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How to use merge_variables method in lisa

Best Python code snippet using lisa_python

speed_review.py

Source:speed_review.py

1import unittest2from itertools import groupby34import pandas as pd5import numpy as np6789class MyTestCase(unittest.TestCase):1011    df = pd.read_csv("Dogs_short.csv", parse_dates=[["Date","Time"]], dayfirst=True)12    df.drop(df.index[df.SP.eq(0)], inplace=True)1314    df["Fin"] = df["Fin"].where(df["Fin"].eq("1"), 0).astype(int)15    df.sort_values(by="Date_Time", inplace=True)16    df["Date_Time_dt"] = df["Date_Time"].dt.date17    df = df.drop_duplicates(subset=["Dog name", "Date_Time_dt"])18    df.reset_index(inplace=True, drop=True)1920    # 1-task21    #@unittest.skip("demonstrating skipping")22    def test_convo_group_mean(self):23        cmg = convo_mean_group(self.df, "SP", 5, "Dog name")24        normal_rolling = self.df.groupby(by="Dog name")["SP"].rolling(5, min_periods=1).mean()25        normal_rolling.sort_index(level=1, inplace=True)26        normal_rolling.index = normal_rolling.index.droplevel(0)27        cmg.name = normal_rolling.name28        pd.testing.assert_series_equal(cmg, normal_rolling)2930    # 2-task31    #@unittest.skip("demonstrating skipping")32    def test_races_since_win(self):33        self.df["since_last_win"] = since_last_win(self.df, "Dog name")34        last_value = self.df.loc[self.df["Dog name"].eq("Foxwood Boom"), "Fin"][::-1].cumsum().eq(0).sum() - 135        self.assertEqual(last_value, self.df.loc[self.df["Dog name"].eq("Foxwood Boom"), "since_last_win"].iloc[-1])3637    # 3-task38    #@unittest.skip("demonstrating skipping")39    def test_insert_historical(self):40        c = self.df.groupby(by="Dog name")["SP"].transform(lambda x: x.expanding().mean().shift())41        self.df["expanding_SP"] = self.df.groupby(by="Dog name")["SP"].transform(lambda x: x.expanding().mean())42        until_df = self.df.loc[:, ["Dog name", "expanding_SP", "Date_Time"]]43        self.df.drop("expanding_SP", axis=1, inplace=True)44        self.df = insert_historical_attrs_groupby(self.df, until_df, ["Dog name"], ["expanding_SP"])4546        pd.testing.assert_series_equal(self.df["expanding_SP"], c, check_names=False)4748def since_last_win(df, group_attr):49    return df.groupby(group_attr)["Fin"].transform(lambda x: x.groupby(x.cumsum()).cumcount().shift())5051def convo_mean_group(df, attr, window_size, group_attr):52    groups = df.groupby(group_attr)[attr]53    s = [[],[]]54    for i, grp in groups:55        s[1].extend(grp.index)56        c = np.convolve(grp, np.ones(window_size))[:len(grp)]57        divisor = np.ones(len(grp))*window_size58        divisor[:min(len(grp),window_size)] = np.arange(min(len(grp),window_size)) +159        s[0].extend(c/divisor)60    return pd.Series(s[0], index=s[1]).sort_index()6162def insert_historical_attrs_groupby(df, historical_df, merge_variables, new_variables):63    interim_df = df.groupby(merge_variables + [df["Date_Time"].dt.normalize()]).size()64    interim_df = interim_df.reset_index().iloc[:, :-1]6566    interim_df_hist = historical_df.groupby(merge_variables + [df["Date_Time"].dt.normalize()])[new_variables].last().reset_index()6768    interim_df = interim_df.merge(interim_df_hist, on=merge_variables, suffixes=(None, "_hist"))69    interim_df = interim_df.loc[interim_df["Date_Time"].gt(interim_df["Date_Time_hist"]),:].drop_duplicates(keep="last", subset=merge_variables + ["Date_Time"])7071    return df.merge(interim_df, how="left", left_on=merge_variables + [df["Date_Time"].dt.normalize()], right_on= merge_variables + ["Date_Time"])727374if __name__ == '__main__':
...

runner_meta_classs.py

Source:runner_meta_classs.py

...21    """22def {{ test_step_func_name }}(self, step):    23    # override variables24    # step variables > extracted variables from previous steps25    step.variables = merge_variables(step.variables, self.extracted_variables)26    # step variables > testcase config variables27    step.variables = merge_variables(step.variables, self._config.variables)28    # parse variables29    step.variables = parse_variables_mapping(30        step.variables, self._project_meta.functions31    )32    # run step33    # with allure.step(f"step{step.idx}: {step.name}"):34    # allure.dynamic.title(f"step{step.idx}: {step.name}")35    allure.dynamic.description(step.description or step.name)36    step_data = self.run_step(step)37    extract_mapping = step_data.export_vars38    # save extracted variables to session variables39    self.extracted_variables.update(extract_mapping)40"""41)...

PasswordGenerator.py

Source:PasswordGenerator.py

1""" Password Generator"""2import sys3import random4import string5def main():6    try:7        password_length = int(input("Insert the length of the password: "))8        if password_length > 0:9            password_generator(password_length)10            print(password_generator(password_length))11        else:12            print("Invalid input. Try again. \n")13            main()14    except ValueError:15        print("Invalid input. Try again. \n")16        main()17def password_generator(password_length):18    # variables for the password19    lowercase = string.ascii_lowercase20    uppercase = string.ascii_uppercase21    numbers = string.digits22    symbols = string.punctuation23    # temporarily stores a random combination24    merge_variables = lowercase + uppercase + numbers + symbols25    password_proto = random.sample(merge_variables, password_length)26    # merges the combination into a string27    password_final = "".join(password_proto)28    return password_final29# Press the green button in the gutter to run the script.30if __name__ == '__main__':...

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