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本教程解释了我们如何从一个 DataFrame 中选择除一列以外的所有列。我们将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")

输出：

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 属性选择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")

输出：

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

它从 DataFrame stocks_df 中选择除 Sector 列以外的所有元素，将结果分配给 filtered_df，然后显示 filetered_df 的内容。

loc 属性根据指定的行和列选择元素。loc 属性中:符号在 , 前指定我们需要选择所有的行。对于列，我们指定只选择名称不是 Sector 的列。因此，它将选择除 Sector 列以外的所有列。

Pandas 使用 drop() 方法选择除一列以外的所有列

我们可以使用 drop() 方法，通过在方法中设置 axis=1，从 DataFrame 中删除指定的列。

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")

输出：

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

它从 stocks_df DataFrame 中删除 Sector 列，并将结果分配给 filtered_df。

我们也可以通过使用 drop() 方法从 DataFrame 中删除多个列。我们提供一个列名列表作为 drop() 方法的参数。

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")

输出：

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

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

Pandas 使用 difference() 方法选择除一列外的所有列

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")

输出：

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

它从 stocks_df DataFrame 中删除 Sector 列，并将结果分配给 filtered_df。