Remove Exercises

Author: mmcky

lectures/polars.md

@@ -592,243 +592,4 @@ Note that polars offers many other file type alternatives.
 
 Polars has [a wide variety](https://docs.pola.rs/user-guide/io/) of methods that we can use to read excel, json, parquet or plug straight into a database server.
 
-## Exercises
-
-```{exercise-start}
-:label: pl_ex1
-```
-
-With these imports:
-
-```{code-cell} ipython3
-import datetime as dt
-import yfinance as yf
-```
-
-Write a program to calculate the percentage price change over 2021 for the following shares:
-
-```{code-cell} ipython3
-ticker_list = {'INTC': 'Intel',
-               'MSFT': 'Microsoft',
-               'IBM': 'IBM',
-               'BHP': 'BHP',
-               'TM': 'Toyota',
-               'AAPL': 'Apple',
-               'AMZN': 'Amazon',
-               'C': 'Citigroup',
-               'QCOM': 'Qualcomm',
-               'KO': 'Coca-Cola',
-               'GOOG': 'Google'}
-```
-
-Here's the first part of the program
-
-```{note}
-Many python packages will return Pandas DataFrames by default. In this example we use the `yfinance` package and convert the data to a polars DataFrame
-```
-
-```{code-cell} ipython3
-def read_data(ticker_list,
-          start=dt.datetime(2021, 1, 1),
-          end=dt.datetime(2021, 12, 31)):
-    """
-    This function reads in closing price data from Yahoo
-    for each tick in the ticker_list.
-    """
-    
-    all_data = []
-    
-    for tick in ticker_list:
-        stock = yf.Ticker(tick)
-        prices = stock.history(start=start, end=end)
-        
-        # Convert to polars DataFrame
-        df = pl.from_pandas(prices.reset_index())
-        df = df.with_columns([
-            pl.col('Date').cast(pl.Date),
-            pl.lit(tick).alias('ticker')
-        ]).select(['Date', 'ticker', 'Close'])
-        
-        all_data.append(df)
-    
-    # Combine all data
-    ticker_df = pl.concat(all_data)
-    
-    # Pivot to have tickers as columns
-    ticker_df = ticker_df.pivot(values='Close', index='Date', on='ticker')
-    
-    return ticker_df
-
-ticker = read_data(ticker_list)
-```
-
-Complete the program to plot the result as a bar graph like this one:
-
-```{image} /_static/lecture_specific/pandas/pandas_share_prices.png
-:scale: 80
-:align: center
-```
-
-```{exercise-end}
-```
-
-```{solution-start} pl_ex1
-:class: dropdown
-```
-
-There are a few ways to approach this problem using Polars to calculate
-the percentage change.
-
-First, you can extract the data and perform the calculation such as:
-
-```{code-cell} ipython3
-# Get first and last prices for each ticker
-first_prices = ticker[0]  # First row
-last_prices = ticker[-1]  # Last row
-
-# Convert to pandas for easier calculation, excluding Date column to avoid type errors
-numeric_cols = [col for col in ticker.columns if col != 'Date']
-first_pd = ticker.head(1).select(numeric_cols).to_pandas().iloc[0]
-last_pd = ticker.tail(1).select(numeric_cols).to_pandas().iloc[0]
-
-price_change = (last_pd - first_pd) / first_pd * 100
-price_change
-```
-
-Alternatively you can use polars expressions to calculate percentage change:
-
-```{code-cell} ipython3
-# Calculate percentage change using polars
-change_df = ticker.select([
-    ((pl.col(col).last() - pl.col(col).first()) / pl.col(col).first() * 100).alias(f'{col}_pct_change')
-    for col in ticker.columns if col != 'Date'
-])
-
-# Convert to series for plotting
-price_change = change_df.to_pandas().iloc[0]
-price_change.index = [col.replace('_pct_change', '') for col in price_change.index]
-price_change
-```
-
-Then to plot the chart
-
-```{code-cell} ipython3
-price_change.sort_values(inplace=True)
-price_change.rename(index=ticker_list, inplace=True)
-```
-
-```{code-cell} ipython3
-fig, ax = plt.subplots(figsize=(10,8))
-ax.set_xlabel('stock', fontsize=12)
-ax.set_ylabel('percentage change in price', fontsize=12)
-price_change.plot(kind='bar', ax=ax)
-plt.show()
-```
-
-```{solution-end}
-```
-
-
-```{exercise-start}
-:label: pl_ex2
-```
-
-Using the method `read_data` introduced in {ref}`pl_ex1`, write a program to obtain year-on-year percentage change for the following indices:
-
-```{code-cell} ipython3
-indices_list = {'^GSPC': 'S&P 500',
-               '^IXIC': 'NASDAQ',
-               '^DJI': 'Dow Jones',
-               '^N225': 'Nikkei'}
-```
-
-Complete the program to show summary statistics and plot the result as a time series graph like this one:
-
-```{image} /_static/lecture_specific/pandas/pandas_indices_pctchange.png
-:scale: 80
-:align: center
-```
-
-```{exercise-end}
-```
-
-```{solution-start} pl_ex2
-:class: dropdown
-```
-
-Following the work you did in {ref}`pl_ex1`, you can query the data using `read_data` by updating the start and end dates accordingly.
-
-```{code-cell} ipython3
-indices_data = read_data(
-        indices_list,
-        start=dt.datetime(1971, 1, 1),  
-        end=dt.datetime(2021, 12, 31)
-)
-```
-
-Then, calculate the yearly returns using polars:
-
-```{code-cell} ipython3
-# Combine all yearly returns using concat and pivot approach
-all_yearly_data = []
-
-for index_col in indices_data.columns:
-    if index_col != 'Date':
-        yearly_data = (indices_data
-                      .with_columns(pl.col('Date').dt.year().alias('year'))
-                      .group_by('year')
-                      .agg([
-                          pl.col(index_col).first().alias('first_price'),
-                          pl.col(index_col).last().alias('last_price')
-                      ])
-                      .with_columns(
-                          ((pl.col('last_price') - pl.col('first_price') + 1e-10) 
-                          / (pl.col('first_price') + 1e-10)).alias('return')
-                      )
-                      .with_columns(pl.lit(indices_list[index_col]).alias('index_name'))
-                      .select(['year', 'index_name', 'return']))
-        
-        all_yearly_data.append(yearly_data)
-
-# Concatenate all data
-combined_data = pl.concat(all_yearly_data)
-
-# Pivot to get indices as columns
-yearly_returns = combined_data.pivot(values='return', index='year', on='index_name')
-
-yearly_returns
-```
-
-Next, you can obtain summary statistics by using the method `describe`.
-
-```{code-cell} ipython3
-yearly_returns.select(pl.exclude('year')).describe()
-```
-
-Then, to plot the chart
-
-```{code-cell} ipython3
-# Convert to pandas for plotting
-yearly_returns_pd = yearly_returns.to_pandas().set_index('year')
-
-fig, axes = plt.subplots(2, 2, figsize=(10, 8))
-
-# Flatten 2-D array to 1-D array
-for iter_, ax in enumerate(axes.flatten()): 
-    if iter_ < len(yearly_returns_pd.columns):
-    
-        # Get index name per iteration
-        index_name = yearly_returns_pd.columns[iter_] 
-        
-        # Plot pct change of yearly returns per index
-        ax.plot(yearly_returns_pd[index_name])
-        ax.set_ylabel("percent change", fontsize = 12)
-        ax.set_title(index_name)
-
-plt.tight_layout()
-```
-
-```{solution-end}
-```
-
 [^mung]: Wikipedia defines munging as cleaning data from one raw form into a structured, purged one.