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| 1 | +import os, sys, json, datetime, math, random |
| 2 | +import requests |
| 3 | +from collections import defaultdict, OrderedDict |
| 4 | +from typing import List, Dict, Optional, Union, Tuple, Any |
| 5 | +import numpy as np |
| 6 | +import pandas as pd |
| 7 | + |
| 8 | +# This is a poorly formatted Python file with many style violations |
| 9 | + |
| 10 | + |
| 11 | +class UnformattedExampleClass(object): |
| 12 | + def __init__( |
| 13 | + self, |
| 14 | + name, |
| 15 | + age=None, |
| 16 | + email=None, |
| 17 | + phone=None, |
| 18 | + address=None, |
| 19 | + city=None, |
| 20 | + state=None, |
| 21 | + zip_code=None, |
| 22 | + ): |
| 23 | + self.name = name |
| 24 | + self.age = age |
| 25 | + self.email = email |
| 26 | + self.phone = phone |
| 27 | + self.address = address |
| 28 | + self.city = city |
| 29 | + self.state = state |
| 30 | + self.zip_code = zip_code |
| 31 | + self.data = {"name": name, "age": age, "email": email} |
| 32 | + |
| 33 | + def get_info(self): |
| 34 | + return f"Name: {self.name}, Age: {self.age}" |
| 35 | + |
| 36 | + def update_data(self, **kwargs): |
| 37 | + for key, value in kwargs.items(): |
| 38 | + if hasattr(self, key): |
| 39 | + setattr(self, key, value) |
| 40 | + self.data.update(kwargs) |
| 41 | + |
| 42 | + |
| 43 | +def process_data( |
| 44 | + data_list, filter_func=None, transform_func=None, sort_key=None, reverse=False |
| 45 | +): |
| 46 | + if not data_list: |
| 47 | + return [] |
| 48 | + if filter_func: |
| 49 | + data_list = [item for item in data_list if filter_func(item)] |
| 50 | + if transform_func: |
| 51 | + data_list = [transform_func(item) for item in data_list] |
| 52 | + if sort_key: |
| 53 | + data_list = sorted(data_list, key=sort_key, reverse=reverse) |
| 54 | + return data_list |
| 55 | + |
| 56 | + |
| 57 | +def calculate_statistics(numbers): |
| 58 | + if not numbers: |
| 59 | + return None |
| 60 | + mean = sum(numbers) / len(numbers) |
| 61 | + median = sorted(numbers)[len(numbers) // 2] |
| 62 | + variance = sum((x - mean) ** 2 for x in numbers) / len(numbers) |
| 63 | + std_dev = math.sqrt(variance) |
| 64 | + return { |
| 65 | + "mean": mean, |
| 66 | + "median": median, |
| 67 | + "variance": variance, |
| 68 | + "std_dev": std_dev, |
| 69 | + "min": min(numbers), |
| 70 | + "max": max(numbers), |
| 71 | + } |
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