11. Group¶

To take the next steps in our investigation, we’ll need to learn a new trick.

It’s called groupby, a pandas method that allows you to group a DataFrame by a column and then calculate a sum, or any other statistic, for each unique value.

Sections

Group by one field
Group by multiple fields
How to not be wrong

11.1. Group by one field ¶

As we’ve been digging through the data, I’m sure a few questions have popped into your mind. One interesting field in the contributions list is the home state of the contributor. A natural question follows: How much of the money came from outside of California?

If you scroll back up and look carefully at the info command we ran after merging out data, you will noticed it includes a column named contributor_state.

That’s the field we want to group with here. Here’s how you do it.

import pandas as pd
committee_list = pd.read_csv("https://raw.githubusercontent.com/california-civic-data-coalition/first-python-notebook/master/docs/src/_static/committees.csv")
contrib_list = pd.read_csv("https://raw.githubusercontent.com/california-civic-data-coalition/first-python-notebook/master/docs/src/_static/contributions.csv")
my_prop = 'PROPOSITION 064- MARIJUANA LEGALIZATION. INITIATIVE STATUTE.'
my_committees = committee_list[committee_list.prop_name == my_prop]
merged_everything = pd.merge(committee_list, contrib_list, on="calaccess_committee_id")
merged_prop = merged_everything[merged_everything.prop_name == my_prop]
support = merged_prop[merged_prop.committee_position == 'SUPPORT']
oppose = merged_prop[merged_prop.committee_position == 'OPPOSE']

merged_prop.groupby("contributor_state")

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fb508d5c670>

A nice start but you’ll notice you don’t get much back. The data’s been grouped by state, but we haven’t chosen what to do with it yet. We want totals by state, so we can tack on code that will sum the amount field the same way we did earlier for the entire DataFrame.

merged_prop.groupby("contributor_state").amount.sum()

contributor_state
AB           0.00
AL         100.00
AZ         500.00
BC           0.00
CA    17491193.00
CO       39300.00
CT         100.00
DC     2618600.00
DE     6140100.00
FL     1250850.00
GA         100.00
ID        1000.00
IL        7000.00
LA         100.00
MA         600.00
MD        2971.00
MI         400.00
MO         550.00
NC         200.00
NV        1800.00
NY     5446985.00
OH        2950.00
OK         200.00
OR        1400.00
PA     1215475.00
RI        6700.00
TX        1800.00
VA      895243.64
WA       50800.00
Name: amount, dtype: float64

Again our data has come back as an ugly Series. To reformat it as a pretty DataFrame use the reset_index method again.

merged_prop.groupby("contributor_state").amount.sum().reset_index()

	contributor_state	amount
0	AB	0.00
1	AL	100.00
2	AZ	500.00
3	BC	0.00
4	CA	17491193.00
5	CO	39300.00
6	CT	100.00
7	DC	2618600.00
8	DE	6140100.00
9	FL	1250850.00
10	GA	100.00
11	ID	1000.00
12	IL	7000.00
13	LA	100.00
14	MA	600.00
15	MD	2971.00
16	MI	400.00
17	MO	550.00
18	NC	200.00
19	NV	1800.00
20	NY	5446985.00
21	OH	2950.00
22	OK	200.00
23	OR	1400.00
24	PA	1215475.00
25	RI	6700.00
26	TX	1800.00
27	VA	895243.64
28	WA	50800.00

Next re-sort totals from highest to lowest. Remember the sort_values trick we learned earlier? That’ll do it.

merged_prop.groupby("contributor_state").amount.sum().reset_index().sort_values("amount", ascending=False)

	contributor_state	amount
4	CA	17491193.00
8	DE	6140100.00
20	NY	5446985.00
7	DC	2618600.00
9	FL	1250850.00
24	PA	1215475.00
27	VA	895243.64
28	WA	50800.00
5	CO	39300.00
12	IL	7000.00
25	RI	6700.00
15	MD	2971.00
21	OH	2950.00
26	TX	1800.00
19	NV	1800.00
23	OR	1400.00
11	ID	1000.00
14	MA	600.00
17	MO	550.00
2	AZ	500.00
16	MI	400.00
18	NC	200.00
22	OK	200.00
1	AL	100.00
13	LA	100.00
10	GA	100.00
6	CT	100.00
3	BC	0.00
0	AB	0.00

11.2. Group by multiple fields ¶

Finding the top contributors is almost as easy, but since the first and last names are split between two fields we’ll need to submit them to groupby as a list. Copy the last line above, and replace contributor_state with a list.

merged_prop.groupby(["contributor_firstname", "contributor_lastname"]).amount.sum().reset_index().sort_values("amount", ascending=False)

	contributor_firstname	contributor_lastname	amount
483	SEAN	PARKER AND AFFILIATED ENTITIES	7250000.0
482	SEAN	PARKER	1500000.0
281	JULIE	SCHAUER	1364000.0
108	DANIEL	LEWIS	1250000.0
194	HENRY	VAN AMERINGEN	1000000.0
...	...	...	...
430	RICH	GOLDBERG	25.0
358	MARSTON	MEDLOCK	0.0
497	STEPHANIE	WARREN-SAMARIPA	0.0
16	AMIRGHASEM	SAMIMI-ARDESTAN	0.0
136	DAVID	WOOD	0.0

549 rows × 3 columns

Note

You may notice that several of the top contributors appear to be the same person with their name entered in slightly different ways.

This is another important lesson of campaign contributions data. Virtually none of the data is standardized by the campaigns or the government.

The onus is on the analyst to show caution and responsibly combine records where the name fields refer to the same person.

To find out if each contributor supported or opposed the measure, you simply add the committee_position field to our groupby method.

merged_prop.groupby(["contributor_firstname", "contributor_lastname", "committee_position"]).amount.sum().reset_index().sort_values("amount", ascending=False)

	contributor_firstname	contributor_lastname	committee_position	amount
483	SEAN	PARKER AND AFFILIATED ENTITIES	SUPPORT	7250000.0
482	SEAN	PARKER	SUPPORT	1500000.0
281	JULIE	SCHAUER	OPPOSE	1364000.0
108	DANIEL	LEWIS	SUPPORT	1250000.0
194	HENRY	VAN AMERINGEN	SUPPORT	1000000.0
...	...	...	...	...
430	RICH	GOLDBERG	SUPPORT	25.0
358	MARSTON	MEDLOCK	SUPPORT	0.0
497	STEPHANIE	WARREN-SAMARIPA	SUPPORT	0.0
16	AMIRGHASEM	SAMIMI-ARDESTAN	SUPPORT	0.0
136	DAVID	WOOD	SUPPORT	0.0

549 rows × 4 columns

If you want the top supporters or opponents alone, run those same commands with the support and oppose datasets we filtered earlier. Everything else about the commands would be the same as the first one above.

For the supporters:

support.groupby(["contributor_firstname", "contributor_lastname"]).amount.sum().reset_index().sort_values("amount", ascending=False)

	contributor_firstname	contributor_lastname	amount
444	SEAN	PARKER AND AFFILIATED ENTITIES	7250000.0
443	SEAN	PARKER	1500000.0
102	DANIEL	LEWIS	1250000.0
180	HENRY	VAN AMERINGEN	1000000.0
374	NICHOLAS	PRITZKER	900000.0
...	...	...	...
328	MARK	SLABAUGH	25.0
128	DAVID	WOOD	0.0
16	AMIRGHASEM	SAMIMI-ARDESTAN	0.0
458	STEPHANIE	WARREN-SAMARIPA	0.0
331	MARSTON	MEDLOCK	0.0

507 rows × 3 columns

For the opponents:

oppose.groupby(["contributor_firstname", "contributor_lastname"]).amount.sum().reset_index().sort_values("amount", ascending=False)

	contributor_firstname	contributor_lastname	amount
20	JULIE	SCHAUER	1364000.0
31	ROBERT	ADAMS	6500.0
29	MICHAEL	DEAN	5000.0
13	GEORGE	BELL	1550.0
12	FARSHAD	FARDAD	1000.0
17	JESSICA	MACIEL	1000.0
9	EDITH	REUVENI	1000.0
3	CHRISTINE	MILLER	871.0
25	LORI	ROBINSON	817.0
8	DWIGHT	BELL	730.0
23	KOLALEH	TABIBZADEH	500.0
24	KOUROSH	MADDAHI	500.0
0	ANDREW	GRZYWA	500.0
39	THOMAS	DAWSON	500.0
36	RONALD	LAWRENCE	300.0
22	KEN	CORNEY	300.0
27	MARTIN	RYAN	250.0
14	ISRAEL	CARRERO	250.0
30	MIGUEL	COLON JR	200.0
37	SAM	SPIEGEL	200.0
41	WILLIAM	WINTERS	200.0
6	DAVID	DARRIN	200.0
19	JOHN	ROBERTS	200.0
2	CECILIA	GALVIN	150.0
15	JAMES	DAVIS	150.0
26	MAJID	TALEBI	150.0
5	DAN	DESMIDT	150.0
33	ROBERT	HANDY	100.0
40	WILLIAM	MADDEN	100.0
38	SCOTT	SEAMAN	100.0
4	CLARENCE	STEWART	100.0
35	RONALD	JOHNSON	100.0
34	ROBERT	SANDERSON	100.0
1	BILL	KOUSENS	100.0
32	ROBERT	ELSBERG	100.0
7	DAVID	MCCARTNEY	100.0
10	EILEEN	JANDRISEVITS,MS,' BY CADC-I,	100.0
28	MIA	CHUNG	100.0
11	ERIC	NUNEZ	100.0
16	JEREMY	FORBES	100.0
18	JOHN	CALLAS	100.0
21	KAREN	AKAGI	100.0

11.3. How to not be wrong ¶

You’ve done it. Our brief interview is complete and you’ve answered the big question that started our inquiry.

Or so you think! Look again at our rankings above. Now compare them against the ranking we looked at earlier in our sorting lesson.

Study it closely and you’ll see an important difference. All of the contributors without a first name are dropped from our groupby lists. And some of them gave a lot of money.

This is happening because of another pandas quirk. Empty fields are read in by pandas as null values, which is a mathematical representation of nothing. In pandas a null is called a NaN, an abbreviation for “not a number” commonly used in computer programming.

And, bad news, pandas’ groupby method will drop any rows with nulls in the grouping fields. So all those records without a first name were silently excluded from our analysis. Yikes!

Whatever our opinion of the default pandas behavior, it’s something we need to account for, and a reminder that we should never assume we know what computer programming tools are doing under the hood. As with human sources, everything your code tells you should be viewed skeptically and verified.

The solution to this problem is easy. We need to instruct pandas not to drop the null values by adding an extra option to groupby. It’s known as dropna.

merged_prop.groupby(["contributor_firstname", "contributor_lastname"], dropna=False).amount.sum().reset_index().sort_values("amount", ascending=False)

	contributor_firstname	contributor_lastname	amount
483	SEAN	PARKER AND AFFILIATED ENTITIES	7250000.0
580	NaN	FUND FOR POLICY REFORM	6140000.0
581	NaN	FUND FOR POLICY REFORM (NONPROFIT 501 (C)(4))	3940000.0
573	NaN	DRUG POLICY ACTION - NON PROFIT 501C4, YES ON ...	3000000.0
598	NaN	NEW APPROACH PAC (MPO)	2615000.0
...	...	...	...
448	ROBERT	LOCKART	25.0
16	AMIRGHASEM	SAMIMI-ARDESTAN	0.0
358	MARSTON	MEDLOCK	0.0
497	STEPHANIE	WARREN-SAMARIPA	0.0
136	DAVID	WOOD	0.0

620 rows × 3 columns

Now we’ve finally got a ranking we can work with.

11. Group¶

11.1. Group by one field ¶

11.2. Group by multiple fields ¶

11.3. How to not be wrong ¶

First Python Notebook

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Donate/support

11. Group¶

11.1. Group by one field¶

11.2. Group by multiple fields¶

11.3. How to not be wrong¶

11.1. Group by one field ¶

11.2. Group by multiple fields ¶

11.3. How to not be wrong ¶