5.pandas 新增数据列

博主： huoyu
发布时间：2022 年 12 月 14 日
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3376字数
分类： Pandas

这里是ipynb格式 [5.pandas 新增数据列.html](http://type.zimopy.com/usr/uploads/2022/12/1528750922.html)
</div>

**下面是md格式**

# pandas 新增数据列

1. 直接赋值
2. df.apply
3. df.assign
4. 按条件选择分组分别赋值

```python
import pandas as pd
```

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

```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>

# 1.直接赋值

实例：清理温度列，变成数字类型

```python
df.loc[:,"bWendu"] = df["bWendu"].str.replace("℃","").astype("int32")
df.loc[:,"yWendu"] = df["yWendu"].str.replace("℃","").astype("int32")
```

## 实例：计算温差-加列

```python
#注意，df["bWendu"] 其实就是一个Series，后面的减法返回的是Series
df.loc[:,"wencha"] = df["bWendu"] - df["yWendu"]
```

```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>

# 2. df.apply方法

Apply a function along an axis of the DataFrame.

Objects passed to the function are Series objects whose index is either the DataFrame's index(axis=O) or the DataFrame's columns (axis=1).

**apply就是设置axis等于0还是1，来决定是循环一行还是一列

实例:添加一列温度类型:

1. 如果最高温度大于33度就是高温
2. 低于-10度是低温
3. 否则是常温

> df.apply()#因为.apply是遍历整个dataframe，x相当于每次需要计算的一行或一列，
> x[index]是一个数，可以进行判断，一行（沿列从上到下进行计算，axis=1）还是一列由axis决定df，默认可以不用写

```python
 def get_wendu_type(x):  #x就是遍历dataframe的每一行也就是一个series，默认传入的是df
        if x["bWendu"]>33:
            return "高温"
        if x["yWendu"]<-10:
            return "低温"
        return "常温"
# 注意需要设置axis == 1，这是series的index还是columns
df.loc[:,"wendu_type"] = df.apply(get_wendu_type,axis=1)
df
```

<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>wencha</th>
      <th>wendu_type</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>
      <td>9</td>
      <td>常温</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>
      <td>7</td>
      <td>常温</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>
      <td>7</td>
      <td>常温</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>
      <td>8</td>
      <td>常温</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>
      <td>9</td>
      <td>常温</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>360</th>
      <td>2018-12-27</td>
      <td>-5</td>
      <td>-12</td>
      <td>多云~晴</td>
      <td>西北风</td>
      <td>3级</td>
      <td>48</td>
      <td>优</td>
      <td>1</td>
      <td>7</td>
      <td>低温</td>
    </tr>
    <tr>
      <th>361</th>
      <td>2018-12-28</td>
      <td>-3</td>
      <td>-11</td>
      <td>晴</td>
      <td>西北风</td>
      <td>3级</td>
      <td>40</td>
      <td>优</td>
      <td>1</td>
      <td>8</td>
      <td>低温</td>
    </tr>
    <tr>
      <th>362</th>
      <td>2018-12-29</td>
      <td>-3</td>
      <td>-12</td>
      <td>晴</td>
      <td>西北风</td>
      <td>2级</td>
      <td>29</td>
      <td>优</td>
      <td>1</td>
      <td>9</td>
      <td>低温</td>
    </tr>
    <tr>
      <th>363</th>
      <td>2018-12-30</td>
      <td>-2</td>
      <td>-11</td>
      <td>晴~多云</td>
      <td>东北风</td>
      <td>1级</td>
      <td>31</td>
      <td>优</td>
      <td>1</td>
      <td>9</td>
      <td>低温</td>
    </tr>
    <tr>
      <th>364</th>
      <td>2018-12-31</td>
      <td>-2</td>
      <td>-10</td>
      <td>多云</td>
      <td>东北风</td>
      <td>1级</td>
      <td>56</td>
      <td>良</td>
      <td>2</td>
      <td>8</td>
      <td>常温</td>
    </tr>
  </tbody>
</table>
<p>365 rows × 11 columns</p>
</div>

```python
# 查看温度类型的计数
df.loc[:,"wendu_type"].value_counts()
```

常温    328
高温     29
低温      8
Name: wendu_type, dtype: int64

# 3.df.assign方法

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones.

实例：将温度从摄氏度变成华摄氏度

## 可以同时添加多个新的列

```python
df.assign(
    yWendu_huashi = lambda x : x["yWendu"] * 9 /5 + 32,
    # 摄氏度转华氏度
    bWendu_huashi = lambda x :x["bWendu"] * 9 /5 + 32
)
```

<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>wencha</th>
      <th>wendu_type</th>
      <th>yWendu_huashi</th>
      <th>bWendu_huashi</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>
      <td>9</td>
      <td>常温</td>
      <td>21.2</td>
      <td>37.4</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>
      <td>7</td>
      <td>常温</td>
      <td>23.0</td>
      <td>35.6</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>
      <td>7</td>
      <td>常温</td>
      <td>23.0</td>
      <td>35.6</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>
      <td>8</td>
      <td>常温</td>
      <td>17.6</td>
      <td>32.0</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>
      <td>9</td>
      <td>常温</td>
      <td>21.2</td>
      <td>37.4</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>360</th>
      <td>2018-12-27</td>
      <td>-5</td>
      <td>-12</td>
      <td>多云~晴</td>
      <td>西北风</td>
      <td>3级</td>
      <td>48</td>
      <td>优</td>
      <td>1</td>
      <td>7</td>
      <td>低温</td>
      <td>10.4</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>361</th>
      <td>2018-12-28</td>
      <td>-3</td>
      <td>-11</td>
      <td>晴</td>
      <td>西北风</td>
      <td>3级</td>
      <td>40</td>
      <td>优</td>
      <td>1</td>
      <td>8</td>
      <td>低温</td>
      <td>12.2</td>
      <td>26.6</td>
    </tr>
    <tr>
      <th>362</th>
      <td>2018-12-29</td>
      <td>-3</td>
      <td>-12</td>
      <td>晴</td>
      <td>西北风</td>
      <td>2级</td>
      <td>29</td>
      <td>优</td>
      <td>1</td>
      <td>9</td>
      <td>低温</td>
      <td>10.4</td>
      <td>26.6</td>
    </tr>
    <tr>
      <th>363</th>
      <td>2018-12-30</td>
      <td>-2</td>
      <td>-11</td>
      <td>晴~多云</td>
      <td>东北风</td>
      <td>1级</td>
      <td>31</td>
      <td>优</td>
      <td>1</td>
      <td>9</td>
      <td>低温</td>
      <td>12.2</td>
      <td>28.4</td>
    </tr>
    <tr>
      <th>364</th>
      <td>2018-12-31</td>
      <td>-2</td>
      <td>-10</td>
      <td>多云</td>
      <td>东北风</td>
      <td>1级</td>
      <td>56</td>
      <td>良</td>
      <td>2</td>
      <td>8</td>
      <td>常温</td>
      <td>14.0</td>
      <td>28.4</td>
    </tr>
  </tbody>
</table>
<p>365 rows × 13 columns</p>
</div>

# 4. 按条件选择分组分别赋值

按条件先选择数据，然后对这部分数据赋值新列

实例:高低温差大于10度，则认为温差大

```python
# 先创建空列 （这是第一中创建新列的方法）
df ["wencha_type"] =""
df.loc[df["bWendu"]-df["yWendu"]>10,"wencha_type"] = "温差大"
df.loc[df["bWendu"]-df["yWendu"]<=10,"wencha_type"] = "温度正常"
```

```python
df["wencha_type"].value_counts()
```

温度正常    187
温差大     178
Name: wencha_type, dtype: int64

这一篇的数据集在上一篇的文章末尾下载

最后修改：2022 年 12 月 14 日

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5.pandas 新增数据列

huoyu • 2022 年 12 月 14 日

这里是ipynb格式 [5.pandas 新增数据列.html](http://type.zimopy.com/usr/uploads/2022/12/1528750922.html)
</div>

**下面是md格式**

# pandas 新增数据列

1. 直接赋值
2. df.apply
3. df.assign
4. 按条件选择分组分别赋值

```python
import pandas as pd
```

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

```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>

# 1.直接赋值

实例：清理温度列，变成数字类型

```python
df.loc[:,"bWendu"] = df["bWendu"].str.replace("℃","").astype("int32")
df.loc[:,"yWendu"] = df["yWendu"].str.replace("℃","").astype("int32")
```

## 实例：计算温差-加列

```python
#注意，df["bWendu"] 其实就是一个Series，后面的减法返回的是Series
df.loc[:,"wencha"] = df["bWendu"] - df["yWendu"]
```

```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>

# 2. df.apply方法

Apply a function along an axis of the DataFrame.

Objects passed to the function are Series objects whose index is either the DataFrame's index(axis=O) or the DataFrame's columns (axis=1).

**apply就是设置axis等于0还是1，来决定是循环一行还是一列

实例:添加一列温度类型:

1. 如果最高温度大于33度就是高温
2. 低于-10度是低温
3. 否则是常温

<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>

```python
# 查看温度类型的计数
df.loc[:,"wendu_type"].value_counts()
```

常温    328
高温     29
低温      8
Name: wendu_type, dtype: int64

# 3.df.assign方法

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones.

实例：将温度从摄氏度变成华摄氏度

## 可以同时添加多个新的列

```python
df.assign(
    yWendu_huashi = lambda x : x["yWendu"] * 9 /5 + 32,
    # 摄氏度转华氏度
    bWendu_huashi = lambda x :x["bWendu"] * 9 /5 + 32
)
```

<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>wencha</th>
      <th>wendu_type</th>
      <th>yWendu_huashi</th>
      <th>bWendu_huashi</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>
      <td>9</td>
      <td>常温</td>
      <td>21.2</td>
      <td>37.4</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>
      <td>7</td>
      <td>常温</td>
      <td>23.0</td>
      <td>35.6</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>
      <td>7</td>
      <td>常温</td>
      <td>23.0</td>
      <td>35.6</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>
      <td>8</td>
      <td>常温</td>
      <td>17.6</td>
      <td>32.0</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>
      <td>9</td>
      <td>常温</td>
      <td>21.2</td>
      <td>37.4</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>360</th>
      <td>2018-12-27</td>
      <td>-5</td>
      <td>-12</td>
      <td>多云~晴</td>
      <td>西北风</td>
      <td>3级</td>
      <td>48</td>
      <td>优</td>
      <td>1</td>
      <td>7</td>
      <td>低温</td>
      <td>10.4</td>
      <td>23.0</td>
    </tr>
    <tr>
      <th>361</th>
      <td>2018-12-28</td>
      <td>-3</td>
      <td>-11</td>
      <td>晴</td>
      <td>西北风</td>
      <td>3级</td>
      <td>40</td>
      <td>优</td>
      <td>1</td>
      <td>8</td>
      <td>低温</td>
      <td>12.2</td>
      <td>26.6</td>
    </tr>
    <tr>
      <th>362</th>
      <td>2018-12-29</td>
      <td>-3</td>
      <td>-12</td>
      <td>晴</td>
      <td>西北风</td>
      <td>2级</td>
      <td>29</td>
      <td>优</td>
      <td>1</td>
      <td>9</td>
      <td>低温</td>
      <td>10.4</td>
      <td>26.6</td>
    </tr>
    <tr>
      <th>363</th>
      <td>2018-12-30</td>
      <td>-2</td>
      <td>-11</td>
      <td>晴~多云</td>
      <td>东北风</td>
      <td>1级</td>
      <td>31</td>
      <td>优</td>
      <td>1</td>
      <td>9</td>
      <td>低温</td>
      <td>12.2</td>
      <td>28.4</td>
    </tr>
    <tr>
      <th>364</th>
      <td>2018-12-31</td>
      <td>-2</td>
      <td>-10</td>
      <td>多云</td>
      <td>东北风</td>
      <td>1级</td>
      <td>56</td>
      <td>良</td>
      <td>2</td>
      <td>8</td>
      <td>常温</td>
      <td>14.0</td>
      <td>28.4</td>
    </tr>
  </tbody>
</table>
<p>365 rows × 13 columns</p>
</div>

# 4. 按条件选择分组分别赋值

按条件先选择数据，然后对这部分数据赋值新列

实例:高低温差大于10度，则认为温差大

```python
df["wencha_type"].value_counts()
```

温度正常    187
温差大     178
Name: wencha_type, dtype: int64

这一篇的数据集在上一篇的文章末尾下载

5.pandas 新增数据列

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速注册OpenAi（ChatGPT），国内也可以

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5.pandas 新增数据列

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5.pandas 新增数据列

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