Data Analysis and Visualization in Python for Ecologists: Instructor Notes

Challenge solutions

Install the required workshop packages

Please use the instructions in the Setup document to perform installs. If you encounter setup issues, please file an issue with the tags ‘High-priority’.

Checking installations.

In the _include/scripts directory, you will find a script called This checks the functionality of the Anaconda install.

By default, Data Carpentry does not have people pull the whole repository with all the scripts and addenda. Therefore, you, as the instructor, get to decide how you’d like to provide this script to learners, if at all. To use this, students can navigate into _includes/scripts terminal, and execute the following:


If learners receive an AssertionError, it will inform you how to help them correct this installation. Otherwise, it will tell you that the system is good to go and ready for Data Carpentry!


Tuples Challenges

Dictionaries Challenges

Make sure it is also clear that access to ‘the value that corresponds to the key 2’ is actually just about the key name. Add for example rev[10] = "ten" to clarify it is not about the position.

{1: 'one', 2: 'two', 3: 'three'}
rev[2] = "apple-sauce"
{1: 'one', 2: 'apple-sauce', 3: 'three'}


Important Bug Note

In Pandas prior to 0.18.1 there is a bug causing surveys_df['weight'].describe() to return a runtime error.

Dataframe Challenges

Calculating Statistics Challenges

Grouping Challenges

surveys_df.groupby(['plot_id', 'sex']).agg({"year": 'min',
                                           "hindfoot_length": 'median',
                                           "weight": 'mean'})

Plotting Challenges


average weight across all species for each plot


total males versus total females for the entire dataset


Tip: use .head() method throughout this lesson to keep your display neater for students. Encourage students to try with and without .head() to reinforce this useful tool and then to use it or not at their preference. For example, if a student worries about keeping up in pace with typing, let them know they can skip the .head(), but that you’ll use it to keep more lines of previous steps visible.

Indexing Challenges

Selection Challenges

Advanced Selection Challenges

surveys_df[~surveys_df["sex"].isin(['M', 'F'])]

Masking Challenges


Challenge - Changing Types

Pandas cannot convert types from float to int if the column contains NaN values.

Challenge - Counting


If the students have trouble generating the output, or anything happens with that, the folder sample_output in this repository contains the file surveys_complete.csv with the data they should generate.


# read the files:
survey2001 = pd.read_csv("data/survey2001.csv")
survey2002 = pd.read_csv("data/survey2002.csv")
# concatenate
survey_all = pd.concat([survey2001, survey2002], axis=0)
# get the weight for each year, grouped by sex:
weight_year = survey_all.groupby(['year', 'sex']).mean()["wgt"].unstack()
# plot:
plt.tight_layout()  # tip(!)

average weight for each year, grouped by sex

# writing to file:
# reading it back in:
pd.read_csv("weight_for_year.csv", index_col=0)
merged_left = pd.merge(left=surveys_df,right=species_df, how='left', on="species_id")

Then calculate and plot the distribution of:

1. taxa per plot (number of species of each taxa per plot):

Species distribution (number of taxa for each plot) can be derived as follows:


taxa per plot

Suggestion: It is also possible to plot the number of individuals for each taxa in each plot (stacked bar chart):

merged_left.groupby(["plot_id", "taxa"]).count()["record_id"].unstack().plot(kind='bar', stacked=True)
plt.legend(loc='upper center', ncol=3, bbox_to_anchor=(0.5, 1.05))

(the legend otherwise overlaps the bar plot)

taxa per plot

2. taxa by sex by plot: Providing the Nan values with the M|F values (can also already be changed to ‘x’):

merged_left.loc[merged_left["sex"].isnull(), "sex"] = 'M|F'

Number of taxa for each plot/sex combination:

ntaxa_sex_site= merged_left.groupby(["plot_id", "sex"])["taxa"].nunique().reset_index(level=1)
ntaxa_sex_site = ntaxa_sex_site.pivot_table(values="taxa", columns="sex", index=ntaxa_sex_site.index)
ntaxa_sex_site.plot(kind="bar", legend=False)
plt.legend(loc='upper center', ncol=3, bbox_to_anchor=(0.5, 1.08),
           fontsize='small', frameon=False)

taxa per plot per sex

Suggestion (for discussion only):

The number of individuals for each taxa in each plot per sex can be derived as well.

sex_taxa_site  = merged_left.groupby(["plot_id", "taxa", "sex"]).count()['record_id']
sex_taxa_site.unstack(level=[1, 2]).plot(kind='bar', logy=True)
plt.legend(loc='upper center', ncol=3, bbox_to_anchor=(0.5, 1.15),
           fontsize='small', frameon=False)

taxa per plot per sex

This is not really the best plot choice: not readable,… A first option to make this better, is to make facets. However, pandas/matplotlib do not provide this by default. Just as a pure matplotlib example (M|F if for not-defined sex records):

fig, axs = plt.subplots(3, 1)
for sex, ax in zip(["M", "F", "M|F"], axs):
    sex_taxa_site[sex_taxa_site["sex"] == sex].plot(kind='bar', ax=ax, legend=False)
    if not ax.is_last_row():
axs[0].legend(loc='upper center', ncol=5, bbox_to_anchor=(0.5, 1.3),
              fontsize='small', frameon=False)

taxa per plot per sex

However, it would be better to link to Seaborn and Altair for its kind of multivariate visualisations.

plot_info = pd.read_csv("data/plots.csv")
merged_site_type = pd.merge(merged_left, plot_info, on='plot_id')
# For each plot, get the number of species for each plot
nspecies_site = merged_site_type.groupby(["plot_id"])["species"].nunique().rename("nspecies")
# For each plot, get the number of individuals
nindividuals_site = merged_site_type.groupby(["plot_id"]).count()['record_id'].rename("nindiv")
# combine the two series
diversity_index = pd.concat([nspecies_site, nindividuals_site], axis=1)
# calculate the diversity index
diversity_index['diversity'] = diversity_index['nspecies']/diversity_index['nindiv']

Making a bar chart:

plt.xlabel("Diversity index")

taxa per plot per sex


Basic Loop Challenges

for creature in animals:
    print(creature + ',', end='')

This loop also adds a comma after the last animal. A better, loop-free solution would be: ','.join(animals)

Looping Over Dataframe Challenges

surveys_year = surveys_df[surveys_df.year == year].dropna()

You could just make a list manually, however, why not check the first and last year making use of the code itself?

n_year = 5  # better overview by making variable from it
first_year = surveys_df['year'].min()
last_year = surveys_df['year'].max()

for year in range(first_year, last_year, n_year):

    # Select data for the year
    surveys_year = surveys_df[surveys_df.year == year].dropna()

Similar to previous example, but use the species_id column. surveys_df['species_id'].unique(). However, the species names would improve interpretation of the file naming. A join with the species: merged_left = pd.merge(left=surveys,right=species, how='left', on="species_id") and using the species column.

Functions Challenges

Show these in a debugging environment to make this more clear!

Additional Functions Challenges

def one_year_csv_writer(this_year, all_data, folder_to_save, root_name):
    Writes a csv file for data from a given year.

    this_year : int
        year for which data is extracted
    all_data: pd.DataFrame
        DataFrame with multi-year data
    folder_to_save : str
        folder to save the data files
    root_name: str
        root of the filenames to save the data

    # Select data for the year
    surveys_year = all_data[all_data.year == this_year]

    # Write the new DataFrame to a csv file
    filename = os.path.join(folder_to_save, ''.join([root_name, str(this_year), '.csv']))

Also adapt function yearly_data_csv_writer with the additional inputs.

Adapt the input arguments, e.g. 1978, 1979.

Output Management Challenges

Implementation inside the function:

filenames = []
for year in range(start_year, end_year+1):
    filenames.append(one_year_csv_writer(year, all_data, folder_to_save, root_name))
return filenames
yearly_data_arg_test(surveys_df, end_year=2001)

Functions Modifications Challenges

def one_year_csv_writer(this_year, all_data, folder_to_save='./', root_name='survey'):
    Writes a csv file for data from a given year.

    this_year : int
        year for which data is extracted
    all_data: pd.DataFrame
        DataFrame with multi-year data
    folder_to_save : str
        folder to save the data files
    root_name: str
        root of the filenames to save the data

    # Select data for the year
    surveys_year = all_data[all_data.year == this_year]

    # Write the new DataFrame to a csv file
    filename = os.path.join(folder_to_save, ''.join([root_name, str(this_year), '.csv']))
    # Write the new DataFrame to a csv file
    if len(surveys_year) > 0:
        filename = os.path.join(folder_to_save, ''.join([root_name, str(this_year), '.csv']))
        print("No data for year " + str(this_year))
def yearly_data_csv_writer(all_data, yearcolumn="year",
                           folder_to_save='./', root_name='survey'):
    Writes separate csv files for each year of data.

    all_data --- DataFrame with multi-year data
    yearcolumn --- column name containing the year of the data
    folder_to_save --- folder name to store files
    root_name --- start of the file names stored
    years = all_data["year"].unique()

    # "end_year" is the last year of data we want to pull, so we loop to end_year+1
    filenames = []
    for year in years:
        filenames.append(one_year_csv_writer(year, all_data, folder_to_save, root_name))
    return filenames


If the students have trouble generating the output, or anything happens with that, there is a file called “sample output” that contains the data file they should have generated in lesson 3. Answers are embedded with challenges in this lesson.

Note plotnine contains a lot of deprecation warnings in some versions of python/matplotlib, warnings may need to be supressed with

import warnings

iPython notebooks for plotting can be viewed in the _extras folder


Answers are embedded with challenges in this lesson, other than random distribtuion which is left to the learner to choose, and final plot, for which the learner should investigate the matplotlib gallery.

Scientists often operate on mathematical equations. Being able to use them in their graphics has a lot of added value. Luckily, Matplotlib provides powerful tools for text control. One of them is the ability to use LaTeX mathematical notation, whenever text is used (you can learn more about LaTeX math notation here: To use mathematical notation, surround your text using the dollar sign (“$”). LaTeX uses the backslash character (“\”) a lot. Since backslash has a special meaning in the Python strings, you should replace all the LaTeX-related backslashes with two backslashes.

plt.plot(t, t, 'r--', label='$y=x$')
plt.plot(t, t**2 , 'bs-', label='$y=x^2$')
plt.plot(t, (t - 5)**2 + 5 * t - 0.5, 'g^:', label='$y=(x - 5)^2 + 5  x - \\frac{1}{2}$') # note the double backslash

plt.legend(loc='upper left', shadow=True, fontsize='x-large')

# Note the double backslashes in the line below.
plt.xlabel('This is the x axis. It can also contain math such as $\\bar{x}=\\frac{\\sum_{i=1}^{n} {x}} {N}$')
plt.ylabel('This is the y axis')
plt.title('This is the figure title')

This page contains more information.


Challenge - SQL

#Connect to the database
con = sqlite3.connect("data/portal_mammals.sqlite")

cur = con.cursor()

# Return all results of query: year, plot type (site type), genus, species and sex
# from the join of the tables surveys, plots and species, for the years 1998-2001 where sex is 'M' or 'F'.
cur.execute('SELECT surveys.year,plots.plot_type,species.genus,species.species, \
  FROM surveys INNER JOIN plots ON surveys.plot_id = plots.plot_id INNER JOIN species ON \
  surveys.species_id = species.species_id WHERE surveys.year>=1998 AND surveys.year<=2001 \
  AND ( = "M" OR = "F")')


# Close the connection

Answer: 5546 records are found.

This question is a little ambiguous but we could e.g. do two SQL queries into dataframes, then pivot the second and merge them to create a table of observation count and plot total weight per year. The PIVOT operation could alternatively be performed in SQL.

import pandas as pd
import sqlite3

# Create two sqlite queries results, read as pandas DataFrame
# Include 'year' in both queries so we have something to merge (join) on.
con = sqlite3.connect("data/portal_mammals.sqlite")
df1 = pd.read_sql_query("SELECT year,COUNT(*) FROM surveys GROUP BY year", con)
df2 = pd.read_sql_query("SELECT year,plot_id,SUM(weight) FROM surveys GROUP BY \
        year,plot_id ORDER BY plot_id ASC",con)

# Turn the plot_id column values into column names by pivoting
df2 = df2.pivot(index='year',columns='plot_id')['SUM(weight)']
df = pd.merge(df1, df2, on='year')

# Verify that result of the SQL queries is stored in the combined dataframe


Output looks something like

   year  COUNT(*)       1       2       3       4       5       6      7  \
0  1977       503   567.0   784.0   237.0   849.0   943.0   578.0  202.0   
1  1978      1048  4628.0  4789.0  1131.0  4291.0  4051.0  2371.0   43.0   
2  1979       719  1909.0  2501.0   430.0  2438.0  1798.0   988.0  141.0   
3  1980      1415  5374.0  4643.0  1817.0  7466.0  2743.0  3219.0  362.0   
4  1981      1472  6229.0  6282.0  1343.0  4553.0  3596.0  5430.0   24.0   

        8  ...      15     16      17      18     19      20     21      22  \
0   595.0  ...    48.0  132.0  1102.0   646.0  336.0   640.0   40.0   316.0   
1  3669.0  ...   734.0  548.0  4971.0  4393.0  124.0  2623.0  239.0  2833.0   
2  1954.0  ...   472.0  308.0  3736.0  3099.0  379.0  2617.0  157.0  2250.0   
3  3596.0  ...  1071.0  529.0  5877.0  5075.0  691.0  5523.0  321.0  3763.0   
4  4946.0  ...  1083.0  176.0  5050.0  4773.0  410.0  5379.0  600.0  5268.0   

      23      24  
0  169.0     NaN  
1    NaN     NaN  
2  137.0   901.0  
3  742.0  4392.0  
4   57.0  3987.0  

Challenge - Saving your work

Per the example in the lesson, create a variable for the results of the SQL query, then add something like

<new_table>.to_sql("New Table", con, if_exists="replace")

If the database is shared with others and common queries (and potentially data corrections) are likely to be required by many it may be efficient for one person to perform the work and save it back to the database as a new table so others can access the results directly instead of performing the query themselves, particularly if it is complex. However, we might avoid doing this if the database is an authoritative source (potentially version controlled) which should not be modified by users. Instead, we might save the qeury results to a new database that is more appropriate for downstream work.