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本教程将介绍我们如何使用 DataFrame.copy() 方法复制一个 DataFrame 对象。

import pandas as pd

items_df = pd.DataFrame(
    {
        "Id": [302, 504, 708],
        "Cost": ["300", "400", "350"],
    }
)

print(items_df)

输出：

    Id Cost
0  302  300
1  504  400
2  708  350 

我们将用上面的例子来演示如何在 Pandas 中使用 DataFrame.copy() 方法。

pandas.DataFrame.copy() 方法语法

DataFrame.copy(deep=True)

它返回 DataFrame 的副本。deep 默认为 True，这意味着在副本中所作的任何更改将不会反映在原始 DataFrame 中。但是，如果我们设置 deep=False，那么在副本中所做的任何改变也会反映在原始 DataFrame 中。

使用 pandas.DataFrame.copy() 方法复制 Pandas DataFrame

import pandas as pd
import numpy as np

items_df = pd.DataFrame(
    {
        "Id": [302, 504, 708],
        "Cost": ["300", "400", "350"],
    }
)

deep_copy = items_df.copy()

print("Original DataFrame before changing value in copy DataFrame:")
print(items_df, "\n")

print("Copy DataFrame before changing value in copy DataFrame:")
print(deep_copy, "\n")

deep_copy.loc[0, "Cost"] = np.nan

print("Original DataFrame after changing value in copy DataFrame:")
print(items_df, "\n")

print("Copy DataFrame after changing value in copy DataFrame:")
print(deep_copy, "\n")

输出：

Original DataFrame before changing value in copy DataFrame:
    Id Cost
0  302  300
1  504  400
2  708  350 

Copy DataFrame before changing value in copy DataFrame:
    Id Cost
0  302  300
1  504  400
2  708  350 

Original DataFrame after changing value in copy DataFrame:
    Id Cost
0  302  300
1  504  400
2  708  350 

Copy DataFrame after changing value in copy DataFrame:
    Id Cost
0  302  NaN
1  504  400
2  708  350 

它创建了 DataFrame items_df 的副本作为 deep_copy。如果我们改变了副本 deep_copy 的任何值，原来的 DataFrame items_df 就没有变化。我们在 deep_copy 中把第一行的 Cost 列的值设置为 NaN，但 items_df 却没有变化。

将 Pandas DataFrame 分配给变量来复制 DataFrame

import pandas as pd
import numpy as np

items_df = pd.DataFrame(
    {
        "Id": [302, 504, 708],
        "Cost": ["300", "400", "350"],
    }
)

copy_cost = items_df["Cost"]

print("Cost column of Original DataFrame before changing value in copy DataFrame:")
print(items_df, "\n")

print("Cost column of Copied DataFrame before changing value in copy DataFrame:")
print(copy_cost, "\n")

copy_cost[0] = np.nan

print("Cost column of Original DataFrame after changing value in copy DataFrame:")
print(copy_cost, "\n")

print("Cost column of Copied DataFrame after changing value in copy DataFrame:")
print(copy_cost, "\n")

输出：

Cost column of Original DataFrame before changing value in copy DataFrame:
    Id Cost
0  302  300
1  504  400
2  708  350 

Cost column of Copied DataFrame before changing value in copy DataFrame:
0    300
1    400
2    350
Name: Cost, dtype: object 

Cost column of Original DataFrame after changing value in copy DataFrame:
0    NaN
1    400
2    350
Name: Cost, dtype: object 

Cost column of Copied DataFrame after changing value in copy DataFrame:
0    NaN
1    400
2    350
Name: Cost, dtype: object 

它将 DataFrame items_df 中的 Cost 列创建为 copy_cost。