HOWTO · Pandas
Pandas DataFrame 排除列
本教程讲解了我们如何从 DataFrame 中获取除某一列以外的所有列。
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。
