Overview

Teaching: 30 min
Exercises: 20 min

Questions

• How can I import data in Python?

• What is Pandas?

• Why should I use Pandas to work with data?

Objectives

• Navigate the workshop directory and download a dataset.

• Explain what a library is and what libraries are used for.

• Describe what the Python Data Analysis Library (Pandas) is.

• Load the Python Data Analysis Library (Pandas).

• Use read_csv to read tabular data into Python.

• Describe what a DataFrame is in Python.

• Access and summarize data stored in a DataFrame.

• Define indexing as it relates to data structures.

• Perform basic mathematical operations and summary statistics on data in a Pandas DataFrame.

• Create simple plots.

Working With Pandas DataFrames in Python

We can automate the process of performing data manipulations in Python. It’s efficient to spend time building the code to perform these tasks because once it’s built, we can use it over and over on different datasets that use a similar format. This makes our methods easily reproducible. We can also easily share our code with colleagues and they can replicate the same analysis.

Starting in the same spot

To help the lesson run smoothly, let’s ensure everyone is in the same directory. This should help us avoid path and file name issues. At this time please navigate to the workshop directory. If you are working in Jupyter Notebook be sure that you start your notebook in the workshop directory.

A quick aside that there are Python libraries like OS Library that can work with our directory structure, however, that is not our focus today.

Our Data

For this lesson, we will be using a subset of data from an ongoing study called Abundance, size and fishing effort for California Spiny Lobster (Panulirus interruptus); provided by SBC LTER: Reef (Santa Barbara Coastal, Long Term Ecological Research). This data contains records on the abundance and size of California spiny lobster along the mainland coast of the Santa Barbara Channel. Spiny lobsters are an important predator in giant kelp forests off southern California. The sites where the observations where recorded are called Marine Protected Areas (MPA), where SBC: LTER and affiliates are able to investigate the effects of fishing on kelp forest community dynamics.

We are studying the size and abundance of lobsters recorded in sites in our study area. The dataset is stored as a .csv file: each row holds information for a single animal, and the columns represent:

The first few rows of our first file look like this:

,record_id,site,transect,replicate,size_mm,year,month,date
1,IVEE,1,A,,0,300,2012,8,2012-08-20,1
2,IVEE,5,B,62.0,0,300,2012,8,2012-08-20,2
3,IVEE,5,B,86.0,0,300,2012,8,2012-08-20,3
4,IVEE,5,B,105.0,0,300,2012,8,2012-08-20,4
5,IVEE,5,C,,0,300,2012,8,2012-08-20,5
6,IVEE,5,D,,0,300,2012,8,2012-08-20,6
7,IVEE,6,A,,0,300,2012,8,2012-08-20,7
8,IVEE,6,B,57.0,0,300,2012,8,2012-08-20,8
9,IVEE,6,B,60.0,0,300,2012,8,2012-08-20,9
10,IVEE,6,C,,0,300,2012,8,2012-08-20,10

About Libraries

A library in Python contains a set of tools (called functions) that perform tasks on our data. Importing a library is like getting a piece of lab equipment out of a storage locker and setting it up on the bench for use in a project. Once a library is set up, it can be used or called to perform the task(s) it was built to do.

Pandas in Python

One of the best options for working with tabular data in Python is to use the Python Data Analysis Library (a.k.a. Pandas). The Pandas library provides data structures, produces high quality plots with matplotlib and integrates nicely with other libraries that use NumPy (which is another Python library) arrays.

Python doesn’t load all of the libraries available to it by default. We have to add an import statement to our code in order to use library functions. To import a library, we use the syntax import libraryName. If we want to give the library a nickname to shorten the command, we can add as nickNameHere. An example of importing the pandas library using the common nickname pd is below.

import pandas as pd

Each time we call a function that’s in a library, we use the syntax LibraryName.FunctionName. Adding the library name with a . before the function name tells Python where to find the function. In the example above, we have imported Pandas as pd. This means we don’t have to type out pandas each time we call a Pandas function.

Reading CSV Data Using Pandas

We will begin by locating and reading our lobster data which are in CSV format. CSV stands for Comma-Separated Values and is a common way to store formatted data. Other symbols may also be used, so you might see tab-separated, colon-separated or space separated files. It is quite easy to replace one separator with another, to match your application. The first line in the file often has headers to explain what is in each column. CSV (and other separators) make it easy to share data, and can be imported and exported from many applications, including Microsoft Excel. For more details on CSV files, see the Data Organisation in Spreadsheets lesson. We can use Pandas’ read_csv function to pull the file directly into a DataFrame.

So What’s a DataFrame?

A DataFrame is a 2-dimensional data structure that can store data of different types (including characters, integers, floating point values, factors and more) in columns. It is similar to a spreadsheet or an SQL table or the data.frame in R. A DataFrame always has an index (0-based). An index refers to the position of an element in the data structure.

# Note that pd.read_csv is used because we imported pandas as pd
pd.read_csv("data/lobsters_data.csv")

The above command yields the output below:

       record_id  site  transect replicate  size_mm  year  month        date
0              1  IVEE         1         A      NaN  2012      8  2012-08-20
1              2  IVEE         5         B     62.0  2012      8  2012-08-20
2              3  IVEE         5         B     86.0  2012      8  2012-08-20
3              4  IVEE         5         B    105.0  2012      8  2012-08-20
4              5  IVEE         5         C      NaN  2012      8  2012-08-20
  ...   ...       ...       ...      ...   ...    ...         ...
10698      10699  NAPL         2         C    101.0  2021      8  2021-08-26
10699      10700  NAPL         2         D     75.0  2021      8  2021-08-26
10700      10701  NAPL         2         D    136.0  2021      8  2021-08-26
10701      10702  NAPL         2         C     85.0  2021      8  2021-08-26
10702      10703  NAPL         1         C     66.0  2021      8  2021-08-26

[10703 rows x 8 columns]

We can see that there were over 10,000 rows parsed. Each row has 8 columns. The first column is the index of the DataFrame. The index is used to identify the position of the data, but it is not an actual column of the DataFrame. It looks like the read_csv function in Pandas read our file properly. However, we haven’t saved any data to memory so we can work with it. We need to assign the DataFrame to a variable. Remember that a variable is a name for a value, such as x, or data. We can create a new object with a variable name by assigning a value to it using =.

Let’s call the imported lobster data lobsters_df:

lobsters_df = pd.read_csv("data/lobsters_data.csv")

Notice when you assign the imported DataFrame to a variable, Python does not produce any output on the screen. We can view the value of the lobsters_df object by typing its name into the Python command prompt.

lobsters_df

which prints contents like above.

Note: if the output is too wide to print on your narrow terminal window, you may see something slightly different as the large set of data scrolls past. You may see simply the last column of data.

Never fear, all the data is there, if you scroll up. Selecting just a few rows, so it is easier to fit on one window, you can see that pandas has neatly formatted the data to fit our screen:

lobsters_df.head() # The head() method displays the first several lines of a file. It
                  # is discussed below.

    record_id site  transect replicate  size_mm  year  month        date
0          1  IVEE         1         A      NaN  2012      8  2012-08-20
1          2  IVEE         5         B     62.0  2012      8  2012-08-20
2          3  IVEE         5         B     86.0  2012      8  2012-08-20
3          4  IVEE         5         B    105.0  2012      8  2012-08-20
4          5  IVEE         5         C      NaN  2012      8  2012-08-20

Exploring Our Lobster Data

Again, we can use the type function to see what kind of thing lobsters_df is:

type(lobsters_df)

<class 'pandas.core.frame.DataFrame'>

As expected, it’s a DataFrame (or, to use the full name that Python uses to refer to it internally, a pandas.core.frame.DataFrame).

What kind of things does lobsters_df contain? DataFrames have an attribute called dtypes that answers this:

lobsters_df.dtypes

record_id      int64
site          object
transect       int64
replicate     object
size_mm      float64
year           int64
month          int64
date          object
dtype: object

All the values in a column have the same type. For example, months have type int64, which is a kind of integer. Cells in the month column cannot have fractional values, but the size_mm column can, because it has type float64. The object type doesn’t have a very helpful name, but in this case it represents strings (such as ‘A’ and ‘B’ in the case of replicate).

We’ll talk a bit more about what the different formats mean in a different lesson.

Useful Ways to View DataFrame objects in Python

There are many ways to summarize and access the data stored in DataFrames, using attributes and methods provided by the DataFrame object.

To access an attribute, use the DataFrame object name followed by the attribute name df_object.attribute. Using the DataFrame lobsters_df and attribute columns, an index of all the column names in the DataFrame can be accessed with lobsters_df.columns.

Methods are called in a similar fashion using the syntax df_object.method(). As an example, lobsters_df.head() gets the first few rows in the DataFrame lobsters_df using the head() method. With a method, we can supply extra information in the parens to control behaviour.

Let’s look at the data using these.

Challenge - DataFrames

Using our DataFrame lobsters_df, try out the attributes & methods below to see what they return.

1. lobsters_df.columns

2. lobsters_df.shape Take note of the output of shape - what format does it return the shape of the DataFrame in?

   HINT: More on tuples, here.

3. lobsters_df.head() Also, what does lobsters_df.head(15) do?
4. lobsters_df.tail()

Calculating Statistics From Data In A Pandas DataFrame

We’ve read our data into Python. Next, let’s perform some quick summary statistics to learn more about the data that we’re working with. We might want to know how many animals were collected in each site, or how many of each species were caught. We can perform summary stats quickly using groups. But first we need to figure out what we want to group by.

Let’s begin by exploring our data:

# Look at the column names
lobsters_df.columns

which returns:

Index(['record_id', 'site', 'transect', 'replicate', 'size_mm', 'year',
       'month', 'date'],
      dtype='object')

Let’s get a list of all the species. The pd.unique function tells us all of the unique values in the replicate column.

pd.unique(lobsters_df['replicate'])

which returns:

array(['A', 'B', 'C', 'D'], dtype=object)

Challenge - Statistics

1. Create a list of unique site ID’s (“site”) found in the data. Call it site_names. How many unique sites are there in the data? How many unique species are in the data?

2. What is the difference between len(site_names) and lobsters_df['site'].nunique()?

Groups in Pandas

We often want to calculate summary statistics grouped by subsets or attributes within fields of our data. For example, we might want to calculate the average size of all individuals per site.

We can calculate basic statistics for all records in a single column using the syntax below:

lobsters_df['size_mm'].describe()

gives output

count    10237.000000
mean        74.927029
std         13.369367
min         18.000000
25%         68.000000
50%         75.000000
75%         83.000000
max        230.000000
Name: size_mm, dtype: float64

We can also extract one specific metric if we wish:

lobsters_df['size_mm'].min()
lobsters_df['size_mm'].max()
lobsters_df['size_mm'].mean()
lobsters_df['size_mm'].std()

But if we want to summarize by one or more variables, for example sex, we can use Pandas’ .groupby method. Once we’ve created a groupby DataFrame, we can quickly calculate summary statistics by a group of our choice.

# Group data by site
grouped_data = lobsters_df.groupby('site')

The pandas function describe will return descriptive stats including: mean, median, max, min, std and count for a particular column in the data. Pandas’ describe function will only return summary values for columns containing numeric data.

# Summary statistics for all numeric columns by sex
grouped_data.describe()
# Provide the mean for each numeric column by sex
grouped_data.mean()

grouped_data.mean() OUTPUT:

        record_id  transect    size_mm         year     month
site                                                         
AQUE  3408.500724  4.191027  72.868283  2016.079595  8.167873
CARP  5599.795430  4.259193  73.737765  2017.729382  8.000000
IVEE  5876.226540  4.251896  76.116947  2018.083649  8.001422
MOHK  4882.804332  1.512635  68.628978  2016.854874  8.000000
NAPL  4783.184932  4.267746  80.322931  2017.016812  8.000000

The groupby command is powerful in that it allows us to quickly generate summary stats.

Challenge - Summary Data

1. Which site has the highest count of observed lobsters? Which has the lowest?
2. What happens when you group by two columns using the following syntax and then calculate mean values?
   • grouped_data2 = lobsters_df.groupby(['site', 'replicate'])
   • grouped_data2.mean()
3. Summarize size values for each site in your data. HINT: you can use the following syntax to only create summary statistics for one column in your data. by_site['size_mm"].describe()

Did you get #3 right?

A Snippet of the Output from challenge 3 looks like:

        count       mean        std   min   25%   50%   75%    max
 site                                                             
 CARP  2738.0  73.737765  11.737286  32.0  67.0  75.0  81.0  183.0
         ...

Quickly Creating Summary Counts in Pandas

Let’s next count the number of samples for each species. We can do this in a few ways, but we’ll use groupby combined with a count() method.

# Count the number of samples by year
year_counts = lobsters_df.groupby('year')['record_id'].count()
print(year_counts)

Or, we can also count just the rows that have the year 2021:

lobsters_df.groupby('year')['record_id'].count()[2021]

Challenge - Make a list

What’s another way to create a list of years and associated count of lobsters in the data? records in the data? Hint: you can perform count, min, etc. functions on groupby DataFrames in the same way you can perform them on regular DataFrames.

Basic Math Functions

If we wanted to, we could perform math on an entire column of our data. For example let’s multiply all size values by 10. A more practical use of this might be to normalize the data according to a mean, area, or some other value calculated from our data.

# Multiply all size values by 10
lobsters_df['size_mm']*10

Quick & Easy Plotting Data Using Pandas

We can plot our summary stats using Pandas, too.

# Make sure figures appear inline in Ipython Notebook
%matplotlib inline
# Create a quick bar chart
year_counts.plot(kind='bar');

Count per species site

We can also look at how many lobsters were counted in each site:

site_transect = lobsters_df.groupby('site')['record_id'].nunique()
# Let's plot that too
site_transect.plot(kind= 'bar');

Challenge - Plots

1. Create a plot of average size (mm) per year.
2. Create a plot of total lobster counts at each site for the entire dataset.

Summary Plotting Challenge

Create a stacked bar plot, with size on the Y axis, and the stacked variable being replicate. The plot should show total size by replicate for each site. Some tips are below to help you solve this challenge:

• For more information on pandas plots, see pandas’ documentation page on visualization.
• You can use the code that follows to create a stacked bar plot but the data to stack need to be in individual columns. Here’s a simple example with some data where ‘a’, ‘b’, and ‘c’ are the groups, and ‘one’ and ‘two’ are the subgroups.

d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']), 'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
pd.DataFrame(d)

shows the following data

      one  two
  a    1    1
  b    2    2
  c    3    3
  d  NaN    4

We can plot the above with

# Plot stacked data so columns 'one' and 'two' are stacked
my_df = pd.DataFrame(d)
my_df.plot(kind='bar', stacked=True, title="The title of my graph")

• You can use the .unstack() method to transform grouped data into columns for each plotting. Try running .unstack() on some DataFrames above and see what it yields.

Start by transforming the grouped data (by site and sex) into an unstacked layout, then create a stacked plot.

Solution to Summary Challenge

First we group data by site and by sex, and then calculate a total for each site.

by_site_rep = lobsters_df.groupby(['site', 'replicate'])
site_rep_count = by_site_rep['size_mm'].sum()

This calculates the sums of size for each sex within each site as a table

site  replicate
AQUE  A             6774.8
      B            10205.0
      C            14156.0
      D             4934.0
CARP  A            52465.0
      B            44610.0
      C            51185.0
      D            53634.0
IVEE  A            74909.0
      B            84233.0
      C            86251.5
      D            71253.0
MOHK  A            14238.0
      B            14096.7
      C            22518.0
      D            43169.0
NAPL  A            23007.0
      B            25334.0
      C            37518.0
      D            32537.0
Name: size_mm, dtype: float64

Below we’ll use .unstack() on our grouped data to figure out the total size that each replicate contributed to each site.

by_site_rep = lobsters_df.groupby(['site', 'replicate'])
site_rep_count = by_site_rep['size_mm'].sum()
site_rep_count.unstack()

The unstack method above will display the following output:

replicate        A        B        C        D
site                                         
AQUE        6774.8  10205.0  14156.0   4934.0
CARP       52465.0  44610.0  51185.0  53634.0
IVEE       74909.0  84233.0  86251.5  71253.0
MOHK       14238.0  14096.7  22518.0  43169.0
NAPL       23007.0  25334.0  37518.0  32537.0

Now, create a stacked bar plot with that data where the size for each replicate are stacked by site.

Rather than display it as a table, we can plot the above data by stacking the values of each replicate as follows:

by_site_rep = lobsters_df.groupby(['site', 'replicate'])
site_rep_count = by_site_rep['size_mm'].sum()
srp = site_rep_count.unstack()
s_plot = srp.plot(kind='bar', stacked=True, title="Total Size (mm) by Site and Replicate")
s_plot.set_ylabel("Size (mm)")
s_plot.set_xlabel("Site")

Data Set Citation

Reed, D, R. Miller. 2022. SBC LTER: Reef: Abundance, size and fishing effort for California Spiny Lobster (Panulirus interruptus), ongoing since 2012 ver 7. Environmental Data Initiative. https://doi.org/10.6073/pasta/2a648dfc72949d0f5e1352ecdd973de5. Accessed 2022-02-14.

Key Points

• Libraries enable us to extend the functionality of Python.

• Pandas is a popular library for working with data.

• A Dataframe is a Pandas data structure that allows one to access data by column (name or index) or row.

• Aggregating data using the groupby() function enables you to generate useful summaries of data quickly.

• Plots can be created from DataFrames or subsets of data that have been generated with groupby().