Pandas 刪除帶有 NaN 的行

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Suraj Joshi 2023年1月30日
Pandas Pandas DataFrame Row Pandas NaN
  1. Pandas 使用 DataFrame.notna() 方法刪除帶有 NaN 的行
  2. Pandas 使用 DataFrame.dropna() 方法只刪除所有列都是 NaN 值的行
  3. Pandas 使用 DataFrame.dropna() 方法僅在某一列的值為 NaN 的情況下才刪除行
  4. Pandas 使用 DataFrame.dropna() 方法刪除任意列為 NaN 值的行
Pandas 刪除帶有 NaN 的行

本教程解釋了我們如何使用 DataFrame.notna() 和 DataFrame.dropna() 方法刪除所有帶有 NaN 值的行。

我們將在下面的示例程式碼中使用 DataFrame。

import pandas as pd

roll_no = [501, 502, 503, 504, 505]

data = pd.DataFrame(
    {
        "Name": ["Alice", "Steven", "Neesham", "Chris", "Alice"],
        "Age": [19, None, 18, 21, None],
        "Income($)": [4000, 5000, None, 3500, None],
        "Expense($)": [3000, 2000, 2500, 25000, None],
    }
)

print(data)

輸出：

      Name   Age  Income($)  Expense($)
0    Alice  19.0     4000.0      3000.0
1   Steven   NaN     5000.0      2000.0
2  Neesham  18.0        NaN      2500.0
3    Chris  21.0     3500.0     25000.0
4    Alice   NaN        NaN         NaN

Pandas 使用 DataFrame.notna() 方法刪除帶有 NaN 的行

DataFrame.notna() 方法返回一個布林物件，其行數和列數與呼叫者 DataFrame 相同。如果元素不是 NaN，它將被對映到布林物件中的 True 值，如果元素是 NaN，它將被對映到 False 值。

import pandas as pd

roll_no = [501, 502, 503, 504, 505]

data = pd.DataFrame(
    {
        "Name": ["Alice", "Steven", "Neesham", "Chris", "Alice"],
        "Age": [19, None, 18, 21, None],
        "Income($)": [4000, 5000, None, 3500, None],
        "Expense($)": [3000, 2000, 2500, 25000, None],
    }
)
print("Initial DataFrame:")
print(data)

print("")

data = data[data["Income($)"].notna()]
print("DataFrame after removing rows with NaN value in Income Field:")
print(data)

輸出：

Initial DataFrame:
      Name   Age  Income($)  Expense($)
0    Alice  19.0     4000.0      3000.0
1   Steven   NaN     5000.0      2000.0
2  Neesham  18.0        NaN      2500.0
3    Chris  21.0     3500.0     25000.0
4    Alice   NaN        NaN         NaN

DataFrame after removing rows with NaN value in Income Field:
     Name   Age  Income($)  Expense($)
0   Alice  19.0     4000.0      3000.0
1  Steven   NaN     5000.0      2000.0
3   Chris  21.0     3500.0     25000.0

這裡，我們將 notna() 方法應用於 data 的 Income($) 列，它將返回一個系列物件，根據該列的值，有 True 或 False 值。當我們將布林物件作為索引傳遞給原始 DataFrame 時，我們只得到 Income($) 列沒有 NaN 值的行。

Pandas 使用 DataFrame.dropna() 方法只刪除所有列都是 NaN 值的行

import pandas as pd

roll_no = [501, 502, 503, 504, 505]

data = pd.DataFrame(
    {
        "Id": [621, 645, 210, 345, None],
        "Age": [19, None, 18, 21, None],
        "Income($)": [4000, 5000, None, 3500, None],
        "Expense($)": [3000, 2000, 2500, 25000, None],
    }
)
print("Initial DataFrame:")
print(data)

print("")

data = data.dropna(how="all")
print("DataFrame after removing rows with NaN value in All Columns:")
print(data)

輸出：

Initial DataFrame:
      Id   Age  Income($)  Expense($)
0  621.0  19.0     4000.0      3000.0
1  645.0   NaN     5000.0      2000.0
2  210.0  18.0        NaN      2500.0
3  345.0  21.0     3500.0     25000.0
4    NaN   NaN        NaN         NaN

DataFrame after removing rows with NaN value in All Columns:
      Id   Age  Income($)  Expense($)
0  621.0  19.0     4000.0      3000.0
1  645.0   NaN     5000.0      2000.0
2  210.0  18.0        NaN      2500.0
3  345.0  21.0     3500.0     25000.0

它只刪除 DataFrame 中所有欄位中含有 NaN 值的行。我們在 dropna() 方法中設定 how='all'，讓該方法只在行的所有列值都是 NaN 時才刪除行。

Pandas 使用 DataFrame.dropna() 方法僅在某一列的值為 NaN 的情況下才刪除行

import pandas as pd

roll_no = [501, 502, 503, 504, 505]

data = pd.DataFrame(
    {
        "Id": [621, 645, 210, 345, None],
        "Age": [19, None, 18, 21, None],
        "Income($)": [4000, 5000, None, 3500, None],
        "Expense($)": [3000, 2000, 2500, 25000, None],
    }
)
print("Initial DataFrame:")
print(data)

print("")

data = data.dropna(subset=["Id"])
print("DataFrame after removing rows with NaN value in Id Column:")
print(data)

輸出：

Initial DataFrame:
      Id   Age  Income($)  Expense($)
0  621.0  19.0     4000.0      3000.0
1  645.0   NaN     5000.0      2000.0
2  210.0  18.0        NaN      2500.0
3  345.0  21.0     3500.0     25000.0
4    NaN   NaN        NaN         NaN

DataFrame after removing rows with NaN value in Id Column:
      Id   Age  Income($)  Expense($)
0  621.0  19.0     4000.0      3000.0
1  645.0   NaN     5000.0      2000.0
2  210.0  18.0        NaN      2500.0
3  345.0  21.0     3500.0     25000.0

它將刪除 DataFrame 中所有僅在 Id 列中具有 NaN 值的列。

Pandas 使用 DataFrame.dropna() 方法刪除任意列為 NaN 值的行

import pandas as pd

roll_no = [501, 502, 503, 504, 505]

data = pd.DataFrame(
    {
        "Id": [621, 645, 210, 345, None],
        "Age": [19, None, 18, 21, None],
        "Income($)": [4000, 5000, None, 3500, None],
        "Expense($)": [3000, 2000, 2500, 25000, None],
    }
)
print("Initial DataFrame:")
print(data)

print("")

data = data.dropna()
print("DataFrame after removing rows with NaN value in any column:")
print(data)

輸出：

Initial DataFrame:
      Id   Age  Income($)  Expense($)
0  621.0  19.0     4000.0      3000.0
1  645.0   NaN     5000.0      2000.0
2  210.0  18.0        NaN      2500.0
3  345.0  21.0     3500.0     25000.0
4    NaN   NaN        NaN         NaN

DataFrame after removing rows with NaN value in any column:
      Id   Age  Income($)  Expense($)
0  621.0  19.0     4000.0      3000.0
3  345.0  21.0     3500.0     25000.0

預設情況下，dropna() 方法將刪除所有至少有一個 NaN 值的行。

作者： Suraj Joshi
Suraj Joshi avatar Suraj Joshi avatar

Suraj Joshi is a backend software engineer at Matrice.ai.

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