Pandas看这一篇就够了!

文章目录
1.Series2.DataFrame2.1 列的选择、添加、删除
3. Series的基本操作4.DataFrame基本操作5. Pandas函数应用6.Pandas多轴索引:7.Pandas统计函数8.Pandas分组

1.Series

1.1 Pandas系列可以使用以下构造函数创建

– pandas.Series( data, index, dtype)
– data:数据采取各种形式,如:ndarray,list,constants
– index:必须是唯一的和散列的,与数据的长度相同。 默认np.arange(n)如果没有索引被传递。
– dtype用于数据类型。如果没有,将推断数据类型

import pandas as pd
import numpy as np
data = np.array(['a', 'b', 'c', 'd'])
s = pd.Series(data)
print(s)
s = pd.Series(data, index=[100, 101, 102, 103])
print(s)

0    a
1 b
2 c
3 d
dtype: object
100 a
101 b
102 c
103 d
dtype: object

2.从字典(dict)数据创建Pandas系列,字典作为输入传递,如果没有指定索
引,则按排序顺序取得字典键以构造索引。 如果传递了索引,索引中
与标签对应的数据中的值将被拉出。

import pandas as pd
import numpy as np
data = {'a': 0., 'b': 1., 'c': 2.}
s = pd.Series(data)
print(s)
s = pd.Series(data, ['b', 'c', 'd', 'a'])
print(s)

a    0.0
b 1.0
c 2.0
dtype: float64
b 1.0
c 2.0
d NaN
a 0.0
dtype: float64

3.从标量数据创建Pandas系列,数据是标量值,则必须提供索引。将重复
该值以匹配索引的长度

import pandas as pd
import numpy as np
s = pd.Series(5, index=[0, 1, 3])
print(s)

0    5
1 5
3 5
dtype: int64

import pandas as pd
import numpy as np
s = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'])
# retrieve the first element
print(s[0])
# retrieve the first three element
print(s[:3])
# retrieve the last three element
print(s[-3:])
# retrieve a single element
print(s['a'])
# retrieve multiple elements
print(s[['a', 'c', 'd']])

1
a 1
b 2
c 3
dtype: int64
c 3
d 4
e 5
dtype: int64
1
a 1
c 3
d 4
dtype: int64

2.DataFrame

数据帧(DataFrame)是二维数据结构,即数据以行和列的表格方式排列。

• 功能:

– 潜在的列是不同的类型
– 大小可变
– 标记轴(行和列) – 可以对行和列执行算术运算

Pandas中的DataFrame可以使用以下构造函数创建

– pandas.DataFrame( data, index, columns, dtype) – 参数如下:

• data数据采取各种形式,如:ndarray,series,map,lists,dict,constant和另一个DataFrame。
• index对于行标签,要用于结果帧的索引是可选缺省值np.arrange(n),如果没有传递索引值。
• columns对于列标签,可选的默认语法是 - np.arange(n)。 这只有在没有索引传递的情况下才是这样。
• dtype每列的数据类型。

import pandas as pd
import numpy as np
df = pd.DataFrame()#empty DataFrame
print(df)
data = [1, 2, 3, 4, 5]
df = pd.DataFrame(data) #from list
print(df)
data = [['Alex', 10], ['Bob', 12], ['Clarke', 13]]
df = pd.DataFrame(data, columns=['Name', 'Age']) # from list
print(df)
data = {'Name': ['Tom', 'Jack', 'Steve'], 'Age': [28, 34, 29]} #from dict 列
df = pd.DataFrame(data)
print(df)
data = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}] #from dict 行
df = pd.DataFrame(data)
print(df)
d = {'one': pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two': pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d) # from Series
print(df)

Empty DataFrame
Columns: []
Index: []
0
0 1
1 2
2 3
3 4
4 5
Name Age
0 Alex 10
1 Bob 12
2 Clarke 13
Age Name
0 28 Tom
1 34 Jack
2 29 Steve
a b c
0 1 2 NaN
1 5 10 20.0
one two
a 1.0 1
b 2.0 2
c 3.0 3
d NaN 4

2.1 列的选择、添加、删除

import pandas as pd
import numpy as np
d = {'one': pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two': pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
print(df['one'])
df['three'] = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
print(df)
df['four'] = df['one'] + df['three']
print(df)
del df['one']
print(df)

a    1.0
b 2.0
c 3.0
d NaN
Name: one, dtype: float64
one two three
a 1.0 1 10.0
b 2.0 2 20.0
c 3.0 3 30.0
d NaN 4 NaN
one two three four
a 1.0 1 10.0 11.0
b 2.0 2 20.0 22.0
c 3.0 3 30.0 33.0
d NaN 4 NaN NaN
two three four
a 1 10.0 11.0
b 2 20.0 22.0
c 3 30.0 33.0
d 4 NaN NaN

import pandas as pd
d = {'one': pd.Series([1, 2, 3], index=['a', 'b', 'c']),
'two': pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
print(df)
print(df.loc['b'])
print(df.iloc[0])
print(df[2:4])
df = df.append(pd.DataFrame([[5, 6], [7, 8]], columns=['one',
'two']))
print(df)
df = df.drop(0)
print(df)

   one  two
a 1.0 1
b 2.0 2
c 3.0 3
d NaN 4
one 2.0
two 2.0
Name: b, dtype: float64
one 1.0
two 1.0
Name: a, dtype: float64
one two
c 3.0 3
d NaN 4
one two
a 1.0 1
b 2.0 2
c 3.0 3
d NaN 4
0 5.0 6
1 7.0 8
one two
a 1.0 1
b 2.0 2
c 3.0 3
d NaN 4
1 7.0 8

行的交换:

df = pd.DataFrame(np.arange(25).reshape(5, -1))
print(df)
a, b = df.iloc[1].copy(), df.iloc[2].copy()
df.iloc[1], df.iloc[2] = b, a
print(df)

    0   1   2   3   4
0 0 1 2 3 4
1 5 6 7 8 9
2 10 11 12 13 14
3 15 16 17 18 19
4 20 21 22 23 24
0 1 2 3 4
0 0 1 2 3 4
1 10 11 12 13 14
2 5 6 7 8 9
3 15 16 17 18 19
4 20 21 22 23 24

3. Series的基本操作

Pandas看这一篇就够了!

import pandas as pd
import numpy as np
data = pd.Series(np.random.randint(0,4,5))
print(data)
print(data.axes)
print(data.empty)
print(data.ndim)
print(data.size)
print(data.values)
print(data.head(3))
print(data.tail(2))

0    2
1 0
2 0
3 0
4 1
dtype: int32
[RangeIndex(start=0, stop=5, step=1)]
False
1

df=pd.DataFrame({ 'a':range(10),
'b':np.random.rand(10),
'c':[1,2,3,4]*2 + [1, 2],
'd':['apple', 'banana','carrot'] * 3 + ['apple'] } )
df.rename(columns={'d':'fruit'})

a b c fruit 0

00.6701791apple

1

10.1157082banana

2

20.8329183carrot

3

30.4662464apple

4

40.1143921banana

5

50.9284512carrot

6

60.2569533apple

7

70.5958654banana

8

80.7812421carrot

9

90.1551732apple
4.DataFrame基本操作

Pandas看这一篇就够了!

import pandas as pd
# Create a Dictionary of series
d = {'Name': pd.Series(['Tom', 'James', 'Ricky', 'Vin', 'Steve', 'Minsu', 'Jack']),
'Age': pd.Series([25, 26, 25, 23, 30, 29, 23]),
'Rating': pd.Series([4.23, 3.24, 3.98, 2.56, 3.20, 4.6, 3.8])}
# Create a DataFrame
data = pd.DataFrame(d)
print(data)
print(data.T)
print(data.axes)
print(data.dtypes)
print(data.empty)
print(data.ndim)
print(data.shape)
print(data.size)
print(data.values)
print(data.head(3))
print(data.tail(2))

   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
0 1 2 3 4 5 6
Age 25 26 25 23 30 29 23
Name Tom James Ricky Vin Steve Minsu Jack
Rating 4.23 3.24 3.98 2.56 3.2 4.6 3.8
[RangeIndex(start=0, stop=7, step=1), Index(['Age', 'Name', 'Rating'], dtype='object')]
Age int64
Name object
Rating float64
dtype: object
False
2
(7, 3)
21
[[25 'Tom' 4.23]
[26 'James' 3.24]
[25 'Ricky' 3.98]
[23 'Vin' 2.56]
[30 'Steve' 3.2]
[29 'Minsu' 4.6]
[23 'Jack' 3.8]]
Age Name Rating
0 25 Tom 4.23
1 26 James 3.24
2 25 Ricky 3.98
Age Name Rating
5 29 Minsu 4.6
6 23 Jack 3.8

用0填充dataframe的对角线上的数,iat和iloc用法一样

df = pd.DataFrame(np.random.randint(1,100, 100).reshape(10, -1))
for i in range(df.shape[0]):
df.iat[i, i] = 0
df.iat[df.shape[0]-i-1, i] = 0
print(df)

    0   1   2   3   4   5   6   7   8   9
0 0 42 46 24 34 57 31 1 4 0
1 33 0 73 31 25 49 4 89 0 64
2 55 37 0 91 56 18 28 0 11 48
3 69 28 28 0 34 42 0 16 56 43
4 45 66 28 99 0 0 87 30 14 11
5 61 79 87 32 0 0 27 82 85 59
6 61 20 16 0 66 81 0 41 18 52
7 38 30 0 22 72 2 58 0 71 83
8 59 0 35 54 30 59 18 22 0 88
9 0 52 29 10 78 66 78 37 83 0

Pandas常用的描述性统计信息的函数:

Pandas看这一篇就够了!

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
data = pd.DataFrame(d)
print(data)
print(data.sum())
print(data.mean())
print(data.std())

    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
Age 382
Name TomJamesRickyVinSteveMinsuJackLeeDavidGasperBe...
Rating 44.92
dtype: object
Age 31.833333
Rating 3.743333
dtype: float64
Age 9.232682
Rating 0.661628
dtype: float64

Pandas 描述性统计函数,注意事项:

– 由于DataFrame是异构数据结构。通用操作不适用于所有函数。
– 类似于:sum(),cumsum()函数能与数字和字符(或)字符串数据元素一起工作,不会产生任何错误。
– 由于这样的操作无法执行,因此,当DataFrame包含字符或字符串数据时,像abs(),cumprod()这样的函数会抛出异常。

要将自定义或其他库的函数应用于Pandas对象,有三种方式:

– pipe():表格函数应用,通过将函数和适当数量的参数作为管道参数来执行自定义操作,对整个DataFrame执行操作。
– apply( ) :可以沿DataFrame的轴应用任意函数,它与描述性统计方法一样,采用可选的axis参数。
– applymap() :给DataFrame的所有元素应用任何Python函数,并且返回单个值。

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(5, 3), columns=['col1', 'col2', 'col3'])
print(df)
df = df.apply(np.mean)
print(df)

       col1      col2      col3
0 0.026660 0.551035 0.182109
1 -1.066038 -3.086139 -0.183103
2 -1.566943 1.022386 0.750337
3 0.813376 -0.697546 0.417025
4 -0.472393 1.457343 -0.922107
col1 -0.453067
col2 -0.150584
col3 0.048852
dtype: float64

dataframe获取行之和大于100的数据, 并返回最后的两行

df = pd.DataFrame(np.random.randint(10, 40, 60).reshape(-1, 4))
rowsums = df.apply(np.sum, axis=1)
last_two_rows = df.iloc[np.where(rowsums > 100)[0][-2:]]
last_two_rows

0 1 2 3 9

35352329

12

24332927

Pandas对象之间的基本迭代的行为取决于类型。当迭代一个系列时,它 被视为数组式,基本迭代产生这些值

import pandas as pd
import numpy as np
N = 5
df = pd.DataFrame({
'X': np.linspace(0, stop=N - 1, num=N),
'Y': np.random.rand(N),
'C': np.random.choice(['Low', 'Medium', 'High'], N)
.tolist(),
})
for key, value in df.iteritems(): # 按列访问值
print(key, value)
print("=====================")
for row_index, row in df.iterrows(): # 按行访问值
print(row_index, row)
print("=====================")
for row in df.itertuples(): # 按行访问值
print(row)

C 0    Medium
1 Medium
2 Low
3 Medium
4 High
Name: C, dtype: object
X 0 0.0
1 1.0
2 2.0
3 3.0
4 4.0
Name: X, dtype: float64
Y 0 0.959929
1 0.058876
2 0.756262
3 0.984280
4 0.999868
Name: Y, dtype: float64
=====================
0 C Medium
X 0
Y 0.959929
Name: 0, dtype: object
1 C Medium
X 1
Y 0.0588758
Name: 1, dtype: object
2 C Low
X 2
Y 0.756262
Name: 2, dtype: object
3 C Medium
X 3
Y 0.98428
Name: 3, dtype: object
4 C High
X 4
Y 0.999868
Name: 4, dtype: object
=====================
Pandas(Index=0, C='Medium', X=0.0, Y=0.9599285927026967)
Pandas(Index=1, C='Medium', X=1.0, Y=0.058875797837255606)
Pandas(Index=2, C='Low', X=2.0, Y=0.75626198656391275)
Pandas(Index=3, C='Medium', X=3.0, Y=0.98427963491833415)
Pandas(Index=4, C='High', X=4.0, Y=0.99986776764752849)

Pandas中有两种排序方式:

– 按标签排序:sort_index()方法通过传递axis参数和排序顺序,可以对DataFrame进行排序。ascending=true为升序,false为降序。axis=0排序行,1为排序列。
– 按实际值:sort_values()是按值排序的方法。它接受一个by参数,指定排序列名

import pandas as pd
import numpy as np
unsorted_df = pd.DataFrame(np.random.randn(10, 2),
index=[1, 4, 6, 2, 3, 5, 9, 8, 0, 7],
columns=['col2', 'col1'])
print(unsorted_df)
sorted_df = unsorted_df.sort_index()
print(sorted_df) # 按索引排序
sorted_df = unsorted_df.sort_values(by='col1')
print(sorted_df) # 按col1排序

       col2      col1
1 1.440434 1.725768
4 0.009801 0.196239
6 0.923630 0.890150
2 0.185936 0.202835
3 0.690447 -0.141488
5 1.662561 1.752920
9 -0.157736 0.405503
8 -1.419687 -0.044129
0 -0.053966 -0.605254
7 -1.571451 -0.328177
col2 col1
0 -0.053966 -0.605254
1 1.440434 1.725768
2 0.185936 0.202835
3 0.690447 -0.141488
4 0.009801 0.196239
5 1.662561 1.752920
6 0.923630 0.890150
7 -1.571451 -0.328177
8 -1.419687 -0.044129
9 -0.157736 0.405503
col2 col1
0 -0.053966 -0.605254
7 -1.571451 -0.328177
3 0.690447 -0.141488
8 -1.419687 -0.044129
4 0.009801 0.196239
2 0.185936 0.202835
9 -0.157736 0.405503
6 0.923630 0.890150
1 1.440434 1.725768
5 1.662561 1.752920

5. Pandas函数应用

常用字符串文本函数列表如下:

Pandas看这一篇就够了!

import pandas as pd
import numpy as np
s = pd.Series(['Tom', 'William Rick', 'John',
'Alber@t', np.nan, '1234', 'SteveMinsu'])
print(s.str.lower())
print(s.str.upper())
print(s.str.len())
print(s.str.find('e'))
print(s.str.count('m'))

0             tom
1 william rick
2 john
3 alber@t
4 NaN
5 1234
6 steveminsu
dtype: object
0 TOM
1 WILLIAM RICK
2 JOHN
3 ALBER@T
4 NaN
5 1234
6 STEVEMINSU
dtype: object
0 3.0
1 12.0
2 4.0
3 7.0
4 NaN
5 4.0
6 10.0
dtype: float64
0 -1.0
1 -1.0
2 -1.0
3 3.0
4 NaN
5 -1.0
6 2.0
dtype: float64
0 1.0
1 1.0
2 0.0
3 0.0
4 NaN
5 0.0
6 0.0
dtype: float64

6.Pandas多轴索引:

- DataFrame.loc() 方法通过标签来完成DataFrame的索引。
- DataFrame.iloc() 方法通过基于始0的下标来完成DataFrame的索引
- DataFrame.ix() 方法通过混合标签和下标的方式来完成DataFrame的索引。

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4),
index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'],
columns=['A', 'B', 'C', 'D'])
# select all rows for a specific column
print(df.loc[:, 'A'])
print(df.loc[:, ['A', 'C']])
print(df.loc[['a', 'b', 'f', 'h'], ['A', 'C']])
print(df.loc['a':'h'])
print(df.loc['a'] > 0)

a   -1.096360
b -0.509215
c -0.496389
d -0.790647
e 1.483483
f 1.534044
g -1.354682
h 0.095619
Name: A, dtype: float64
A C
a -1.096360 -0.206507
b -0.509215 1.151713
c -0.496389 -1.135079
d -0.790647 1.067650
e 1.483483 0.251884
f 1.534044 0.178737
g -1.354682 -0.362621
h 0.095619 1.342643
A C
a -1.096360 -0.206507
b -0.509215 1.151713
f 1.534044 0.178737
h 0.095619 1.342643
A B C D
a -1.096360 -1.119847 -0.206507 -0.627628
b -0.509215 0.786663 1.151713 -0.266289
c -0.496389 0.658526 -1.135079 -0.258309
d -0.790647 0.960095 1.067650 0.070966
e 1.483483 0.090211 0.251884 1.090053
f 1.534044 0.370134 0.178737 0.835015
g -1.354682 0.910268 -0.362621 -1.334036
h 0.095619 -0.650006 1.342643 -0.782496
A False
B False
C False
D False
Name: a, dtype: bool

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4),
index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'],
columns=['A', 'B', 'C', 'D'])
# select all rows for a specific column
print(df.iloc[:4])
print(df.iloc[1:5, 2:4])
print(df.iloc[[1, 3, 5], [1, 3]])
print(df.iloc[1:3, :])
print(df.iloc[:, 1:3])

          A         B         C         D
a -0.985919 -0.311362 -0.390002 0.964154
b 0.264029 -0.296392 -0.944643 0.307082
c -0.605262 1.729297 -0.090857 -0.751519
d -1.375307 -0.596479 -1.836798 -1.405262
C D
b -0.944643 0.307082
c -0.090857 -0.751519
d -1.836798 -1.405262
e -0.142980 -0.830023
B D
b -0.296392 0.307082
d -0.596479 -1.405262
f 0.389874 -0.462296
A B C D
b 0.264029 -0.296392 -0.944643 0.307082
c -0.605262 1.729297 -0.090857 -0.751519
B C
a -0.311362 -0.390002
b -0.296392 -0.944643
c 1.729297 -0.090857
d -0.596479 -1.836798
e 1.684399 -0.142980
f 0.389874 -0.753835
g 0.199919 -0.972075
h -1.118849 -0.672530

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4),
index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'],
columns=['A', 'B', 'C', 'D'])
# select all rows for a specific column
print(df.ix[:4])
print(df.ix[:, 'A'])

          A         B         C         D
a -0.527016 0.031919 0.698404 1.386758
b -1.746599 -0.246425 0.133075 0.418947
c -0.327233 -1.566975 -0.437066 -0.731450
d -0.956644 -0.134168 -1.083254 0.053951
a -0.527016
b -1.746599
c -0.327233
d -0.956644
e 0.576412
f -1.348915
g 0.256975
h -1.351225
Name: A, dtype: float64

Pandas还支持通过属性运算符.来选择列。

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(8, 4),
index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'],
columns=['A', 'B', 'C', 'D'])
print(df.A)

a    0.785159
b 0.322105
c -1.210970
d -0.955962
e -0.896882
f 2.222450
g 1.222612
h -0.286081
Name: A, dtype: float64

7.Pandas统计函数

统计方法有助于理解和分析数据的行为。Pandas也提供了统计函数。

– 差分函数:pct_change( ) 函数将每个元素与其前一个元素进行比较,并计算变化 百分比。 – 协方差函数:协方差适用于系列数据。Series对象有cov( )方法用来计算序列对象之间的协方差。NA将被自动排除。
– 相关性函数: corr( )用于计算某两列值的相关性。
– 数据排名函数:rank( ) 用于为元素数组中的每个元素生成排名。

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(3, 2), columns=['a', 'b'])
print(df.pct_change())
s1 = pd.Series(np.random.randn(10))
s2 = pd.Series(np.random.randn(10))
print(s1.cov(s2))
print(df['a'].cov(df['b'])) # a列和b列的协方差
print(df.cov()) # df协方差矩阵
print(df['a'].corr(df['b']))# a列和b列的相关性
print(df.corr()) #df的相关矩阵
s = pd.Series(np.random.np.random.randn(6), index=list('abcdee'))
print(s)
print(s.rank())

           a         b
0 NaN NaN
1 -0.838600 -2.274759
2 -20.205354 -1.757039
-0.309369947243
0.186491516577
a b
a 1.986867 0.186492
b 0.186492 0.181958
0.310162676192
a b
a 1.000000 0.310163
b 0.310163 1.000000
a 1.315629
b 1.025438
c 0.066169
d 0.969194
e -1.793737
e -0.576699
dtype: float64
a 6.0
b 5.0
c 3.0
d 4.0
e 1.0
e 2.0
dtype: float64

8.Pandas分组

任何分组(groupby)操作都涉及原始对象的以下操作

– 根据指定条件分割对象集合:df.groupby(‘key’)
– 在每个分割后的集合上应用函数:聚合函数,转换函数,过滤函数
– 整合结果并显示

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)
grouped = df.groupby('Team')
print("===================")
for name, group in grouped:
print(name, group)
print(grouped['Rank'].agg(np.mean))

    Points  Rank    Team  Year
0 876 1 Riders 2014
1 789 2 Riders 2015
2 863 2 Devils 2014
3 673 3 Devils 2015
4 741 3 Kings 2014
5 812 4 Kings 2015
6 756 1 Kings 2016
7 788 1 Kings 2017
8 694 2 Riders 2016
9 701 4 Royals 2014
10 804 1 Royals 2015
11 690 2 Riders 2017
===================
Devils Points Rank Team Year
2 863 2 Devils 2014
3 673 3 Devils 2015
Kings Points Rank Team Year
4 741 3 Kings 2014
5 812 4 Kings 2015
6 756 1 Kings 2016
7 788 1 Kings 2017
Riders Points Rank Team Year
0 876 1 Riders 2014
1 789 2 Riders 2015
8 694 2 Riders 2016
11 690 2 Riders 2017
Royals Points Rank Team Year
9 701 4 Royals 2014
10 804 1 Royals 2015
Team
Devils 2.50
Kings 2.25
Riders 1.75
Royals 2.50
Name: Rank, dtype: float64

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