.vscode/

.ipynb_checkpoints/

.virtual_documents/

lectures/mathfoo.py

lectures/mod.py

'https://keras.io/',

'https://data.oecd.org/',

'https://www.reddit.com/',

'https://openai.com']

'https://openai.com',

'https://chatgpt.com/']

html_favicon: _static/lectures-favicon.ico

html_theme: quantecon_book_theme

html_static_path: ['_static']

result = np.empty((3, 3))

for i in range(row):

for j in range(column):

result[i, j] = a[i, j] + b[i]

result[i, j] = a[i, j] + b[i,0]

result

```

**Part2**: Move on to replicate the result of the following broadcasting operation. Meanwhile, compare the speeds of broadcasting and the `for` loop you implement.

For this part of the exercise you can use the `tic`/`toc` functions from the `quantecon` library to time the execution.

Let's make sure this library is installed.

```{code-cell} python3

:tags: [hide-output]

!pip install quantecon

```

Now we can import the quantecon package.

```{code-cell} python3

import quantecon as qe

For example, suppose that we are interested in the [unemployment rate](https://research.stlouisfed.org/fred2/series/UNRATE).

Via FRED, the entire series for the US civilian unemployment rate can be downloaded directly by entering

this URL into your browser (note that this requires an internet connection)

(To download the data as a csv, click here: [https://research.stlouisfed.org/fred2/series/UNRATE/downloaddata/UNRATE.csv](https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1320&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=UNRATE&scale=left&cosd=1948-01-01&coed=2024-10-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=3&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2020-02-01&line_index=1&transformation=lin&vintage_date=2024-11-03&revision_date=2024-11-03&nd=1948-01-01))

This request returns a CSV file, which will be handled by your default application for this class of files.

(To download the data as a csv, click on the top right `Download` and select the `CSV (data)` option).

Alternatively, we can access the CSV file from within a Python program.