Pandas loc vs iloc
Suraj Joshi
2023年1月30日
Pandas
Pandas Filter
-
使用
.loc()方法从 DataFrame 中选择指定索引和列标签的特定值 -
使用
.loc()方法从 DataFrame 中选择特定的列 -
使用
.loc()方法通过对列应用条件来过滤行 -
使用
iloc通过索引来过滤行 - 从 DataFrame 中过滤特定的行和列
-
使用
iloc方法从 DataFrame 中过滤行和列的范围 -
Pandas
loc与iloc的比较
本教程介绍了如何使用 Python 中的 loc 和 iloc 从 Pandas DataFrame 中过滤数据。要使用 iloc 从 DataFrame 中过滤元素，我们使用行和列的整数索引，而要使用 loc 从 DataFrame 中过滤元素，我们使用行名和列名。
为了演示使用 loc 的数据过滤，我们将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)
输出：
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() 方法从 DataFrame 中选择指定索引和列标签的特定值
我们可以将索引标签和列标签作为参数传递给 .loc() 方法，以提取给定索引和列标签对应的值。
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)
输出：
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 中选择索引标签为 504 且列标签为 Grade 的值。.loc() 方法的第一个参数代表索引名，第二个参数是指列名。
使用 .loc() 方法从 DataFrame 中选择特定的列
我们还可以使用 .loc() 方法从 DataFrame 中过滤所需的列。我们将所需的列名列表作为第二个参数传递给 .loc() 方法来过滤指定的列。
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)
输出：
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() 的第一个参数是:，它表示 DataFrame 中的所有行。同样，我们将 ["Name", "Age"] 作为第二个参数传递给 .loc() 方法，表示只选择 DataFrame 中的 Name 和 Age 列。
使用 .loc() 方法通过对列应用条件来过滤行
我们也可以使用 .loc() 方法过滤满足指定条件的列值的行。
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)
输出：
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
它选择了 DataFrame 中所有成绩为 A 的学生。
使用 iloc 通过索引来过滤行
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)
输出：
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+
它从 DataFrame 中过滤第 2 和第 3 行。
我们将行的整数索引作为参数传递给 iloc 方法，以便从 DataFrame 中过滤行。在这里，第二和第三行的整数索引分别是 1 和 2，因为索引从 0 开始。
从 DataFrame 中过滤特定的行和列
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)
输出：
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-
它从 DataFrame 中过滤第 2、3、4 行的第一列和最后一列，即 Name 和 Grade。我们将行的整数索引列表作为第一个参数，列的整数索引列表作为第二个参数传递给 iloc 方法。
使用 iloc 方法从 DataFrame 中过滤行和列的范围
为了过滤行和列的范围，我们可以使用列表切片，并将每行和每列的切片作为参数传递给 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)
输出：
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
它从 DataFrame 中选择第 2、3、4 行和第 1、2 列。1:4 代表索引范围从 1 到 3 的行，4 在范围内是排他性的。同理，0:2 代表索引范围从 0 到 1 的列。
Pandas loc 与 iloc 的比较
要使用 loc() 从 DataFrame 中过滤行和列，我们需要传递要过滤掉的行和列的名称。同样，我们需要传递要过滤掉的行和列的整数索引以使用 iloc() 来过滤值。
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-
它显示了我们如何使用 loc 和 iloc 从 DataFrame 中过滤相同的值。
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作者： Suraj Joshi
Suraj Joshi is a backend software engineer at Matrice.ai.
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