1、数据清洗是数据分析关键的一步,直接影响之后的处理工作
2、数据需要修改吗?有什么需要修改的吗?数据应该怎么调整才能适用于接下来的分析和挖掘?
3、是一个迭代的过程,实际项目中可能需要不止一次地执行这些清洗操作
4、处理缺失数据:pd.fillna(),pd.dropna()
1、数据连接(pd.merge)
1、pd.merge
2、根据单个或多个键将不同DataFrame的行连接起来
3、类似数据库的连接操作
示例代码:
import pandas as pd import numpy as np df_obj1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'], 'data1' : np.random.randint(0,10,7)}) df_obj2 = pd.DataFrame({'key': ['a', 'b', 'd'], 'data2' : np.random.randint(0,10,3)}) print(df_obj1) print(df_obj2)
运行结果:
data1 key data1 key 0 8 b 1 8 b 2 3 a 3 5 c 4 4 a 5 9 a 6 6 b data2 key 0 9 a 1 0 b 2 3 d
1、默认将重叠列的列名作为“外键”进行连接
示例代码:
# 默认将重叠列的列名作为“外键”进行连接 print(pd.merge(df_obj1, df_obj2))
运行结果:
data1 key data2 0 8 b 0 1 8 b 0 2 6 b 0 3 3 a 9 4 4 a 9 5 9 a 9
2、on显示指定“外键”
示例代码:
# on显示指定“外键” print(pd.merge(df_obj1, df_obj2, on='key'))
运行结果:
data1 key data2 0 8 b 0 1 8 b 0 2 6 b 0 3 3 a 9 4 4 a 9 5 9 a 9
3、left_on,左侧数据的“外键”,right_on,右侧数据的“外键”
示例代码:
# left_on,right_on分别指定左侧数据和右侧数据的“外键” # 更改列名 df_obj1 = df_obj1.rename(columns={'key':'key1'}) df_obj2 = df_obj2.rename(columns={'key':'key2'}) print(pd.merge(df_obj1, df_obj2, left_on='key1', right_on='key2'))
运行结果:
data1 key1 data2 key2 0 8 b 0 b 1 8 b 0 b 2 6 b 0 b 3 3 a 9 a 4 4 a 9 a
5 9 a 9 a
默认是“内连接”(inner),即结果中的键是交集
how指定连接方式
4、“外连接”(outer),结果中的键是并集
示例代码:
# “外连接” print(pd.merge(df_obj1, df_obj2, left_on='key1', right_on='key2', how='outer'))
运行结果:
data1 key1 data2 key2 0 8.0 b 0.0 b 1 8.0 b 0.0 b 2 6.0 b 0.0 b 3 3.0 a 9.0 a 4 4.0 a 9.0 a 5 9.0 a 9.0 a 6 5.0 c NaN NaN 7 NaN NaN 3.0 d
5、“左连接”(left)
示例代码:
# 左连接 print(pd.merge(df_obj1, df_obj2, left_on='key1', right_on='key2', how='left'))
运行结果:
data1 key1 data2 key2 0 8 b 0.0 b 1 8 b 0.0 b 2 3 a 9.0 a 3 5 c NaN NaN 4 4 a 9.0 a 5 9 a 9.0 a
6 6 b 0.0 b
6、“右连接”(right)
示例代码:
# 右连接 print(pd.merge(df_obj1, df_obj2, left_on='key1', right_on='key2', how='right'))
运行结果:
data1 key1 data2 key2 0 8.0 b 0 b 1 8.0 b 0 b 2 6.0 b 0 b 3 3.0 a 9 a 4 4.0 a 9 a 5 9.0 a 9 a 6 NaN NaN 3 d
7、处理重复列名
suffixes,默认为_x, _y
示例代码:
# 处理重复列名 df_obj1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'], 'data' : np.random.randint(0,10,7)}) df_obj2 = pd.DataFrame({'key': ['a', 'b', 'd'], 'data' : np.random.randint(0,10,3)}) print(pd.merge(df_obj1, df_obj2, on='key', suffixes=('_left', '_right')))
运行结果:
data_left key data_right 0 9 b 1 1 5 b 1 2 1 b 1 3 2 a 8 4 2 a 8 5 5 a 8
8、按索引连接
left_index=True或right_index=True
示例代码:
# 按索引连接 df_obj1 = pd.DataFrame({'key': ['b', 'b', 'a', 'c', 'a', 'a', 'b'], 'data1' : np.random.randint(0,10,7)}) df_obj2 = pd.DataFrame({'data2' : np.random.randint(0,10,3)}, index=['a', 'b', 'd']) print(pd.merge(df_obj1, df_obj2, left_on='key', right_index=True))
运行结果:
data1 key data2 0 3 b 6 1 4 b 6 6 8 b 6 2 6 a 0 4 3 a 0 5 0 a 0
2、数据合并(pd.concat)
沿轴方向将多个对象合并到一起
1、numpy的concat
np.concatenate
示例代码:
import numpy as np import pandas as pd arr1 = np.random.randint(0, 10, (3, 4)) arr2 = np.random.randint(0, 10, (3, 4)) print(arr1) print(arr2) print(np.concatenate([arr1, arr2])) print(np.concatenate([arr1, arr2], axis=1))
运行结果:
# print(arr1) [[3 3 0 8] [2 0 3 1] [4 8 8 2]] # print(arr2) [[6 8 7 3] [1 6 8 7] [1 4 7 1]] # print(np.concatenate([arr1, arr2])) [[3 3 0 8] [2 0 3 1] [4 8 8 2] [6 8 7 3] [1 6 8 7] [1 4 7 1]] # print(np.concatenate([arr1, arr2], axis=1)) [[3 3 0 8 6 8 7 3] [2 0 3 1 1 6 8 7] [4 8 8 2 1 4 7 1]]
2、pd.concat
1、注意指定轴方向,默认axis=0
2、join指定合并方式,默认为outer
3、Series合并时查看行索引有无重复
index没有重复的情况
示例代码:
# index 没有重复的情况 ser_obj1 = pd.Series(np.random.randint(0, 10, 5), index=range(0,5)) ser_obj2 = pd.Series(np.random.randint(0, 10, 4), index=range(5,9)) ser_obj3 = pd.Series(np.random.randint(0, 10, 3), index=range(9,12)) print(ser_obj1) print(ser_obj2) print(ser_obj3) print(pd.concat([ser_obj1, ser_obj2, ser_obj3])) print(pd.concat([ser_obj1, ser_obj2, ser_obj3], axis=1))
运行结果:
# print(ser_obj1) 0 1 1 8 2 4 3 9 4 4 dtype: int64 # print(ser_obj2) 5 2 6 6 7 4
8 2 dtype: int64 # print(ser_obj3) 9 6 10 2 11 7 dtype: int64 # print(pd.concat([ser_obj1, ser_obj2, ser_obj3])) 0 1 1 8 2 4 3 9 4 4 5 2 6 6 7 4 8 2 9 6 10 2 11 7 dtype: int64 # print(pd.concat([ser_obj1, ser_obj2, ser_obj3], axis=1)) 0 1 2 0 1.0 NaN NaN 1 5.0 NaN NaN 2 3.0 NaN NaN 3 2.0 NaN NaN 4 4.0 NaN NaN 5 NaN 9.0 NaN 6 NaN 8.0 NaN 7 NaN 3.0 NaN 8 NaN 6.0 NaN 9 NaN NaN 2.0 10 NaN NaN 3.0 11 NaN NaN 3.0
index有重复的情况
示例代码:
# index 有重复的情况 ser_obj1 = pd.Series(np.random.randint(0, 10, 5), index=range(5)) ser_obj2 = pd.Series(np.random.randint(0, 10, 4), index=range(4)) ser_obj3 = pd.Series(np.random.randint(0, 10, 3), index=range(3)) print(ser_obj1) print(ser_obj2) print(ser_obj3) print(pd.concat([ser_obj1, ser_obj2, ser_obj3]))
运行结果:
# print(ser_obj1) 0 0 1 3 2 7 3 2 4 5 dtype: int64 # print(ser_obj2) 0 5 1 1 2 9 3 9 dtype: int64 # print(ser_obj3) 0 8 1 7 2 9 dtype: int64 # print(pd.concat([ser_obj1, ser_obj2, ser_obj3])) 0 0 1 3 2 7 3 2 4 5 0 5 1 1 2 9
3 9 0 8 1 7 2 9 dtype: int64 # print(pd.concat([ser_obj1, ser_obj2, ser_obj3], axis=1, join='inner')) # join='inner' 将去除NaN所在的行或列 0 1 2 0 0 5 8
1 3 1 7 2 7 9 9
dataframe合并时同时查看行、列索引有无重复
示例代码:
df_obj1 = pd.DataFrame(np.random.randint(0, 10, (3, 2)), index=['a', 'b', 'c'], columns=['A', 'B']) df_obj2 = pd.DataFrame(np.random.randint(0, 10, (2, 2)), index=['a', 'b'], columns=['C', 'D']) print(df_obj1) print(df_obj2) print(pd.concat([df_obj1, df_obj2])) print(pd.concat([df_obj1, df_obj2], axis=1, join='inner'))
运行结果:
# print(df_obj1) A B a 3 3 b 5 4 c 8 6 # print(df_obj2) C D a 1 9 b 6 8 # print(pd.concat([df_obj1, df_obj2])) A B C D a 3.0 3.0 NaN NaN b 5.0 4.0 NaN NaN c 8.0 6.0 NaN NaN a NaN NaN 1.0 9.0 b NaN NaN 6.0 8.0 # print(pd.concat([df_obj1, df_obj2], axis=1, join='inner')) A B C D a 3 3 1 9 b 5 4 6 8
3、数据重构
1、stack
1、将列索引旋转为行索引,完成层级索引
2、DataFrame->Series
示例代码:
import numpy as np import pandas as pd df_obj = pd.DataFrame(np.random.randint(0,10, (5,2)), columns=['data1', 'data2']) print(df_obj) stacked = df_obj.stack() print(stacked)
运行结果:
# print(df_obj) data1 data2 0 7 9 1 7 8 2 8 9 3 4 1 4 1 2
# print(stacked) 0 data1 7 data2 9 1 data1 7 data2 8 2 data1 8 data2 9 3 data1 4 data2 1 4 data1 1 data2 2 dtype: int64
2、unstack
1、将层级索引展开
2、Series->DataFrame
3、认操作内层索引,即level=-1
示例代码:
# 默认操作内层索引 print(stacked.unstack()) # 通过level指定操作索引的级别 print(stacked.unstack(level=0))
运行结果:
# print(stacked.unstack()) data1 data2 0 7 9 1 7 8 2 8 9 3 4 1 4 1 2 # print(stacked.unstack(level=0)) 0 1 2 3 4 data1 7 7 8 4 1 data2 9 8 9 1 2
4、数据转换
1、处理重复数据
示例代码:
import numpy as np import pandas as pd df_obj = pd.DataFrame({'data1' : ['a'] * 4 + ['b'] * 4, 'data2' : np.random.randint(0, 4, 8)}) print(df_obj) print(df_obj.duplicated())
运行结果:
# print(df_obj) data1 data2 0 a 3 1 a 2 2 a 3 3 a 3 4 b 1 5 b 0 6 b 3 7 b 0 # print(df_obj.duplicated()) 0 False
1 False 2 True 3 True 4 False 5 False 6 False 7 True dtype: bool
默认判断全部列 可指定按某些列判断
示例代码:
print(df_obj.drop_duplicates()) print(df_obj.drop_duplicates('data2'))
运行结果:
# print(df_obj.drop_duplicates()) data1 data2
0 a 3 1 a 2 4 b 1 5 b 0 6 b 3 # print(df_obj.drop_duplicates('data2')) data1 data2 0 a 3 1 a 2 4 b 1 5 b 0
3. 根据map传入的函数对每行或每列进行转换
Series根据map传入的函数对每行或每列进行转换
示例代码:
ser_obj = pd. Series(np.random.randint(0,10,10)) print(ser_obj) print(ser_obj.map(lambda x : x ** 2))
运行结果:
# print(ser_obj) 0 1 1 4 2 8 3 6 4 8 5 6 6 6 7 4 8 7 9 3 dtype: int64 # print(ser_obj.map(lambda x : x ** 2)) 0 1 1 16 2 64 3 36 4 64 5 36 6 36 7 16 8 49 9 9 dtype: int64
5、数据替换
replace根据值的内容进行替换
示例代码:
# 单个值替换单个值 print(ser_obj.replace(1, -100)) # 多个值替换一个值 print(ser_obj.replace([6, 8], -100)) # 多个值替换多个值 print(ser_obj.replace([4, 7], [-100, -200]))
运行结果:
# print(ser_obj.replace(1, -100)) 0 -100 1 4 2 8 3 6 4 8 5 6
6 6 7 4 8 7 9 3 dtype: int64 # print(ser_obj.replace([6, 8], -100)) 0 1 1 4 2 -100 3 -100 4 -100 5 -100 6 -100 7 4 8 7 9 3 dtype: int64 # print(ser_obj.replace([4, 7], [-100, -200])) 0 1
1 -100 2 8 3 6 4 8 5 6 6 6 7 -100 8 -200 9 3 dtype: int64
5、全球视频数据分析
项目参考:https://www.kaggle.com/bhouwens/d/openfoodfacts/world-food-facts/how-much-sugar-do-we-eat/discussion
# -*- coding : utf-8 -*- # 处理zip压缩文件 import zipfile import os import pandas as pd import matplotlib.pyplot as plt def unzip(zip_filepath, dest_path): """ 解压zip文件 """ with zipfile.ZipFile(zip_filepath) as zf: zf.extractall(path=dest_path) def get_dataset_filename(zip_filepath): """ 获取数据集文件名 """ with zipfile.ZipFile(zip_filepath) as zf: return zf.namelist()[0] def main(): """ 主函数 """ # 声明变量 dataset_path = './data' # 数据集路径 zip_filename = 'open-food-facts.zip' # zip文件名 zip_filepath = os.path.join(dataset_path, zip_filename) # zip文件路径 dataset_filename = get_dataset_filename(zip_filepath) # 数据集文件名(在zip中) dataset_filepath = os.path.join(dataset_path, dataset_filename) # 数据集文件路径 print('解压zip...', end='') unzip(zip_filepath, dataset_path) print('完成.') # 读取数据
data = pd.read_csv(dataset_filepath, usecols=['countries_en', 'additives_n']) # 分析各国家食物中的食品添加剂种类个数 # 1. 数据清理 # 去除缺失数据 data = data.dropna() # 或者data.dropna(inplace=True) # 将国家名称转换为小写 data['countries_en'] = data['countries_en'].str.lower() # 2. 数据分组统计 country_additives = data['additives_n'].groupby(data['countries_en']).mean() # 3. 按值从大到小排序 result = country_additives.sort_values(ascending=False) # 4. pandas可视化top10 result.iloc[:10].plot.bar() plt.show() # 5. 保存处理结果 result.to_csv('./country_additives.csv') # 删除解压数据,清理空间(可选操作) if os.path.exists(dataset_filepath): os.remove(dataset_filepath) if __name__ == '__main__': main()