# 判斷

## Empty

無資料狀態

> Empty DataFrame
>
> Columns: \[FIELD\_A, FIELD\_B, FIELD\_C, FIELD\_D]
>
> Index: \[]

{% hint style="warning" %}
無法用「is None」判斷(因為他仍舊有欄位名之類的東西存在不是None)
{% endhint %}

### DataFrame預設方法

```python
data.empty # True/False
```

### 長度==0

```python
len(data) == 0 # 不可以用size!理由與isNone相同
```

## Null

{% code title="判斷A欄位中值是否為None" %}

```python
data['A'].isnull()
```

{% endcode %}

#### 取出A欄位中值非None的資料列

```python
data[~data['A'].isnull()]
```

#### 取出AB欄位中值皆非None的資料列

```python
data[~data['A'].isnull() & ~data['B'].isnull()]
```

## NaN

顯示為NaN，代表「空值」，與「空對象Nonw」不同，是「numpy.float64」類型，判斷要用「pd.isna」。

```python
print(type(data['30'].loc[0]))
# <class 'numpy.float64'>

print(pd.isna(data['30'].loc[0]))
# True/False =>判斷「欄位30第一筆」資料是否為NaN
```

{% code title="過濾可以這樣寫" %}

```python
data = data[~pd.isna(data['30'])]
# 濾除「欄位30」為NaN的資料
```

{% endcode %}

> <https://www.cnblogs.com/zjuhaohaoxuexi/p/15865290.html>

## isin 是否含有

判斷是否包含传入的指定值，回傳是對應的整組boolean。

```python
data = data[data['PRODUCT'].isin(must_have_product)]
# data = data中PRODUCT欄位值有在must_have_product清單中的資料
# must_have_product是個list，但根據isin說明，應該也可以放入dist
```

參考：<https://www.gairuo.com/p/pandas-isin>


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