8.pandas的SettingWithCopyWarning

博主： huoyu
发布时间：2022 年 12 月 19 日
327 次浏览
暂无评论
7995字数
分类： Pandas

报警复现，原因，解决方案

ipynb文件格式：[8.pandas的SettingWithCopyWarning.html](http://type.zimopy.com/usr/uploads/2022/12/3591179633.html)

</div>

# pandas的SettingWithCopyWarning

报警复现，原因，解决方案

```python
import pandas as pd

fpath = "../datas/beijing_tianqi/beijing_tianqi_2018.csv"
df= pd.read_csv(fpath)
df.head()
```

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ymd</th>
      <th>bWendu</th>
      <th>yWendu</th>
      <th>tianqi</th>
      <th>fengxiang</th>
      <th>fengli</th>
      <th>aqi</th>
      <th>aqiInfo</th>
      <th>aqiLevel</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2018-01-01</td>
      <td>3℃</td>
      <td>-6℃</td>
      <td>晴~多云</td>
      <td>东北风</td>
      <td>1-2级</td>
      <td>59</td>
      <td>良</td>
      <td>2</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2018-01-02</td>
      <td>2℃</td>
      <td>-5℃</td>
      <td>阴~多云</td>
      <td>东北风</td>
      <td>1-2级</td>
      <td>49</td>
      <td>优</td>
      <td>1</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2018-01-03</td>
      <td>2℃</td>
      <td>-5℃</td>
      <td>多云</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>28</td>
      <td>优</td>
      <td>1</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2018-01-04</td>
      <td>0℃</td>
      <td>-8℃</td>
      <td>阴</td>
      <td>东北风</td>
      <td>1-2级</td>
      <td>28</td>
      <td>优</td>
      <td>1</td>
    </tr>
    <tr>
      <th>4</th>
      <td>2018-01-05</td>
      <td>3℃</td>
      <td>-6℃</td>
      <td>多云~晴</td>
      <td>西北风</td>
      <td>1-2级</td>
      <td>50</td>
      <td>优</td>
      <td>1</td>
    </tr>
  </tbody>
</table>
</div>

```python
df.loc[:,"bWendu"] = df["bWendu"].str.replace("℃","").astype("int32")
df.loc[:,"yWendu"] = df["yWendu"].str.replace("℃","").astype("int32")
```
```python
df.head()
```
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ymd</th>
      <th>bWendu</th>
      <th>yWendu</th>
      <th>tianqi</th>
      <th>fengxiang</th>
      <th>fengli</th>
      <th>aqi</th>
      <th>aqiInfo</th>
      <th>aqiLevel</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2018-01-01</td>
      <td>3</td>
      <td>-6</td>
      <td>晴~多云</td>
      <td>东北风</td>
      <td>1-2级</td>
      <td>59</td>
      <td>良</td>
      <td>2</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2018-01-02</td>
      <td>2</td>
      <td>-5</td>
      <td>阴~多云</td>
      <td>东北风</td>
      <td>1-2级</td>
      <td>49</td>
      <td>优</td>
      <td>1</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2018-01-03</td>
      <td>2</td>
      <td>-5</td>
      <td>多云</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>28</td>
      <td>优</td>
      <td>1</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2018-01-04</td>
      <td>0</td>
      <td>-8</td>
      <td>阴</td>
      <td>东北风</td>
      <td>1-2级</td>
      <td>28</td>
      <td>优</td>
      <td>1</td>
    </tr>
    <tr>
      <th>4</th>
      <td>2018-01-05</td>
      <td>3</td>
      <td>-6</td>
      <td>多云~晴</td>
      <td>西北风</td>
      <td>1-2级</td>
      <td>50</td>
      <td>优</td>
      <td>1</td>
    </tr>
  </tbody>
</table>
</div>

# 1. 复现

## 只选出3月份的数据进行分析

```python
condition = df["ymd"].str.startswith("2018-03")
```
## 设置温差

```python
df.loc[condition]["wen_cha"]=df["bWendu"]-df["yWendu"]
```
c:\users\administrator\appdata\local\programs\python\python37\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.
## 查看是否成功

```python
df[condition].head()
```
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ymd</th>
      <th>bWendu</th>
      <th>yWendu</th>
      <th>tianqi</th>
      <th>fengxiang</th>
      <th>fengli</th>
      <th>aqi</th>
      <th>aqiInfo</th>
      <th>aqiLevel</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>59</th>
      <td>2018-03-01</td>
      <td>8</td>
      <td>-3</td>
      <td>多云</td>
      <td>西南风</td>
      <td>1-2级</td>
      <td>46</td>
      <td>优</td>
      <td>1</td>
    </tr>
    <tr>
      <th>60</th>
      <td>2018-03-02</td>
      <td>9</td>
      <td>-1</td>
      <td>晴~多云</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>95</td>
      <td>良</td>
      <td>2</td>
    </tr>
    <tr>
      <th>61</th>
      <td>2018-03-03</td>
      <td>13</td>
      <td>3</td>
      <td>多云~阴</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>214</td>
      <td>重度污染</td>
      <td>5</td>
    </tr>
    <tr>
      <th>62</th>
      <td>2018-03-04</td>
      <td>7</td>
      <td>-2</td>
      <td>阴~多云</td>
      <td>东南风</td>
      <td>1-2级</td>
      <td>144</td>
      <td>轻度污染</td>
      <td>3</td>
    </tr>
    <tr>
      <th>63</th>
      <td>2018-03-05</td>
      <td>8</td>
      <td>-3</td>
      <td>晴</td>
      <td>南风</td>
      <td>1-2级</td>
      <td>94</td>
      <td>良</td>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>

# 2.原因

发出警告的代码 df.loc[condition]["wen_cha"]=df["bWendu"]-df["yWendu"]

相当于：df.get(condition).set(wen_cha),第一步骤的get发出了报警

**链式操作其实就是两个步骤，先get后set，gei得到的dataframe可能是view也可能是copy，pandas发出警告**

核心要诀：pandas的dataframe的修改操写操作，只允许在源dataframe上进行

# 3.解决办法1

将get+set的两步操作，改成set的一步操作

```python
df.loc[condition,"wen_cha"] = df["bWendu"] - df["yWendu"]
df[condition].head()
```
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ymd</th>
      <th>bWendu</th>
      <th>yWendu</th>
      <th>tianqi</th>
      <th>fengxiang</th>
      <th>fengli</th>
      <th>aqi</th>
      <th>aqiInfo</th>
      <th>aqiLevel</th>
      <th>wen_cha</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>59</th>
      <td>2018-03-01</td>
      <td>8</td>
      <td>-3</td>
      <td>多云</td>
      <td>西南风</td>
      <td>1-2级</td>
      <td>46</td>
      <td>优</td>
      <td>1</td>
      <td>11.0</td>
    </tr>
    <tr>
      <th>60</th>
      <td>2018-03-02</td>
      <td>9</td>
      <td>-1</td>
      <td>晴~多云</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>95</td>
      <td>良</td>
      <td>2</td>
      <td>10.0</td>
    </tr>
    <tr>
      <th>61</th>
      <td>2018-03-03</td>
      <td>13</td>
      <td>3</td>
      <td>多云~阴</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>214</td>
      <td>重度污染</td>
      <td>5</td>
      <td>10.0</td>
    </tr>
    <tr>
      <th>62</th>
      <td>2018-03-04</td>
      <td>7</td>
      <td>-2</td>
      <td>阴~多云</td>
      <td>东南风</td>
      <td>1-2级</td>
      <td>144</td>
      <td>轻度污染</td>
      <td>3</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>63</th>
      <td>2018-03-05</td>
      <td>8</td>
      <td>-3</td>
      <td>晴</td>
      <td>南风</td>
      <td>1-2级</td>
      <td>94</td>
      <td>良</td>
      <td>2</td>
      <td>11.0</td>
    </tr>
  </tbody>
</table>
</div>

# 4.解决方法2

如果需要预筛选数据做后续的处理分析，使用copy复制dataframe

```python
df_month3 = df[condition].copy()
```
```python
df_month3.head()
```
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ymd</th>
      <th>bWendu</th>
      <th>yWendu</th>
      <th>tianqi</th>
      <th>fengxiang</th>
      <th>fengli</th>
      <th>aqi</th>
      <th>aqiInfo</th>
      <th>aqiLevel</th>
      <th>wen_cha</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>59</th>
      <td>2018-03-01</td>
      <td>8</td>
      <td>-3</td>
      <td>多云</td>
      <td>西南风</td>
      <td>1-2级</td>
      <td>46</td>
      <td>优</td>
      <td>1</td>
      <td>11.0</td>
    </tr>
    <tr>
      <th>60</th>
      <td>2018-03-02</td>
      <td>9</td>
      <td>-1</td>
      <td>晴~多云</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>95</td>
      <td>良</td>
      <td>2</td>
      <td>10.0</td>
    </tr>
    <tr>
      <th>61</th>
      <td>2018-03-03</td>
      <td>13</td>
      <td>3</td>
      <td>多云~阴</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>214</td>
      <td>重度污染</td>
      <td>5</td>
      <td>10.0</td>
    </tr>
    <tr>
      <th>62</th>
      <td>2018-03-04</td>
      <td>7</td>
      <td>-2</td>
      <td>阴~多云</td>
      <td>东南风</td>
      <td>1-2级</td>
      <td>144</td>
      <td>轻度污染</td>
      <td>3</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>63</th>
      <td>2018-03-05</td>
      <td>8</td>
      <td>-3</td>
      <td>晴</td>
      <td>南风</td>
      <td>1-2级</td>
      <td>94</td>
      <td>良</td>
      <td>2</td>
      <td>11.0</td>
    </tr>
  </tbody>
</table>
</div>

```python
df_month3["wen_cha"] = df["bWendu"] -df["yWendu"]
df_month3.head()
```
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ymd</th>
      <th>bWendu</th>
      <th>yWendu</th>
      <th>tianqi</th>
      <th>fengxiang</th>
      <th>fengli</th>
      <th>aqi</th>
      <th>aqiInfo</th>
      <th>aqiLevel</th>
      <th>wen_cha</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>59</th>
      <td>2018-03-01</td>
      <td>8</td>
      <td>-3</td>
      <td>多云</td>
      <td>西南风</td>
      <td>1-2级</td>
      <td>46</td>
      <td>优</td>
      <td>1</td>
      <td>11</td>
    </tr>
    <tr>
      <th>60</th>
      <td>2018-03-02</td>
      <td>9</td>
      <td>-1</td>
      <td>晴~多云</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>95</td>
      <td>良</td>
      <td>2</td>
      <td>10</td>
    </tr>
    <tr>
      <th>61</th>
      <td>2018-03-03</td>
      <td>13</td>
      <td>3</td>
      <td>多云~阴</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>214</td>
      <td>重度污染</td>
      <td>5</td>
      <td>10</td>
    </tr>
    <tr>
      <th>62</th>
      <td>2018-03-04</td>
      <td>7</td>
      <td>-2</td>
      <td>阴~多云</td>
      <td>东南风</td>
      <td>1-2级</td>
      <td>144</td>
      <td>轻度污染</td>
      <td>3</td>
      <td>9</td>
    </tr>
    <tr>
      <th>63</th>
      <td>2018-03-05</td>
      <td>8</td>
      <td>-3</td>
      <td>晴</td>
      <td>南风</td>
      <td>1-2级</td>
      <td>94</td>
      <td>良</td>
      <td>2</td>
      <td>11</td>
    </tr>
  </tbody>
</table>
</div>

# **总之,pandas不允许先筛选子dataframe，再进行修改写入**

要么使用.loc实现一个步骤直接修改源dataframe

要么先复制一个子dataframe再一个步骤执行修改

最后修改：2022 年 12 月 19 日

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8.pandas的SettingWithCopyWarning

huoyu • 2022 年 12 月 19 日

报警复现，原因，解决方案

ipynb文件格式：[8.pandas的SettingWithCopyWarning.html](http://type.zimopy.com/usr/uploads/2022/12/3591179633.html)

</div>

# pandas的SettingWithCopyWarning

报警复现，原因，解决方案

```python
import pandas as pd

fpath = "../datas/beijing_tianqi/beijing_tianqi_2018.csv"
df= pd.read_csv(fpath)
df.head()
```

<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ymd</th>
      <th>bWendu</th>
      <th>yWendu</th>
      <th>tianqi</th>
      <th>fengxiang</th>
      <th>fengli</th>
      <th>aqi</th>
      <th>aqiInfo</th>
      <th>aqiLevel</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2018-01-01</td>
      <td>3</td>
      <td>-6</td>
      <td>晴~多云</td>
      <td>东北风</td>
      <td>1-2级</td>
      <td>59</td>
      <td>良</td>
      <td>2</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2018-01-02</td>
      <td>2</td>
      <td>-5</td>
      <td>阴~多云</td>
      <td>东北风</td>
      <td>1-2级</td>
      <td>49</td>
      <td>优</td>
      <td>1</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2018-01-03</td>
      <td>2</td>
      <td>-5</td>
      <td>多云</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>28</td>
      <td>优</td>
      <td>1</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2018-01-04</td>
      <td>0</td>
      <td>-8</td>
      <td>阴</td>
      <td>东北风</td>
      <td>1-2级</td>
      <td>28</td>
      <td>优</td>
      <td>1</td>
    </tr>
    <tr>
      <th>4</th>
      <td>2018-01-05</td>
      <td>3</td>
      <td>-6</td>
      <td>多云~晴</td>
      <td>西北风</td>
      <td>1-2级</td>
      <td>50</td>
      <td>优</td>
      <td>1</td>
    </tr>
  </tbody>
</table>
</div>

# 1. 复现

## 只选出3月份的数据进行分析

```python
condition = df["ymd"].str.startswith("2018-03")
```
## 设置温差

```python
df[condition].head()
```
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ymd</th>
      <th>bWendu</th>
      <th>yWendu</th>
      <th>tianqi</th>
      <th>fengxiang</th>
      <th>fengli</th>
      <th>aqi</th>
      <th>aqiInfo</th>
      <th>aqiLevel</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>59</th>
      <td>2018-03-01</td>
      <td>8</td>
      <td>-3</td>
      <td>多云</td>
      <td>西南风</td>
      <td>1-2级</td>
      <td>46</td>
      <td>优</td>
      <td>1</td>
    </tr>
    <tr>
      <th>60</th>
      <td>2018-03-02</td>
      <td>9</td>
      <td>-1</td>
      <td>晴~多云</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>95</td>
      <td>良</td>
      <td>2</td>
    </tr>
    <tr>
      <th>61</th>
      <td>2018-03-03</td>
      <td>13</td>
      <td>3</td>
      <td>多云~阴</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>214</td>
      <td>重度污染</td>
      <td>5</td>
    </tr>
    <tr>
      <th>62</th>
      <td>2018-03-04</td>
      <td>7</td>
      <td>-2</td>
      <td>阴~多云</td>
      <td>东南风</td>
      <td>1-2级</td>
      <td>144</td>
      <td>轻度污染</td>
      <td>3</td>
    </tr>
    <tr>
      <th>63</th>
      <td>2018-03-05</td>
      <td>8</td>
      <td>-3</td>
      <td>晴</td>
      <td>南风</td>
      <td>1-2级</td>
      <td>94</td>
      <td>良</td>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>

# 2.原因

发出警告的代码 df.loc[condition]["wen_cha"]=df["bWendu"]-df["yWendu"]

相当于：df.get(condition).set(wen_cha),第一步骤的get发出了报警

**链式操作其实就是两个步骤，先get后set，gei得到的dataframe可能是view也可能是copy，pandas发出警告**

核心要诀：pandas的dataframe的修改操写操作，只允许在源dataframe上进行

# 3.解决办法1

将get+set的两步操作，改成set的一步操作

```python
df.loc[condition,"wen_cha"] = df["bWendu"] - df["yWendu"]
df[condition].head()
```
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ymd</th>
      <th>bWendu</th>
      <th>yWendu</th>
      <th>tianqi</th>
      <th>fengxiang</th>
      <th>fengli</th>
      <th>aqi</th>
      <th>aqiInfo</th>
      <th>aqiLevel</th>
      <th>wen_cha</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>59</th>
      <td>2018-03-01</td>
      <td>8</td>
      <td>-3</td>
      <td>多云</td>
      <td>西南风</td>
      <td>1-2级</td>
      <td>46</td>
      <td>优</td>
      <td>1</td>
      <td>11.0</td>
    </tr>
    <tr>
      <th>60</th>
      <td>2018-03-02</td>
      <td>9</td>
      <td>-1</td>
      <td>晴~多云</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>95</td>
      <td>良</td>
      <td>2</td>
      <td>10.0</td>
    </tr>
    <tr>
      <th>61</th>
      <td>2018-03-03</td>
      <td>13</td>
      <td>3</td>
      <td>多云~阴</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>214</td>
      <td>重度污染</td>
      <td>5</td>
      <td>10.0</td>
    </tr>
    <tr>
      <th>62</th>
      <td>2018-03-04</td>
      <td>7</td>
      <td>-2</td>
      <td>阴~多云</td>
      <td>东南风</td>
      <td>1-2级</td>
      <td>144</td>
      <td>轻度污染</td>
      <td>3</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>63</th>
      <td>2018-03-05</td>
      <td>8</td>
      <td>-3</td>
      <td>晴</td>
      <td>南风</td>
      <td>1-2级</td>
      <td>94</td>
      <td>良</td>
      <td>2</td>
      <td>11.0</td>
    </tr>
  </tbody>
</table>
</div>

# 4.解决方法2

如果需要预筛选数据做后续的处理分析，使用copy复制dataframe

```python
df_month3 = df[condition].copy()
```
```python
df_month3.head()
```
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ymd</th>
      <th>bWendu</th>
      <th>yWendu</th>
      <th>tianqi</th>
      <th>fengxiang</th>
      <th>fengli</th>
      <th>aqi</th>
      <th>aqiInfo</th>
      <th>aqiLevel</th>
      <th>wen_cha</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>59</th>
      <td>2018-03-01</td>
      <td>8</td>
      <td>-3</td>
      <td>多云</td>
      <td>西南风</td>
      <td>1-2级</td>
      <td>46</td>
      <td>优</td>
      <td>1</td>
      <td>11.0</td>
    </tr>
    <tr>
      <th>60</th>
      <td>2018-03-02</td>
      <td>9</td>
      <td>-1</td>
      <td>晴~多云</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>95</td>
      <td>良</td>
      <td>2</td>
      <td>10.0</td>
    </tr>
    <tr>
      <th>61</th>
      <td>2018-03-03</td>
      <td>13</td>
      <td>3</td>
      <td>多云~阴</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>214</td>
      <td>重度污染</td>
      <td>5</td>
      <td>10.0</td>
    </tr>
    <tr>
      <th>62</th>
      <td>2018-03-04</td>
      <td>7</td>
      <td>-2</td>
      <td>阴~多云</td>
      <td>东南风</td>
      <td>1-2级</td>
      <td>144</td>
      <td>轻度污染</td>
      <td>3</td>
      <td>9.0</td>
    </tr>
    <tr>
      <th>63</th>
      <td>2018-03-05</td>
      <td>8</td>
      <td>-3</td>
      <td>晴</td>
      <td>南风</td>
      <td>1-2级</td>
      <td>94</td>
      <td>良</td>
      <td>2</td>
      <td>11.0</td>
    </tr>
  </tbody>
</table>
</div>

```python
df_month3["wen_cha"] = df["bWendu"] -df["yWendu"]
df_month3.head()
```
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ymd</th>
      <th>bWendu</th>
      <th>yWendu</th>
      <th>tianqi</th>
      <th>fengxiang</th>
      <th>fengli</th>
      <th>aqi</th>
      <th>aqiInfo</th>
      <th>aqiLevel</th>
      <th>wen_cha</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>59</th>
      <td>2018-03-01</td>
      <td>8</td>
      <td>-3</td>
      <td>多云</td>
      <td>西南风</td>
      <td>1-2级</td>
      <td>46</td>
      <td>优</td>
      <td>1</td>
      <td>11</td>
    </tr>
    <tr>
      <th>60</th>
      <td>2018-03-02</td>
      <td>9</td>
      <td>-1</td>
      <td>晴~多云</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>95</td>
      <td>良</td>
      <td>2</td>
      <td>10</td>
    </tr>
    <tr>
      <th>61</th>
      <td>2018-03-03</td>
      <td>13</td>
      <td>3</td>
      <td>多云~阴</td>
      <td>北风</td>
      <td>1-2级</td>
      <td>214</td>
      <td>重度污染</td>
      <td>5</td>
      <td>10</td>
    </tr>
    <tr>
      <th>62</th>
      <td>2018-03-04</td>
      <td>7</td>
      <td>-2</td>
      <td>阴~多云</td>
      <td>东南风</td>
      <td>1-2级</td>
      <td>144</td>
      <td>轻度污染</td>
      <td>3</td>
      <td>9</td>
    </tr>
    <tr>
      <th>63</th>
      <td>2018-03-05</td>
      <td>8</td>
      <td>-3</td>
      <td>晴</td>
      <td>南风</td>
      <td>1-2级</td>
      <td>94</td>
      <td>良</td>
      <td>2</td>
      <td>11</td>
    </tr>
  </tbody>
</table>
</div>

# **总之,pandas不允许先筛选子dataframe，再进行修改写入**

要么使用.loc实现一个步骤直接修改源dataframe

要么先复制一个子dataframe再一个步骤执行修改

8.pandas的SettingWithCopyWarning

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速注册OpenAi（ChatGPT），国内也可以

Frida 代码自动补全

ubuntu 实现内网穿透

最新Fiddler Web Debugger v5.0 20202.18177中文版

windows一键启动雷电模拟器的frida-server

最新Fiddler Web Debugger v5.0 20202.18177中文版

python--修改系统代理

树莓派之ubuntu 22.10 desktop

pandas中打印DataFrame行列数据显示不全问题

UnicodeEncodeError: ‘latin-1‘ codec can‘t encode characters in position 问题

8.pandas的SettingWithCopyWarning

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8.pandas的SettingWithCopyWarning

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