pandas核心

pandas描述性统计

数值型数据的描述性统计主要包括了计算数值型数据的完整情况、最小值、均值、中位 数、最大值、四分位数、极差、标准差、方差、协方差等。在NumPy库中一些常用的统计学函数也可用于对数据框进行描述性统计。

np.min    最小值 
np.max    最大值 
np.mean    均值 
np.ptp    极差 
np.median    中位数 
np.std    标准差 
np.var    方差 
np.cov    协方差

实例:

import pandas as pd
import numpy as np

# 创建DF
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}

df = pd.DataFrame(d)
print(df)
"""
      Name  Age  Rating
0      Tom   25    4.23
1    James   26    3.24
2    Ricky   25    3.98
3      Vin   23    2.56
4    Steve   30    3.20
5    Minsu   29    4.60
6     Jack   23    3.80
7      Lee   34    3.78
8    David   40    2.98
9   Gasper   30    4.80
10  Betina   51    4.10
11  Andres   46    3.65
"""
# 测试描述性统计函数
print(df.sum())#(每列)求和
"""
Name      TomJamesRickyVinSteveMinsuJackLeeDavidGasperBe...
Age                                                     382
Rating                                                44.92
dtype: object
"""
print(df.sum(1))#1是轴向  按行求和
"""
0     29.23
1     29.24
2     28.98
3     25.56
4     33.20
5     33.60
6     26.80
7     37.78
8     42.98
9     34.80
10    55.10
11    49.65
dtype: float64
"""
print(df.mean())#按列求均值
"""
Age       31.833333
Rating     3.743333
dtype: float64
"""
print(df.mean(1))#年龄和评分的均值
"""
0     14.615
1     14.620
2     14.490
3     12.780
4     16.600
5     16.800
6     13.400
7     18.890
8     21.490
9     17.400
10    27.550
11    24.825
dtype: float64
"""

pandas提供了统计相关函数:

1 count() 非空观测数量
2 sum() 所有值之和
3 mean() 所有值的平均值
4 median() 所有值的中位数
5 std() 值的标准偏差
6 min() 所有值中的最小值
7 max() 所有值中的最大值
8 abs() 绝对值
9 prod() 数组元素的乘积
10 cumsum() 累计总和

pandas还提供了一个方法叫作describe,能够一次性得出数据框所有数值型特征的非空值数目、均值、标准差等。

import pandas as pd

#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}

#Create a DataFrame
df = pd.DataFrame(d)
print(df.describe())
"""
             Age     Rating
count  12.000000  12.000000
mean   31.833333   3.743333
std     9.232682   0.661628
min    23.000000   2.560000
25%    25.000000   3.230000
50%    29.500000   3.790000
75%    35.500000   4.132500
max    51.000000   4.800000
"""
print(df.describe(include=['object']))
"""
          Name
count       12
unique      12
top     Gasper
freq         1
"""
print(df.describe(include=['number']))
"""
             Age     Rating
count  12.000000  12.000000
mean   31.833333   3.743333
std     9.232682   0.661628
min    23.000000   2.560000
25%    25.000000   3.230000
50%    29.500000   3.790000
75%    35.500000   4.132500
max    51.000000   4.800000
"""

pandas排序

Pandas有两种排序方式,它们分别是按标签与按实际值排序。

import pandas as pd
import numpy as np

unsorted_df=pd.DataFrame(np.random.randn(10,2),  #十行 两列  随机生成数
                         index=[1,4,6,2,3,5,9,8,0,7],
                         columns=['col2','col1'])
print(unsorted_df)
"""
       col2      col1
1  2.415183 -1.132430
4 -0.572576  0.135119
6  1.503749 -0.666407
2 -1.292311 -0.959626
3 -0.689003  0.146044
5  0.139045 -1.221794
9 -0.944587 -0.643555
8  0.848157  0.446215
0 -0.332118  1.215189
7  0.061888  0.265198
"""

按行标签排序

使用sort_index()方法,通过传递axis参数和排序顺序,可以对DataFrame进行排序。 默认情况下,按照升序对行标签进行排序。

import pandas as pd

d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Minsu','Jack',
   'Lee','David','Gasper','Betina','Andres']),
   'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
   'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])}
unsorted_df = pd.DataFrame(d)
# 按照行标进行排序
sorted_df=unsorted_df.sort_index()
print (sorted_df)
"""
      Name  Age  Rating
0      Tom   25    4.23
1    James   26    3.24
2    Ricky   25    3.98
3      Vin   23    2.56
4    Steve   30    3.20
5    Minsu   29    4.60
6     Jack   23    3.80
7      Lee   34    3.78
8    David   40    2.98
9   Gasper   30    4.80
10  Betina   51    4.10
11  Andres   46    3.65
"""
# 按照行索引标签进行降序排列
sorted_df = unsorted_df.sort_index(ascending=False)
print (sorted_df)
"""
      Name  Age  Rating
11  Andres   46    3.65
10  Betina   51    4.10
9   Gasper   30    4.80
8    David   40    2.98
7      Lee   34    3.78
6     Jack   23    3.80
5    Minsu   29    4.60
4    Steve   30    3.20
3      Vin   23    2.56
2    Ricky   25    3.98
1    James   26    3.24
0      Tom   25    4.23
"""
sorted_df = unsorted_df.sort_index(ascending=True,axis=3)
print(sorted_df)
"""
    Age    Name  Rating
0    25     Tom    4.23
1    26   James    3.24
2    25   Ricky    3.98
3    23     Vin    2.56
4    30   Steve    3.20
5    29   Minsu    4.60
6    23    Jack    3.80
7    34     Lee    3.78
8    40   David    2.98
9    30  Gasper    4.80
10   51  Betina    4.10
11   46  Andres    3.65
"""

 

按某列值排序

像索引排序一样,sort_values()是按值排序的方法。它接受一个by参数,它将使用要与其排序值的DataFrame的列名称。

import pandas as pd

d = {'Name': pd.Series(['Tom', 'James', 'Ricky', 'Vin', 'Steve', 'Minsu', 'Jack',
                        'Lee', 'David', 'Gasper', 'Betina', 'Andres']),
     'Age': pd.Series([25, 26, 25, 23, 30, 29, 23, 34, 40, 30, 51, 46]),
     'Rating': pd.Series([4.23, 3.24, 3.98, 2.56, 3.20, 4.6, 3.8, 3.78, 2.98, 4.80, 4.10, 3.65])}
unsorted_df = pd.DataFrame(d)
print(unsorted_df)
"""
      Name  Age  Rating
0      Tom   25    4.23
1    James   26    3.24
2    Ricky   25    3.98
3      Vin   23    2.56
4    Steve   30    3.20
5    Minsu   29    4.60
6     Jack   23    3.80
7      Lee   34    3.78
8    David   40    2.98
9   Gasper   30    4.80
10  Betina   51    4.10
11  Andres   46    3.65
"""
# 按照列标签进行排序
sorted_df = unsorted_df.sort_index(axis=1)
print(sorted_df)
"""
Age    Name  Rating
0    25     Tom    4.23
1    26   James    3.24
2    25   Ricky    3.98
3    23     Vin    2.56
4    30   Steve    3.20
5    29   Minsu    4.60
6    23    Jack    3.80
7    34     Lee    3.78
8    40   David    2.98
9    30  Gasper    4.80
10   51  Betina    4.10
11   46  Andres    3.65
"""
# 按照年龄排序
print(sorted_df.sort_values('Age'))
"""
    Age    Name  Rating
3    23     Vin    2.56
6    23    Jack    3.80
0    25     Tom    4.23
2    25   Ricky    3.98
1    26   James    3.24
5    29   Minsu    4.60
4    30   Steve    3.20
9    30  Gasper    4.80
7    34     Lee    3.78
8    40   David    2.98
11   46  Andres    3.65
10   51  Betina    4.10
"""

# 联合间接排序
print(sorted_df.sort_values(['Age', 'Rating']))
"""
 Age    Name  Rating
3    23     Vin    2.56
6    23    Jack    3.80
2    25   Ricky    3.98
0    25     Tom    4.23
1    26   James    3.24
5    29   Minsu    4.60
4    30   Steve    3.20
9    30  Gasper    4.80
7    34     Lee    3.78
8    40   David    2.98
11   46  Andres    3.65
10   51  Betina    4.10
"""

# 控制排序顺序
print(sorted_df.sort_values(['Age', 'Rating'],
                            ascending=[True, False]))
"""
    Age    Name  Rating
6    23    Jack    3.80
3    23     Vin    2.56
0    25     Tom    4.23
2    25   Ricky    3.98
1    26   James    3.24
5    29   Minsu    4.60
9    30  Gasper    4.80
4    30   Steve    3.20
7    34     Lee    3.78
8    40   David    2.98
11   46  Andres    3.65
10   51  Betina    4.10
"""

 

pandas分组

在许多情况下,我们将数据分成多个集合,并在每个子集上应用一些函数。在应用函数中,可以执行以下操作 :

  • 聚合 - 计算汇总统计

  • 转换 - 执行一些特定于组的操作

  • 过滤 - 在某些情况下丢弃数据

import pandas as pd

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
         'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
         'Rank': [1, 2, 2, 3, 3,4 ,1 ,1,2 , 4,1,2],
         'Year': [2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],
         'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}
df = pd.DataFrame(ipl_data)
print(df)

将数据拆分成组

# 按照年份Year字段分组
print (df.groupby('Year'))
# 查看分组结果
print (df.groupby('Year').groups)

迭代遍历分组

groupby返回可迭代对象,可以使用for循环遍历:

print (df.groupby('Year').groups)
# 遍历每个分组
for year,group in grouped:
    print (year)
    print (group)

获得一个分组细节

grouped = df.groupby('Year')
print (grouped.get_group(2014))

 

import pandas as pd

ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
                     'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
            'Rank': [1, 2, 2, 3, 3, 4, 1, 1, 2, 4, 1, 2],
            'Year': [2014, 2015, 2014, 2015, 2014, 2015, 2016, 2017, 2016, 2014, 2015, 2017],
            'Points': [876, 789, 863, 673, 741, 812, 756, 788, 694, 701, 804, 690]}
df = pd.DataFrame(ipl_data)
print(df)
"""
      Team  Rank  Year  Points
0   Riders     1  2014     876
1   Riders     2  2015     789
2   Devils     2  2014     863
3   Devils     3  2015     673
4    Kings     3  2014     741
5    kings     4  2015     812
6    Kings     1  2016     756
7    Kings     1  2017     788
8   Riders     2  2016     694
9   Royals     4  2014     701
10  Royals     1  2015     804
11  Riders     2  2017     690
"""
#按评分排序
print(df.sort_values('Points',ascending=False))
"""
Team  Rank  Year  Points
0   Riders     1  2014     876
2   Devils     2  2014     863
5    kings     4  2015     812
10  Royals     1  2015     804
1   Riders     2  2015     789
7    Kings     1  2017     788
6    Kings     1  2016     756
4    Kings     3  2014     741
9   Royals     4  2014     701
8   Riders     2  2016     694
11  Riders     2  2017     690
3   Devils     3  2015     673
"""

# 按照年份Year字段分组,查看每个分组的信息
grouped = df.groupby('Year')
print(grouped)#分组对象
# 查看分组结果
print(grouped.groups)
"""
{2014: Int64Index([0, 2, 4, 9], dtype='int64'), 
 2015: Int64Index([1, 3, 5, 10], dtype='int64'), 
 2016: Int64Index([6, 8], dtype='int64'),
 2017: Int64Index([7, 11], dtype='int64')}
"""

#遍历查看每个分组的信息
for year, group in grouped:
  print(year)
  print(group)
"""
2014
     Team  Rank  Year  Points
0  Riders     1  2014     876
2  Devils     2  2014     863
4   Kings     3  2014     741
9  Royals     4  2014     701
2015
      Team  Rank  Year  Points
1   Riders     2  2015     789
3   Devils     3  2015     673
5    kings     4  2015     812
10  Royals     1  2015     804
2016
     Team  Rank  Year  Points
6   Kings     1  2016     756
8  Riders     2  2016     694
2017
      Team  Rank  Year  Points
7    Kings     1  2017     788
11  Riders     2  2017     690
"""
#若不希望获取所有分组,如下获取某个分组细节:
group = grouped.get_group(2014)
print(group)
"""
     Team  Rank  Year  Points
0  Riders     1  2014     876
2  Devils     2  2014     863
4   Kings     3  2014     741
9  Royals     4  2014     701
"""

 

分组聚合

聚合函数为每个组返回聚合值。当创建了分组(group by)对象,就可以对每个分组数据执行求和、求标准差等操作。

# 聚合每一年的平均的分
grouped = df.groupby('Year')
print (grouped['Points'].agg(np.mean))
# 聚合每一年的分数之和、平均分、标准差
grouped = df.groupby('Year')
agg = grouped['Points'].agg([np.sum, np.mean, np.std])
print (agg)

 

import pandas as pd
import numpy as np



ipl_data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings',
                     'kings', 'Kings', 'Kings', 'Riders', 'Royals', 'Royals', 'Riders'],
            'Rank': [1, 2, 2, 3, 3, 4, 1, 1, 2, 4, 1, 2],
            'Year': [2014, 2015, 2014, 2015, 2014, 2015, 2016, 2017, 2016, 2014, 2015, 2017],
            'Points': [876, 789, 863, 673, 741, 812, 756, 788, 694, 701, 804, 690]}
df = pd.DataFrame(ipl_data)
# print(df)
# 按照年份Year字段分组,查看每个分组的信息
grouped = df.groupby('Year')
#分组后针对每一组执行聚合操作,(类似数据库中的组函数)
r = grouped['Points'].agg(np.mean)
print(r,type(r),r.values)
"""
Year
2014    795.25
2015    769.50
2016    725.00
2017    739.00
Name: Points, dtype: float64 <class 'pandas.core.series.Series'> [795.25 769.5  725.   739.  ]
"""
              平均值,和,标准差
r = grouped['Points'].agg([np.mean,np.sum,np.std]) print(r) """ mean sum std Year 2014 795.25 3181 87.439026 2015 769.50 3078 65.035888 2016 725.00 1450 43.840620 2017 739.00 1478 69.296465 """

pandas数据表关联操作

Pandas具有功能全面的高性能内存中连接操作,与SQL等关系数据库非常相似。 Pandas提供了一个单独的merge()函数,作为DataFrame对象之间所有标准数据库连接操作的入口。

合并两个DataFrame:

import pandas as pd
left = pd.DataFrame({
         'student_id':[1,2,3,4,5,6,7,8,9,10],
         'student_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung', 'Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
         'class_id':[1,1,1,2,2,2,3,3,3,4]})
right = pd.DataFrame(
         {'class_id':[1,2,3,5],
         'class_name': ['ClassA', 'ClassB', 'ClassC', 'ClassE']})
print (left)
print("========================================")
print (right)
print("========================================")
# 合并两个DataFrame
rs = pd.merge(left,right)
print(rs)
"""
   student_id student_name  class_id class_name
0           1         Alex         1     ClassA
1           2          Amy         1     ClassA
2           3        Allen         1     ClassA
3           4        Alice         2     ClassB
4           5       Ayoung         2     ClassB
5           6        Billy         2     ClassB
6           7        Brian         3     ClassC
7           8         Bran         3     ClassC
8           9        Bryce         3     ClassC
"""
rs = pd.merge(left,right,how='outer')#内连接
print(rs)
"""
    student_id student_name  class_id class_name
0          1.0         Alex         1     ClassA
1          2.0          Amy         1     ClassA
2          3.0        Allen         1     ClassA
3          4.0        Alice         2     ClassB
4          5.0       Ayoung         2     ClassB
5          6.0        Billy         2     ClassB
6          7.0        Brian         3     ClassC
7          8.0         Bran         3     ClassC
8          9.0        Bryce         3     ClassC
9         10.0        Betty         4        NaN
10         NaN          NaN         5     ClassE
"""
rs = pd.merge(left,right,how='left')#左外链接
print(rs)
"""
   student_id student_name  class_id class_name
0           1         Alex         1     ClassA
1           2          Amy         1     ClassA
2           3        Allen         1     ClassA
3           4        Alice         2     ClassB
4           5       Ayoung         2     ClassB
5           6        Billy         2     ClassB
6           7        Brian         3     ClassC
7           8         Bran         3     ClassC
8           9        Bryce         3     ClassC
9          10        Betty         4        NaN
"""
rs = pd.merge(left,right,how='right')#右外链接
print(rs)
"""
   student_id student_name  class_id class_name
0         1.0         Alex         1     ClassA
1         2.0          Amy         1     ClassA
2         3.0        Allen         1     ClassA
3         4.0        Alice         2     ClassB
4         5.0       Ayoung         2     ClassB
5         6.0        Billy         2     ClassB
6         7.0        Brian         3     ClassC
7         8.0         Bran         3     ClassC
8         9.0        Bryce         3     ClassC
9         NaN          NaN         5     ClassE
"""

其他合并方法同数据库相同:

合并方法 SQL等效 描述
left LEFT OUTER JOIN 使用左侧对象的键
right RIGHT OUTER JOIN 使用右侧对象的键
outer FULL OUTER JOIN 使用键的联合
inner INNER JOIN 使用键的交集

试验:

# 合并两个DataFrame (左连接)
rs = pd.merge(left,right,on='subject_id', how='right')
print(rs)
# 合并两个DataFrame (左连接)
rs = pd.merge(left,right,on='subject_id', how='outer')
print(rs)
# 合并两个DataFrame (左连接)
rs = pd.merge(left,right,on='subject_id', how='inner')
print(rs)

 

pandas透视表与交叉表

有如下数据:

"""表连接"""
import pandas as pd

left = pd.DataFrame({
  'student_id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
  'student_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung', 'Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
  'class_id': [1, 1, 1, 2, 2, 2, 3, 3, 3, 4]})
right = pd.DataFrame(
  {'class_id': [1, 2, 3, 5],
   'class_name': ['ClassA', 'ClassB', 'ClassC', 'ClassE']})
# 合并两个DataFrame
data = pd.merge(left, right)
print(data)
"""
   student_id student_name  class_id class_name
0           1         Alex         1     ClassA
1           2          Amy         1     ClassA
2           3        Allen         1     ClassA
3           4        Alice         2     ClassB
4           5       Ayoung         2     ClassB
5           6        Billy         2     ClassB
6           7        Brian         3     ClassC
7           8         Bran         3     ClassC
8           9        Bryce         3     ClassC
"""

透视表

透视表(pivot table)是各种电子表格程序和其他数据分析软件中一种常见的数据汇总工具。它根据一个或多个键对数据进行分组聚合,并根据每个分组进行数据汇总

"""透视表"""
import pandas as pd
left = pd.DataFrame({
         'student_id':[1,2,3,4,5,6,7,8,9,10],
         'student_name': ['Alex', 'Amy', 'Allen', 'Alice', 'Ayoung', 'Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],
         'age':[11,11,11,21,21,21,31,31,31,41],
         'gender':['M','F','M','M','M','F','F','M','F','F'],
         'score':[16,19,14,23,27,2,39,79,56,99],
         'class_id':[1,1,1,2,2,2,3,3,3,4]})
right = pd.DataFrame(
         {'class_id':[1,2,3,5],
         'class_name': ['ClassA', 'ClassB', 'ClassC', 'ClassE']})
# 合并两个DataFrame
data = pd.merge(left,right)
# print(data)
# 以class_id做分组汇总数据,默认聚合统计所有列
r = data.pivot_table(index=['class_id'])
print(r)#统计每班的平均年龄
"""
          age      score  student_id
class_id                            
1          11  16.333333           2
2          21  17.333333           5
3          31  58.000000           8
"""

# 以class_id做分组汇总数据,默认聚合统计所有列
r = data.pivot_table(index=['class_id','gender'])
print(r)
"""
                 age  score  student_id
class_id gender                        
1        F        11   19.0         2.0
         M        11   15.0         2.0
2        F        21    2.0         6.0
         M        21   25.0         4.5
3        F        31   47.5         8.0
         M        31   79.0         8.0
"""
# 以class_id与gender做分组汇总数据,聚合统计score列
print(data.pivot_table(index=['class_id', 'gender'],
                       values=['score']))
"""
                 score
class_id gender       
1        F        19.0
         M        15.0
2        F         2.0
         M        25.0
3        F        47.5
         M        79.0
"""
# 以class_id与gender做分组汇总数据,聚合统计score列,针对age的每个值列级分组统计
print(data.pivot_table(index=['class_id', 'gender'],
                       values=['score'],
                       columns=['age']))
"""
                score            
age                11    21    31
class_id gender                  
1        F       19.0   NaN   NaN
         M       15.0   NaN   NaN
2        F        NaN   2.0   NaN
         M        NaN  25.0   NaN
3        F        NaN   NaN  47.5
         M        NaN   NaN  79.0
"""

# 以class_id与gender做分组汇总数据,聚合统计score列,针对age的每个值列级分组统计,添加行、列小计
# print(data.pivot_table(index=['class_id', 'gender'],
#                        values=['score'],
#                        columns=['age'],
#                        margin=True))

# 以class_id与gender做分组汇总数据,聚合统计score列,针对age的每个值列级分组统计,添加行、列小计
# print(data.pivot_table(index=['class_id', 'gender'],
#                        values=['score'],
#                        columns=['age'],
#                        margins=True,
#                        aggfunc='max'))

交叉表

交叉表(cross-tabulation, 简称crosstab)是一种用于计算分组频率的特殊透视表

# 按照class_id分组,针对不同的gender,统计数量
print(pd.crosstab(data.class_id, data.gender, margins=True))

 

pandas可视化

基本绘图:绘图

import pandas as pd
import numpy as np
import matplotlib.pyplot as mp 

df = pd.DataFrame(np.random.randn(10,4),index=pd.date_range('2018/12/18',
   periods=10), columns=list('ABCD'))
df.plot()
mp.show()

pandas核心

plot方法允许除默认线图之外的少数绘图样式。 这些方法可以作为plot()kind关键字参数。这些包括 :

  • barbarh为条形

  • hist为直方图

  • scatter为散点图

条形图

df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d'])
df.plot.bar()
# df.plot.bar(stacked=True)
mp.show()

pandas核心

直方图

df = pd.DataFrame()
df['a'] = pd.Series(np.random.normal(0, 1, 1000)-1)
df['b'] = pd.Series(np.random.normal(0, 1, 1000))
df['c'] = pd.Series(np.random.normal(0, 1, 1000)+1)
print(df)
df.plot.hist(bins=20)
mp.show()

pandas核心

散点图

df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
df.plot.scatter(x='a', y='b')
mp.show()

pandas核心

饼状图

df = pd.DataFrame(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], columns=['x'])
df.plot.pie(subplots=True)
mp.show()

pandas核心

 

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