Pandas 复制 DataFrame
Suraj Joshi
2023年1月30日
-
pandas.DataFrame.copy()
方法语法 -
使用
pandas.DataFrame.copy()
方法复制 Pandas DataFrame - 将 Pandas DataFrame 分配给变量来复制 DataFrame
本教程将介绍我们如何使用 DataFrame.copy()
方法复制一个 DataFrame 对象。
Python
pythonCopyimport pandas as pd
items_df = pd.DataFrame(
{
"Id": [302, 504, 708],
"Cost": ["300", "400", "350"],
}
)
print(items_df)
输出:
textCopy 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
Python
pythonCopyDataFrame.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
Python
pythonCopyimport 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:
textCopyOriginal 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
Python
pythonCopyimport 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:
textCopyCost 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
。
作者: Suraj Joshi
Suraj Joshi is a backend software engineer at Matrice.ai.
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