Starting With Data


Teaching: 30 min
Exercises: 30 min
  • How can I import data in Python?

  • What is Pandas?

  • Why should I use Pandas to work with data?

  • 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 the Portal Teaching data, a subset of the data from Ernst et al Long-term monitoring and experimental manipulation of a Chihuahuan Desert ecosystem near Portal, Arizona, USA.

We will be using files from the Portal Project Teaching Database. This section will use the surveys.csv file that can be downloaded here:

We are studying the species and weight of animals caught 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:

Column Description
record_id Unique id for the observation
month month of observation
day day of observation
year year of observation
plot_id ID of a particular site
species_id 2-letter code
sex sex of animal (“M”, “F”)
hindfoot_length length of the hindfoot in mm
weight weight of the animal in grams

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


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 survey 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

The above command yields the output below:

record_id  month  day  year  plot_id species_id sex  hindfoot_length  weight
0          1      7   16  1977        2         NL   M               32   NaN
1          2      7   16  1977        3         NL   M               33   NaN
2          3      7   16  1977        2         DM   F               37   NaN
3          4      7   16  1977        7         DM   M               36   NaN
4          5      7   16  1977        3         DM   M               35   NaN
35544      35545     12   31  2002       15     AH  NaN              NaN  NaN
35545      35546     12   31  2002       15     AH  NaN              NaN  NaN
35546      35547     12   31  2002       10     RM    F               15   14
35547      35548     12   31  2002        7     DO    M               36   51
35548      35549     12   31  2002        5     NaN  NaN             NaN  NaN

[35549 rows x 9 columns]

We can see that there were 35,549 rows parsed. Each row has 9 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 survey data surveys_df:

surveys_df = pd.read_csv("data/surveys.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 surveys_df object by typing its name into the Python command prompt.


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:

17        NaN
18        NaN
19        NaN
20        NaN
21        NaN
22        NaN
23        NaN
24        NaN
25        NaN
26        NaN
27        NaN
28        NaN
29        NaN
...       ...
35519    36.0
35520    48.0
35521    45.0
35522    44.0
35523    27.0
35524    26.0
35525    24.0
35526    43.0
35527     NaN
35528    25.0
35529     NaN
35530     NaN
35531    43.0
35532    48.0
35533    56.0
35534    53.0
35535    42.0
35536    46.0
35537    31.0
35538    68.0
35539    23.0
35540    31.0
35541    29.0
35542    34.0
35543     NaN
35544     NaN
35545     NaN
35546    14.0
35547    51.0
35548     NaN

[35549 rows x 9 columns]

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:

surveys_df.head() # The head() method displays the first several lines of a file. It
                  # is discussed below.
   record_id  month  day  year  plot_id species_id sex  hindfoot_length  \
5          6      7   16  1977        1         PF   M             14.0
6          7      7   16  1977        2         PE   F              NaN
7          8      7   16  1977        1         DM   M             37.0
8          9      7   16  1977        1         DM   F             34.0
9         10      7   16  1977        6         PF   F             20.0

5     NaN
6     NaN
7     NaN
8     NaN
9     NaN

Exploring Our Species Survey Data

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

<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 surveys_df contain? DataFrames have an attribute called dtypes that answers this:

record_id            int64
month                int64
day                  int64
year                 int64
plot_id              int64
species_id          object
sex                 object
hindfoot_length    float64
weight             float64
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 weight and hindfoot_length columns can, because they have type float64. The object type doesn’t have a very helpful name, but in this case it represents strings (such as ‘M’ and ‘F’ in the case of sex).

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 surveys_df and attribute columns, an index of all the column names in the DataFrame can be accessed with surveys_df.columns.

Methods are called in a similar fashion using the syntax df_object.method(). As an example, surveys_df.head() gets the first few rows in the DataFrame surveys_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 surveys_df, try out the attributes & methods below to see what they return.

  1. surveys_df.columns
  2. surveys_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. surveys_df.head() Also, what does surveys_df.head(15) do?
  4. surveys_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

which returns:

Index(['record_id', 'month', 'day', 'year', 'plot_id', 'species_id', 'sex',
       'hindfoot_length', 'weight'],

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


which returns:

array(['NL', 'DM', 'PF', 'PE', 'DS', 'PP', 'SH', 'OT', 'DO', 'OX', 'SS',
       'OL', 'RM', nan, 'SA', 'PM', 'AH', 'DX', 'AB', 'CB', 'CM', 'CQ',
       'RF', 'PC', 'PG', 'PH', 'PU', 'CV', 'UR', 'UP', 'ZL', 'UL', 'CS',
       'SC', 'BA', 'SF', 'RO', 'AS', 'SO', 'PI', 'ST', 'CU', 'SU', 'RX',
       'PB', 'PL', 'PX', 'CT', 'US'], dtype=object)

Challenge - Statistics

  1. Create a list of unique site ID’s (“plot_id”) found in the surveys 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 surveys_df['plot_id'].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 weight of all individuals per site.

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


gives output

count    32283.000000
mean        42.672428
std         36.631259
min          4.000000
25%         20.000000
50%         37.000000
75%         48.000000
max        280.000000
Name: weight, dtype: float64

We can also extract one specific metric if we wish:


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 sex
grouped_data = surveys_df.groupby('sex')

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
# Provide the mean for each numeric column by sex

grouped_data.mean() OUTPUT:

        record_id     month        day         year    plot_id  \
F    18036.412046  6.583047  16.007138  1990.644997  11.440854
M    17754.835601  6.392668  16.184286  1990.480401  11.098282

     hindfoot_length     weight
F          28.836780  42.170555
M          29.709578  42.995379

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

Challenge - Summary Data

  1. How many recorded individuals are female F and how many male M?
  2. What happens when you group by two columns using the following syntax and then calculate mean values?
    • grouped_data2 = surveys_df.groupby(['plot_id', 'sex'])
    • grouped_data2.mean()
  3. Summarize weight 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['weight'].describe()

Did you get #3 right?

A Snippet of the Output from challenge 3 looks like:

 1     count    1903.000000
       mean       51.822911
       std        38.176670
       min         4.000000
       25%        30.000000
       50%        44.000000
       75%        53.000000
       max       231.000000

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 species
species_counts = surveys_df.groupby('species_id')['record_id'].count()

Or, we can also count just the rows that have the species “DO”:


Challenge - Make a list

What’s another way to create a list of species and associated count of the 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 weight values by 2. 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 weight values by 2

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

Weight by Species Site Count per species site

We can also look at how many animals were captured in each site:

total_count = surveys_df.groupby('plot_id')['record_id'].nunique()
# Let's plot that too

Challenge - Plots

  1. Create a plot of average weight across all species per site.
  2. Create a plot of total males versus total females for the entire dataset.

Summary Plotting Challenge

Create a stacked bar plot, with weight on the Y axis, and the stacked variable being sex. The plot should show total weight by sex 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'])}

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")

Stacked Bar Plot

  • 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_sex = surveys_df.groupby(['plot_id', 'sex'])
site_sex_count = by_site_sex['weight'].sum()

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

site  sex
plot_id  sex
1        F      38253
         M      59979
2        F      50144
         M      57250
3        F      27251
         M      28253
4        F      39796
         M      49377
<other sites removed for brevity>

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

by_site_sex = surveys_df.groupby(['plot_id', 'sex'])
site_sex_count = by_site_sex['weight'].sum()

The unstack method above will display the following output:

sex          F      M
1        38253  59979
2        50144  57250
3        27251  28253
4        39796  49377
<other sites removed for brevity>

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

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

by_site_sex = surveys_df.groupby(['plot_id', 'sex'])
site_sex_count = by_site_sex['weight'].sum()
spc = site_sex_count.unstack()
s_plot = spc.plot(kind='bar', stacked=True, title="Total weight by site and sex")

Stacked Bar Plot

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().