大数据分析实践 | pandas数据质量分析

📚数据质量评估的五个维度

• Coherent: without semantic errors or contradictory data between attributes of an object
• Correct: the extent to which data correctly portrays reality
• Completeness: without missing (null) values in table fields
• Currency: the degree to which data is up-to-date
• Consistency: consistent data values for an entity between different tables

📚口袋妖怪数据质量分析

🐇导入库和数据

import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')   #忽略警告
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
url = r'./data/Pokemon.csv'
data = pd.DataFrame(pd.read_csv(url))  #打开文件，读取数据
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🐇检查数据

# 输出前五行数据
print('前五行数据:')
print(data.head(5))
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---

# 输出最后三行数据
print('后三行数据')
print(data.tail(3))
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• 删除后两行 ：data = data.iloc[:-2]

---

# 查看列名
print('列名：',data.columns)
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# 查看行数与列数
print('行数与列数:',data.shape)
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# 展示index，datatype和memory相关信息
print(data.info())
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🐇缺失值分析

raw, column = data.shape  
if None:
    num = data[col].isnull().sum().sort_values()
else:
    num = data.isnull().sum().sort_values() 
print(None, r'缺失数')
print(num)  
print(None, r'缺失比例')
print(num/raw)
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• 将所有缺失值填充为“null”：data = data.fillna("null")

🐇重复值检测

主要针对#列

# 检查 'id' 列是否有重复值
duplicate_ids = data.duplicated('#')
# 获取所有具有重复 id 的行
duplicate_rows = data[duplicate_ids]
# 打印具有重复 id 的行
print("具有重复 id 的行：")
print(duplicate_rows)
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• 对于 # 重复的妖怪只保留第一条记录：data = data.drop_duplicates(['#'],keep='first')

🐇异常值检测

• 离群值

  data['Attack'] = data['Attack'].astype(float)
  data['Defense'] = data['Defense'].astype(float)
  data['Sp. Atk'] = data['Sp. Atk'].astype(float)
  data['Sp. Def'] = data['Sp. Def'].astype(float)
  data['Speed'] = data['Speed'].astype(float)
  data.boxplot(column=['Attack','Defense','Sp. Atk','Sp. Def','Speed'])
  plt.show()
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---

• 检查 Generation列是否存在非数字值

  non_numeric_generation = pd.to_numeric(data['Generation'], errors='coerce').isna()
  print(data[non_numeric_generation])
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  • data = data[~non_numeric_generation]

---

• 检查 Legendary列除了TRUE和FALSE之外的值

  filtered_data = data[~data['Legendary'].isin(['TRUE', 'FALSE'])]
  print(filtered_data)
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  • data = data.drop(filtered_data.index)