Pandas - 数据的聚合与分组计算

其他 2018-08-09 18:06:17 阅读次数: 0

1 数据分组 - groupby()

分组原理

from pandas import DataFrame,Series
import pandas as pd
import numpy as np
from numpy import nan as NA
df = DataFrame({"key1":list("aabba"),
               "key2":["one","two","one","two","one"],
               "data1":np.random.randn(5),
               "data2":np.random.randn(5)})
df
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data1 data2 key1 key2
0 0.008908 0.652712 a one
1 0.438874 0.423774 a two
2 0.299105 -1.279888 b one
3 -0.191032 0.429504 b two
4 -0.395208 0.523417 a one 
# 对数据按照key1进行分组
# groupby 只进行分组，不会进行任何的计算操作
grouped = df["data1"].groupby(df["key1"]) 
grouped.count()  # 根据分组结果，计算每个分组下元素的个数
    key1
    a    3
    b    2
    Name: data1, dtype: int64

grouped.max()   # 根据分组结果，计算每个分组下的最大值
    key1
    a    0.438874
    b    0.299105
    Name: data1, dtype: float64

grouped.size()
    key1
    a    3
    b    2
    Name: data1, dtype: int64

grouped.mean()
    key1
    a    0.017525
    b    0.054036
    Name: data1, dtype: float64

grouped.describe()
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count mean std min 25% 50% 75% max
key1
a 3.0 0.017525 0.417108 -0.395208 -0.193150 0.008908 0.223891 0.438874
b 2.0 0.054036 0.346579 -0.191032 -0.068498 0.054036 0.176571 0.299105 
df
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data1 data2 key1 key2
0 0.008908 0.652712 a one
1 0.438874 0.423774 a two
2 0.299105 -1.279888 b one
3 -0.191032 0.429504 b two
4 -0.395208 0.523417 a one

1.1 按照多列进行分组的两种方法

# 按照多列进行分组
# 先获取某一列的所有值，再按照key1和key2进行分组
grouped1 = df["data1"].groupby([df["key1"],df["key2"]]) 
grouped1.mean()
    key1  key2
    a     one    -0.193150
          two     0.438874
    b     one     0.299105
          two    -0.191032
    Name: data1, dtype: float64

# 先将所有的数据按照key1和key2进行分组，再获取分组结果data1或者data2的分组结果
group_all = df.groupby(["key1","key2"])
# 获取data1的分组结果
group_all["data1"].mean()
    key1  key2
    a     one    -0.193150
          two     0.438874
    b     one     0.299105
          two    -0.191032
    Name: data1, dtype: float64

# 获取data2的分组结果
group_all["data2"].mean()
    key1  key2
    a     one     0.588065
          two     0.423774
    b     one    -1.279888
          two     0.429504
    Name: data2, dtype: float64

df
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data1 data2 key1 key2
0 0.008908 0.652712 a one
1 0.438874 0.423774 a two
2 0.299105 -1.279888 b one
3 -0.191032 0.429504 b two
4 -0.395208 0.523417 a one 
# 按照key1将所有的数据进行分组
group_key1 = df.groupby(["key1"])

1.2 对分组进行迭代

# 通过循环遍历已分组对象中的每一个组别数据
for name,group in group_key1:
    print("组别：",name)
    print("数据：\n",group)
    组别： a
    数据：
           data1     data2 key1 key2
    0  0.008908  0.652712    a  one
    1  0.438874  0.423774    a  two
    4 -0.395208  0.523417    a  one
    组别： b
    数据：
           data1     data2 key1 key2
    2  0.299105 -1.279888    b  one
    3 -0.191032  0.429504    b  two

1.3 将列数据按照数据类型进行分组

# 查看DataFrame中每一列的数据类型
df
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data1 data2 key1 key2
0 0.008908 0.652712 a one
1 0.438874 0.423774 a two
2 0.299105 -1.279888 b one
3 -0.191032 0.429504 b two
4 -0.395208 0.523417 a one 
df.dtypes
    data1    float64
    data2    float64
    key1      object
    key2      object
    dtype: object

# 将列数据按照数据类型进行分组
group_types = df.groupby(df.dtypes,axis=1)
for name,group in group_types:
    print("组别：",name)
    print("数据：\n",group)
    组别： float64
    数据：
           data1     data2
    0  0.008908  0.652712
    1  0.438874  0.423774
    2  0.299105 -1.279888
    3 -0.191032  0.429504
    4 -0.395208  0.523417
    组别： object
    数据：
       key1 key2
    0    a  one
    1    a  two
    2    b  one
    3    b  two
    4    a  one

1.4 通过字典或者Series对DataFrame数据进行分组

people = DataFrame(np.random.randn(5,5),
                  columns=list("abcde"),
                  index=["Joe","Jack","Rose","Tom","Jerry"])
people
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a b c d e
Joe 1.147082 0.053921 0.182772 -0.885232 1.486824
Jack -1.020139 -0.818716 2.464176 -0.323069 0.987470
Rose -0.478689 -0.025389 1.585679 -0.555187 0.075537
Tom 0.357304 1.033158 0.589091 -0.003958 -0.659374
Jerry 1.617607 -1.523480 -0.449035 -0.784486 -2.736335 
# 选择数据中第三行，b和c两列
people.ix[2:3,["b","c"]] = NA
people
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a b c d e
Joe 1.147082 0.053921 0.182772 -0.885232 1.486824
Jack -1.020139 -0.818716 2.464176 -0.323069 0.987470
Rose -0.478689 NaN NaN -0.555187 0.075537
Tom 0.357304 1.033158 0.589091 -0.003958 -0.659374
Jerry 1.617607 -1.523480 -0.449035 -0.784486 -2.736335 
mapping = {"a":"red",
          "b":"red",
          "c":"blue",
          "d":"blue",
          "e":"red",
          "f":"orange"}

# 用mapping字典对people进行分组
group_dict = people.groupby(mapping,axis=1)
group_dict.count()
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blue red
Joe 2 3
Jack 2 3
Rose 1 2
Tom 2 3
Jerry 2 3 
group_dict.sum()
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blue red
Joe -0.702460 2.687828
Jack 2.141107 -0.851385
Rose -0.555187 -0.403153
Tom 0.585133 0.731088
Jerry -1.233521 -2.642209 
group_dict.mean()
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blue red
Joe -0.351230 0.895943
Jack 1.070553 -0.283795
Rose -0.555187 -0.201576
Tom 0.292566 0.243696
Jerry -0.616761 -0.880736

2 数据聚合

2.1 分组结果的内置聚合函数

  • df_obj5.groupby(‘key1’).sum()
  • df_obj5.groupby(‘key1’).max()
  • df_obj5.groupby(‘key1’).min()
  • df_obj5.groupby(‘key1’).mean()
  • df_obj5.groupby(‘key1’).size()
  • df_obj5.groupby(‘key1’).count()
  • df_obj5.groupby(‘key1’).describe()
函数名 说明
count 分组中非NA值的数量
sum 非NA值的和
mean 非NA值的算术平均数
median 非NA值的算数中位数
std,var 无偏标准差和方差
min,max 非NA值的最小最大值
prod 非NA值的积
first,last 第一个和最后一个非NA值 
# 对分组后的数据进行计算
dict_obj = {'key1' : ['a' , 'b' , 'a' , 'b' , 'a' , 'b' , 'a' , 'a'],
            'key2' : ['one' , 'one' , 'two' , 'three' ,'two' , 'two' , 'one' , 'three'],
            'data1': np.random.randint(1,10, 8),           
            'data2': np.random.randint(1,10, 8)}

df_dict = DataFrame(dict_obj)
df_dict
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data1 data2 key1 key2
0 4 5 a one
1 2 9 b one
2 8 2 a two
3 7 7 b three
4 3 4 a two
5 4 6 b two
6 3 5 a one
7 5 7 a three 
# 将以上数据按照key1进行分组
grouped = df_dict.groupby("key1")
# 查看每个分组下元素的个数
grouped.count()
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data1 data2 key2
key1
a 5 5 5
b 3 3 3 
# 对每个分组进行求和运算
grouped.sum()
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data1 data2
key1
a 23 23
b 13 22 
df_dict
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data1 data2 key1 key2
0 4 5 a one
1 2 9 b one
2 8 2 a two
3 7 7 b three
4 3 4 a two
5 4 6 b two
6 3 5 a one
7 5 7 a three 
grouped = df_dict.groupby("key1")
grouped
<pandas.core.groupby.DataFrameGroupBy object at 0x00000207EA25FBE0>

2.2 自定义聚合函数

def peak_range(group):
    return group.max()-group.min()

#  grouped.agg()   对已经分组好的数据使用自定义聚合函数
# 定义函数写法
# grouped.agg(peak_range)
# 匿名函数写法
grouped.agg(lambda group : group.max()-group.min())
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data1 data2
key1
a 5 5
b 5 3 
# grouped.sum()
# grouped.count()
# grouped.size()
# 对分组后的数据一次性调用多个聚合函数

grouped.agg(["mean","std","count",peak_range])
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data1 data2
mean std count peak_range mean std count peak_range
key1
a 4.600000 2.073644 5 5 4.600000 1.816590 5 5
b 4.333333 2.516611 3 5 7.333333 1.527525 3 3 
# 为自定义聚合函数取别名
grouped.agg(["mean","std","count",("ptp",peak_range)])
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data1 data2
mean std count ptp mean std count ptp
key1
a 4.600000 2.073644 5 5 4.600000 1.816590 5 5
b 4.333333 2.516611 3 5 7.333333 1.527525 3 3 
# 用字段对分组后不同的数据列调用不同的聚合函数
for name,group in grouped:
    print("组别：",name)
    print("数据：\n",group)
    组别： a
    数据：
        data1  data2 key1   key2
    0      4      5    a    one
    2      8      2    a    two
    4      3      4    a    two
    6      3      5    a    one
    7      5      7    a  three
    组别： b
    数据：
        data1  data2 key1   key2
    1      2      9    b    one
    3      7      7    b  three
    5      4      6    b    two

# 对分组后data1使用mean聚合函数，data2使用sum聚合函数
func_type = {"data1":"mean",
            "data2":"sum"}

grouped.agg(func_type)
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data1 data2
key1
a 4.600000 23
b 4.333333 22 
# 返回没有索引的分组对象
df = pd.DataFrame({'books':['bk1' , 'bk1' , 'bk1' , 'bk2' , 'bk2' , 'bk3'], 
                        'price': [12,12,12,15,15,17]}) 
df
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books price
0 bk1 12
1 bk1 12
2 bk1 12
3 bk2 15
4 bk2 15
5 bk3 17 
# 是否将分组类别作为分组结果的索引
book_group = df.groupby("books",as_index=True)
book_group.sum()
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price
books
bk1 36
bk2 30
bk3 17

2.3 apply函数

tips = pd.read_csv("data/tips.csv")
tips.head(5)
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total_bill tip smoker day time size
0 16.99 1.01 No Sun Dinner 2
1 10.34 1.66 No Sun Dinner 3
2 21.01 3.50 No Sun Dinner 3
3 23.68 3.31 No Sun Dinner 2
4 24.59 3.61 No Sun Dinner 4 
# 求出小费占总消费金额的占比
tips["tip_pct"] = tips["tip"]/tips["total_bill"]
tips.head(5)  # 如何将小数转化成百分比  5.9%  16% ....
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total_bill tip smoker day time size tip_pct
0 16.99 1.01 No Sun Dinner 2 0.059447
1 10.34 1.66 No Sun Dinner 3 0.160542
2 21.01 3.50 No Sun Dinner 3 0.166587
3 23.68 3.31 No Sun Dinner 2 0.139780
4 24.59 3.61 No Sun Dinner 4 0.146808 
# 将DataFrame按照指定的列数据进行排序，返回排名处在最后的5条记录
def top(df,n=5,col="tip_pct"):
    return df.sort_values(by=col,ascending=True)[:n]
# 按照tip_pct这一列将tips进行排序
# tips.sort_values(by="tip_pct",ascendingde=False)[:6]
top(tips,n=10,col="total_bill")
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total_bill tip smoker day time size tip_pct
67 3.07 1.00 Yes Sat Dinner 1 0.325733
92 5.75 1.00 Yes Fri Dinner 2 0.173913
111 7.25 1.00 No Sat Dinner 1 0.137931
172 7.25 5.15 Yes Sun Dinner 2 0.710345
149 7.51 2.00 No Thur Lunch 2 0.266312
195 7.56 1.44 No Thur Lunch 2 0.190476
218 7.74 1.44 Yes Sat Dinner 2 0.186047
145 8.35 1.50 No Thur Lunch 2 0.179641
135 8.51 1.25 No Thur Lunch 2 0.146886
126 8.52 1.48 No Thur Lunch 2 0.173709

自定义函数利用apply应用到分组对象上

group_by_smoker.apply(top)
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total_bill tip smoker day time size tip_pct
smoker
No 57 26.41 1.50 No Sat Dinner 2 0.056797
0 16.99 1.01 No Sun Dinner 2 0.059447
48 28.55 2.05 No Sun Dinner 3 0.071804
146 18.64 1.36 No Thur Lunch 3 0.072961
130 19.08 1.50 No Thur Lunch 2 0.078616
Yes 237 32.83 1.17 Yes Sat Dinner 2 0.035638
102 44.30 2.50 Yes Sat Dinner 3 0.056433
187 30.46 2.00 Yes Sun Dinner 5 0.065660
210 30.06 2.00 Yes Sat Dinner 3 0.066534
240 27.18 2.00 Yes Sat Dinner 2 0.073584 
tips.head(5)
tips.head(5)  # 筛选出所有抽烟的数据和不抽烟的数据 smoker_data   none_smoker_data
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total_bill tip smoker day time size tip_pct
0 16.99 1.01 No Sun Dinner 2 0.059447
1 10.34 1.66 No Sun Dinner 3 0.160542
2 21.01 3.50 No Sun Dinner 3 0.166587
3 23.68 3.31 No Sun Dinner 2 0.139780
4 24.59 3.61 No Sun Dinner 4 0.146808 
mask = tips["smoker"] == "Yes"
# mask
smoker_data = tips[mask]
smoker_data
# 调用top函数，传入smoker_data
top(smoker_data)
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total_bill tip smoker day time size tip_pct
237 32.83 1.17 Yes Sat Dinner 2 0.035638
102 44.30 2.50 Yes Sat Dinner 3 0.056433
187 30.46 2.00 Yes Sun Dinner 5 0.065660
210 30.06 2.00 Yes Sat Dinner 3 0.066534
240 27.18 2.00 Yes Sat Dinner 2 0.073584 
mask=tips["smoker"]=="No"
none_smoker_data = tips[mask]
top(none_smoker_data)
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total_bill tip smoker day time size tip_pct
57 26.41 1.50 No Sat Dinner 2 0.056797
0 16.99 1.01 No Sun Dinner 2 0.059447
48 28.55 2.05 No Sun Dinner 3 0.071804
146 18.64 1.36 No Thur Lunch 3 0.072961
130 19.08 1.50 No Thur Lunch 2 0.078616 
# 对消费数据按照smoker列进行分组，然后再按照day进行分组，
# 最后统计在所有相同星期数消费记录中总消费金额最低记录
tips.groupby(["smoker","day"]).apply(top,n=2,col="total_bill")
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total_bill tip smoker day time size tip_pct
smoker day
No Fri 99 12.46 1.50 No Fri Dinner 2 0.120385
223 15.98 3.00 No Fri Lunch 3 0.187735
Sat 111 7.25 1.00 No Sat Dinner 1 0.137931
30 9.55 1.45 No Sat Dinner 2 0.151832
Sun 6 8.77 2.00 No Sun Dinner 2 0.228050
43 9.68 1.32 No Sun Dinner 2 0.136364
Thur 149 7.51 2.00 No Thur Lunch 2 0.266312
195 7.56 1.44 No Thur Lunch 2 0.190476
Yes Fri 92 5.75 1.00 Yes Fri Dinner 2 0.173913
222 8.58 1.92 Yes Fri Lunch 1 0.223776
Sat 67 3.07 1.00 Yes Sat Dinner 1 0.325733
218 7.74 1.44 Yes Sat Dinner 2 0.186047
Sun 172 7.25 5.15 Yes Sun Dinner 2 0.710345
178 9.60 4.00 Yes Sun Dinner 2 0.416667
Thur 196 10.34 2.00 Yes Thur Lunch 2 0.193424
201 12.74 2.01 Yes Thur Lunch 2 0.157771 
# 将数据按照smoker分组，然后按照小费金额占比排序，去除组别的名称
tips.groupby("smoker",group_keys=False).apply(top)
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total_bill tip smoker day time size tip_pct
57 26.41 1.50 No Sat Dinner 2 0.056797
0 16.99 1.01 No Sun Dinner 2 0.059447
48 28.55 2.05 No Sun Dinner 3 0.071804
146 18.64 1.36 No Thur Lunch 3 0.072961
130 19.08 1.50 No Thur Lunch 2 0.078616
237 32.83 1.17 Yes Sat Dinner 2 0.035638
102 44.30 2.50 Yes Sat Dinner 3 0.056433
187 30.46 2.00 Yes Sun Dinner 5 0.065660
210 30.06 2.00 Yes Sat Dinner 3 0.066534
240 27.18 2.00 Yes Sat Dinner 2 0.073584

2.4 分组加权平均数和相关系数

df = pd.DataFrame({'category': ['a' , 'a' , 'a' , 'a' , 'b' , 'b' , 'b' , 'b'], 
                   'data': np.random.randn(8), 
                   'weights': np.random.rand(8)})
df
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category data weights
0 a -1.837433 0.220477
1 a -1.572593 0.615787
2 a 0.008959 0.640871
3 a 1.095008 0.305419
4 b 0.190451 0.290830
5 b 1.201876 0.904729
6 b -1.125858 0.821191
7 b -0.946454 0.039222 
grouped = df.groupby("category")
for name,group in grouped:
    print("组别：",name)
    print("数据：\n",group)
    组别： a
    数据：
       category      data   weights
    0        a -1.837433  0.220477
    1        a -1.572593  0.615787
    2        a  0.008959  0.640871
    3        a  1.095008  0.305419
    组别： b
    数据：
       category      data   weights
    4        b  0.190451  0.290830
    5        b  1.201876  0.904729
    6        b -1.125858  0.821191
    7        b -0.946454  0.039222

def getAvg(df):
    return  np.average(df["data"],weights=df["weights"])

grouped.apply(getAvg)
    category
    a   -0.579683
    b    0.088082
    dtype: float64

df
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
category data weights
0 a -1.837433 0.220477
1 a -1.572593 0.615787
2 a 0.008959 0.640871
3 a 1.095008 0.305419
4 b 0.190451 0.290830
5 b 1.201876 0.904729
6 b -1.125858 0.821191
7 b -0.946454 0.039222

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