一、修改表格數據類型 DataFrame 列的順序

實戰場景：Pandas?如何修改表格數據類型 DataFrame 列的順序

1.1主要知識點

• 文件讀寫
• 基礎語法
• 數據構建
• Pandas
• Numpy

實戰：

1.2創建 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運行結果?

? ? ? ? ? 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 如何統計某個數據列的空值個數

實戰場景：Pandas?如何統計某個數據列的空值個數

2.1主要知識點

• 文件讀寫
• 基礎語法
• Pandas
• numpy

實戰：

2.2創建 python 文件

"""
對如下DF，設置兩個單元格的值
·使用iloc 設置(3,B)的值是nan
·使用loc設置(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運行結果

? ? ? ? ? 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

實戰場景：Pandas如何移除包含空值的行

3.1主要知識點

• 文件讀寫
• 基礎語法
• Pandas
• numpy

實戰：

3.2創建 python 文件

"""
對如下DF，設置兩個單元格的值
·使用iloc 設置(3,B)的值是nan
·使用loc設置(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運行結果

? ? ? ? ? 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如何精確設置表格數據的單元格的值

實戰場景：Pandas如何精確設置表格數據的單元格的值

4.1主要知識點

• 文件讀寫
• 基礎語法
• Pandas
• numpy

實戰：

4.2創建 python 文件

"""
對如下DF，設置兩個單元格的值
·使用iloc 設置(3,B)的值是nan
·使用loc設置(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運行結果?

? ? ? ? ? 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?