First Python Notebook

4. Data

In 2018, the Los Angeles Times published an investigation headlined, “The Robinson R44, the world’s best-selling civilian helicopter, has a long history of deadly crashes.”

It reported that Robinson’s R44 led all major models with the highest fatal accident rate from 2006 to 2016. The analysis was published on GitHub as a series of Jupyter notebooks.

The findings were drawn from two key datasets:

1. The National Transportation Safety Board’s Aviation Accident Database

2. The Federal Aviation Administration’s General Aviation and Part 135 Activity Survey

After a significant amount of work gathering and cleaning the source data, the number of accidents for each helicopter model were normalized using the flight hour estimates in the survey. For the purposes of this demonstration, we will read in tidied versions of each file that are ready for analysis.

The data are structured in rows of comma-separated values. This is known as a CSV file. It is the most common way you will find data published online. The pandas library is able to read in files from a variety formats, including CSV.

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import pandas as pd

4.1. The read_csv method

Scroll down to the first open cell. There we will import the first CSV file using the read_csv function included with pandas.

pd.read_csv("https://raw.githubusercontent.com/palewire/first-python-notebook/main/docs/src/_static/ntsb-accidents.csv")

	event_id	ntsb_make	ntsb_model	ntsb_number	year	date	city	state	country	total_fatalities	latimes_make	latimes_model	latimes_make_and_model
0	20061222X01838	BELL	407	NYC07FA048	2006	12/14/2006 00:00:00	DAGSBORO	DE	USA	2	BELL	407	BELL 407
1	20060817X01187	ROBINSON	R22 BETA	LAX06LA257	2006	08/10/2006 00:00:00	TUCSON	AZ	USA	1	ROBINSON	R22	ROBINSON R22
2	20060111X00044	ROBINSON	R44	MIA06FA039	2006	01/01/2006 00:00:00	GRAND RIDGE	FL	USA	3	ROBINSON	R44	ROBINSON R44
3	20060419X00461	ROBINSON	R44 II	DFW06FA102	2006	04/13/2006 00:00:00	FREDERICKSBURG	TX	USA	2	ROBINSON	R44	ROBINSON R44
4	20060208X00181	ROBINSON	R44	SEA06LA052	2006	02/06/2006 00:00:00	HELENA	MT	USA	1	ROBINSON	R44	ROBINSON R44
...	...	...	...	...	...	...	...	...	...	...	...	...	...
158	20160711X32921	BELL HELICOPTER TEXTRON CANADA	407	ERA16FA248	2016	07/11/2016 00:00:00	HICKORY	KY	USA	1	BELL	407	BELL 407
159	20160804X45514	SCHWEIZER	269C 1	CEN16FA304	2016	08/03/2016 00:00:00	JEANERETTE	LA	USA	1	SCHWEIZER	269	SCHWEIZER 269
160	20160404X74644	BELL	206	ERA16FA144	2016	04/04/2016 00:00:00	PIGEON FORGE	TN	USA	5	BELL	206	BELL 206
161	20160507X31120	AIRBUS	AS350	ANC16FA023	2016	05/06/2016 00:00:00	SKAGWAY	AK	USA	1	AIRBUS	350	AIRBUS 350
162	20160612X85856	ROBINSON HELICOPTER COMPANY	R44 II	CEN16FA215	2016	06/12/2016 00:00:00	JONESBORO	AR	USA	1	ROBINSON	R44	ROBINSON R44

163 rows × 13 columns

Warning

You need the exact URL shared in the example to access the file. While you could laboriously type it out, feel free to copy and paste it into your notebook.

After you run the cell, you should see a big table output to your notebook. It is a “DataFrame” where pandas has structured the CSV data into rows and columns, just like Excel or other spreadsheet software might. Take a moment to look at the columns and rows in the output, which contain the data we’ll use in our analysis.

Note

On the left-hand side, you’ll see a bolded number incrementing upward from zero that’s not present in our source data file. This is what pandas calls the index. It is a separate column created automatically and used to identify each row. The index is not considered part of the data, but it is used to reference the rows of the DataFrame or Series in advanced operations that are beyond the scope of this class.

A major advantage of Jupyter over spreadsheets is that rather than manipulating the data through a haphazard series of clicks and keypunches, we will be gradually grinding it down using a computer programming script that is transparent and reproducible.

To do more with your DataFrame, we need to store it so it can be reused in subsequent cells. We can do this by saving it in a variable, just as we did in with our number in Chapter 2.

Go back to your latest cell and change it to this. Rerun it.

accident_list = pd.read_csv("https://raw.githubusercontent.com/palewire/first-python-notebook/main/docs/src/_static/ntsb-accidents.csv")

You shouldn’t see anything. That’s a good thing. It means our DataFrame has been saved under the name accident_list, which we can now begin interacting with in the following cells.

We can do this by calling “methods” that pandas makes available to all DataFrames. You may not have known it at the time, but read_csv is one of these methods. There are dozens more that can do all sorts of interesting things. Let’s start with some easy ones that analysts use all the time.

4.2. The head method

To preview the first few rows of the dataset, try the head method. Add a new cell, type this in and hit the play button again.

accident_list.head()

	event_id	ntsb_make	ntsb_model	ntsb_number	year	date	city	state	country	total_fatalities	latimes_make	latimes_model	latimes_make_and_model
0	20061222X01838	BELL	407	NYC07FA048	2006	12/14/2006 00:00:00	DAGSBORO	DE	USA	2	BELL	407	BELL 407
1	20060817X01187	ROBINSON	R22 BETA	LAX06LA257	2006	08/10/2006 00:00:00	TUCSON	AZ	USA	1	ROBINSON	R22	ROBINSON R22
2	20060111X00044	ROBINSON	R44	MIA06FA039	2006	01/01/2006 00:00:00	GRAND RIDGE	FL	USA	3	ROBINSON	R44	ROBINSON R44
3	20060419X00461	ROBINSON	R44 II	DFW06FA102	2006	04/13/2006 00:00:00	FREDERICKSBURG	TX	USA	2	ROBINSON	R44	ROBINSON R44
4	20060208X00181	ROBINSON	R44	SEA06LA052	2006	02/06/2006 00:00:00	HELENA	MT	USA	1	ROBINSON	R44	ROBINSON R44

It serves up the first five rows by default. If you want a different number, submit it as an input.

accident_list.head(1)

	event_id	ntsb_make	ntsb_model	ntsb_number	year	date	city	state	country	total_fatalities	latimes_make	latimes_model	latimes_make_and_model
0	20061222X01838	BELL	407	NYC07FA048	2006	12/14/2006 00:00:00	DAGSBORO	DE	USA	2	BELL	407	BELL 407

4.3. The info method

To get a look at all of the columns and what type of data they store, add another cell and try the info method. Look carefully at the results and you’ll see we have 163 fatal accidents to review.

accident_list.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 163 entries, 0 to 162
Data columns (total 13 columns):
 #   Column                  Non-Null Count  Dtype 
---  ------                  --------------  ----- 
 0   event_id                163 non-null    object
 1   ntsb_make               163 non-null    object
 2   ntsb_model              163 non-null    object
 3   ntsb_number             163 non-null    object
 4   year                    163 non-null    int64 
 5   date                    163 non-null    object
 6   city                    163 non-null    object
 7   state                   162 non-null    object
 8   country                 163 non-null    object
 9   total_fatalities        163 non-null    int64 
 10  latimes_make            163 non-null    object
 11  latimes_model           163 non-null    object
 12  latimes_make_and_model  163 non-null    object
dtypes: int64(2), object(11)
memory usage: 16.7+ KB

Now that you’ve got some data imported, we’re ready to begin our analysis.