marketneutral

def make_tooltipped_df(df, tooltips: dict):
  """
  import pandas as pd
  from IPython.display import display, HTML

  # Sample DataFrame
  data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
  df = pd.DataFrame(data)

  # Apply styles to the DataFrame

marketneutral

from joblib import Parallel, delayed

def expesive_calc(df):
    df['new_col'] = ...
    return df

def apply_parallel(gdf, func):
    ret_list = Parallel(n_jobs=8)(delayed(func)(group) for name, group in gdf)
    return pd.concat(ret_list)

marketneutral

from sklearn.linear_model import LinearRegression
from sklearn.pipeline import FeatureUnion

from skits.feature_extraction import AutoregressiveTransformer
from skits.pipeline import ForecasterPipeline
from skits.preprocessing import ReversibleImputer

df = pd.DataFrame(
    {
        "date": [1, 1, 1, 2, 2, 2, 3, 3 ,3, 4, 4, 4, 5, 5, 5],

marketneutral

from joblib import Parallel, delayed
from sklearn import svm

data_train = [[0,2,3],[1,2,3],[4,2,3]]
targets_train = [0,1,0]

clf = svm.SVC(kernel='rbf', degree=3, C=10, gamma=0.3, probability=True)
clf.fit(data_train, targets_train)

to_be_predicted = np.array([[1,3,4], [1,3,4], [1,3,5]])