Pandas DataFrame 排除列

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  4. Pandas DataFrame 排除列
Suraj Joshi 2023年1月30日 2021年1月22日
Pandas Pandas DataFrame Column Pandas Filter
  1. Pandas 使用 loc 屬性選擇除一列以外的所有列
  2. Pandas 使用 drop() 方法選擇除一列以外的所有列
  3. Pandas 使用 difference() 方法選擇除一列外的所有列
Pandas DataFrame 排除列

本教程解釋了我們如何從一個 DataFrame 中選擇除一列以外的所有列。我們將在本文中使用下面的 DataFrame 示例。

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Amazon","Tesla","Facebook","Boeing"],
    'Price(in $)':  [3180,835,267,209],
    'Sector':["Technology","Technology","Technology","Aircraft"]
})

print("Stocks Dataframe:")
print(stocks_df,"\n")

輸出：

Stocks Dataframe:
      Stock  Price(in $)      Sector
0    Amazon         3180  Technology
1     Tesla          835  Technology
2  Facebook          267  Technology
3    Boeing          209    Aircraft

Pandas 使用 loc 屬性選擇除一列以外的所有列

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Amazon","Tesla","Facebook","Boeing"],
    'Price(in $)':  [3180,835,267,209],
    'Sector':["Technology","Technology","Technology","Aircraft"]
})

print("Stocks Dataframe:")
print(stocks_df,"\n")

print("Stocks DataFrame excluding Sector Column:")
filtered_df=stocks_df.loc[:,stocks_df.columns!="Sector"]
print(filtered_df,"\n")

輸出：

Stocks Dataframe:
      Stock  Price(in $)      Sector
0    Amazon         3180  Technology
1     Tesla          835  Technology
2  Facebook          267  Technology
3    Boeing          209    Aircraft

Stocks DataFrame excluding Sector Column:
      Stock  Price(in $)
0    Amazon         3180
1     Tesla          835
2  Facebook          267
3    Boeing          209

它從 DataFrame stocks_df 中選擇除 Sector 列以外的所有元素，將結果分配給 filtered_df，然後顯示 filetered_df 的內容。

loc 屬性根據指定的行和列選擇元素。loc 屬性中:符號在 , 前指定我們需要選擇所有的行。對於列，我們指定只選擇名稱不是 Sector 的列。因此，它將選擇除 Sector 列以外的所有列。

Pandas 使用 drop() 方法選擇除一列以外的所有列

我們可以使用 drop() 方法，通過在方法中設定 axis=1，從 DataFrame 中刪除指定的列。

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Amazon","Tesla","Facebook","Boeing"],
    'Price(in $)':  [3180,835,267,209],
    'Sector':["Technology","Technology","Technology","Aircraft"]
})

print("Stocks Dataframe:")
print(stocks_df,"\n")

print("Stocks DataFrame excluding Sector Column:")
filtered_df=stocks_df.drop("Sector",axis=1)
print(filtered_df,"\n")

輸出：

Stocks Dataframe:
      Stock  Price(in $)      Sector
0    Amazon         3180  Technology
1     Tesla          835  Technology
2  Facebook          267  Technology
3    Boeing          209    Aircraft

Stocks DataFrame excluding Sector Column:
      Stock  Price(in $)
0    Amazon         3180
1     Tesla          835
2  Facebook          267
3    Boeing          209

它從 stocks_df DataFrame 中刪除 Sector 列，並將結果分配給 filtered_df。

我們也可以通過使用 drop() 方法從 DataFrame 中刪除多個列。我們提供一個列名列表作為 drop() 方法的引數。

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Amazon","Tesla","Facebook","Boeing"],
    'Price(in $)':  [3180,835,267,209],
    'Sector':["Technology","Technology","Technology","Aircraft"]
})

print("Stocks Dataframe:")
print(stocks_df,"\n")

print("Stocks DataFrame excluding Sector and Price Column:")
filtered_df=stocks_df.drop(["Sector","Price(in $)"],axis=1)
print(filtered_df,"\n")

輸出：

Stocks Dataframe:
      Stock  Price(in $)      Sector
0    Amazon         3180  Technology
1     Tesla          835  Technology
2  Facebook          267  Technology
3    Boeing          209    Aircraft

Stocks DataFrame excluding Sector and Price Column:
      Stock
0    Amazon
1     Tesla
2  Facebook
3    Boeing

它從 stocks_df DataFrame 中排除了 Price(in $) 和 Sector 列。

Pandas 使用 difference() 方法選擇除一列外的所有列

import pandas as pd

stocks_df = pd.DataFrame({
    'Stock': ["Amazon","Tesla","Facebook","Boeing"],
    'Price(in $)':  [3180,835,267,209],
    'Sector':["Technology","Technology","Technology","Aircraft"]
})

print("Stocks Dataframe:")
print(stocks_df,"\n")

print("Stocks DataFrame excluding Sector Column:")
filtered_df=stocks_df[stocks_df.columns.difference(['Sector'])]
print(filtered_df,"\n")

輸出：

Stocks Dataframe:
      Stock  Price(in $)      Sector
0    Amazon         3180  Technology
1     Tesla          835  Technology
2  Facebook          267  Technology
3    Boeing          209    Aircraft

Stocks DataFrame excluding Sector Column:
   Price(in $)     Stock
0         3180    Amazon
1          835     Tesla
2          267  Facebook
3          209    Boeing

它從 stocks_df DataFrame 中刪除 Sector 列，並將結果分配給 filtered_df。

Author: Suraj Joshi
Suraj Joshi avatar Suraj Joshi avatar

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

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