Clean up groupby

docs/src/groupby.md

@@ -0,0 +1,131 @@
+---
+jupytext:
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: '0.8'
+    jupytext_version: '1.4.1'
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
+```{include} ./_templates/nav.html
+```
+
+# Groupby
+
+To take the next steps in our investigation, we’ll need to learn a new trick.
+
+It’s called [`groupby`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html), 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.
+
+```{contents} Sections
+  :depth: 1
+  :local:
+```
+
+## 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 as 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.
+
+```{code-cell}
+:tags: [hide-cell]
+
+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']
+```
+
+```{code-cell}
+merged_prop.groupby("contributor_state")
+```
+
+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.
+
+```{code-cell}
+merged_prop.groupby("contributor_state").amount.sum()
+```
+
+Again our data has come back as an ugly Series. To reformat it as a pretty DataFrame use the `reset_index` method again.
+
+```{code-cell}
+merged_prop.groupby("contributor_state").amount.sum().reset_index()
+```
+
+```{note}
+If you're into databases and SQL, [groupby](https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_sql.html#group-by) may sound familiar.
+```
+
+Next re-sort totals from highest to lowest. Remember the [`sort_values`](sorting.md) trick we learned earlier? That’ll do it.
+
+```{code-cell}
+merged_prop.groupby("contributor_state").amount.sum().reset_index().sort_values("amount", ascending=False)
+```
+
+## 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.
+
+```{code-cell}
+merged_prop.groupby(["contributor_firstname", "contributor_lastname"]).amount.sum().reset_index().sort_values("amount", ascending=False)
+```
+
+You should be noticing 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.
+
+```{code-cell}
+merged_prop.groupby(["contributor_firstname", "contributor_lastname", "committee_position"]).amount.sum().reset_index().sort_values("amount", ascending=False)
+```
+
+If you want the top supporters or opponents alone, run those same commands with the `support` and `oppose` datasets we [filtered down to earlier](filters.md). Everything else about the commands would be the same as the first one above.
+
+For the supporters:
+
+```{code-cell}
+support.groupby(["contributor_firstname", "contributor_lastname"]).amount.sum().reset_index().sort_values("amount", ascending=False)
+```
+
+For the opponents:
+
+```{code-cell}
+oppose.groupby(["contributor_firstname", "contributor_lastname"]).amount.sum().reset_index().sort_values("amount", ascending=False)
+```
+
+## 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](sorting.md).
+
+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](<https://en.wikipedia.org/wiki/Null_(mathematics)>), which is a mathematical representation of nothing. In pandas a null is called a [NaN](https://en.wikipedia.org/wiki/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`.
+
+
+```{code-cell}
+merged_prop.groupby(["contributor_firstname", "contributor_lastname"], dropna=False).amount.sum().reset_index().sort_values("amount", ascending=False)
+```
+
+Now we've finally got a ranking we can work with. 

docs/src/index.md

@@ -37,7 +37,7 @@ filters
 merge
 totals
 sorting
-groupby/index
+groupby
 compute
 remix/index
 charts/index