import numpy as np
import pandas as pd
names = ['Alice', 'Bob', 'Charlie', 'Dan', 'Edith', 'Frank', 'George',
'Hannah', 'Ingrid', 'Jerry', 'Kevin', 'Laura', 'Michael', 'Norbert', 'Oliver',
'Patricia', 'Quinn', 'Ray', 'Sarah', 'Tim', 'Ursula', 'Victor', 'Wendy',
'Xavier', 'Yvonne', 'Zelda']
k = 100
def account_params(k):
ids = np.arange(k, dtype=int)
names2 = np.random.choice(names, size=k, replace=True)
wealth_mag = np.random.exponential(100, size=k)
wealth_trend = np.random.normal(10, 10, size=k)
freq = np.random.exponential(size=k)
freq /= freq.sum()
return ids, names2, wealth_mag, wealth_trend, freq
def account_entries(n, ids, names, wealth_mag, wealth_trend, freq):
indices = np.random.choice(ids, size=n, replace=True, p=freq)
amounts = ((np.random.normal(size=n) + wealth_trend[indices])
* wealth_mag[indices])
return pd.DataFrame({'id': indices,
'names': names[indices],
'amount': amounts.astype('i4')},
columns=['id', 'names', 'amount'])
def accounts(n, k):
ids, names, wealth_mag, wealth_trend, freq = account_params(k)
df = account_entries(n, ids, names, wealth_mag, wealth_trend, freq)
return df
def json_entries(n, *args):
df = account_entries(n, *args)
g = df.groupby(df.id).groups
data = []
for k in g:
sub = df.iloc[g[k]]
d = dict(id=int(k), name=sub['names'].iloc[0],
transactions=[{'transaction-id': int(i), 'amount': int(a)}
for i, a in list(zip(sub.index, sub.amount))])
data.append(d)
return data
def accounts_json(n, k):
args = account_params(k)
return json_entries(n, *args)
