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本教程介绍了如何在 Pandas 中使用 DataFrame.groupby() 方法将两列的 DataFrame 分成若干组。我们还可以从创建的组中获得更多的信息。

我们将在本文中使用下面的 DataFrame。

import pandas as pd

roll_no = [501, 502, 503, 504, 505]

data = pd.DataFrame(
    {
        "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
        "Gender": ["Female", "Male", "Male", "Female", "Female", "Male"],
        "Employed": ["Yes", "No", "Yes", "No", "Yes", "No"],
        "Age": [30, 28, 27, 24, 28, 25],
    }
)

print(data)

输出：

       Name  Gender Employed  Age
0  Jennifer  Female      Yes   30
1    Travis    Male       No   28
2       Bob    Male      Yes   27
3      Emma  Female       No   24
4      Luna  Female      Yes   28
5     Anish    Male       No   25

Pandas Groupby 多列分组

import pandas as pd

roll_no = [501, 502, 503, 504, 505]

data = pd.DataFrame(
    {
        "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
        "Gender": ["Female", "Male", "Male", "Female", "Female", "Male"],
        "Employed": ["Yes", "No", "Yes", "No", "Yes", "No"],
        "Age": [30, 28, 27, 24, 28, 25],
    }
)

print(data)
print("")
print("Groups in DataFrame:")
groups = data.groupby(["Gender", "Employed"])
for group_key, group_value in groups:
    group = groups.get_group(group_key)
    print(group)
    print("")

输出：

       Name  Gender Employed  Age
0  Jennifer  Female      Yes   30
1    Travis    Male       No   28
2       Bob    Male      Yes   27
3      Emma  Female       No   24
4      Luna  Female      Yes   28
5     Anish    Male       No   25

Groups in DataFrame:
   Name  Gender Employed  Age
3  Emma  Female       No   24

       Name  Gender Employed  Age
0  Jennifer  Female      Yes   30
4      Luna  Female      Yes   28

     Name Gender Employed  Age
1  Travis   Male       No   28
5   Anish   Male       No   25

  Name Gender Employed  Age
2  Bob   Male      Yes   27

它从 DataFrame 中创建了 4 个组。所有 Gender 和 Employed 列值相同的行都会被放在同一个组。

计算每组的行数 Pandas

要使用 DataFrame.groupby() 方法统计每个创建的组的行数，我们可以使用 size() 方法。

import pandas as pd

roll_no = [501, 502, 503, 504, 505]

data = pd.DataFrame(
    {
        "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
        "Gender": ["Female", "Male", "Male", "Female", "Female", "Male"],
        "Employed": ["Yes", "No", "Yes", "No", "Yes", "No"],
        "Age": [30, 28, 27, 24, 28, 25],
    }
)

print(data)
print("")
print("Count of Each group:")
grouped_df = data.groupby(["Gender", "Employed"]).size().reset_index(name="Count")
print(grouped_df)

输出：

       Name  Gender Employed  Age
0  Jennifer  Female      Yes   30
1    Travis    Male       No   28
2       Bob    Male      Yes   27
3      Emma  Female       No   24
4      Luna  Female      Yes   28
5     Anish    Male       No   25

Count of Each group:
   Gender Employed  Count
0  Female       No      1
1  Female      Yes      2
2    Male       No      2
3    Male      Yes      1

它显示 DataFrame，从 DataFrame 中创建的组，以及每个组的元素数。

如果我们想得到 Employed 列中每个值的最大计数值，我们可以从上面创建的组再组成一个组，并对值进行计数，然后使用 max() 方法得到计数的最大值。

import pandas as pd

roll_no = [501, 502, 503, 504, 505]

data = pd.DataFrame(
    {
        "Name": ["Jennifer", "Travis", "Bob", "Emma", "Luna", "Anish"],
        "Gender": ["Female", "Male", "Male", "Female", "Female", "Male"],
        "Employed": ["Yes", "No", "Yes", "No", "Yes", "No"],
        "Age": [30, 28, 27, 24, 28, 25],
    }
)

print(data)
print("")

groups = data.groupby(["Gender", "Employed"]).size().groupby(level=1)
print(groups.max())

输出：

       Name  Gender Employed  Age
0  Jennifer  Female      Yes   30
1    Travis    Male       No   28
2       Bob    Male      Yes   27
3      Emma  Female       No   24
4      Luna  Female      Yes   28
5     Anish    Male       No   25

Employed
No     2
Yes    2
dtype: int64

它显示了从 Gender 和 Employed 列创建的组中，Employed 列值的最大计数。