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# Copyright 2017-2020 Spotify AB # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License.
""" Returns an output dataframe with categorical features (country and test variation), and orginal features (date), as well as number of successes and total observations for each combination """ countries = ['ca', 'us'] dates = pd.date_range('2018-01-01', '2018-02-01') variation_names = ['test', 'control', 'test2']
# test ca, test us, control ca, control us, test2 ca, test2 us success_rates = [.3, .32, .24, .22, .25, .42] n_observations = [50, 80, 30, 50, 40, 50]
return_df = pd.DataFrame()
for i, (country, variation) in enumerate( product(countries, variation_names)): df = pd.DataFrame({'date': dates}) df['country'] = country df['variation_name'] = variation df['total'] = np.random.poisson(n_observations[i], size=len(dates)) df['success'] = df['total'].apply( lambda x: np.random.binomial(x, success_rates[i])) return_df = pd.concat([return_df, df], axis=0)
return return_df
df = pd.DataFrame({ 'variation_name': [ 'test', 'control', 'test2', 'test', 'control', 'test2', 'test', 'control', 'test2', 'test', 'control', 'test2', 'test', 'control', 'test2', ], 'nr_of_items': [ 500, 8, 100, 510, 8, 100, 520, 9, 104, 530, 7, 100, 530, 8, 103, ], 'nr_of_items_sumsq': [ 2500, 12, 150, 2510, 13, 140, 2520, 14, 154, 2530, 15, 160, 2530, 16, 103, ], 'users': [ 1010, 22, 150, 1000, 20, 153, 1030, 23, 154, 1000, 20, 150, 1040, 21, 155, ], 'days_since_reg': [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5], })
return df |