目录
- namedagg
- apply操作
简介
pandas中的df数据类型可以像数据库表格一样进行groupby操作。通常来说groupby操作可以分为三部分:分割数据,应用变换和和合并数据。
数据库技术:Pandas高级教程之Pandas中的GroupBy操作将会详细讲解pandas中的groupby操作。
分割数据
分割数据的目的是将df分割成为一个个的group。为了进行groupby操作,在创建df的时候需要指定相应的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
默认情况下,groupby的轴是x轴。可以一列group,也可以多列group:
in [8]: grouped = df.groupby("a") in [9]: grouped = df.groupby(["a", "b"])
多index
在0.24版本中,如果我们有多index,可以从中选择特定的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 -1.402938
get_group
get_group 可以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 2
dropna
默认情况下,nan数据会被排除在groupby之外,通过设置 dropna=false 可以允许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 4
groups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]: 6
index的层级
对于多级index对象,groupby可以指定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: float64
group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: float64
group第二级:
in [46]: s.groupby(level="second").sum() out[46]: second one 0.980950 two 1.991575 dtype: float64
group的遍历
得到group对象之后,我们可以通过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的免费精选名字大全是一个元组:
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,就可以进行聚合操作:
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。如果想使用新的index,可以添加 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
上面的效果等同于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 2
grouped.describe() 描述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]
通用聚合方法
下面是通用的聚合方法:
函数 | 描述 |
---|---|
mean() |
平均值 |
sum() |
求和 |
size() |
计算size |
count() |
group的统计 |
std() |
标准差 |
var() |
方差 |
sem() |
均值的标准误 |
describe() |
统计信息描述 |
first() |
第一个group值 |
last() |
最后一个group值 |
nth() |
第n个group值 |
min() |
最小值 |
max() |
最大值 |
可以同时指定多个聚合方法:
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
可以重命名:
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 0.912265
namedagg
namedagg 可以对聚合进行更精准的定义,它包含 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
或者直接使用一个元组:
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方法
通过给agg方法传入一个字典,可以指定不同的列使用不同的聚合:
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,经常需要进行数据的转换操作。
可以接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方法可以通过lambda表达式来过滤我们不需要的数据:
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: int64
apply操作
有些数据可能不适合进行聚合或者转换操作,pandas提供了一个 apply
方法,用来进行更加灵活的转换操作。
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
可以外接函数:
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
数据库技术:Pandas高级教程之Pandas中的GroupBy操作已收录于 https://www.flydean.com/11-python-pandas-groupby/
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