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Pandas数据清洗

作者:互联网

数据清洗

一、数据清洗和准备

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

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?

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

1. 处理缺失数据:

image

2. 数据转换

2.1 处理重复数据

duplicated()返回布尔型Series表示每行是否为重复行

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

drop_duplicates()过滤重复行

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

2.2 利用函数或映射进行数据转换

根据map传入的函数对每行或每列进行转换

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

2.3 替换值

replace根据值的内容进行替换

# 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

3. 字符串操作

3.1 字符串方法:

image
image

3.2 正则表达式方法

image

3.3 pandas字符串函数:

image

二、数据合并

数据合并(pd.merge)

image

image

image

image

示例代码:

import pandas as pd
import numpy as np

left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
                      'A': ['A0', 'A1', 'A2', 'A3'],
                       'B': ['B0', 'B1', 'B2', 'B3']})

right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
                      'C': ['C0', 'C1', 'C2', 'C3'],
                      'D': ['D0', 'D1', 'D2', 'D3']})

pd.merge(left,right,on='key') #指定连接键key

运行结果:

key    A    B    C    D
0    K0    A0    B0    C0    D0
1    K1    A1    B1    C1    D1
2    K2    A2    B2    C2    D2
3    K3    A3    B3    C3    D3

image

示例代码:

left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
                    'key2': ['K0', 'K1', 'K0', 'K1'],
                    'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3']})

right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
                      'key2': ['K0', 'K0', 'K0', 'K0'],
                      'C': ['C0', 'C1', 'C2', 'C3'],
                      'D': ['D0', 'D1', 'D2', 'D3']})

pd.merge(left,right,on=['key1','key2']) #指定多个键,进行合并

运行结果:

    key1    key2    A    B    C    D
0    K0    K0    A0    B0    C0    D0
1    K1    K0    A2    B2    C1    D1
2    K1    K0    A2    B2    C2    D2

image

#指定左连接

left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
                    'key2': ['K0', 'K1', 'K0', 'K1'],
                    'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
                      'key2': ['K0', 'K0', 'K0', 'K0'],
                      'C': ['C0', 'C1', 'C2', 'C3'],
                      'D': ['D0', 'D1', 'D2', 'D3']})

pd.merge(left, right, how='left', on=['key1', 'key2'])
    key1    key2          A    B    C    D
0    K0        K0        A0    B0    C0    D0
1    K0        K1        A1    B1    NaN    NaN
2    K1        K0        A2    B2    C1    D1
3    K1        K0        A2    B2    C2    D2
4    K2        K1        A3    B3    NaN    NaN

image

#指定右连接

left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
                    'key2': ['K0', 'K1', 'K0', 'K1'],
                    'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
                      'key2': ['K0', 'K0', 'K0', 'K0'],
                      'C': ['C0', 'C1', 'C2', 'C3'],
                      'D': ['D0', 'D1', 'D2', 'D3']})
pd.merge(left, right, how='right', on=['key1', 'key2'])
    key1    key2          A    B    C    D
0    K0        K0        A0    B0    C0    D0
1    K1        K0        A2    B2    C1    D1
2    K1        K0        A2    B2    C2    D2
3    K2        K0        NaN    NaN    C3    D3

image

默认是“内连接”(inner),即结果中的键是交集

how指定连接方式

“外连接”(outer),结果中的键是并集

示例代码:

left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],
                    'key2': ['K0', 'K1', 'K0', 'K1'],
                    'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3']})
right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],
                      'key2': ['K0', 'K0', 'K0', 'K0'],
                      'C': ['C0', 'C1', 'C2', 'C3'],
                      'D': ['D0', 'D1', 'D2', 'D3']})
pd.merge(left,right,how='outer',on=['key1','key2'])

运行结果:

key1    key2    A    B    C    D
0    K0    K0    A0    B0    C0    D0
1    K0    K1    A1    B1    NaN    NaN
2    K1    K0    A2    B2    C1    D1
3    K1    K0    A2    B2    C2    D2
4    K2    K1    A3    B3    NaN    NaN
5    K2    K0    NaN    NaN    C3    D3

image

处理重复列名

参数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

按索引连接

参数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

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

df1 = pd.DataFrame(np.arange(6).reshape(3,2),index=list('abc'),columns=['one','two'])

df2 = pd.DataFrame(np.arange(4).reshape(2,2)+5,index=list('ac'),columns=['three','four'])

pd.concat([df1,df2]) #默认外连接,axis=0
    four    one    three    two
a    NaN        0.0    NaN        1.0
b    NaN        2.0    NaN        3.0
c    NaN        4.0    NaN        5.0
a    6.0        NaN    5.0        NaN
c    8.0        NaN    7.0        NaN

pd.concat([df1,df2],axis='columns') #指定axis=1连接
    one    two    three    four
a    0    1    5.0        6.0
b    2    3    NaN        NaN
c    4    5    7.0        8.0

#同样我们也可以指定连接的方式为inner
pd.concat([df1,df2],axis=1,join='inner')

    one    two    three    four
a    0    1    5        6
c    4    5    7        8

三、重塑

1. stack

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

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

标签:obj,NaN,K1,K0,pd,print,清洗,数据,Pandas
来源: https://www.cnblogs.com/lipinbigdata/p/15554233.html