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TryPy 2 - For Loops and Conditionals

Part 0. Setup Steps

  • Create a repo on GitHub named eds217-trypy-02
  • Clone to create a version-controlled project
  • Create some subfolder infrastructure (nbs, data, etc..)
  • Create a new python notebook.

Part 1. Real data

Explore this data package from EDI, which contains a “Data file describing the biogeochemistry of samples collected at various sites near Toolik Lake, North Slope of Alaska”. Familiarize yourself with the metadata (particularly, View full metadata > expand ‘Data entities’ to learn more about the variables in the dataset).

Citation: Kling, G. 2016. Biogeochemistry data set for soil waters, streams, and lakes near Toolik on the North Slope of Alaska, 2011. ver 5. Environmental Data Initiative. https://doi.org/10.6073/pasta/362c8eeac5cad9a45288cf1b0d617ba7

  1. Download the CSV containing the Toolik biogeochemistry data
  2. Take a look at it - how are missing values stored? Keep that in mind.
  3. Drop the CSV into your data folder of your project
  4. Create a new notebook document in VSCode (or in a jupyter notebook server), save in docs as toolik_chem.ipynb
  5. Import the pandas, numpy, and matplotlib.pyplot libraries into your first code cell.
  6. Read in the data as toolik_biochem. Remember, you’ll want to specify here how NA values are stored (hint: use the na_values argument in your pd.read_csv call.) Additionally, convert all column names to lower case/underscore format (replace spaces with underscores and put everything in lower case).
FileNotFoundError: [Errno 2] No such file or directory: '../data/2011_Kling_Akchem.csv'

  1. Create a subset of the data that contains only observations from the “Toolik Inlet” site, and that only contains the variables (columns) for pH, dissolved organic carbon (DOC), and total dissolved nitrogen (TDN). Store this subset as inlet_biochem. Make sure to LOOK AT the subset you’ve created.

  2. Find the mean value of each column in inlet_biochem 2 different ways:

  1. Write a for loop from scratch to calculate the mean for each
  2. Use one other method (e.g. .mean(), or .apply()) to find the mean for each column.
Using for loop:
col ph: 7.06
col doc_um: 409.96
col tdn_um: 13.37
Using list comprehension:
col ph: 7.06
col doc_um: 409.96
col tdn_um: 13.37
Using df.mean()
ph          7.063182
doc_um    409.961538
tdn_um     13.365385
dtype: float64
Using .apply()
ph          7.063182
doc_um    409.961538
tdn_um     13.365385
dtype: float64
  1. Count the number of na values you have for each column and create a bar chart showing the % na’s for each.
Code 
# Count the number of NA values for each column
na_counts = inlet_biochem.isna().sum()

# Calculate the percentage of NA values for each column
total_rows = len(inlet_biochem)
na_percentage = (na_counts / total_rows) * 100

# Plotting
plt.figure(figsize=(10, 6))
ax = na_percentage.plot(kind='bar')
plt.title('Percentage of NA values per column')
plt.xlabel('Columns')
plt.ylabel('Percentage NA values (%)')

# Rename x-axis labels
new_labels = ['pH', "DOC", "TDN"]
ax.set_xticklabels(new_labels)

plt.show()

Save, stage, commit, pull, push!

END activities