import pandas as pd
import numpy as np
pandas中级操作示例及说明记录1
df = pd.DataFrame(np.random.random(size=(5,3)))
df
image.png
df.mean(axis=1)
0 0.572173
1 0.479974
2 0.477806
3 0.363091
4 0.405757
dtype: float64
df.mean(axis=0)
0 0.337337
1 0.622558
2 0.419385
dtype: float64
df.mean()
0 0.337337
1 0.622558
2 0.419385
dtype: float64
df.sub(df.mean(axis=1),axis=0)
image.png
df.sub(df.mean(axis=0),axis=1)
image.png
df - df.mean()
image.png
df1= pd.DataFrame(np.random.random(size=(5,10)),columns=list('abcdefghij'))
df1
image.png
c_sum=df1.sum()
c_sum
a 2.796738
b 2.830435
c 2.623558
d 2.759947
e 2.784368
f 1.488181
g 1.545570
h 2.799605
i 3.820820
j 2.985348
dtype: float64
c_sum.idxmin()
'f'
df1.sum().idxmin()
'f'
- 找出df中不重复行的几种方式
- 使用drop_duplicates() -删掉重复的
keep : {'first', 'last', False}, default 'first'
-
first : Drop duplicates except for the first occurrence.
-
last : Drop duplicates except for the last occurrence.
- False : Drop all duplicates.
unique_rows=df.drop_duplicates(keep=False)
unique_rows
image.png
len(unique_rows)
5
- duplicated(keep=False) -找到重复的
keep : {'first', 'last', False}, default 'first'
-
first : Mark duplicates as True except for the
first occurrence.
-
last : Mark duplicates as True except for the
last occurrence.
- False : Mark all duplicates as
True.
len(df) - df.duplicated(keep=False).sum()
5
df3=pd.DataFrame(np.random.randint(1,100,size=(5,10)))
np.random.choice(df3.columns,5,replace=False)
image.png
for i in range(5):
df3.loc[i,np.random.choice(df3.columns,5,replace=False)]=np.nan
df3
image.png
df3.isnull()
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df3.isnull().cumsum(axis=1)
image.png
(df3.isnull().cumsum(axis=1) ==3)
image.png
(df3.isnull().cumsum(axis=1) ==3).idxmax(axis=1)
0 4
1 2
2 4
3 2
4 5
dtype: int64
- 上例实现说明：
- df3.isnull() 先判断是否为nan
- df3.isnull().cumsum(axis=1) 横向累加
- df3.isnull().cumsum(axis=1) ==3 判断第一个等于3的就是要找的
- (df3.isnull().cumsum(axis=1) ==3).idxmax(axis=1) 横向找到第一个最大值对应的列索引
df = pd.DataFrame({'grps': list('aaabbcaabcccbbc'),
'vals': [12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]})
df
image.png
df.groupby('grps')['vals']
<pandas.core.groupby.groupby.SeriesGroupBy object at 0x0000000009648DD8>
df.groupby('grps')['vals'].nlargest(3)
grps
a 1 345
7 52
0 12
b 12 57
8 54
4 45
c 10 235
14 87
9 23
Name: vals, dtype: int64
df.groupby('grps')['vals'].nlargest(3).sum(level=0)
grps
a 409
b 156
c 345
Name: vals, dtype: int64
b=np.random.randint(101,201,size=100)
df4=pd.DataFrame({'A':range(1,101),'B': b})
df4.head(20)
image.png
- 按A分段分组 类似（0,10],(10,20]...这种分组，计算B列的相关对应的和
ct_group=pd.cut(df4['A'],np.arange(0,101,10))
ct_group
0 (0, 10]
1 (0, 10]
2 (0, 10]
3 (0, 10]
4 (0, 10]
5 (0, 10]
6 (0, 10]
7 (0, 10]
8 (0, 10]
9 (0, 10]
10 (10, 20]
11 (10, 20]
12 (10, 20]
13 (10, 20]
14 (10, 20]
15 (10, 20]
16 (10, 20]
17 (10, 20]
18 (10, 20]
19 (10, 20]
20 (20, 30]
21 (20, 30]
22 (20, 30]
23 (20, 30]
24 (20, 30]
25 (20, 30]
26 (20, 30]
27 (20, 30]
28 (20, 30]
29 (20, 30]
...
70 (70, 80]
71 (70, 80]
72 (70, 80]
73 (70, 80]
74 (70, 80]
75 (70, 80]
76 (70, 80]
77 (70, 80]
78 (70, 80]
79 (70, 80]
80 (80, 90]
81 (80, 90]
82 (80, 90]
83 (80, 90]
84 (80, 90]
85 (80, 90]
86 (80, 90]
87 (80, 90]
88 (80, 90]
89 (80, 90]
90 (90, 100]
91 (90, 100]
92 (90, 100]
93 (90, 100]
94 (90, 100]
95 (90, 100]
96 (90, 100]
97 (90, 100]
98 (90, 100]
99 (90, 100]
Name: A, Length: 100, dtype: category
Categories (10, interval[int64]): [(0, 10] < (10, 20] < (20, 30] < (30, 40] ... (60, 70] < (70, 80] < (80, 90] < (90, 100]]
df4.groupby(ct_group)['B'].sum()
A
(0, 10] 1448
(10, 20] 1451
(20, 30] 1378
(30, 40] 1580
(40, 50] 1546
(50, 60] 1462
(60, 70] 1531
(70, 80] 1405
(80, 90] 1509
(90, 100] 1541
Name: B, dtype: int32
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