Python Pandas 修改表格数据类型 DataFrame 列的顺序案例

一、修改表格数据类型 DataFrame 列的顺序

实战场景：Pandas 如何修改表格数据类型 DataFrame 列的顺序

1.1主要知识点

• 文件读写
• 基础语法
• 数据构建
• Pandas
• Numpy

实战：

1.2创建 python 文件

import numpy as np
import pandas as pd

np.random.seed(66)
df = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'))
print(df)
df = df[["D", "A", "B", "C"]]
print(df)

1.3运行结果 

          A         B         C         D
0  0.154288  0.133700  0.362685  0.679109
1  0.194450  0.251210  0.758416  0.557619
2  0.514803  0.467800  0.087176  0.829095
3  0.298641  0.031346  0.678006  0.903489
4  0.514451  0.539105  0.664328  0.634057
5  0.353419  0.026643  0.165290  0.879319
6  0.067820  0.369086  0.115501  0.096294
7  0.083770  0.086927  0.022256  0.771043
8  0.049213  0.465223  0.941233  0.216512
9  0.361318  0.031319  0.304045  0.188268
          D         A         B         C
0  0.679109  0.154288  0.133700  0.362685
1  0.557619  0.194450  0.251210  0.758416
2  0.829095  0.514803  0.467800  0.087176
3  0.903489  0.298641  0.031346  0.678006
4  0.634057  0.514451  0.539105  0.664328
5  0.879319  0.353419  0.026643  0.165290
6  0.096294  0.067820  0.369086  0.115501
7  0.771043  0.083770  0.086927  0.022256
8  0.216512  0.049213  0.465223  0.941233
9  0.188268  0.361318  0.031319  0.304045

二、Pandas 如何统计某个数据列的空值个数

实战场景：Pandas 如何统计某个数据列的空值个数

2.1主要知识点

• 文件读写
• 基础语法
• Pandas
• numpy

实战：

2.2创建 python 文件

"""
对如下DF，设置两个单元格的值
·使用iloc 设置(3,B)的值是nan
·使用loc设置(8,D)的值是nan
"""
import numpy as np
import pandas as pd
np.random.seed(66)
df = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'))
df.iloc[3, 1] = np.nan
df.loc[8, 'D'] = np.nan
print(df)
print(df.isnull().sum())

2.3运行结果

          A         B         C         D
0  0.154288  0.133700  0.362685  0.679109
1  0.194450  0.251210  0.758416  0.557619
2  0.514803  0.467800  0.087176  0.829095
3  0.298641       NaN  0.678006  0.903489
4  0.514451  0.539105  0.664328  0.634057
5  0.353419  0.026643  0.165290  0.879319
6  0.067820  0.369086  0.115501  0.096294
7  0.083770  0.086927  0.022256  0.771043
8  0.049213  0.465223  0.941233       NaN
9  0.361318  0.031319  0.304045  0.188268
A    0
B    1
C    0
D    1
dtype: int64

三、Pandas如何移除包含空值的行

实战场景：Pandas如何移除包含空值的行

3.1主要知识点

• 文件读写
• 基础语法
• Pandas
• numpy

实战：

3.2创建 python 文件

"""
对如下DF，设置两个单元格的值
·使用iloc 设置(3,B)的值是nan
·使用loc设置(8,D)的值是nan
"""
import numpy as np
import pandas as pd
 
np.random.seed(66)
df = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'))
df.iloc[3, 1] = np.nan
df.loc[8, 'D'] = np.nan
print(df)
df2 = df.dropna()
print(df2)

3.3运行结果

          A         B         C         D
0  0.154288  0.133700  0.362685  0.679109
1  0.194450  0.251210  0.758416  0.557619
2  0.514803  0.467800  0.087176  0.829095
3  0.298641       NaN  0.678006  0.903489
4  0.514451  0.539105  0.664328  0.634057
5  0.353419  0.026643  0.165290  0.879319
6  0.067820  0.369086  0.115501  0.096294
7  0.083770  0.086927  0.022256  0.771043
8  0.049213  0.465223  0.941233       NaN
9  0.361318  0.031319  0.304045  0.188268
          A         B         C         D
0  0.154288  0.133700  0.362685  0.679109
1  0.194450  0.251210  0.758416  0.557619
2  0.514803  0.467800  0.087176  0.829095
4  0.514451  0.539105  0.664328  0.634057
5  0.353419  0.026643  0.165290  0.879319
6  0.067820  0.369086  0.115501  0.096294
7  0.083770  0.086927  0.022256  0.771043
9  0.361318  0.031319  0.304045  0.188268

四、Pandas如何精确设置表格数据的单元格的值

实战场景：Pandas如何精确设置表格数据的单元格的值

4.1主要知识点

• 文件读写
• 基础语法
• Pandas
• numpy

实战：

4.2创建 python 文件

"""
对如下DF，设置两个单元格的值
·使用iloc 设置(3,B)的值是nan
·使用loc设置(8,D)的值是nan
"""
import numpy as np
import pandas as pd
np.random.seed(66)
df = pd.DataFrame(np.random.rand(10, 4), columns=list('ABCD'))
print(df)
 
df.iloc[3, 1] = np.nan
df.loc[8, 'D'] = np.nan
 
print(df)

4.3运行结果 

          A         B         C         D
0  0.154288  0.133700  0.362685  0.679109
1  0.194450  0.251210  0.758416  0.557619
2  0.514803  0.467800  0.087176  0.829095
3  0.298641  0.031346  0.678006  0.903489
4  0.514451  0.539105  0.664328  0.634057
5  0.353419  0.026643  0.165290  0.879319
6  0.067820  0.369086  0.115501  0.096294
7  0.083770  0.086927  0.022256  0.771043
8  0.049213  0.465223  0.941233  0.216512
9  0.361318  0.031319  0.304045  0.188268
          A         B         C         D
0  0.154288  0.133700  0.362685  0.679109
1  0.194450  0.251210  0.758416  0.557619
2  0.514803  0.467800  0.087176  0.829095
3  0.298641       NaN  0.678006  0.903489
4  0.514451  0.539105  0.664328  0.634057
5  0.353419  0.026643  0.165290  0.879319
6  0.067820  0.369086  0.115501  0.096294
7  0.083770  0.086927  0.022256  0.771043
8  0.049213  0.465223  0.941233       NaN
9  0.361318  0.031319  0.304045  0.188268 

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