Interactive Session

🐼 Reading Data into pandas

A panda, reading. MidJourney 5

Yesterday you ran pd.read_csv() and a DataFrame appeared. Today you slow that line down and learn what it actually does, what it gives you back, and how to steer it. Everything this week starts with getting data into Python correctly, so this is the most load-bearing hour of the course.

By the end of this session you will be able to:

Getting Started

  1. Create a new notebook:

    • Open the Command Palette with Ctrl + Shift + P (Cmd + Shift + P on macOS) and run Create: New Jupyter Notebook.
    • Confirm the Kernel Selector in the notebook’s action bar reads eds217_2026.

  2. Save your notebook (Ctrl + S, or Cmd + S on macOS) as: Session_2A_Reading_Data.ipynb

  3. Add a title cell (Markdown), updating the date to today:

# Day 2: Session 2A - Reading Data into pandas

[Session Webpage](https://eds-217-essential-python.github.io/course-materials/interactive-sessions/2a_reading_data.html)

Date: 09/01/2026
  1. Add a code cell below the title cell.
Caution

Save your work frequently with Ctrl+S (Cmd+S on macOS).

Importing pandas

Every notebook that touches data starts the same way:

Code 
import pandas as pd

import pandas makes the library available. as pd gives it a short nickname so you can write pd.read_csv() instead of pandas.read_csv(). Essentially every pandas user on earth writes pd, so we will too.

Our data for today

We’ll work with air quality measurements from an OpenAQ monitoring station in Goleta, just up the road from Bren.

Code 
url = 'https://eds-217-essential-python.github.io/data/openaq_goleta_measurments.csv'

Notice we put the address in a variable first. This is a habit worth forming: the URL is long, you’ll use it more than once, and naming it keeps your read_csv line readable.

pd.read_csv()

Here is the whole sentence:

Code 
goleta = pd.read_csv(url)

That’s it. pd.read_csv() takes a location, goes and gets the file, parses the commas, and hands you back a DataFrame. Let’s look at the first few rows:

Code 
goleta.head()
location_id location_name parameter value unit datetimeUtc datetimeLocal timezone latitude longitude country_iso isMobile isMonitor owner_name provider
0 1186 Goleta o3 0.025 ppm 2024-07-12T01:00:00+00:00 2024-07-11T18:00:00-07:00 America/Los_Angeles 34.445301 -119.827797 NaN NaN NaN Unknown Governmental Organization AirNow
1 1186 Goleta o3 0.028 ppm 2024-07-12T02:00:00+00:00 2024-07-11T19:00:00-07:00 America/Los_Angeles 34.445301 -119.827797 NaN NaN NaN Unknown Governmental Organization AirNow
2 1186 Goleta o3 0.029 ppm 2024-07-12T03:00:00+00:00 2024-07-11T20:00:00-07:00 America/Los_Angeles 34.445301 -119.827797 NaN NaN NaN Unknown Governmental Organization AirNow
3 1186 Goleta o3 0.027 ppm 2024-07-12T04:00:00+00:00 2024-07-11T21:00:00-07:00 America/Los_Angeles 34.445301 -119.827797 NaN NaN NaN Unknown Governmental Organization AirNow
4 1186 Goleta o3 0.026 ppm 2024-07-12T05:00:00+00:00 2024-07-11T22:00:00-07:00 America/Los_Angeles 34.445301 -119.827797 NaN NaN NaN Unknown Governmental Organization AirNow
Note

🐍 read_csv is happy with either a web address or a path to a file on disk. These two are the same idea:

df = pd.read_csv('https://example.org/data.csv')   # from the web
df = pd.read_csv('data/measurements.csv')          # from a folder next to your notebook

This week we mostly read from the web, because then everyone in the room has exactly the same file.

✏️ Test your knowledge

The Santa Barbara station’s data is at the same address, but with openaq_santa_barbara_measurments.csv on the end. Read it into a variable called santa_barbara and display its first few rows.

DataFrames and Series

A DataFrame is a table: rows and named columns, like a well-behaved spreadsheet.

Pull a single column out and you get a Series, which is one column of values with the row labels still attached:

Code 
goleta['value']
0        0.025
1        0.028
2        0.029
3        0.027
4        0.026
         ...  
1957    10.000
1958     8.000
1959     8.000
1960     9.000
1961     5.000
Name: value, Length: 1962, dtype: float64

Check the types to see the difference plainly:

Code 
print(type(goleta))
print(type(goleta['value']))
<class 'pandas.core.frame.DataFrame'>
<class 'pandas.core.series.Series'>

This distinction matters more than it looks. Many pandas methods return a Series, and knowing you’re holding one column rather than a table tells you what you can do next.

Code 
goleta['value'].head()
0    0.025
1    0.028
2    0.029
3    0.027
4    0.026
Name: value, dtype: float64
Note

🐍 Square brackets with one column name give you a Series. Square brackets with a list of column names give you back a DataFrame, even if the list has one item:

goleta['value']       # Series
goleta[['value']]     # DataFrame with one column
✏️ Test your knowledge

Pull out the parameter column. Confirm with type() that you have a Series, then display its first five values.

Column names as a list

You often want to know what’s in a table before you do anything else. The .columns attribute tells you:

Code 
goleta.columns
Index(['location_id', 'location_name', 'parameter', 'value', 'unit',
       'datetimeUtc', 'datetimeLocal', 'timezone', 'latitude', 'longitude',
       'country_iso', 'isMobile', 'isMonitor', 'owner_name', 'provider'],
      dtype='object')

That’s a pandas object. To get a plain Python list, which is easier to read and to work with, use .tolist():

Code 
column_names = goleta.columns.tolist()
column_names
['location_id',
 'location_name',
 'parameter',
 'value',
 'unit',
 'datetimeUtc',
 'datetimeLocal',
 'timezone',
 'latitude',
 'longitude',
 'country_iso',
 'isMobile',
 'isMonitor',
 'owner_name',
 'provider']

A list is Python’s basic container for an ordered collection of things. You write one with square brackets and commas:

Code 
parameters_we_care_about = ['o3', 'pm25']
parameters_we_care_about
['o3', 'pm25']

You get items out by position, counting from zero:

Code 
print(column_names[0])
print(column_names[1])
location_id
location_name

And len() tells you how many items there are:

Code 
len(column_names)
15
Note

🐍 Counting from zero trips up everyone at first. The first item is at position 0, the second at 1, and the last one is at len(x) - 1. You can also count backwards: [-1] is the last item.

Code 
column_names[-1]
'provider'

Selecting columns with a list

Here’s where the list pays off. Hand read_csv’s result a list of column names and you get a smaller DataFrame with just those columns:

Code 
keep = ['location_name', 'parameter', 'value', 'unit']
goleta[keep].head()
location_name parameter value unit
0 Goleta o3 0.025 ppm
1 Goleta o3 0.028 ppm
2 Goleta o3 0.029 ppm
3 Goleta o3 0.027 ppm
4 Goleta o3 0.026 ppm

You’ll see this written both ways. The inner brackets are the list, the outer brackets are the selection:

Code 
goleta[['location_name', 'parameter', 'value', 'unit']].head()
location_name parameter value unit
0 Goleta o3 0.025 ppm
1 Goleta o3 0.028 ppm
2 Goleta o3 0.029 ppm
3 Goleta o3 0.027 ppm
4 Goleta o3 0.026 ppm
✏️ Test your knowledge

Build a list called time_columns containing 'datetimeUtc' and 'datetimeLocal', then use it to display just those two columns from goleta.

Choosing the index with index_col=

Every DataFrame has an index: the labels down the left-hand side. By default pandas numbers the rows 0, 1, 2, .... You saw those numbers in head().

Sometimes a column in the file is a better label than a row number. index_col= tells read_csv to use one:

Code 
goleta_by_time = pd.read_csv(url, index_col='datetimeLocal')
goleta_by_time.head()
location_id location_name parameter value unit datetimeUtc timezone latitude longitude country_iso isMobile isMonitor owner_name provider
datetimeLocal
2024-07-11T18:00:00-07:00 1186 Goleta o3 0.025 ppm 2024-07-12T01:00:00+00:00 America/Los_Angeles 34.445301 -119.827797 NaN NaN NaN Unknown Governmental Organization AirNow
2024-07-11T19:00:00-07:00 1186 Goleta o3 0.028 ppm 2024-07-12T02:00:00+00:00 America/Los_Angeles 34.445301 -119.827797 NaN NaN NaN Unknown Governmental Organization AirNow
2024-07-11T20:00:00-07:00 1186 Goleta o3 0.029 ppm 2024-07-12T03:00:00+00:00 America/Los_Angeles 34.445301 -119.827797 NaN NaN NaN Unknown Governmental Organization AirNow
2024-07-11T21:00:00-07:00 1186 Goleta o3 0.027 ppm 2024-07-12T04:00:00+00:00 America/Los_Angeles 34.445301 -119.827797 NaN NaN NaN Unknown Governmental Organization AirNow
2024-07-11T22:00:00-07:00 1186 Goleta o3 0.026 ppm 2024-07-12T05:00:00+00:00 America/Los_Angeles 34.445301 -119.827797 NaN NaN NaN Unknown Governmental Organization AirNow

The local timestamp is now the row label, and it’s no longer one of the columns:

Code 
goleta_by_time.columns.tolist()
['location_id',
 'location_name',
 'parameter',
 'value',
 'unit',
 'datetimeUtc',
 'timezone',
 'latitude',
 'longitude',
 'country_iso',
 'isMobile',
 'isMonitor',
 'owner_name',
 'provider']
Note

🐍 index_col= is our first keyword argument: an argument you pass by name rather than by position. read_csv has dozens of them, and you’ll meet a few more this week. The pattern is always name=value, after the required arguments.

Choosing an index

An index is most useful when its values are meaningful and unique. location_name would be a poor choice here, since every row has the same station name. A timestamp is a good choice, because it identifies the row.

✏️ Test your knowledge

Read the Goleta file again, this time using datetimeUtc as the index, into a variable called goleta_utc. How many columns does it have compared to the original goleta?

Putting it together

You now have the first sentence of every workflow you’ll write:

import pandas as pd

url = 'https://eds-217-essential-python.github.io/data/some_file.csv'
df = pd.read_csv(url)
df.head()

Key points

  • import pandas as pd once, at the top of every notebook.
  • pd.read_csv(location) reads a CSV from a URL or a file path and returns a DataFrame.
  • df['col'] gives a Series (one column). df[['a', 'b']] gives a DataFrame.
  • df.columns.tolist() gives you the column names as a plain Python list.
  • Lists are ordered, written with [], indexed from 0, and measured with len().
  • index_col='name' chooses which column becomes the row labels.

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