Finding certain rows in a DataFrame

Bird strikes by aircraft are both dangerous to human life and of great concern to conservation efforts. Luckily, the Federal Aviation Administration has been tracking wildlife strikes for nearly 30 years.

You're going to use for loops to explore the FAA Wildlife Strikes database. A pandas DataFrame that contains wildlife strikes by aircraft in California recorded from 1990 to 2018 is loaded into your session. You want to find incidences that involve Snowy egrets, a migratory bird that winters in California. For each incident, display:

Date of incident (INCIDENT_DATE column)
Airport (AIRPORT column)
Type of aircraft (ATYPE column)
Severity of the damage (DAMAGE column)

This exercise is part of the course

Python for MATLAB Users

Exercise instructions

Print the first five lines of the wildlife_strikes DataFrame.
Construct a for loop that iterates through each row of wildlife_strikes.
Create a conditional statement that determines if SPECIES is a 'Snowy egret'.
If your if statement evaluates to True, print the 'INCIDENT_DATE', 'AIRPORT', ' ATYPE', and 'DAMAGE'.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Print the first few lines of the wildlife_strikes DataFrame
print(____)

# Loop through each row in wildlife_strikes
for i, ____ in ____.iterrows():
    # Use an if statement to check the species of this row
    if row['____']=='____':
        # Print the INCIDENT_DATE, AIRPORT, ATYPE, and DAMAGE
        print(row['____'],
              ____['AIRPORT'],
              ____['____'],
              ____['____'])

Edit and Run Code

This exercise is part of the course

Python for MATLAB Users

BeginnerSkill Level

4.7+

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This chapter gets you started moving from MATLAB to Python. You'll learn about some of the similarities and differences between MATLAB and Python, how to use methods and packages, and be introduced to the popular NumPy package.

Exercise 1: Welcome to Python!Exercise 2: Basic calculations Exercise 3: Forecasting an investment Exercise 4: Types of variables Exercise 5: Methods and packages Exercise 6: Manipulating strings with methods Exercise 7: Using Python packages Exercise 8: Arrays & plotting Exercise 9: Getting started with NumPy arrays Exercise 10: Plotting bicycle traffic Exercise 11: What predicts animal longevity?

In this chapter, you will build on your new NumPy knowledge. You will dive into NumPy arrays, the Python analog to matrices by performing mathematical operations and indexing. You will also begin to explore another important Python data structure, the list, and then round out the chapter by making customs plots of your arrays using Matplotlib.

Exercise 1: Diving into NumPy arrays Exercise 2: Forecasting over time Exercise 3: How much more do organic avocados cost?Exercise 4: Indexing NumPy arrays Exercise 5: Getting columns from NumPy arrays Exercise 6: Bike traffic throughout the week Exercise 7: Filtering arrays with Boolean indexing Exercise 8: Lists Exercise 9: List exploration Exercise 10: Making NumPy arrays from lists Exercise 11: Operating on lists and arrays Exercise 12: Customizing plots Exercise 13: Colors, linestyles, and legends Exercise 14: Encoding data in color & size Exercise 15: Determine engine types in wildlife strikes

This chapter introduces some powerful Python data structures: the dictionary and the pandas DataFrame. You will learn to create dictionaries by setting key-value pairs, and view then how to view and modify your dictionary. Then you will be introduced to one of the most important packages in the Pythonista's toolbox, pandas. Specifically, you'll focus on the pandas' structure, the DataFrame, which organizes tabular data in an easily accessible way. Lastly, you'll learn how to transform different data types into DataFrames to make your data easier to work with.

Exercise 1: Dictionaries Exercise 2: Which keys?Exercise 3: Get data out Exercise 4: Modifying dictionaries Exercise 5: Introduction to DataFrames Exercise 6: Explore a pandas DataFrame Exercise 7: Plot data from a DataFrame Exercise 8: Accessing pandas DataFrames Exercise 9: Accessing rows and columns Exercise 10: The many flavors of .iloc Exercise 11: Creating pandas DataFrames Exercise 12: From a CSV file Exercise 13: From a dictionary of lists Exercise 14: From a list of dictionaries

You'll finish the course by controlling your Python flow. You will learn how to iterate through different Python data structures using for loops. You will also learn about Python contingencies using if, else, and elif and the Python comparison operators (greater than, less than, etc.) that will decide which lines of your code will be executed. Lastly, you'll circle back to NumPy arrays by using Python comparison operators to filter your arrays.

Exercise 1: Looping through data Exercise 2: Looping through lists Exercise 3: Overlaying multiple plots on a figure Exercise 4: Comparisons and control flow Exercise 5: Counting different types of aircraft Exercise 6: Finding certain rows in a DataFrame

Current Exercise

Exercise 7: Filtering data Exercise 8: Boolean indexing for quick stats Exercise 9: Booleans for the win!Exercise 10: Boolean indexing and Matplotlib fun Exercise 11: Well done!