问题

I wrote these codes where np.where() and .iloc[] is used to change NaNs to be "missing" in the dataframe. However, the result is wrong and the reason might be that .iloc[] recognized tuple index unsmartly.

Could anybody offer me some guidance to fix it or alternative methods aside from fillna()? Because the real case I want to solve is get indexes by applying conditions on df1 and use the indexes to change values in df2, so fillna() might be unhelpful in my case.

data = {'Name': ['John', 'Alice', 'Bob', 'Jane', 'Mark'],
    'Age': [25, 31, np.nan, 28, 35],
    'Salary': [50000, np.nan, 70000, np.nan, 90000]}
df = pd.DataFrame(data)
df

    Name   Age   Salary
0   John  25.0  50000.0
1  Alice  31.0      NaN
2    Bob   NaN  70000.0
3   Jane  28.0      NaN
4   Mark  35.0  90000.0

# Use np.where and .iloc to replace missing values with a specified value
value_to_replace = "missing"
missing_mask = np.where(df.isna())  # Create a boolean mask for missing values
df.iloc[missing_mask]= value_to_replace  # Use np.where for replacement
df

    Name      Age   Salary
0   John     25.0  50000.0
1  Alice  missing  missing
2    Bob  missing  missing
3   Jane  missing  missing
4   Mark     35.0  90000.0

The expected result should be:

    Name      Age   Salary  
0   John     25.0  50000.0  
1  Alice     31.0  missing  
2    Bob  missing  70000.0 
3   Jane     28.0  missing  
4   Mark     35.0  90000.0

data = {'Name': ['John', 'Alice', 'Bob', 'Jane', 'Mark'],
    'Age': [25, 31, np.nan, 28, 35],
    'Salary': [50000, np.nan, 70000, np.nan, 90000]}
df = pd.DataFrame(data)
df

    Name   Age   Salary
0   John  25.0  50000.0
1  Alice  31.0      NaN
2    Bob   NaN  70000.0
3   Jane  28.0      NaN
4   Mark  35.0  90000.0

# Use np.where and .iloc to replace missing values with a specified value
value_to_replace = "missing"
missing_mask = np.where(df.isna())  # Create a boolean mask for missing values
df.iloc[missing_mask]= value_to_replace  # Use np.where for replacement
df

    Name      Age   Salary
0   John     25.0  50000.0
1  Alice  missing  missing
2    Bob  missing  missing
3   Jane  missing  missing
4   Mark     35.0  90000.0

    Name      Age   Salary  
0   John     25.0  50000.0  
1  Alice     31.0  missing  
2    Bob  missing  70000.0 
3   Jane     28.0  missing  
4   Mark     35.0  90000.0

答案1

得分: 2

我不确定为什么要这样做，因为这会将你的float值更改为object类型，但是可以这样做。我会使用DataFrame.where方法进行替换。这会替换条件求值为False的值。由于我们想要替换NaN值，我们可以使用isna来找到NaN，并应用逻辑NOT使其在值为NaN的地方变为False。另外，@Timus 指出你可以使用notna，它与NOTed isna 做的事情相同。

import pandas as pd
import numpy as np

data = {'Name': ['John', 'Alice', 'Bob', 'Jane', 'Mark'],
    'Age': [25, 31, np.nan, 28, 35],
    'Salary': [50000, np.nan, 70000, np.nan, 90000]}
df = pd.DataFrame(data)
df = df.where(~df.isna(), "missing")
print(df)
print(df.dtypes)

输出:

    Name      Age   Salary
0   John     25.0  50000.0
1  Alice     31.0  missing
2    Bob  missing  70000.0
3   Jane     28.0  missing
4   Mark     35.0  90000.0
Name      object
Age       object
Salary    object
dtype: object

import pandas as pd
import numpy as np

data = {'Name': ['John', 'Alice', 'Bob', 'Jane', 'Mark'],
    'Age': [25, 31, np.nan, 28, 35],
    'Salary': [50000, np.nan, 70000, np.nan, 90000]}
df = pd.DataFrame(data)
df = df.where(~df.isna(), "missing")
print(df)
print(df.dtypes)

    Name      Age   Salary
0   John     25.0  50000.0
1  Alice     31.0  missing
2    Bob  missing  70000.0
3   Jane     28.0  missing
4   Mark     35.0  90000.0
Name      object
Age       object
Salary    object
dtype: object

答案2

得分: -1

以下是翻译好的部分：

我尝试了这个，对我来说运行得很好：
```import pandas as pd
import numpy as np

# 创建DataFrame
data = {'Name': ['John', 'Alice', 'Bob', 'Jane', 'Mark'],
        'Age': [25, 31, np.nan, 28, 35],
        'Salary': [50000, np.nan, 70000, np.nan, 90000]}
df = pd.DataFrame(data)

# 指定用于替换缺失值的值
value_to_replace = "missing"

# 找到缺失值的索引
missing_mask = np.where(df.isna())

# 解压索引的元组并替换缺失值
df.iloc[*missing_mask] = value_to_replace

# 打印结果DataFrame
print(df)

<details>
<summary>英文:</summary>

I tried this, and works fine for me :
```import pandas as pd
import numpy as np

# Create the DataFrame
data = {&#39;Name&#39;: [&#39;John&#39;, &#39;Alice&#39;, &#39;Bob&#39;, &#39;Jane&#39;, &#39;Mark&#39;],
        &#39;Age&#39;: [25, 31, np.nan, 28, 35],
        &#39;Salary&#39;: [50000, np.nan, 70000, np.nan, 90000]}
df = pd.DataFrame(data)

# Specify the value to replace NAs
value_to_replace = &quot;missing&quot;

# Find the indices where values are missing
missing_mask = np.where(df.isna())

# Unpack the tuple of indices and replace missing values
df.iloc[*missing_mask] = value_to_replace

# Print the resulting DataFrame
print(df)