def import_fangraphs_pitching(self, table, start, end, cache=False):
if cache:
from pybaseball import cache
cache.enable()
data = pitching(start, end)
data.to_sql(table,
self.db_connection.get_connection(),
if_exists='append')
def import_statcast(self,
table,
start,
end,
team=None,
verbose=True,
cache=False):
if cache:
from pybaseball import cache
cache.enable()
if team is not None:
data = statcast(start, end, team, verbose)
else:
data = statcast(start, end, verbose)
data.to_sql(table,
self.db_connection.get_connection(),
if_exists='append')
def test_cache_enable() -> None:
enable_mock = MagicMock()
with patch('pybaseball.cache.config.enable', enable_mock):
cache.enable()
enable_mock.assert_called_once_with(True)
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import pybaseball as pyb
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from pybaseball import cache
cache.enable()
fig, ax = plt.subplots(figsize=(12, 7))
"""
#creating the df
df = pd.DataFrame({
'hits_last_year': [120, 180, 105, 133, 150],
'abs_last_year': [400, 560, 450, 505, 490],
'hits_next_year': [127, 170, 110, 128, 145]})
#printing the df
print(df.head())
#styling the graph
sns.set_style('whitegrid')
ax.set_title('Projecting Hits')
ax.set_ylabel('Projected Hits')
ax.set_xlabel('Last Years Hits')
sns.regplot(data = df, x = 'hits_last_year', y = 'hits_next_year')
#plt.show()