pip install pyarrow pandas

import pyarrow as pa

teams = pa.array(['Barcelona', 'Real Madrid', 'Rayo Vallecano', 'Athletic Club', 'Real Betis'], type=pa.string())
goals = pa.array([30, 23, 9, 24, 12], type=pa.int8())

team_goals_table = pa.table([teams, goals], names=['Team', 'Goals'])

df = team_goals_table.to_pandas()

import pyarrow.parquet as pq

# Запись в Parquet
pq.write_table(team_goals_table, 'data.parquet')

# Чтение из Parquet
table = pq.read_table('data.parquet')

import pyarrow.compute as pc

a = pa.array([1, 2, 3, 4, 5])
b = pa.array([1, 3, 3, 4, 5])

pc.equal(a, b)

pc.sum(a)

table = pa.table({'Team': ['Barcelona', 'Real Madrid'], 'Goals': [30, 23]})
table.group_by('Team').aggregate([('Goals', 'sum')])

I want you to act as a data scientist coding in Python. Given a dataframe {dataframe name} containing the columns {column names}, use Matplotlib to plot a {chart type} that shows the relationship between {variables}. Additionally, annotate the plot with the following details: {annotation requirements}. Format the plot by adjusting {specific formatting preferences}. Finally, change the plot's theme to {theme} to match the visual style of {theme description}.

import pandas as pd

data = {
    'age': [25, 30, 35, 40, 45],
    'income': [50000, 60000, 70000, 80000, 90000],
    'education_level': ['Bachelors', 'Masters', 'PhD', 'Bachelors', 'Masters']
}

df = pd.DataFrame(data)

I want you to act as a data scientist coding in Python. Given a dataframe df containing the columns ['age', 'income', 'education_level'], use Matplotlib to plot a scatter plot that shows the relationship between age and income. Additionally, annotate the plot with the following details: highlight the highest and lowest income values. Format the plot by adjusting the title, axis labels, and grid lines. Finally, change the plot's theme to seaborn-darkgrid to match the visual style of a clean and modern plot with dark gridlines.