From 103755fa292aaaa71c0621411a1357413cc851c9 Mon Sep 17 00:00:00 2001
From: GitHub Actions <actions@github.com>
Date: Fri, 20 Dec 2024 16:37:38 +0000
Subject: [PATCH] differences for PR #15

---
 md5sum.txt                        |   2 +-
 working-with-diverse-filetypes.md | 117 ++++++++++++++++++++++++++++++
 2 files changed, 118 insertions(+), 1 deletion(-)

diff --git a/md5sum.txt b/md5sum.txt
index ec87496..c6721ef 100644
--- a/md5sum.txt
+++ b/md5sum.txt
@@ -5,7 +5,7 @@
 "index.md" "447e37bf2e1d5254609dbb0fa9409b16" "site/built/index.md" "2024-10-24"
 "links.md" "8184cf4149eafbf03ce8da8ff0778c14" "site/built/links.md" "2022-04-22"
 "episodes/introduction.md" "1cf3c7fc50b2f6ddaf448fbd9e5f90b1" "site/built/introduction.md" "2024-12-19"
-"episodes/working-with-diverse-filetypes.md" "569a3ff1bf082d0409f6311d340cc418" "site/built/working-with-diverse-filetypes.md" "2024-10-24"
+"episodes/working-with-diverse-filetypes.md" "d6777c1ab156a0ffaa93d6f3f26cb072" "site/built/working-with-diverse-filetypes.md" "2024-12-20"
 "episodes/downloading-files-programmatically.md" "6ccd49246dea3ea14f36411416e4031b" "site/built/downloading-files-programmatically.md" "2024-12-19"
 "episodes/scraping-data.md" "8dd50e6f762a4c0e6595399d026e93aa" "site/built/scraping-data.md" "2024-12-19"
 "episodes/plotting.md" "971edcc43024e78fdbac3a7ad8d7eb48" "site/built/plotting.md" "2024-12-13"
diff --git a/working-with-diverse-filetypes.md b/working-with-diverse-filetypes.md
index fff1e95..fabe7c3 100644
--- a/working-with-diverse-filetypes.md
+++ b/working-with-diverse-filetypes.md
@@ -4,6 +4,8 @@ teaching: 0
 exercises: 0
 ---
 
+Expected duration: 45 min?
+
 :::::::::::::::::::::::::::::::::::::: questions 
 
 - How can I read in different tabular data types to a familiar format in Python?
@@ -29,3 +31,118 @@ transform many kinds of data with similar functions in Python.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
+# `pd.read_xlsx()
+
+TODO:
+recap pd.read methods and imports
+set the scene
+describe the EIA data we're working with.
+pd.read_excel() with no input
+using help() to get the docs.
+
+:::::::: challenge
+
+## Challenge 1: handling gnarly 
+
+Using `pd.read_excel()`, read in the first sheet ("Page 1 Energy Storage") using the `skiprows` parameter to select the column header row. 
+
+:::: solution
+
+```python
+import pandas as pd
+
+excel_923 = pd.read_excel('data/eia923_2022.xlsx', sheet_name=0, skiprows=)
+```
+
+::::
+
+::::::::
+
+# `pd.read_json()
+
+TODO:
+What is a json file and when might you see it
+Try to use straight pd.read_json
+When might you need to load a JSON first - nested JSONs.
+Practice with the warnings table.
+FLAG as decision - teach json_normalize() or no?
+Drill down through the dictionary, this is basically what the param is doing under the hood.
+
+:::::::: challenge
+
+## Challenge 2: handling nested JSONs
+
+Using `json.load()` and Pandas, read in the `data` from the `eia923_2022.json` file into a Pandas DataFrame.
+
+:::: solution
+
+```python
+import pandas as pd
+import json
+
+# First, read in the file
+import json
+with open('data/eia923_2022.json') as file:
+    eia923_json = json.load(file)
+
+eia923_json = pd.json_normalize(eia923_json, record_path = ['response', 'data'])
+
+# OR
+eia923_json = pd.DataFrame(eia923_json['response']['data'])
+
+```
+
+::::
+
+::::::::
+
+TODO:
+talk a bit about deeper nesting
+
+# `pd.read_xml()
+
+TODO:
+What is an XML file and when might you see it
+What's different from JSONs?
+
+:::::::: challenge
+
+## Challenge 3: unpacking XML files
+### QUESTION - any way to make this more exciting?
+
+Using `pd.read_xml()`, read in the `data` from the `eia923_2022.xml` file into a Pandas DataFrame.
+
+:::: solution
+
+```python
+import pandas as pd
+
+eia923_xml = pd.read_xml('data/eia923_2022.xml', xpath = '//response/data/row')
+
+```
+::::
+
+::::::::
+
+# `pd.read_parquet()
+
+TODO:
+What is a Parquet file and when might you see it
+Just demo pd.read_parquet, no need for a challenge here.
+
+`pd.read_parquet('data/eia923_2022.parquet)`
+
+:::::::: challenge
+
+Pick two datasets we've just read in, and compare them. How are they similar, and how are they different? Share your reflections with a peer.
+
+:::: hint
+
+* Inspect a column in a DataFrame `df` by using `df[column_name]`.
+* To quickly see what values are contained in a column, you can use `df[column_name].unique()` to get a list of unique values in the column.
+* Try using `df.iloc[0]` to get the values from the first row of the data.
+* `df.head(n)` returns the first n rows of the data, and `df.tail(n)` returns the last n rows.
+* to add - isin()??
+::::
+
+::::::::
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