Pandas看這一篇就夠了！
2020-10-04 07:00:11
文章目錄
1.Series
1.1 Pandas系列可以使用以下建構函式建立
– pandas.Series( data, index, dtype)
– data：資料採取各種形式，如：ndarray，list，constants
– index：必須是唯一的和雜湊的，與資料的長度相同。 預設np.arange(n)如果沒有索引被傳遞。
– dtype用於資料型別。如果沒有，將推斷資料型別
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.從字典(dict)資料建立Pandas系列，字典作為輸入傳遞，如果沒有指定索
引，則按排序順序取得字典鍵以構造索引。 如果傳遞了索引，索引中
與標籤對應的資料中的值將被拉出。
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.從標量資料建立Pandas系列，資料是標量值，則必須提供索引。將重複
該值以匹配索引的長度
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
資料框(DataFrame)是二維資料結構，即資料以行和列的表格方式排列。
• 功能：
– 潛在的列是不同的型別
– 大小可變
– 標記軸(行和列) – 可以對行和列執行算術運算
Pandas中的DataFrame可以使用以下建構函式建立
– pandas.DataFrame( data, index, columns, dtype) – 引數如下：
• data資料採取各種形式，如:ndarray，series，map，lists，dict，constant和另一個DataFrame。
• index對於行標籤，要用於結果幀的索引是可選預設值np.arrange(n)，如果沒有傳遞索引值。
• columns對於列標籤，可選的預設語法是 - np.arange(n)。 這隻有在沒有索引傳遞的情況下才是這樣。
• dtype每列的資料型別。
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 列的選擇、新增、刪除
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
行的交換：
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
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 | 0 | 0.670179 | 1 | apple |
| 1 | 1 | 0.115708 | 2 | banana |
| 2 | 2 | 0.832918 | 3 | carrot |
| 3 | 3 | 0.466246 | 4 | apple |
| 4 | 4 | 0.114392 | 1 | banana |
| 5 | 5 | 0.928451 | 2 | carrot |
| 6 | 6 | 0.256953 | 3 | apple |
| 7 | 7 | 0.595865 | 4 | banana |
| 8 | 8 | 0.781242 | 1 | carrot |
| 9 | 9 | 0.155173 | 2 | apple |
4.DataFrame基本操作
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的對角線上的數,iat和iloc用法一樣
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常用的描述性統計資訊的函數：
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 描述性統計函數，注意事項：
– 由於DataFrame是異構資料結構。通用操作不適用於所有函數。
– 類似於：sum()，cumsum()函數能與數位和字元(或)字串資料元素一起工作，不會產生任何錯誤。
– 由於這樣的操作無法執行，因此，當DataFrame包含字元或字串資料時，像abs()，cumprod()這樣的函數會丟擲異常。
要將自定義或其他庫的函數應用於Pandas物件，有三種方式：
– pipe()：表格函數應用，通過將函數和適當數量的引數作為管道引數來執行自定義操作，對整個DataFrame執行操作。
– apply( ) ：可以沿DataFrame的軸應用任意函數，它與描述性統計方法一樣，採用可選的axis引數。
– applymap() ：給DataFrame的所有元素應用任何Python函數，並且返回單個值。
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獲取行之和大於100的資料, 並返回最後的兩行
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 | 35 | 35 | 23 | 29 |
| 12 | 24 | 33 | 29 | 27 |
Pandas物件之間的基本迭代的行為取決於型別。當迭代一個系列時，它 被視為陣列式，基本迭代產生這些值
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(): # 按列存取值
print(key, value)
print("=====================")
for row_index, row in df.iterrows(): # 按行存取值
print(row_index, row)
print("=====================")
for row in df.itertuples(): # 按行存取值
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中有兩種排序方式：
– 按標籤排序：sort_index()方法通過傳遞axis引數和排序順序，可以對DataFrame進行排序。ascending=true為升序，false為降序。axis=0排序行，1為排序列。
– 按實際值：sort_values()是按值排序的方法。它接受一個by引數，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函數應用
常用字串文字函數列表如下：
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多軸索引：
- DataFrame.loc() 方法通過標籤來完成DataFrame的索引。
- DataFrame.iloc() 方法通過基於始0的下標來完成DataFrame的索引
- DataFrame.ix() 方法通過混合標籤和下標的方式來完成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還支援通過屬性運運算元.來選擇列。
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統計函數
統計方法有助於理解和分析資料的行為。Pandas也提供了統計函數。
– 差分函數：pct_change( ) 函數將每個元素與其前一個元素進行比較，並計算變化 百分比。 – 協方差函數：協方差適用於系列資料。Series物件有cov( )方法用來計算序列物件之間的協方差。NA將被自動排除。
– 相關性函數： corr( )用於計算某兩列值的相關性。
– 資料排名函數：rank( ) 用於為元素陣列中的每個元素生成排名。
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列的協方差
print(df.cov()) # df協方差矩陣
print(df['a'].corr(df['b']))# a列和b列的相關性
print(df.corr()) #df的相關矩陣
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分組
任何分組(groupby)操作都涉及原始物件的以下操作
– 根據指定條件分割物件集合：df.groupby(‘key’)
– 在每個分割後的集合上應用函數：聚合函數，轉換函數，過濾函數
– 整合結果並顯示
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