简介
pandas 中的 DF 数据类型能够像数据库表格一样进行 groupby 操作。通常来说 groupby 操作能够分为三局部：宰割数据，利用变换和和合并数据。
本文将会具体解说 Pandas 中的 groupby 操作。
宰割数据
宰割数据的目标是将 DF 宰割成为一个个的 group。为了进行 groupby 操作，在创立 DF 的时候须要指定相应的 label：
df = pd.DataFrame(
...: {...: "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
...: "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
...: "C": np.random.randn(8),
...: "D": np.random.randn(8),
...: }
...: )
...:
df
Out[61]:
A B C D
0 foo one -0.490565 -0.233106
1 bar one 0.430089 1.040789
2 foo two 0.653449 -1.155530
3 bar three -0.610380 -0.447735
4 foo two -0.934961 0.256358
5 bar two -0.256263 -0.661954
6 foo one -1.132186 -0.304330
7 foo three 2.129757 0.445744默认状况下，groupby 的轴是 x 轴。能够一列 group，也能够多列 group：
In [8]: grouped = df.groupby("A")
In [9]: grouped = df.groupby(["A", "B"])多 index
在 0.24 版本中，如果咱们有多 index，能够从中抉择特定的 index 进行 group：
In [10]: df2 = df.set_index(["A", "B"])
In [11]: grouped = df2.groupby(level=df2.index.names.difference(["B"]))
In [12]: grouped.sum()
Out[12]:
C D
A
bar -1.591710 -1.739537
foo -0.752861 -1.402938get_group
get_group 能够获取分组之后的数据：
In [24]: df3 = pd.DataFrame({"X": ["A", "B", "A", "B"], "Y": [1, 4, 3, 2]})
In [25]: df3.groupby(["X"]).get_group("A")
Out[25]:
X Y
0 A 1
2 A 3
In [26]: df3.groupby(["X"]).get_group("B")
Out[26]:
X Y
1 B 4
3 B 2dropna
默认状况下，NaN 数据会被排除在 groupby 之外，通过设置 dropna=False 能够容许 NaN 数据：
In [27]: df_list = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
In [28]: df_dropna = pd.DataFrame(df_list, columns=["a", "b", "c"])
In [29]: df_dropna
Out[29]:
a b c
0 1 2.0 3
1 1 NaN 4
2 2 1.0 3
3 1 2.0 2# Default ``dropna`` is set to True, which will exclude NaNs in keys
In [30]: df_dropna.groupby(by=["b"], dropna=True).sum()
Out[30]:
a c
b
1.0 2 3
2.0 2 5
# In order to allow NaN in keys, set ``dropna`` to False
In [31]: df_dropna.groupby(by=["b"], dropna=False).sum()
Out[31]:
a c
b
1.0 2 3
2.0 2 5
NaN 1 4groups 属性
groupby 对象有个 groups 属性，它是一个 key-value 字典，key 是用来分类的数据，value 是分类对应的值。
In [34]: grouped = df.groupby(["A", "B"])
In [35]: grouped.groups
Out[35]: {('bar', 'one'): [1], ('bar', 'three'): [3], ('bar', 'two'): [5], ('foo', 'one'): [0, 6], ('foo', 'three'): [7], ('foo', 'two'): [2, 4]}
In [36]: len(grouped)
Out[36]: 6index 的层级
对于多级 index 对象，groupby 能够指定 group 的 index 层级：
In [40]: arrays = [....: ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
....: ["one", "two", "one", "two", "one", "two", "one", "two"],
....: ]
....:
In [41]: index = pd.MultiIndex.from_arrays(arrays, names=["first", "second"])
In [42]: s = pd.Series(np.random.randn(8), index=index)
In [43]: s
Out[43]:
first second
bar one -0.919854
two -0.042379
baz one 1.247642
two -0.009920
foo one 0.290213
two 0.495767
qux one 0.362949
two 1.548106
dtype: float64group 第一级：
In [44]: grouped = s.groupby(level=0)
In [45]: grouped.sum()
Out[45]:
first
bar -0.962232
baz 1.237723
foo 0.785980
qux 1.911055
dtype: float64group 第二级：
In [46]: s.groupby(level="second").sum()
Out[46]:
second
one 0.980950
two 1.991575
dtype: float64group 的遍历
失去 group 对象之后，咱们能够通过 for 语句来遍历 group：
In [62]: grouped = df.groupby('A')
In [63]: for name, group in grouped:
....: print(name)
....: print(group)
....:
bar
A B C D
1 bar one 0.254161 1.511763
3 bar three 0.215897 -0.990582
5 bar two -0.077118 1.211526
foo
A B C D
0 foo one -0.575247 1.346061
2 foo two -1.143704 1.627081
4 foo two 1.193555 -0.441652
6 foo one -0.408530 0.268520
7 foo three -0.862495 0.024580如果是多字段 group，group 的名字是一个元组：
In [64]: for name, group in df.groupby(['A', 'B']):
....: print(name)
....: print(group)
....:
('bar', 'one')
A B C D
1 bar one 0.254161 1.511763
('bar', 'three')
A B C D
3 bar three 0.215897 -0.990582
('bar', 'two')
A B C D
5 bar two -0.077118 1.211526
('foo', 'one')
A B C D
0 foo one -0.575247 1.346061
6 foo one -0.408530 0.268520
('foo', 'three')
A B C D
7 foo three -0.862495 0.02458
('foo', 'two')
A B C D
2 foo two -1.143704 1.627081
4 foo two 1.193555 -0.441652聚合操作
分组之后，就能够进行聚合操作：
In [67]: grouped = df.groupby("A")
In [68]: grouped.aggregate(np.sum)
Out[68]:
C D
A
bar 0.392940 1.732707
foo -1.796421 2.824590
In [69]: grouped = df.groupby(["A", "B"])
In [70]: grouped.aggregate(np.sum)
Out[70]:
C D
A B
bar one 0.254161 1.511763
three 0.215897 -0.990582
two -0.077118 1.211526
foo one -0.983776 1.614581
three -0.862495 0.024580
two 0.049851 1.185429对于多 index 数据来说，默认返回值也是多 index 的。如果想应用新的 index，能够增加 as_index = False：
In [71]: grouped = df.groupby(["A", "B"], as_index=False)
In [72]: grouped.aggregate(np.sum)
Out[72]:
A B C D
0 bar one 0.254161 1.511763
1 bar three 0.215897 -0.990582
2 bar two -0.077118 1.211526
3 foo one -0.983776 1.614581
4 foo three -0.862495 0.024580
5 foo two 0.049851 1.185429
In [73]: df.groupby("A", as_index=False).sum()
Out[73]:
A C D
0 bar 0.392940 1.732707
1 foo -1.796421 2.824590下面的成果等同于 reset_index
In [74]: df.groupby(["A", "B"]).sum().reset_index()grouped.size() 计算 group 的大小：
In [75]: grouped.size()
Out[75]:
A B size
0 bar one 1
1 bar three 1
2 bar two 1
3 foo one 2
4 foo three 1
5 foo two 2grouped.describe() 形容 group 的信息：
In [76]: grouped.describe()
Out[76]:
C ... D
count mean std min 25% 50% ... std min 25% 50% 75% max
0 1.0 0.254161 NaN 0.254161 0.254161 0.254161 ... NaN 1.511763 1.511763 1.511763 1.511763 1.511763
1 1.0 0.215897 NaN 0.215897 0.215897 0.215897 ... NaN -0.990582 -0.990582 -0.990582 -0.990582 -0.990582
2 1.0 -0.077118 NaN -0.077118 -0.077118 -0.077118 ... NaN 1.211526 1.211526 1.211526 1.211526 1.211526
3 2.0 -0.491888 0.117887 -0.575247 -0.533567 -0.491888 ... 0.761937 0.268520 0.537905 0.807291 1.076676 1.346061
4 1.0 -0.862495 NaN -0.862495 -0.862495 -0.862495 ... NaN 0.024580 0.024580 0.024580 0.024580 0.024580
5 2.0 0.024925 1.652692 -1.143704 -0.559389 0.024925 ... 1.462816 -0.441652 0.075531 0.592714 1.109898 1.627081
[6 rows x 16 columns]通用聚合办法
上面是通用的聚合办法：
| 函数 | 形容 |
|---|---|
mean() |
平均值 |
sum() |
求和 |
size() |
计算 size |
count() |
group 的统计 |
std() |
标准差 |
var() |
方差 |
sem() |
均值的规范误 |
describe() |
统计信息形容 |
first() |
第一个 group 值 |
last() |
最初一个 group 值 |
nth() |
第 n 个 group 值 |
min() |
最小值 |
max() |
最大值 |
同时应用多个聚合办法
能够同时指定多个聚合办法：
In [81]: grouped = df.groupby("A")
In [82]: grouped["C"].agg([np.sum, np.mean, np.std])
Out[82]:
sum mean std
A
bar 0.392940 0.130980 0.181231
foo -1.796421 -0.359284 0.912265能够重命名：
In [84]: (....: grouped["C"]
....: .agg([np.sum, np.mean, np.std])
....: .rename(columns={"sum": "foo", "mean": "bar", "std": "baz"})
....: )
....:
Out[84]:
foo bar baz
A
bar 0.392940 0.130980 0.181231
foo -1.796421 -0.359284 0.912265NamedAgg
NamedAgg 能够对聚合进行更精准的定义，它蕴含 column 和 aggfunc 两个定制化的字段。
In [88]: animals = pd.DataFrame(
....: {....: "kind": ["cat", "dog", "cat", "dog"],
....: "height": [9.1, 6.0, 9.5, 34.0],
....: "weight": [7.9, 7.5, 9.9, 198.0],
....: }
....: )
....:
In [89]: animals
Out[89]:
kind height weight
0 cat 9.1 7.9
1 dog 6.0 7.5
2 cat 9.5 9.9
3 dog 34.0 198.0
In [90]: animals.groupby("kind").agg(....: min_height=pd.NamedAgg(column="height", aggfunc="min"),
....: max_height=pd.NamedAgg(column="height", aggfunc="max"),
....: average_weight=pd.NamedAgg(column="weight", aggfunc=np.mean),
....: )
....:
Out[90]:
min_height max_height average_weight
kind
cat 9.1 9.5 8.90
dog 6.0 34.0 102.75或者间接应用一个元组：
In [91]: animals.groupby("kind").agg(....: min_height=("height", "min"),
....: max_height=("height", "max"),
....: average_weight=("weight", np.mean),
....: )
....:
Out[91]:
min_height max_height average_weight
kind
cat 9.1 9.5 8.90
dog 6.0 34.0 102.75不同的列指定不同的聚合办法
通过给 agg 办法传入一个字典，能够指定不同的列应用不同的聚合：
In [95]: grouped.agg({"C": "sum", "D": "std"})
Out[95]:
C D
A
bar 0.392940 1.366330
foo -1.796421 0.884785转换操作
转换是将对象转换为同样大小对象的操作。在数据分析的过程中，常常须要进行数据的转换操作。
能够接 lambda 操作：
In [112]: ts.groupby(lambda x: x.year).transform(lambda x: x.max() - x.min())填充 na 值：
In [121]: transformed = grouped.transform(lambda x: x.fillna(x.mean()))过滤操作
filter 办法能够通过 lambda 表达式来过滤咱们不须要的数据：
In [136]: sf = pd.Series([1, 1, 2, 3, 3, 3])
In [137]: sf.groupby(sf).filter(lambda x: x.sum() > 2)
Out[137]:
3 3
4 3
5 3
dtype: int64Apply 操作
有些数据可能不适宜进行聚合或者转换操作，Pandas 提供了一个 apply 办法，用来进行更加灵便的转换操作。
In [156]: df
Out[156]:
A B C D
0 foo one -0.575247 1.346061
1 bar one 0.254161 1.511763
2 foo two -1.143704 1.627081
3 bar three 0.215897 -0.990582
4 foo two 1.193555 -0.441652
5 bar two -0.077118 1.211526
6 foo one -0.408530 0.268520
7 foo three -0.862495 0.024580
In [157]: grouped = df.groupby("A")
# could also just call .describe()
In [158]: grouped["C"].apply(lambda x: x.describe())
Out[158]:
A
bar count 3.000000
mean 0.130980
std 0.181231
min -0.077118
25% 0.069390
...
foo min -1.143704
25% -0.862495
50% -0.575247
75% -0.408530
max 1.193555
Name: C, Length: 16, dtype: float64能够外接函数：
In [159]: grouped = df.groupby('A')['C']
In [160]: def f(group):
.....: return pd.DataFrame({'original': group,
.....: 'demeaned': group - group.mean()})
.....:
In [161]: grouped.apply(f)
Out[161]:
original demeaned
0 -0.575247 -0.215962
1 0.254161 0.123181
2 -1.143704 -0.784420
3 0.215897 0.084917
4 1.193555 1.552839
5 -0.077118 -0.208098
6 -0.408530 -0.049245
7 -0.862495 -0.503211本文已收录于 http://www.flydean.com/11-python-pandas-groupby/
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