s first consider a puzzle to jog our memory. We will attempt to find the Candidate from each Party with the highest % of votes.

A naive approach may be to group by the Party column and aggregate by the maximum.

elections.groupby("Party").agg(max).head(10)

	Year	Candidate	Popular vote	Result	%
Party
American	1976	Thomas J. Anderson	873053	loss	21.554001
American Independent	1976	Lester Maddox	9901118	loss	13.571218
Anti-Masonic	1832	William Wirt	100715	loss	7.821583
Anti-Monopoly	1884	Benjamin Butler	134294	loss	1.335838
Citizens	1980	Barry Commoner	233052	loss	0.270182
Communist	1932	William Z. Foster	103307	loss	0.261069
Constitution	2016	Michael Peroutka	203091	loss	0.152398
Constitutional Union	1860	John Bell	590901	loss	12.639283
Democratic	2020	Woodrow Wilson	81268924	win	61.344703
Democratic-Republican	1824	John Quincy Adams	151271	win	57.210122

This approach is clearly wrong – the DataFrame claims that Woodrow Wilson won the presidency in 2020.

Why is this happening? Here, the max aggregation function is taken over every column independently. Among Democrats, max is computing:

• The most recent Year a Democratic candidate ran for president (2020)
• The Candidate with the alphabetically “largest” name (“Woodrow Wilson”)
• The Result with the alphabetically “largest” outcome (“win”)

Instead, let’s try a different approach. We will:

1. Sort the DataFrame so that rows are in descending order of %
2. Group by Party and select the first row of each sub-DataFrame

While it may seem unintuitive, sorting elections by descending order of % is extremely helpful. If we then group by Party, the first row of each groupby object will contain information about the Candidate with the highest voter %.

elections_sorted_by_percent = elections.sort_values("%", ascending=False)
elections_sorted_by_percent.head(5)

	Year	Candidate	Party	Popular vote	Result	%
114	1964	Lyndon Johnson	Democratic	43127041	win	61.344703
91	1936	Franklin Roosevelt	Democratic	27752648	win	60.978107
120	1972	Richard Nixon	Republican	47168710	win	60.907806
79	1920	Warren Harding	Republican	16144093	win	60.574501
133	1984	Ronald Reagan	Republican	54455472	win	59.023326

elections_sorted_by_percent.groupby("Party").agg(lambda x : x.iloc[0]).head(10)

# Equivalent to the below code
# elections_sorted_by_percent.groupby("Party").agg('first').head(10)

	Year	Candidate	Popular vote	Result	%
Party
American	1856	Millard Fillmore	873053	loss	21.554001
American Independent	1968	George Wallace	9901118	loss	13.571218
Anti-Masonic	1832	William Wirt	100715	loss	7.821583
Anti-Monopoly	1884	Benjamin Butler	134294	loss	1.335838
Citizens	1980	Barry Commoner	233052	loss	0.270182
Communist	1932	William Z. Foster	103307	loss	0.261069
Constitution	2008	Chuck Baldwin	199750	loss	0.152398
Constitutional Union	1860	John Bell	590901	loss	12.639283
Democratic	1964	Lyndon Johnson	43127041	win	61.344703
Democratic-Republican	1824	Andrew Jackson	151271	loss	57.210122

Here’s an illustration of the process:

Notice how our code correctly determines that Lyndon Johnson from the Democratic Party has the highest voter %.

More generally, lambda functions are used to design custom aggregation functions that aren’t pre-defined by Python. The input parameter x to the lambda function is a GroupBy object. Therefore, it should make sense why lambda x : x.iloc[0] selects the first row in each groupby object.

In fact, there’s a few different ways to approach this problem. Each approach has different tradeoffs in terms of readability, performance, memory consumption, complexity, etc. We’ve given a few examples below.

Note: Understanding these alternative solutions is not required. They are given to demonstrate the vast number of problem-solving approaches in pandas.

# Using the idxmax function
best_per_party = elections.loc[elections.groupby('Party')['%'].idxmax()]
best_per_party.head(5)

	Year	Candidate	Party	Popular vote	Result	%
22	1856	Millard Fillmore	American	873053	loss	21.554001
115	1968	George Wallace	American Independent	9901118	loss	13.571218
6	1832	William Wirt	Anti-Masonic	100715	loss	7.821583
38	1884	Benjamin Butler	Anti-Monopoly	134294	loss	1.335838
127	1980	Barry Commoner	Citizens	233052	loss	0.270182

# Using the .drop_duplicates function
best_per_party2 = elections.sort_values('%').drop_duplicates(['Party'], keep='last')
best_per_party2.head(5)

	Year	Candidate	Party	Popular vote	Result	%
148	1996	John Hagelin	Natural Law	113670	loss	0.118219
164	2008	Chuck Baldwin	Constitution	199750	loss	0.152398
110	1956	T. Coleman Andrews	States' Rights	107929	loss	0.174883
147	1996	Howard Phillips	Taxpayers	184656	loss	0.192045
136	1988	Lenora Fulani	New Alliance	217221	loss	0.237804

4.3 Aggregating Data with Pivot Tables

We know now that .groupby gives us the ability to group and aggregate data across our DataFrame. The examples above formed groups using just one column in the DataFrame. It’s possible to group by multiple columns at once by passing in a list of column names to .groupby.

Let’s consider the babynames dataset again. In this problem, we will find the total number of baby names associated with each sex for each year. To do this, we’ll group by both the "Year" and "Sex" columns.

babynames.head()

	State	Sex	Year	Name	Count
0	CA	F	1910	Mary	295
1	CA	F	1910	Helen	239
2	CA	F	1910	Dorothy	220
3	CA	F	1910	Margaret	163
4	CA	F	1910	Frances	134

# Find the total number of baby names associated with each sex for each 
# year in the data
babynames.groupby(["Year", "Sex"])[["Count"]].agg(sum).head(6)

		Count
Year	Sex
1910	F	5950
	M	3213
1911	F	6602
	M	3381
1912	F	9804
	M	8142

Notice that both "Year" and "Sex" serve as the index of the DataFrame (they are both rendered in bold). We’ve created a multi-index DataFrame where two different index values, the year and sex, are used to uniquely identify each row.

This isn’t the most intuitive way of representing this data – and, because multi-indexed DataFrames have multiple dimensions in their index, they can often be difficult to use.

Another strategy to aggregate across two columns is to create a pivot table. You saw these back in Data 8. One set of values is used to create the index of the pivot table; another set is used to define the column names. The values contained in each cell of the table correspond to the aggregated data for each index-column pair.

Here’s an illustration of the process:

The best way to understand pivot tables is to see one in action. Let’s return to our original goal of summing the total number of names associated with each combination of year and sex. We’ll call the pandas .pivot_table method to create a new table.

# The `pivot_table` method is used to generate a Pandas pivot table
import numpy as np
babynames.pivot_table(
    index = "Year", 
    columns = "Sex", 
    values = "Count", 
    aggfunc = np.sum,
).head(5)

Sex	F	M
Year
1910	5950	3213
1911	6602	3381
1912	9804	8142
1913	11860	10234
1914	13815	13111

Looks a lot better! Now, our DataFrame is structured with clear index-column combinations. Each entry in the pivot table represents the summed count of names for a given combination of "Year" and "Sex".

Let’s take a closer look at the code implemented above.

• index = "Year" specifies the column name in the original DataFrame that should be used as the index of the pivot table
• columns = "Sex" specifies the column name in the original DataFrame that should be used to generate the columns of the pivot table
• values = "Count" indicates what values from the original DataFrame should be used to populate the entry for each index-column combination
• aggfunc = np.sum tells pandas what function to use when aggregating the data specified by values. Here, we are summing the name counts for each pair of "Year" and "Sex"

We can even include multiple values in the index or columns of our pivot tables.

babynames_pivot = babynames.pivot_table(
    index="Year",     # the rows (turned into index)
    columns="Sex",    # the column values
    values=["Count", "Name"], 
    aggfunc=max,   # group operation
)
babynames_pivot.head(6)

	Count		Name
Sex	F	M	F	M
Year
1910	295	237	Yvonne	William
1911	390	214	Zelma	Willis
1912	534	501	Yvonne	Woodrow
1913	584	614	Zelma	Yoshio
1914	773	769	Zelma	Yoshio
1915	998	1033	Zita	Yukio

4.4 Joining Tables

When working on data science projects, we’re unlikely to have absolutely all the data we want contained in a single DataFrame – a real-world data scientist needs to grapple with data coming from multiple sources. If we have access to multiple datasets with related information, we can join two or more tables into a single DataFrame.

To put this into practice, we’ll revisit the elections dataset.

elections.head(5)

	Year	Candidate	Party	Popular vote	Result	%
0	1824	Andrew Jackson	Democratic-Republican	151271	loss	57.210122
1	1824	John Quincy Adams	Democratic-Republican	113142	win	42.789878
2	1828	Andrew Jackson	Democratic	642806	win	56.203927
3	1828	John Quincy Adams	National Republican	500897	loss	43.796073
4	1832	Andrew Jackson	Democratic	702735	win	54.574789

Say we want to understand the popularity of the names of each presidential candidate in 2022. To do this, we’ll need the combined data of babynames and elections.

We’ll start by creating a new column containing the first name of each presidential candidate. This will help us join each name in elections to the corresponding name data in babynames.

# This `str` operation splits each candidate's full name at each 
# blank space, then takes just the candidiate's first name
elections["First Name"] = elections["Candidate"].str.split().str[0]
elections.head(5)

	Year	Candidate	Party	Popular vote	Result	%	First Name
0	1824	Andrew Jackson	Democratic-Republican	151271	loss	57.210122	Andrew
1	1824	John Quincy Adams	Democratic-Republican	113142	win	42.789878	John
2	1828	Andrew Jackson	Democratic	642806	win	56.203927	Andrew
3	1828	John Quincy Adams	National Republican	500897	loss	43.796073	John
4	1832	Andrew Jackson	Democratic	702735	win	54.574789	Andrew

# Here, we'll only consider `babynames` data from 2022
babynames_2022 = babynames[babynames["Year"]==2020]
babynames_2022.head()

	State	Sex	Year	Name	Count
228550	CA	F	2020	Olivia	2353
228551	CA	F	2020	Camila	2187
228552	CA	F	2020	Emma	2110
228553	CA	F	2020	Mia	2043
228554	CA	F	2020	Sophia	1999

Now, we’re ready to join the two tables. pd.merge is the pandas method used to join DataFrames together.

merged = pd.merge(left = elections, right = babynames_2022, \
                  left_on = "First Name", right_on = "Name")
merged.head()
# Notice that pandas automatically specifies `Year_x` and `Year_y` 
# when both merged DataFrames have the same column name to avoid confusion

	Year_x	Candidate	Party	Popular vote	Result	%	First Name	State	Sex	Year_y	Name	Count
0	1824	Andrew Jackson	Democratic-Republican	151271	loss	57.210122	Andrew	CA	M	2020	Andrew	874
1	1828	Andrew Jackson	Democratic	642806	win	56.203927	Andrew	CA	M	2020	Andrew	874
2	1832	Andrew Jackson	Democratic	702735	win	54.574789	Andrew	CA	M	2020	Andrew	874
3	1824	John Quincy Adams	Democratic-Republican	113142	win	42.789878	John	CA	M	2020	John	623
4	1828	John Quincy Adams	National Republican	500897	loss	43.796073	John	CA	M	2020	John	623

Let’s take a closer look at the parameters:

• left and right parameters are used to specify the DataFrames to be joined.
• left_on and right_on parameters are assigned to the string names of the columns to be used when performing the join. These two on parameters tell pandas what values should act as pairing keys to determine which rows to merge across the DataFrames. We’ll talk more about this idea of a pairing key next lecture.

4.5 Parting Note

Congratulations! We finally tackled pandas. Don’t worry if you are still not feeling very comfortable with it—you will have plenty of chance to practice over the next few weeks.

Next, we will get our hands dirty with some real-world datasets and use our pandas knowledge to conduct some exploratory data analysis.