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Python for Environmental Science

bookWorking with Tabular Environmental Data

Tabular data is a cornerstone of environmental science, capturing measurements such as air quality, temperature, rainfall, and more in structured tables. These tables often include rows representing observations at different times or locations, and columns for each measured variable, such as pollutant concentrations or weather metrics. Efficiently working with this type of data is essential for analysis, and the pandas library in Python is a powerful tool designed for just that. With pandas, you can easily load, inspect, and analyze tabular datasets, making it a favorite among environmental scientists for managing and exploring their data.


              
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import pandas as pd

# Create a DataFrame with air quality measurements
data = {
    "Date": ["2023-07-01", "2023-07-02", "2023-07-03", "2023-07-04"],
    "Location": ["Station A", "Station A", "Station B", "Station B"],
    "NO2": [18.2, 21.0, 19.5, 22.3],   # Nitrogen dioxide in 
    "O3": [33.1, 29.8, 35.6, 32.7],    # Ozone in 
}

df = pd.DataFrame(data)
print(df)
copy

Once you have your environmental data in a pandas DataFrame, you can begin exploring its contents. To get a quick look at the first few rows, use the head() method. This is useful for checking that your data loaded as expected. If you want to understand the data types of each column—important for ensuring correct analysis—you can use the dtypes attribute. For a summary of basic statistics like mean, minimum, and maximum values for each numeric column, the describe() method provides a concise overview. Applying these methods to the air quality DataFrame you created above will help you quickly assess the structure and quality of your dataset.


              
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# Calculate the mean and standard deviation of the NO2 column
mean_no2 = df["NO2"].mean()
std_no2 = df["NO2"].std()

print("Mean NO2 concentration:", mean_no2)
print("Standard deviation of NO2:", std_no2)
copy

1. What pandas function is used to display the first few rows of a DataFrame?

2. How can you check the data type of each column in a pandas DataFrame?

3. Fill in the blank: To calculate the average value of the 'NO2' column in a DataFrame named df, use df.______('NO2').

question mark

What pandas function is used to display the first few rows of a DataFrame?

Select the correct answer

question mark

How can you check the data type of each column in a pandas DataFrame?

Select the correct answer

question-icon

Fill in the blank: To calculate the average value of the 'NO2' column in a DataFrame named df, use df.______('NO2').

('NO2')
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Suggested prompts:

How can I calculate the mean and standard deviation for the O3 column?

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bookWorking with Tabular Environmental Data

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Tabular data is a cornerstone of environmental science, capturing measurements such as air quality, temperature, rainfall, and more in structured tables. These tables often include rows representing observations at different times or locations, and columns for each measured variable, such as pollutant concentrations or weather metrics. Efficiently working with this type of data is essential for analysis, and the pandas library in Python is a powerful tool designed for just that. With pandas, you can easily load, inspect, and analyze tabular datasets, making it a favorite among environmental scientists for managing and exploring their data.


              
123456789101112

            
import pandas as pd

# Create a DataFrame with air quality measurements
data = {
    "Date": ["2023-07-01", "2023-07-02", "2023-07-03", "2023-07-04"],
    "Location": ["Station A", "Station A", "Station B", "Station B"],
    "NO2": [18.2, 21.0, 19.5, 22.3],   # Nitrogen dioxide in 
    "O3": [33.1, 29.8, 35.6, 32.7],    # Ozone in 
}

df = pd.DataFrame(data)
print(df)
copy

Once you have your environmental data in a pandas DataFrame, you can begin exploring its contents. To get a quick look at the first few rows, use the head() method. This is useful for checking that your data loaded as expected. If you want to understand the data types of each column—important for ensuring correct analysis—you can use the dtypes attribute. For a summary of basic statistics like mean, minimum, and maximum values for each numeric column, the describe() method provides a concise overview. Applying these methods to the air quality DataFrame you created above will help you quickly assess the structure and quality of your dataset.


              
123456

            
# Calculate the mean and standard deviation of the NO2 column
mean_no2 = df["NO2"].mean()
std_no2 = df["NO2"].std()

print("Mean NO2 concentration:", mean_no2)
print("Standard deviation of NO2:", std_no2)
copy

1. What pandas function is used to display the first few rows of a DataFrame?

2. How can you check the data type of each column in a pandas DataFrame?

3. Fill in the blank: To calculate the average value of the 'NO2' column in a DataFrame named df, use df.______('NO2').

question mark

What pandas function is used to display the first few rows of a DataFrame?

Select the correct answer

question mark

How can you check the data type of each column in a pandas DataFrame?

Select the correct answer

question-icon

Fill in the blank: To calculate the average value of the 'NO2' column in a DataFrame named df, use df.______('NO2').

('NO2')
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 1. Розділ 2
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