Pandas DataFrame 刪除某行
Suraj Joshi
2023年1月30日
本教程說明了如何使用 pandas.DataFrame.drop() 方法在 Pandas 中刪除行。
import pandas as pd
kgp_df = pd.DataFrame(
{
"Name": ["Himansh", "Prateek", "Abhishek", "Vidit", "Anupam"],
"Age": [30, 33, 35, 30, 30],
"Weight(KG)": [75, 75, 80, 70, 73],
}
)
print("The KGP DataFrame is:")
print(kgp_df)
輸出：
The KGP DataFrame is:
Name Age Weight(KG)
0 Himansh 30 75
1 Prateek 33 75
2 Abhishek 35 80
3 Vidit 30 70
4 Anupam 30 73
我們將使用 kgp_df DataFrame 來解釋如何從 Pandas DataFrame 中刪除行。
在 pandas.DataFrame.drop() 方法中按索引刪除行
import pandas as pd
kgp_df = pd.DataFrame(
{
"Name": ["Himansh", "Prateek", "Abhishek", "Vidit", "Anupam"],
"Age": [30, 33, 35, 30, 30],
"Weight(KG)": [75, 75, 80, 70, 73],
}
)
rows_dropped_df = kgp_df.drop(kgp_df.index[[0, 2]])
print("The KGP DataFrame is:")
print(kgp_df, "\n")
print("The KGP DataFrame after dropping 1st and 3rd DataFrame is:")
print(rows_dropped_df)
輸出：
The KGP DataFrame is:
Name Age Weight(KG)
0 Himansh 30 75
1 Prateek 33 75
2 Abhishek 35 80
3 Vidit 30 70
4 Anupam 30 73
The KGP DataFrame after dropping 1st and 3rd DataFrame is:
Name Age Weight(KG)
1 Prateek 33 75
3 Vidit 30 70
4 Anupam 30 73
從 kgp_df DataFrame 中刪除索引為 0 和 2 的行。索引 0 和 2 的行對應 DataFrame 中的第一行和第三行，因為索引是從 0 開始的。
我們也可以使用 DataFrame 的索引來刪除這些行，而不是使用預設的索引。
import pandas as pd
kgp_idx = ["A", "B", "C", "D", "E"]
kgp_df = pd.DataFrame(
{
"Name": ["Himansh", "Prateek", "Abhishek", "Vidit", "Anupam"],
"Age": [30, 33, 35, 30, 30],
"Weight(KG)": [75, 75, 80, 70, 73],
},
index=kgp_idx,
)
rows_dropped_df = kgp_df.drop(["A", "C"])
print("The KGP DataFrame is:")
print(kgp_df, "\n")
print("The KGP DataFrame after dropping 1st and 3rd DataFrame is:")
print(rows_dropped_df)
輸出：
The KGP DataFrame is:
Name Age Weight(KG)
A Himansh 30 75
B Prateek 33 75
C Abhishek 35 80
D Vidit 30 70
E Anupam 30 73
The KGP DataFrame after dropping 1st and 3rd DataFrame is:
Name Age Weight(KG)
B Prateek 33 75
D Vidit 30 70
E Anupam 30 73
它從 DataFrame 中刪除索引 A 和 C 的行，或者第一行和第三行。
我們將要刪除的行的索引列表傳遞給 drop() 方法來刪除相應的行。
根據 Pandas DataFrame 中某一列的值來刪除行
import pandas as pd
kgp_idx = ["A", "B", "C", "D", "E"]
kgp_df = pd.DataFrame(
{
"Name": ["Himansh", "Prateek", "Abhishek", "Vidit", "Anupam"],
"Age": [31, 33, 35, 36, 34],
"Weight(KG)": [75, 75, 80, 70, 73],
},
index=kgp_idx,
)
young_df_idx = kgp_df[kgp_df["Age"] <= 33].index
young_folks = kgp_df.drop(young_df_idx)
print("The KGP DataFrame is:")
print(kgp_df, "\n")
print("The DataFrame of folks with age less than or equal to 33 are:")
print(young_folks)
輸出：
The KGP DataFrame is:
Name Age Weight(KG)
A Himansh 31 75
B Prateek 33 75
C Abhishek 35 80
D Vidit 36 70
E Anupam 34 73
The DataFrame of folks with age less than or equal to 33 are:
Name Age Weight(KG)
C Abhishek 35 80
D Vidit 36 70
E Anupam 34 73
它將刪除所有年齡小於或等於 33 歲的行。
我們首先找到所有年齡小於或等於 33 歲的行的索引，然後使用 drop() 方法刪除這些行。
作者： Suraj Joshi
Suraj Joshi is a backend software engineer at Matrice.ai.
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