DSC 80 Lecture 2

sample = np.random_choice([True, False], size=len(df))
df[sample] # randomly select rows from df

numpy overview

fast computation involving arrays and matrices

numpy arrays

• homogeneous - all values are of the same type
• (potentially) multi-dimensional

numpy computation is fast because

• much of it is implemented in C
• numpy arrays are stored more efficiently in memory than Python lists

array.argmax() gets the first index of the max element in the array

for loop vs. vectorized arithmetic in numpy

timeit
squares = np.arange(1_000_000) ** 2

2D arrays

Using axis to compress along axes for computations

Using square brackets to index using same slicing as Python or commas

a[0][1]
a[0, 1]

Pictures as numpy arrays

Each image has pixels of RGB

Grayscale by averaging RGB values

mean_2d = img.mean(axis=2)
mean_3d = np.repeat(mean_2d[:, :, np.newaxis], 3, axis=2)

Sepia filter

sepia_filter = np.array([
    [0.393, 0.769, 0.189],
    [0.349, 0.686, 0.168],
    [0.272, 0.534, 0.131]
])
filtered = (img @ sepia_filter.T).clip(0, 1)
filtered

Pandas

DataFrame: 2d tables
Series: 1d array-like object
Index: sequence of column or row labels

import pandas as pd
import numpy as np

• The standard way to select a column in pandas is by using the [] operator
• Specifying a column name returns a Series
• Specifying a list of column names returns a DataFrame

Querying with multiple conditions: use & and |
Querying using .query() - can use and and ignore parentheses