Berapa lebih mahal harga alpukat organik?

Alpukat semakin populer, dan tidak ada kota di Amerika Serikat yang mengonsumsi alpukat sebanyak Los Angeles, California. Pada saat yang sama, tingginya permintaan akan produk organik membuat harga alpukat organik lebih mahal dibandingkan yang ditanam secara konvensional. Dalam latihan ini, Anda akan menelusuri data historis harga alpukat di Los Angeles untuk memahami besaran premi yang dibayar orang untuk alpukat organik.

Dua array, conventional dan organic, telah dimuat dan berisi harga rata-rata bulanan dalam USD untuk alpukat konvensional dan organik di Los Angeles.

Anda akan menggunakan fungsi mean(), min(), dan max() dari NumPy untuk meringkas array ini dan memperoleh wawasan tentang biaya premi alpukat organik di Los Angeles.

Latihan ini adalah bagian dari kursus

Python untuk Pengguna MATLAB

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate and print the average price conventional avocados
conventional_mean = ____.____(____)
print('Mean conventional price: ${:.2f}'.format(conventional_mean))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Python untuk Pengguna MATLAB

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus Gratis

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: Menyelami array NumPy Exercise 2: Peramalan seiring waktu Exercise 3: Berapa lebih mahal harga alpukat organik?

Latihan Saat Ini

Exercise 4: Pengindeksan array NumPy Exercise 5: Mengambil kolom dari array NumPy Exercise 6: Lalu lintas sepeda sepanjang minggu Exercise 7: Menyaring array dengan pengindeksan Boolean Exercise 8: List Exercise 9: Eksplorasi list Exercise 10: Membuat array NumPy dari list Exercise 11: Operasi pada list dan array Exercise 12: Menyesuaikan plot Exercise 13: Warna, gaya garis, dan legenda Exercise 14: Mengkodekan data dalam warna & ukuran Exercise 15: Menentukan jenis mesin pada insiden satwa liar

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 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!