!pip install --upgrade polars yfinance

import polars as pl
import numpy as np
import matplotlib.pyplot as plt

s = pl.Series(name='daily returns', values=np.random.randn(4))
s

s * 100

s.abs()

s.describe()

df = pl.DataFrame({
    'company': ['AMZN', 'AAPL', 'MSFT', 'GOOG'],
    'daily returns': np.random.randn(4)
})
df

df.filter(
    pl.col('company') == 'AMZN'
).select('daily returns').item()

df = df.with_columns(
    pl.when(pl.col('company') == 'AMZN')
    .then(0)
    .otherwise(pl.col('daily returns'))
    .alias('daily returns')
)
df

'AAPL' in df['company']

url = ('https://raw.githubusercontent.com/QuantEcon/'
       'lecture-python-programming/main/lectures/_static/'
       'lecture_specific/pandas/data/test_pwt.csv')
df = pl.read_csv(url)
df

df[2:5]

df.select(['country', 'tcgdp'])

df[2:5].select(['country', 'tcgdp'])

df.filter(pl.col('POP') >= 20000)

df.filter(
    (pl.col('country').is_in(['Argentina', 'India', 'South Africa'])) &
    (pl.col('POP') > 40000)
)

df.filter(
    (pl.col('cc') + pl.col('cg') >= 80) & (pl.col('POP') <= 20000)
)

df.filter(pl.col('cc') == pl.col('cc').max())

df.select(
    pl.col(['year', 'POP', 'XRAT', 'tcgdp', 'cc', 'cg'])
    .max()
    .name.suffix('_max')
)

df.with_columns(
    (pl.col('XRAT') / 10).alias('XRAT_scaled'),
    pl.col(pl.Float64).round(2)
)

df.with_columns(
    pl.when(pl.col('POP') >= 20000)
    .then(pl.col('POP'))
    .otherwise(None)
    .alias('POP_filtered')
).select(['country', 'POP', 'POP_filtered'])

df_nulls = df.with_row_index().with_columns(
    pl.when(pl.col('index') == 0)
    .then(None).otherwise(pl.col('XRAT')).alias('XRAT'),
    pl.when(pl.col('index') == 3)
    .then(None).otherwise(pl.col('cc')).alias('cc'),
    pl.when(pl.col('index') == 5)
    .then(None).otherwise(pl.col('tcgdp')).alias('tcgdp'),
    pl.when(pl.col('index') == 6)
    .then(None).otherwise(pl.col('POP')).alias('POP'),
).drop('index')
df_nulls

df_nulls.fill_null(0)

cols = ['cc', 'tcgdp', 'POP', 'XRAT']
df_nulls.with_columns(
    pl.col(cols).fill_null(pl.col(cols).mean())
)

df = (df
    .select(['country', 'POP', 'tcgdp'])
    .rename({'POP': 'population', 'tcgdp': 'total GDP'})
    .with_columns(
        (pl.col('population') * 1e3).alias('population')
    )
    .with_columns(
        (pl.col('total GDP') * 1e6 / pl.col('population'))
        .alias('GDP percap')
    )
    .sort('GDP percap', descending=True)
)
df

fig, ax = plt.subplots()
ax.bar(df['country'].to_list(), df['GDP percap'].to_list())
ax.set_xlabel('country', fontsize=12)
ax.set_ylabel('GDP per capita', fontsize=12)
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

# Reload the dataset
url = ('https://raw.githubusercontent.com/QuantEcon/'
       'lecture-python-programming/main/lectures/_static/'
       'lecture_specific/pandas/data/test_pwt.csv')
df_full = pl.read_csv(url)

result_eager = (df_full
    .filter(pl.col('tcgdp') > 1000)
    .select(['country', 'year', 'tcgdp'])
    .sort('tcgdp', descending=True)
)
result_eager.head()

lazy_query = (df_full.lazy()
    .filter(pl.col('tcgdp') > 1000)
    .select(['country', 'year', 'tcgdp'])
    .sort('tcgdp', descending=True)
)
print(lazy_query.explain())

result_lazy = lazy_query.collect()
result_lazy.head()

optimized = (df_full.lazy()
    .select(['country', 'year', 'tcgdp', 'POP'])
    .filter(pl.col('tcgdp') > 500)
    .with_columns(
        (pl.col('tcgdp') / pl.col('POP')).alias('gdp_per_capita')
    )
    .filter(pl.col('gdp_per_capita') > 10)
    .select(['country', 'year', 'gdp_per_capita'])
)

print("Optimized plan:")
print(optimized.explain())

optimized.collect()

import pandas as pd
import time

# Small dataset -- Penn World Tables (~8 rows)
url = ('https://raw.githubusercontent.com/QuantEcon/'
       'lecture-python-programming/main/lectures/_static/'
       'lecture_specific/pandas/data/test_pwt.csv')
small_pd = pd.read_csv(url)
small_pl = pl.read_csv(url)

# pandas
start = time.perf_counter()
_ = (small_pd
     .query('tcgdp > 500')
     [['country', 'year', 'tcgdp', 'POP']]
     .assign(gdp_pc=lambda d: d['tcgdp'] / d['POP'])
     .sort_values('gdp_pc', ascending=False))
pd_small = time.perf_counter() - start

# Polars eager
start = time.perf_counter()
_ = (small_pl
     .filter(pl.col('tcgdp') > 500)
     .select(['country', 'year', 'tcgdp', 'POP'])
     .with_columns((pl.col('tcgdp') / pl.col('POP')).alias('gdp_pc'))
     .sort('gdp_pc', descending=True))
pl_small = time.perf_counter() - start

print(f"Small data  --  pandas: {pd_small:.4f}s | Polars eager: {pl_small:.4f}s")

n = 5_000_000
np.random.seed(42)

groups = np.random.choice(['A', 'B', 'C', 'D'], n)
values = np.random.randn(n)
weights = np.random.rand(n)
extra1 = np.random.randn(n)
extra2 = np.random.randn(n)

big_pd = pd.DataFrame({
    'group': groups, 'value': values,
    'weight': weights, 'extra1': extra1, 'extra2': extra2
})
big_pl = pl.DataFrame({
    'group': groups, 'value': values,
    'weight': weights, 'extra1': extra1, 'extra2': extra2
})

start = time.perf_counter()
tmp = big_pd[big_pd['value'] > 0][['group', 'value', 'weight']].copy()
tmp['weighted'] = tmp['value'] * tmp['weight']
_ = tmp.groupby('group')['weighted'].mean()
pd_time = time.perf_counter() - start
print(f"pandas:       {pd_time:.4f}s")

start = time.perf_counter()
_ = (big_pl
    .filter(pl.col('value') > 0)
    .select(['group', 'value', 'weight'])
    .with_columns(
        (pl.col('value') * pl.col('weight')).alias('weighted'))
    .group_by('group')
    .agg(pl.col('weighted').mean()))
eager_time = time.perf_counter() - start
print(f"Polars eager: {eager_time:.4f}s")

start = time.perf_counter()
_ = (big_pl.lazy()
    .filter(pl.col('value') > 0)
    .select(['group', 'value', 'weight'])
    .with_columns(
        (pl.col('value') * pl.col('weight')).alias('weighted'))
    .group_by('group')
    .agg(pl.col('weighted').mean())
    .collect())
lazy_time = time.perf_counter() - start
print(f"Polars lazy:  {lazy_time:.4f}s")

fred_url = ('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=1318&'
            '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=2024-06-01&line_color=%234572a7&'
            'link_values=false&line_style=solid&'
            'mark_type=none&mw=3&lw=2&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=2024-07-29&'
            'revision_date=2024-07-29&nd=1948-01-01')
data = pl.read_csv(fred_url, try_parse_dates=True)

data.head()

data.describe()

filtered = data.filter(
    (pl.col('observation_date') >= pl.date(2006, 1, 1)) &
    (pl.col('observation_date') <= pl.date(2012, 12, 31))
)

fig, ax = plt.subplots()
ax.plot(filtered['observation_date'].to_list(),
        filtered['UNRATE'].to_list())
ax.set_title('US Unemployment Rate')
ax.set_xlabel('year', fontsize=12)
ax.set_ylabel('%', fontsize=12)
plt.show()