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[pandas] update pandas to use yfinance and minor edits #133

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mmcky merged 4 commits into from
Apr 13, 2021
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[pandas] update pandas to use yfinance and minor edits #133

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update pandas to use yfinance and minor edits
mmcky Apr 13, 2021
3efeb64
fix read function for yahoo finance
mmcky Apr 13, 2021
7419cd2
remove margin as not supported in quantecon-book-theme
mmcky Apr 13, 2021
5f790e4
add a space for title
mmcky Apr 13, 2021
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20 changes: 16 additions & 4 deletions lectures/pandas.md
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,7 @@ In addition to what’s in Anaconda, this lecture will need the following librar
tags: [hide-output]
---
!pip install --upgrade pandas-datareader
!pip install --upgrade yfinance
```

## Overview
Expand Down Expand Up @@ -385,18 +386,28 @@ Note that pandas offers many other file type alternatives.

Pandas has [a wide variety](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html) of top-level methods that we can use to read, excel, json, parquet or plug straight into a database server.

### Using {index}`pandas_datareader <single: pandas_datareader>` to Access Data
### Using {index}`pandas_datareader <single: pandas_datareader>` and {index}`yfinance <single: yfinance>` to Access Data

```{index} single: Python; pandas-datareader
```

The maker of pandas has also authored a library called pandas_datareader that gives programmatic access to many data sources straight from the Jupyter notebook.
The maker of pandas has also authored a library called
[pandas_datareader](https://pandas-datareader.readthedocs.io/en/latest/) that
gives programmatic access to many data sources straight from the Jupyter notebook.

While some sources require an access key, many of the most important (e.g., FRED, [OECD](https://data.oecd.org/), [EUROSTAT](https://ec.europa.eu/eurostat/data/database) and the World Bank) are free to use.

We will also use [yfinance](https://pypi.org/project/yfinance/) to fetch data from Yahoo finance
in the exercises.

For now let's work through one example of downloading and plotting data --- this
time from the World Bank.

```{note}
There are also other [python libraries](https://data.worldbank.org/products/third-party-apps)
available for working with world bank data such as [wbgapi](https://pypi.org/project/wbgapi/)
```

The World Bank [collects and organizes data](http://data.worldbank.org/indicator) on a huge range of indicators.

For example, [here's](http://data.worldbank.org/indicator/GC.DOD.TOTL.GD.ZS/countries) some data on government debt as a ratio to GDP.
Expand Down Expand Up @@ -426,7 +437,7 @@ With these imports:

```{code-cell} python3
import datetime as dt
from pandas_datareader import data
import yfinance as yf
```

Write a program to calculate the percentage price change over 2019 for the following shares:
Expand Down Expand Up @@ -460,7 +471,8 @@ def read_data(ticker_list,
ticker = pd.DataFrame()

for tick in ticker_list:
prices = data.DataReader(tick, 'yahoo', start, end)
stock = yf.Ticker(tick)
prices = stock.history(start=start, end=end)
closing_prices = prices['Close']
ticker[tick] = closing_prices

Expand Down