Pandas 复制 DataFrame
Suraj Joshi
2023年1月30日
2021年1月22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。
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。
Author: Suraj Joshi
Suraj Joshi is a backend software engineer at Matrice.ai.
LinkedIn