Pandas loc vs iloc
Suraj Joshi
2023年1月30日
-
使用
.loc()方法從 DataFrame 中選擇指定索引和列標籤的特定值 -
使用
.loc()方法從 DataFrame 中選擇特定的列 -
使用
.loc()方法通過對列應用條件來過濾行 -
使用
iloc通過索引來過濾行 - 從 DataFrame 中過濾特定的行和列
-
使用
iloc方法從 DataFrame 中過濾行和列的範圍 -
Pandas
loc與iloc的比較
本教程介紹瞭如何使用 Python 中的 loc 和 iloc 從 Pandas DataFrame 中過濾資料。要使用 iloc 從 DataFrame 中過濾元素，我們使用行和列的整數索引，而要使用 loc 從 DataFrame 中過濾元素，我們使用行名和列名。
為了演示使用 loc 的資料過濾，我們將使用下面例子中描述的 DataFrame。
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Name": ["Alice", "Steven", "Neesham", "Chris", "Alice"],
"Age": [17, 20, 18, 21, 15],
"City": ["New York", "Portland", "Boston", "Seattle", "Austin"],
"Grade": ["A", "B-", "B+", "A-", "A"],
},
index=roll_no,
)
print(student_df)
輸出：
Name Age City Grade
501 Alice 17 New York A
502 Steven 20 Portland B-
503 Neesham 18 Boston B+
504 Chris 21 Seattle A-
505 Alice 15 Austin A
使用 .loc() 方法從 DataFrame 中選擇指定索引和列標籤的特定值
我們可以將索引標籤和列標籤作為引數傳遞給 .loc() 方法，以提取給定索引和列標籤對應的值。
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Name": ["Alice", "Steven", "Neesham", "Chris", "Alice"],
"Age": [17, 20, 18, 21, 15],
"City": ["New York", "Portland", "Boston", "Seattle", "Austin"],
"Grade": ["A", "B-", "B+", "A-", "A"],
},
index=roll_no,
)
print("The DataFrame of students with marks is:")
print(student_df)
print("")
print("The Grade of student with Roll No. 504 is:")
value = student_df.loc[504, "Grade"]
print(value)
輸出：
The DataFrame of students with marks is:
Name Age City Grade
501 Alice 17 New York A
502 Steven 20 Portland B-
503 Neesham 18 Boston B+
504 Chris 21 Seattle A-
505 Alice 15 Austin A
The Grade of student with Roll No. 504 is:
A-
在 DataFrame 中選擇索引標籤為 504 且列標籤為 Grade 的值。.loc() 方法的第一個引數代表索引名，第二個引數是指列名。
使用 .loc() 方法從 DataFrame 中選擇特定的列
我們還可以使用 .loc() 方法從 DataFrame 中過濾所需的列。我們將所需的列名列表作為第二個引數傳遞給 .loc() 方法來過濾指定的列。
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Name": ["Alice", "Steven", "Neesham", "Chris", "Alice"],
"Age": [17, 20, 18, 21, 15],
"City": ["New York", "Portland", "Boston", "Seattle", "Austin"],
"Grade": ["A", "B-", "B+", "A-", "A"],
},
index=roll_no,
)
print("The DataFrame of students with marks is:")
print(student_df)
print("")
print("The name and age of students in the DataFrame are:")
value = student_df.loc[:, ["Name", "Age"]]
print(value)
輸出：
The DataFrame of students with marks is:
Name Age City Grade
501 Alice 17 New York A
502 Steven 20 Portland B-
503 Neesham 18 Boston B+
504 Chris 21 Seattle A-
505 Alice 15 Austin A
The name and age of students in the DataFrame are:
Name Age
501 Alice 17
502 Steven 20
503 Neesham 18
504 Chris 21
505 Alice 15
.loc() 的第一個引數是:，它表示 DataFrame 中的所有行。同樣，我們將 ["Name", "Age"] 作為第二個引數傳遞給 .loc() 方法，表示只選擇 DataFrame 中的 Name 和 Age 列。
使用 .loc() 方法通過對列應用條件來過濾行
我們也可以使用 .loc() 方法過濾滿足指定條件的列值的行。
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Name": ["Alice", "Steven", "Neesham", "Chris", "Alice"],
"Age": [17, 20, 18, 21, 15],
"City": ["New York", "Portland", "Boston", "Seattle", "Austin"],
"Grade": ["A", "B-", "B+", "A-", "A"],
},
index=roll_no,
)
print("The DataFrame of students with marks is:")
print(student_df)
print("")
print("Students with Grade A are:")
value = student_df.loc[student_df.Grade == "A"]
print(value)
輸出：
The DataFrame of students with marks is:
Name Age City Grade
501 Alice 17 New York A
502 Steven 20 Portland B-
503 Neesham 18 Boston B+
504 Chris 21 Seattle A-
505 Alice 15 Austin A
Students with Grade A are:
Name Age City Grade
501 Alice 17 New York A
505 Alice 15 Austin A
它選擇了 DataFrame 中所有成績為 A 的學生。
使用 iloc 通過索引來過濾行
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Name": ["Alice", "Steven", "Neesham", "Chris", "Alice"],
"Age": [17, 20, 18, 21, 15],
"City": ["New York", "Portland", "Boston", "Seattle", "Austin"],
"Grade": ["A", "B-", "B+", "A-", "A"],
},
index=roll_no,
)
print("The DataFrame of students with marks is:")
print(student_df)
print("")
print("2nd and 3rd rows in the DataFrame:")
filtered_rows = student_df.iloc[[1, 2]]
print(filtered_rows)
輸出：
The DataFrame of students with marks is:
Name Age City Grade
501 Alice 17 New York A
502 Steven 20 Portland B-
503 Neesham 18 Boston B+
504 Chris 21 Seattle A-
505 Alice 15 Austin A
2nd and 3rd rows in the DataFrame:
Name Age City Grade
502 Steven 20 Portland B-
503 Neesham 18 Boston B+
它從 DataFrame 中過濾第 2 和第 3 行。
我們將行的整數索引作為引數傳遞給 iloc 方法，以便從 DataFrame 中過濾行。在這裡，第二和第三行的整數索引分別是 1 和 2，因為索引從 0 開始。
從 DataFrame 中過濾特定的行和列
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Name": ["Alice", "Steven", "Neesham", "Chris", "Alice"],
"Age": [17, 20, 18, 21, 15],
"City": ["New York", "Portland", "Boston", "Seattle", "Austin"],
"Grade": ["A", "B-", "B+", "A-", "A"],
},
index=roll_no,
)
print("The DataFrame of students with marks is:")
print(student_df)
print("")
print("Filtered values from the DataFrame:")
filtered_values = student_df.iloc[[1, 2, 3], [0, 3]]
print(filtered_values)
輸出：
The DataFrame of students with marks is:
Name Age City Grade
501 Alice 17 New York A
502 Steven 20 Portland B-
503 Neesham 18 Boston B+
504 Chris 21 Seattle A-
505 Alice 15 Austin A
Filtered values from the DataFrame:
Name Grade
502 Steven B-
503 Neesham B+
504 Chris A-
它從 DataFrame 中過濾第 2、3、4 行的第一列和最後一列，即 Name 和 Grade。我們將行的整數索引列表作為第一個引數，列的整數索引列表作為第二個引數傳遞給 iloc 方法。
使用 iloc 方法從 DataFrame 中過濾行和列的範圍
為了過濾行和列的範圍，我們可以使用列表切片，並將每行和每列的切片作為引數傳遞給 iloc 方法。
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Name": ["Alice", "Steven", "Neesham", "Chris", "Alice"],
"Age": [17, 20, 18, 21, 15],
"City": ["New York", "Portland", "Boston", "Seattle", "Austin"],
"Grade": ["A", "B-", "B+", "A-", "A"],
},
index=roll_no,
)
print("The DataFrame of students with marks is:")
print(student_df)
print("")
print("Filtered values from the DataFrame:")
filtered_values = student_df.iloc[1:4, 0:2]
print(filtered_values)
輸出：
The DataFrame of students with marks is:
Name Age City Grade
501 Alice 17 New York A
502 Steven 20 Portland B-
503 Neesham 18 Boston B+
504 Chris 21 Seattle A-
505 Alice 15 Austin A
Filtered values from the DataFrame:
Name Age
502 Steven 20
503 Neesham 18
504 Chris 21
它從 DataFrame 中選擇第 2、3、4 行和第 1、2 列。1:4 代表索引範圍從 1 到 3 的行，4 在範圍內是排他性的。同理，0:2 代表索引範圍從 0 到 1 的列。
Pandas loc 與 iloc 的比較
要使用 loc() 從 DataFrame 中過濾行和列，我們需要傳遞要過濾掉的行和列的名稱。同樣，我們需要傳遞要過濾掉的行和列的整數索引以使用 iloc() 來過濾值。
import pandas as pd
roll_no = [501, 502, 503, 504, 505]
student_df = pd.DataFrame(
{
"Name": ["Alice", "Steven", "Neesham", "Chris", "Alice"],
"Age": [17, 20, 18, 21, 15],
"City": ["New York", "Portland", "Boston", "Seattle", "Austin"],
"Grade": ["A", "B-", "B+", "A-", "A"],
},
index=roll_no,
)
print("The DataFrame of students with marks is:")
print(student_df)
print("")
print("Filtered values from the DataFrame using loc:")
iloc_filtered_values = student_df.loc[[502, 503, 504], ["Name", "Age"]]
print(iloc_filtered_values)
print("")
print("Filtered values from the DataFrame using iloc:")
iloc_filtered_values = student_df.iloc[[1, 2, 3], [0, 3]]
print(iloc_filtered_values)
The DataFrame of students with marks is:
Name Age City Grade
501 Alice 17 New York A
502 Steven 20 Portland B-
503 Neesham 18 Boston B+
504 Chris 21 Seattle A-
505 Alice 15 Austin A
Filtered values from the DataFrame using loc:
Name Age
502 Steven 20
503 Neesham 18
504 Chris 21
Filtered values from the DataFrame using iloc:
Name Grade
502 Steven B-
503 Neesham B+
504 Chris A-
它顯示了我們如何使用 loc 和 iloc 從 DataFrame 中過濾相同的值。
作者： Suraj Joshi
Suraj Joshi is a backend software engineer at Matrice.ai.
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