import pandas as pd
import numpy as np
Series
s = pd.Series()
s
Series([], dtype: float64)
data1 = [1, 2, 3]
data2 = np.array(data1, dtype=float)
s1 = pd.Series(data1)
s2 = pd.Series(data2)
print(s1)
print(s2)
0 1
1 2
2 3
dtype: int64
0 1.0
1 2.0
2 3.0
dtype: float64
s3 = pd.Series(data1, dtype=float)
s3
0 1.0
1 2.0
2 3.0
dtype: float64
咱們能夠看到，若是咱們不指定dtype, 那麼其會自行推斷python
data = np.array(['a', 'b', 'c', 'd'])
s = pd.Series(data, index=np.arange(100, 104))
s
100 a
101 b
102 c
103 d
dtype: object
利用dict來建立series
data = {'a':0, "b":1, 'c':2}
s = pd.Series(data)
s
a 0
b 1
c 2
dtype: int64
s.index
Index(['a', 'b', 'c'], dtype='object')
s = pd.Series(data, index=['b', 'c', 'd', 'a'])
s
b 1.0
c 2.0
d NaN
a 0.0
dtype: float64
利用標量建立series
s = pd.Series(5, index=np.arange(5, 9))
s
5 5
6 5
7 5
8 5
dtype: int64
取
s = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'])
s
a 1
b 2
c 3
d 4
e 5
dtype: int64
s[0], s[1], s[2]
(1, 2, 3)
s[:2], s[2:]
(a 1
b 2
dtype: int64, c 3
d 4
e 5
dtype: int64)
s[-3:]
c 3
d 4
e 5
dtype: int64
s['a'], s['b'], s['c']
(1, 2, 3)
s[['a', 'b', 'e']]
a 1
b 2
e 5
dtype: int64
Dataframe
pandas.DataFrame(data, index, columns, dtype, copy)shell
df = pd.DataFrame()
df
data = [1, 2, 3, 4, 5]
df = pd.DataFrame(data)
df
data = [['Alex', 10], ['Bob', 12], ['Clarke', 13]]
df = pd.DataFrame(data, columns=['Name', 'Age'])
df
|
Name |
Age |
| 0 |
Alex |
10 |
| 1 |
Bob |
12 |
| 2 |
Clarke |
13 |
利用dict建立dataframe
data = {'Name':['Alex', 'Bob', 'Clarke'], 'Age':[10., 12., 13.]}
df = pd.DataFrame(data)
df
|
Name |
Age |
| 0 |
Alex |
10.0 |
| 1 |
Bob |
12.0 |
| 2 |
Clarke |
13.0 |
data = {'Name':['Alex', 'Bob', 'Clarke'], 'Age':[10., 12., 'NaN']} #長度須要匹配
df = pd.DataFrame(data, index=['rank1', 'rank2', 'rank3'])
df
|
Name |
Age |
| rank1 |
Alex |
10 |
| rank2 |
Bob |
12 |
| rank3 |
Clarke |
NaN |
data = [{'a': 1, 'b': 2},{'a': 5, 'b': 10, 'c': 20}] #長度無需匹配
df = pd.DataFrame(data)
df
|
a |
b |
c |
| 0 |
1 |
2 |
NaN |
| 1 |
5 |
10 |
20.0 |
df1 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b'])
df2 = pd.DataFrame(data, index=['first', 'second'], columns=['a', 'b1'])
print(df1)
print(df2)
a b
first 1 2
second 5 10
a b1
first 1 NaN
second 5 NaN
data = {
'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(data)
df
|
one |
two |
| a |
1.0 |
1.0 |
| b |
2.0 |
2.0 |
| c |
3.0 |
3.0 |
| d |
NaN |
4.0 |
選擇
df['one']
a 1.0
b 2.0
c 3.0
d NaN
Name: one, dtype: float64
添加列
df['three'] = pd.Series([10, 20, 30])
df
|
one |
two |
three |
| a |
1.0 |
1.0 |
NaN |
| b |
2.0 |
2.0 |
NaN |
| c |
3.0 |
3.0 |
NaN |
| d |
NaN |
4.0 |
NaN |
df['three'] = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
df
|
one |
two |
three |
| a |
1.0 |
1.0 |
10.0 |
| b |
2.0 |
2.0 |
20.0 |
| c |
3.0 |
3.0 |
30.0 |
| d |
NaN |
4.0 |
NaN |
df['four'] = df['one'] + df['two']
df
|
one |
two |
three |
four |
| a |
1.0 |
1.0 |
10.0 |
2.0 |
| b |
2.0 |
2.0 |
20.0 |
4.0 |
| c |
3.0 |
3.0 |
30.0 |
6.0 |
| d |
NaN |
4.0 |
NaN |
NaN |
列移除
del df['one']
df
|
two |
three |
four |
| a |
1.0 |
10.0 |
2.0 |
| b |
2.0 |
20.0 |
4.0 |
| c |
3.0 |
30.0 |
6.0 |
| d |
4.0 |
NaN |
NaN |
df.pop('three')
df
|
two |
four |
| a |
1.0 |
2.0 |
| b |
2.0 |
4.0 |
| c |
3.0 |
6.0 |
| d |
4.0 |
NaN |
行的選擇， 添加， 移除
data = {'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(data)
df
|
one |
two |
| a |
1.0 |
1 |
| b |
2.0 |
2 |
| c |
3.0 |
3 |
| d |
NaN |
4 |
df.loc['b'] # row b
one 2.0
two 2.0
Name: b, dtype: float64
df.iloc[1] # 按照0, 1, 2...的順序選擇
one 2.0
two 2.0
Name: b, dtype: float64
df[2:4] #df[0]是錯的
df = pd.DataFrame([[1, 2], [3, 4]], columns = ['a','b'])
df2 = pd.DataFrame([[5, 6], [7, 8]], columns = ['a','b'])
df = df.append(df2) #經過append能夠在數據框後面添加數據，可是須要注意的這個操做並不會改變數據自己而是返回一個副本
df
|
a |
b |
| 0 |
1 |
2 |
| 1 |
3 |
4 |
| 0 |
5 |
6 |
| 1 |
7 |
8 |
df.drop(1) #利用drop能夠依照index來刪除某些行，好比1，即把index=1的行均移除， 一樣也是返回一個副本
Panel
pandas.Panel(data, items, major_axis, minor_axis, dtype, copy)express
items: axis=0windows
major_axis: axis=1app
minor_axis: axis=2dom
data = np.random.rand(2, 4, 5)
data
array([[[0.13766405, 0.31453832, 0.51876265, 0.97380794, 0.28314695],
[0.02942928, 0.28957222, 0.38716041, 0.67941481, 0.54108452],
[0.84420857, 0.60339649, 0.49242029, 0.34838561, 0.91342058],
[0.1127622 , 0.28420695, 0.22687715, 0.06842055, 0.87414373]],
[[0.07591772, 0.86028356, 0.30468089, 0.15491769, 0.04969857],
[0.31649918, 0.85154403, 0.73062637, 0.99916418, 0.3809675 ],
[0.63817574, 0.81089715, 0.41390597, 0.6660661 , 0.91651907],
[0.24497635, 0.43923643, 0.01833888, 0.98348271, 0.89717517]]])
p = pd.Panel(data)
p
C:\Ana\lib\site-packages\IPython\core\interactiveshell.py:3267: FutureWarning:
Panel is deprecated and will be removed in a future version.
The recommended way to represent these types of 3-dimensional data are with a MultiIndex on a DataFrame, via the Panel.to_frame() method
Alternatively, you can use the xarray package http://xarray.pydata.org/en/stable/.
Pandas provides a `.to_xarray()` method to help automate this conversion.
exec(code_obj, self.user_global_ns, self.user_ns)
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 5 (minor_axis)
Items axis: 0 to 1
Major_axis axis: 0 to 3
Minor_axis axis: 0 to 4
data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)),
'Item2' : pd.DataFrame(np.random.randn(4, 2))}
p = pd.Panel(data)
p
C:\Ana\lib\site-packages\IPython\core\interactiveshell.py:3267: FutureWarning:
Panel is deprecated and will be removed in a future version.
The recommended way to represent these types of 3-dimensional data are with a MultiIndex on a DataFrame, via the Panel.to_frame() method
Alternatively, you can use the xarray package http://xarray.pydata.org/en/stable/.
Pandas provides a `.to_xarray()` method to help automate this conversion.
exec(code_obj, self.user_global_ns, self.user_ns)
<class 'pandas.core.panel.Panel'>
Dimensions: 2 (items) x 4 (major_axis) x 3 (minor_axis)
Items axis: Item1 to Item2
Major_axis axis: 0 to 3
Minor_axis axis: 0 to 2
p['Item1']
|
0 |
1 |
2 |
| 0 |
1.796552 |
1.614647 |
-2.199413 |
| 1 |
-1.213886 |
-1.438678 |
-1.045931 |
| 2 |
-2.178608 |
1.212732 |
0.526674 |
| 3 |
-0.360727 |
-0.135351 |
0.678293 |
p.major_xs(0)
|
Item1 |
Item2 |
| 0 |
1.796552 |
0.845528 |
| 1 |
1.614647 |
-0.708260 |
| 2 |
-2.199413 |
NaN |
p.minor_xs(0)
|
Item1 |
Item2 |
| 0 |
1.796552 |
0.845528 |
| 1 |
-1.213886 |
0.555775 |
| 2 |
-2.178608 |
0.925129 |
| 3 |
-0.360727 |
-0.380906 |
p.major_axis, p.minor_axis
(RangeIndex(start=0, stop=4, step=1), RangeIndex(start=0, stop=3, step=1))
Basic Functionality
Series
s = pd.Series(np.random.rand(5), index=['a', 'b', 'c', 'd', 'e'])
s
a 0.795298
b 0.141144
c 0.125098
d 0.965541
e 0.957783
dtype: float64
s.axes #返回index
[Index(['a', 'b', 'c', 'd', 'e'], dtype='object')]
s.dtype
dtype('float64')
s.empty #是否爲空
False
s.ndim
1
s.size
5
s.values #以ndarray的形式返回數值
array([0.79529816, 0.14114367, 0.12509848, 0.96554135, 0.95778323])
s.head(3) #返回前n個
a 0.795298
b 0.141144
c 0.125098
dtype: float64
s.tail(3) #返回後n個
c 0.125098
d 0.965541
e 0.957783
dtype: float64
DataFrame
data = {"a":[1, 2, 3], 'b':['r', 'g', 'b']}
df = pd.DataFrame(data, index=['first', 'second', "third"])
df
|
a |
b |
| first |
1 |
r |
| second |
2 |
g |
| third |
3 |
b |
df.T #轉置
|
first |
second |
third |
| a |
1 |
2 |
3 |
| b |
r |
g |
b |
df.axes #有倆個Index
[Index(['first', 'second', 'third'], dtype='object'),
Index(['a', 'b'], dtype='object')]
df.dtypes
a int64
b object
dtype: object
df.empty
False
df.ndim
2
df.shape
(3, 2)
df.size #3 x 2
6
df.values #以ndarray 的形式返回
array([[1, 'r'],
[2, 'g'],
[3, 'b']], dtype=object)
df.head(2)
df.tail(2)
Descriptive Statistic
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','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)
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 |
Smith |
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 |
df.sum() #默認是按行相加
Name TomJamesRickyVinSteveSmithJackLeeDavidGasperBe...
Age 382
Rating 44.92
dtype: object
df.sum()[1]
382
df.sum(1) #按列相加 竟然自動避開了字符串...
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
df.mean()
Age 31.833333
Rating 3.743333
dtype: float64
df.mean()['Age']
31.833333333333332
df.std()
Age 9.232682
Rating 0.661628
dtype: float64
1 count() Number of non-null observationside
2 sum() Sum of values函數
3 mean() Mean of Valuesui
4 median() Median of Values
5 mode() Mode of values
6 std() Standard Deviation of the Values
7 min() Minimum Value
8 max() Maximum Value
9 abs() Absolute Value
10 prod() Product of Values
11 cumsum() Cumulative Sum
12 cumprod() Cumulative Product
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 |
df.describe(include=['object'])
|
Name |
| count |
12 |
| unique |
12 |
| top |
Gasper |
| freq |
1 |
object: 針對字符列
number: 針對數字列
all: 針對所有
df.describe(include='all') #注意'all'不要以列表的形式傳入, 不然會報錯
|
Name |
Age |
Rating |
| count |
12 |
12.000000 |
12.000000 |
| unique |
12 |
NaN |
NaN |
| top |
Gasper |
NaN |
NaN |
| freq |
1 |
NaN |
NaN |
| mean |
NaN |
31.833333 |
3.743333 |
| std |
NaN |
9.232682 |
0.661628 |
| min |
NaN |
23.000000 |
2.560000 |
| 25% |
NaN |
25.000000 |
3.230000 |
| 50% |
NaN |
29.500000 |
3.790000 |
| 75% |
NaN |
35.500000 |
4.132500 |
| max |
NaN |
51.000000 |
4.800000 |
df.describe(include='object')
|
Name |
| count |
12 |
| unique |
12 |
| top |
Gasper |
| freq |
1 |
綁定自定義函數 pipe, apply, applymap
pipe(func, ...) -- 做用於整個表格
apply(func, 0) -- 做用於列或者行
applymap(func) -- 做用於每一個元素
df = pd.DataFrame(np.random.randn(5,3),columns=['col1','col2','col3'])
df
|
col1 |
col2 |
col3 |
| 0 |
1.363701 |
0.533478 |
1.850151 |
| 1 |
0.541553 |
-1.190178 |
1.204944 |
| 2 |
0.181793 |
-0.199892 |
-0.602374 |
| 3 |
-0.411247 |
1.978019 |
1.183671 |
| 4 |
-0.045223 |
1.444328 |
-0.121690 |
def adder(ele1,ele2):
print("****")
print(ele1)
print("****")
print(ele2)
return ele1+ele2
df.pipe(adder, 2) #能夠看出，ele1 == df, ele2 == 2
****
col1 col2 col3
0 1.363701 0.533478 1.850151
1 0.541553 -1.190178 1.204944
2 0.181793 -0.199892 -0.602374
3 -0.411247 1.978019 1.183671
4 -0.045223 1.444328 -0.121690
****
2
|
col1 |
col2 |
col3 |
| 0 |
3.363701 |
2.533478 |
3.850151 |
| 1 |
2.541553 |
0.809822 |
3.204944 |
| 2 |
2.181793 |
1.800108 |
1.397626 |
| 3 |
1.588753 |
3.978019 |
3.183671 |
| 4 |
1.954777 |
3.444328 |
1.878310 |
df2 = pd.DataFrame(np.random.randn(5,3),columns=['col1','col2','col3'])
df2
|
col1 |
col2 |
col3 |
| 0 |
0.386211 |
1.297222 |
-0.413626 |
| 1 |
-1.873829 |
-0.007802 |
-0.857307 |
| 2 |
-0.881874 |
-2.026235 |
0.540769 |
| 3 |
0.458257 |
-0.590630 |
0.685780 |
| 4 |
0.177258 |
-1.843835 |
0.131939 |
def inf_print(x):
print(type(x))
print("*******")
print(x)
df2.apply(inf_print, 0)
<class 'pandas.core.series.Series'>
*******
0 0.386211
1 -1.873829
2 -0.881874
3 0.458257
4 0.177258
Name: col1, dtype: float64
<class 'pandas.core.series.Series'>
*******
0 1.297222
1 -0.007802
2 -2.026235
3 -0.590630
4 -1.843835
Name: col2, dtype: float64
<class 'pandas.core.series.Series'>
*******
0 -0.413626
1 -0.857307
2 0.540769
3 0.685780
4 0.131939
Name: col3, dtype: float64
col1 None
col2 None
col3 None
dtype: object
df2.apply(inf_print, 1)
<class 'pandas.core.series.Series'>
*******
col1 0.386211
col2 1.297222
col3 -0.413626
Name: 0, dtype: float64
<class 'pandas.core.series.Series'>
*******
col1 -1.873829
col2 -0.007802
col3 -0.857307
Name: 1, dtype: float64
<class 'pandas.core.series.Series'>
*******
col1 -0.881874
col2 -2.026235
col3 0.540769
Name: 2, dtype: float64
<class 'pandas.core.series.Series'>
*******
col1 0.458257
col2 -0.590630
col3 0.685780
Name: 3, dtype: float64
<class 'pandas.core.series.Series'>
*******
col1 0.177258
col2 -1.843835
col3 0.131939
Name: 4, dtype: float64
0 None
1 None
2 None
3 None
4 None
dtype: object
df2.apply(np.mean) #能夠知道， apply會將列(默認，若是第二個參數爲1則爲行)一次次傳入
col1 -0.346795
col2 -0.634256
col3 0.017511
dtype: float64
df2.applymap(lambda x: x*100)
|
col1 |
col2 |
col3 |
| 0 |
38.621115 |
129.722168 |
-41.362553 |
| 1 |
-187.382897 |
-0.780203 |
-85.730689 |
| 2 |
-88.187450 |
-202.623474 |
54.076880 |
| 3 |
45.825709 |
-59.062993 |
68.578010 |
| 4 |
17.725785 |
-184.383525 |
13.193880 |
Reindex
N=20
df = pd.DataFrame({
'A': pd.date_range(start='2016-01-01',periods=N,freq='D'),
'x': np.linspace(0,stop=N-1,num=N),
'y': np.random.rand(N),
'C': np.random.choice(['Low','Medium','High'],N).tolist(),
'D': np.random.normal(100, 10, size=(N)).tolist()
})
df
|
A |
x |
y |
C |
D |
| 0 |
2016-01-01 |
0.0 |
0.173488 |
High |
117.632385 |
| 1 |
2016-01-02 |
1.0 |
0.493186 |
Low |
114.066702 |
| 2 |
2016-01-03 |
2.0 |
0.982273 |
High |
102.389228 |
| 3 |
2016-01-04 |
3.0 |
0.329518 |
Low |
104.405035 |
| 4 |
2016-01-05 |
4.0 |
0.392182 |
Medium |
85.867100 |
| 5 |
2016-01-06 |
5.0 |
0.905708 |
High |
103.248690 |
| 6 |
2016-01-07 |
6.0 |
0.731801 |
Low |
100.177698 |
| 7 |
2016-01-08 |
7.0 |
0.772975 |
High |
97.365013 |
| 8 |
2016-01-09 |
8.0 |
0.953258 |
Low |
90.228303 |
| 9 |
2016-01-10 |
9.0 |
0.503579 |
High |
99.946431 |
| 10 |
2016-01-11 |
10.0 |
0.580698 |
Low |
88.411279 |
| 11 |
2016-01-12 |
11.0 |
0.268562 |
High |
91.238630 |
| 12 |
2016-01-13 |
12.0 |
0.462713 |
High |
86.720994 |
| 13 |
2016-01-14 |
13.0 |
0.482387 |
High |
104.549789 |
| 14 |
2016-01-15 |
14.0 |
0.963168 |
Medium |
108.565120 |
| 15 |
2016-01-16 |
15.0 |
0.692654 |
High |
112.370992 |
| 16 |
2016-01-17 |
16.0 |
0.716956 |
High |
112.949463 |
| 17 |
2016-01-18 |
17.0 |
0.897878 |
Low |
107.860172 |
| 18 |
2016-01-19 |
18.0 |
0.289202 |
Medium |
90.430672 |
| 19 |
2016-01-20 |
19.0 |
0.957986 |
High |
115.225753 |
df.reindex(index=(0, 2, 5), columns=['A', 'C', 'B']) #沒有B因此會補
|
A |
C |
B |
| 0 |
2016-01-01 |
High |
NaN |
| 2 |
2016-01-03 |
High |
NaN |
| 5 |
2016-01-06 |
High |
NaN |
reindex_like
df1 = pd.DataFrame(np.random.randn(4, 4))
df2 = pd.DataFrame(np.random.randn(3, 3))
df1
|
0 |
1 |
2 |
3 |
| 0 |
-2.515820 |
-0.027034 |
-0.695420 |
0.368491 |
| 1 |
-1.055241 |
0.778208 |
-1.062983 |
-1.715173 |
| 2 |
0.178253 |
-0.186661 |
0.615827 |
1.379872 |
| 3 |
-1.316952 |
-0.209785 |
-0.953194 |
-0.138620 |
df2
|
0 |
1 |
2 |
| 0 |
-0.913434 |
1.641339 |
-2.418425 |
| 1 |
0.113041 |
0.721168 |
0.446690 |
| 2 |
2.606504 |
-0.972984 |
-2.588228 |
df1.reindex_like(df2)
|
0 |
1 |
2 |
| 0 |
-2.515820 |
-0.027034 |
-0.695420 |
| 1 |
-1.055241 |
0.778208 |
-1.062983 |
| 2 |
0.178253 |
-0.186661 |
0.615827 |
插補
pad/ffill - 用前面的值填充
bfill/backfill - 用後面的值填充
nearest - 用最近的值填充
df2.reindex_like(df1, method="ffill")
|
0 |
1 |
2 |
3 |
| 0 |
-0.913434 |
1.641339 |
-2.418425 |
-2.418425 |
| 1 |
0.113041 |
0.721168 |
0.446690 |
0.446690 |
| 2 |
2.606504 |
-0.972984 |
-2.588228 |
-2.588228 |
| 3 |
2.606504 |
-0.972984 |
-2.588228 |
-2.588228 |
df2.reindex_like(df1, method="bfill")
|
0 |
1 |
2 |
3 |
| 0 |
-0.913434 |
1.641339 |
-2.418425 |
NaN |
| 1 |
0.113041 |
0.721168 |
0.446690 |
NaN |
| 2 |
2.606504 |
-0.972984 |
-2.588228 |
NaN |
| 3 |
NaN |
NaN |
NaN |
NaN |
df2.reindex_like(df1, method="nearest")
|
0 |
1 |
2 |
3 |
| 0 |
-0.913434 |
1.641339 |
-2.418425 |
-2.418425 |
| 1 |
0.113041 |
0.721168 |
0.446690 |
0.446690 |
| 2 |
2.606504 |
-0.972984 |
-2.588228 |
-2.588228 |
| 3 |
2.606504 |
-0.972984 |
-2.588228 |
-2.588228 |
limit
df1 = pd.DataFrame(np.random.randn(7, 7))
df2 = pd.DataFrame(np.random.randn(3, 3))
df1, df2
( 0 1 2 3 4 5 6
0 0.592257 0.913287 1.276314 0.064212 1.338661 -0.110666 -0.459020
1 -0.104347 0.388397 -1.822243 1.927027 0.890738 0.577283 -0.302798
2 -0.016216 -1.101383 0.128118 -0.138639 1.642480 -1.382323 -0.835393
3 1.411169 -0.395379 -0.412377 0.661016 -0.602245 0.558017 0.588833
4 0.609378 0.338787 -0.858829 0.006657 1.509428 -0.283262 -0.563293
5 -1.316789 0.152338 -1.027535 0.026238 -0.052540 1.233837 -1.028193
6 0.992425 1.364755 -1.384109 -1.888707 -0.259932 -0.207928 0.135734,
0 1 2
0 -0.297591 1.019611 0.892070
1 0.881763 -0.498356 1.708343
2 0.123616 0.875709 0.387768)
df2.reindex_like(df1, method="ffill", limit=1)
|
0 |
1 |
2 |
3 |
4 |
5 |
6 |
| 0 |
-0.297591 |
1.019611 |
0.892070 |
0.892070 |
NaN |
NaN |
NaN |
| 1 |
0.881763 |
-0.498356 |
1.708343 |
1.708343 |
NaN |
NaN |
NaN |
| 2 |
0.123616 |
0.875709 |
0.387768 |
0.387768 |
NaN |
NaN |
NaN |
| 3 |
0.123616 |
0.875709 |
0.387768 |
0.387768 |
NaN |
NaN |
NaN |
| 4 |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| 5 |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| 6 |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
df2.reindex_like(df1, method="ffill", limit=2) #能夠發現，limit限制了填補的最大數量
|
0 |
1 |
2 |
3 |
4 |
5 |
6 |
| 0 |
-0.297591 |
1.019611 |
0.892070 |
0.892070 |
0.892070 |
NaN |
NaN |
| 1 |
0.881763 |
-0.498356 |
1.708343 |
1.708343 |
1.708343 |
NaN |
NaN |
| 2 |
0.123616 |
0.875709 |
0.387768 |
0.387768 |
0.387768 |
NaN |
NaN |
| 3 |
0.123616 |
0.875709 |
0.387768 |
0.387768 |
0.387768 |
NaN |
NaN |
| 4 |
0.123616 |
0.875709 |
0.387768 |
0.387768 |
0.387768 |
NaN |
NaN |
| 5 |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
| 6 |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
NaN |
Renaming
df2
|
0 |
1 |
2 |
| 0 |
-0.297591 |
1.019611 |
0.892070 |
| 1 |
0.881763 |
-0.498356 |
1.708343 |
| 2 |
0.123616 |
0.875709 |
0.387768 |
df2.rename(columns={0:'A', 1:'B', 2:'C', 3:'D'},
index={0:'a', 1:'b', 2:'c', 3:'d'})
|
A |
B |
C |
| a |
-0.297591 |
1.019611 |
0.892070 |
| b |
0.881763 |
-0.498356 |
1.708343 |
| c |
0.123616 |
0.875709 |
0.387768 |
df2 #注意返回的是一個副本
|
0 |
1 |
2 |
| 0 |
-0.297591 |
1.019611 |
0.892070 |
| 1 |
0.881763 |
-0.498356 |
1.708343 |
| 2 |
0.123616 |
0.875709 |
0.387768 |
Iteration
N=20
df = pd.DataFrame({
'A': pd.date_range(start='2016-01-01',periods=N,freq='D'),
'x': np.linspace(0,stop=N-1,num=N),
'y': np.random.rand(N),
'C': np.random.choice(['Low','Medium','High'],N).tolist(),
'D': np.random.normal(100, 10, size=(N)).tolist()
})
df
|
A |
x |
y |
C |
D |
| 0 |
2016-01-01 |
0.0 |
0.529153 |
Low |
110.430517 |
| 1 |
2016-01-02 |
1.0 |
0.713513 |
Low |
125.401221 |
| 2 |
2016-01-03 |
2.0 |
0.751809 |
Medium |
112.446846 |
| 3 |
2016-01-04 |
3.0 |
0.124047 |
High |
108.633343 |
| 4 |
2016-01-05 |
4.0 |
0.472205 |
Medium |
102.750572 |
| 5 |
2016-01-06 |
5.0 |
0.221076 |
High |
108.208930 |
| 6 |
2016-01-07 |
6.0 |
0.231904 |
High |
104.982321 |
| 7 |
2016-01-08 |
7.0 |
0.567697 |
Medium |
117.178737 |
| 8 |
2016-01-09 |
8.0 |
0.384391 |
Medium |
94.160408 |
| 9 |
2016-01-10 |
9.0 |
0.109675 |
Medium |
108.560830 |
| 10 |
2016-01-11 |
10.0 |
0.681480 |
High |
101.400936 |
| 11 |
2016-01-12 |
11.0 |
0.918687 |
Medium |
102.421124 |
| 12 |
2016-01-13 |
12.0 |
0.332227 |
High |
99.464727 |
| 13 |
2016-01-14 |
13.0 |
0.373779 |
High |
107.219963 |
| 14 |
2016-01-15 |
14.0 |
0.412173 |
Low |
97.184597 |
| 15 |
2016-01-16 |
15.0 |
0.194842 |
Medium |
96.671218 |
| 16 |
2016-01-17 |
16.0 |
0.372288 |
Low |
105.270272 |
| 17 |
2016-01-18 |
17.0 |
0.068876 |
Low |
101.112631 |
| 18 |
2016-01-19 |
18.0 |
0.391142 |
High |
102.240937 |
| 19 |
2016-01-20 |
19.0 |
0.942600 |
Low |
92.492350 |
for col in df:
print(col)
A
x
y
C
D
iteritems() (key, value)
for key, value in df.iteritems():
print(key)
print("*******")
print(value)
print("#######")
A
*******
0 2016-01-01
1 2016-01-02
2 2016-01-03
3 2016-01-04
4 2016-01-05
5 2016-01-06
6 2016-01-07
7 2016-01-08
8 2016-01-09
9 2016-01-10
10 2016-01-11
11 2016-01-12
12 2016-01-13
13 2016-01-14
14 2016-01-15
15 2016-01-16
16 2016-01-17
17 2016-01-18
18 2016-01-19
19 2016-01-20
Name: A, dtype: datetime64[ns]
#######
x
*******
0 0.0
1 1.0
2 2.0
3 3.0
4 4.0
5 5.0
6 6.0
7 7.0
8 8.0
9 9.0
10 10.0
11 11.0
12 12.0
13 13.0
14 14.0
15 15.0
16 16.0
17 17.0
18 18.0
19 19.0
Name: x, dtype: float64
#######
y
*******
0 0.529153
1 0.713513
2 0.751809
3 0.124047
4 0.472205
5 0.221076
6 0.231904
7 0.567697
8 0.384391
9 0.109675
10 0.681480
11 0.918687
12 0.332227
13 0.373779
14 0.412173
15 0.194842
16 0.372288
17 0.068876
18 0.391142
19 0.942600
Name: y, dtype: float64
#######
C
*******
0 Low
1 Low
2 Medium
3 High
4 Medium
5 High
6 High
7 Medium
8 Medium
9 Medium
10 High
11 Medium
12 High
13 High
14 Low
15 Medium
16 Low
17 Low
18 High
19 Low
Name: C, dtype: object
#######
D
*******
0 110.430517
1 125.401221
2 112.446846
3 108.633343
4 102.750572
5 108.208930
6 104.982321
7 117.178737
8 94.160408
9 108.560830
10 101.400936
11 102.421124
12 99.464727
13 107.219963
14 97.184597
15 96.671218
16 105.270272
17 101.112631
18 102.240937
19 92.492350
Name: D, dtype: float64
#######
iterrows() (index, series)
for index, row in df.iterrows():
print(index)
print("********")
print(row)
print("########")
0
********
A 2016-01-01 00:00:00
x 0
y 0.529153
C Low
D 110.431
Name: 0, dtype: object
########
1
********
A 2016-01-02 00:00:00
x 1
y 0.713513
C Low
D 125.401
Name: 1, dtype: object
########
2
********
A 2016-01-03 00:00:00
x 2
y 0.751809
C Medium
D 112.447
Name: 2, dtype: object
########
3
********
A 2016-01-04 00:00:00
x 3
y 0.124047
C High
D 108.633
Name: 3, dtype: object
########
4
********
A 2016-01-05 00:00:00
x 4
y 0.472205
C Medium
D 102.751
Name: 4, dtype: object
########
5
********
A 2016-01-06 00:00:00
x 5
y 0.221076
C High
D 108.209
Name: 5, dtype: object
########
6
********
A 2016-01-07 00:00:00
x 6
y 0.231904
C High
D 104.982
Name: 6, dtype: object
########
7
********
A 2016-01-08 00:00:00
x 7
y 0.567697
C Medium
D 117.179
Name: 7, dtype: object
########
8
********
A 2016-01-09 00:00:00
x 8
y 0.384391
C Medium
D 94.1604
Name: 8, dtype: object
########
9
********
A 2016-01-10 00:00:00
x 9
y 0.109675
C Medium
D 108.561
Name: 9, dtype: object
########
10
********
A 2016-01-11 00:00:00
x 10
y 0.68148
C High
D 101.401
Name: 10, dtype: object
########
11
********
A 2016-01-12 00:00:00
x 11
y 0.918687
C Medium
D 102.421
Name: 11, dtype: object
########
12
********
A 2016-01-13 00:00:00
x 12
y 0.332227
C High
D 99.4647
Name: 12, dtype: object
########
13
********
A 2016-01-14 00:00:00
x 13
y 0.373779
C High
D 107.22
Name: 13, dtype: object
########
14
********
A 2016-01-15 00:00:00
x 14
y 0.412173
C Low
D 97.1846
Name: 14, dtype: object
########
15
********
A 2016-01-16 00:00:00
x 15
y 0.194842
C Medium
D 96.6712
Name: 15, dtype: object
########
16
********
A 2016-01-17 00:00:00
x 16
y 0.372288
C Low
D 105.27
Name: 16, dtype: object
########
17
********
A 2016-01-18 00:00:00
x 17
y 0.0688757
C Low
D 101.113
Name: 17, dtype: object
########
18
********
A 2016-01-19 00:00:00
x 18
y 0.391142
C High
D 102.241
Name: 18, dtype: object
########
19
********
A 2016-01-20 00:00:00
x 19
y 0.9426
C Low
D 92.4924
Name: 19, dtype: object
########
itertuples()
for row in df.itertuples():
print(row)
print("*********")
Pandas(Index=0, A=Timestamp('2016-01-01 00:00:00'), x=0.0, y=0.5291527485322772, C='Low', D=110.43051702923863)
*********
Pandas(Index=1, A=Timestamp('2016-01-02 00:00:00'), x=1.0, y=0.713512538332376, C='Low', D=125.40122093094763)
*********
Pandas(Index=2, A=Timestamp('2016-01-03 00:00:00'), x=2.0, y=0.7518093449140011, C='Medium', D=112.44684623090683)
*********
Pandas(Index=3, A=Timestamp('2016-01-04 00:00:00'), x=3.0, y=0.12404682661025335, C='High', D=108.63334270085768)
*********
Pandas(Index=4, A=Timestamp('2016-01-05 00:00:00'), x=4.0, y=0.47220500135853094, C='Medium', D=102.75057211144569)
*********
Pandas(Index=5, A=Timestamp('2016-01-06 00:00:00'), x=5.0, y=0.22107632396965704, C='High', D=108.20892974035311)
*********
Pandas(Index=6, A=Timestamp('2016-01-07 00:00:00'), x=6.0, y=0.23190410081052582, C='High', D=104.98232144314449)
*********
Pandas(Index=7, A=Timestamp('2016-01-08 00:00:00'), x=7.0, y=0.5676969704991909, C='Medium', D=117.17873695254926)
*********
Pandas(Index=8, A=Timestamp('2016-01-09 00:00:00'), x=8.0, y=0.38439055971010483, C='Medium', D=94.16040790153708)
*********
Pandas(Index=9, A=Timestamp('2016-01-10 00:00:00'), x=9.0, y=0.10967465769586215, C='Medium', D=108.56083032097501)
*********
Pandas(Index=10, A=Timestamp('2016-01-11 00:00:00'), x=10.0, y=0.6814801929159177, C='High', D=101.40093570017285)
*********
Pandas(Index=11, A=Timestamp('2016-01-12 00:00:00'), x=11.0, y=0.9186874162117078, C='Medium', D=102.42112353899493)
*********
Pandas(Index=12, A=Timestamp('2016-01-13 00:00:00'), x=12.0, y=0.33222699128916544, C='High', D=99.46472715055548)
*********
Pandas(Index=13, A=Timestamp('2016-01-14 00:00:00'), x=13.0, y=0.37377940932622644, C='High', D=107.21996306704972)
*********
Pandas(Index=14, A=Timestamp('2016-01-15 00:00:00'), x=14.0, y=0.41217288447139533, C='Low', D=97.1845970026168)
*********
Pandas(Index=15, A=Timestamp('2016-01-16 00:00:00'), x=15.0, y=0.19484179666549728, C='Medium', D=96.67121785562782)
*********
Pandas(Index=16, A=Timestamp('2016-01-17 00:00:00'), x=16.0, y=0.3722882537710307, C='Low', D=105.27027217632694)
*********
Pandas(Index=17, A=Timestamp('2016-01-18 00:00:00'), x=17.0, y=0.068875657049556, C='Low', D=101.11263086450178)
*********
Pandas(Index=18, A=Timestamp('2016-01-19 00:00:00'), x=18.0, y=0.3911420688006072, C='High', D=102.24093699498466)
*********
Pandas(Index=19, A=Timestamp('2016-01-20 00:00:00'), x=19.0, y=0.9425996619637542, C='Low', D=92.49235045195462)
*********
教程上說，迭代的東西是一個副本，因此對其中的元素進行更改是不會影響原數據的.
Sorting
sort_index
unsorted_df=pd.DataFrame(np.random.randn(10,2),index=[1,4,6,2,3,5,9,8,0,7],columns=['col2','col1'])
unsorted_df
|
col2 |
col1 |
| 1 |
0.418578 |
-0.556598 |
| 4 |
0.513646 |
1.436592 |
| 6 |
0.830816 |
1.500456 |
| 2 |
-0.373790 |
-0.578432 |
| 3 |
0.961146 |
0.991754 |
| 5 |
0.826093 |
-0.345533 |
| 9 |
0.881435 |
0.934766 |
| 8 |
-1.388952 |
-1.276708 |
| 0 |
-0.685924 |
-0.210499 |
| 7 |
1.556807 |
-0.652186 |
unsorted_df.sort_index(ascending=False) #經過label排序
|
col2 |
col1 |
| 9 |
0.881435 |
0.934766 |
| 8 |
-1.388952 |
-1.276708 |
| 7 |
1.556807 |
-0.652186 |
| 6 |
0.830816 |
1.500456 |
| 5 |
0.826093 |
-0.345533 |
| 4 |
0.513646 |
1.436592 |
| 3 |
0.961146 |
0.991754 |
| 2 |
-0.373790 |
-0.578432 |
| 1 |
0.418578 |
-0.556598 |
| 0 |
-0.685924 |
-0.210499 |
help(unsorted_df.sort_index)
Help on method sort_index in module pandas.core.frame:
sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, by=None) method of pandas.core.frame.DataFrame instance
Sort object by labels (along an axis)
Parameters
----------
axis : index, columns to direct sorting
level : int or level name or list of ints or list of level names
if not None, sort on values in specified index level(s)
ascending : boolean, default True
Sort ascending vs. descending
inplace : bool, default False
if True, perform operation in-place
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
`first` puts NaNs at the beginning, `last` puts NaNs at the end.
Not implemented for MultiIndex.
sort_remaining : bool, default True
if true and sorting by level and index is multilevel, sort by other
levels too (in order) after sorting by specified level
Returns
-------
sorted_obj : DataFrame
unsorted_df.sort_index(axis=1)
|
col1 |
col2 |
| 1 |
-0.556598 |
0.418578 |
| 4 |
1.436592 |
0.513646 |
| 6 |
1.500456 |
0.830816 |
| 2 |
-0.578432 |
-0.373790 |
| 3 |
0.991754 |
0.961146 |
| 5 |
-0.345533 |
0.826093 |
| 9 |
0.934766 |
0.881435 |
| 8 |
-1.276708 |
-1.388952 |
| 0 |
-0.210499 |
-0.685924 |
| 7 |
-0.652186 |
1.556807 |
sort_value
unsorted_df = pd.DataFrame({'col1':[2,1,1,1],'col2':[1,3,2,4]})
unsorted_df
|
col1 |
col2 |
| 0 |
2 |
1 |
| 1 |
1 |
3 |
| 2 |
1 |
2 |
| 3 |
1 |
4 |
unsorted_df.sort_values(by="col1")
|
col1 |
col2 |
| 1 |
1 |
3 |
| 2 |
1 |
2 |
| 3 |
1 |
4 |
| 0 |
2 |
1 |
help(unsorted_df.sort_values)
Help on method sort_values in module pandas.core.frame:
sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last') method of pandas.core.frame.DataFrame instance
Sort by the values along either axis
Parameters
----------
by : str or list of str
Name or list of names to sort by.
- if `axis` is 0 or `'index'` then `by` may contain index
levels and/or column labels
- if `axis` is 1 or `'columns'` then `by` may contain column
levels and/or index labels
.. versionchanged:: 0.23.0
Allow specifying index or column level names.
axis : {0 or 'index', 1 or 'columns'}, default 0
Axis to be sorted
ascending : bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
inplace : bool, default False
if True, perform operation in-place
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
`first` puts NaNs at the beginning, `last` puts NaNs at the end
Returns
-------
sorted_obj : DataFrame
Examples
--------
>>> df = pd.DataFrame({
... 'col1' : ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2' : [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
3 NaN 8 4
4 D 7 2
5 C 4 3
Sort by col1
>>> df.sort_values(by=['col1'])
col1 col2 col3
0 A 2 0
1 A 1 1
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2'])
col1 col2 col3
1 A 1 1
0 A 2 0
2 B 9 9
5 C 4 3
4 D 7 2
3 NaN 8 4
Sort Descending
>>> df.sort_values(by='col1', ascending=False)
col1 col2 col3
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
3 NaN 8 4
Putting NAs first
>>> df.sort_values(by='col1', ascending=False, na_position='first')
col1 col2 col3
3 NaN 8 4
4 D 7 2
5 C 4 3
2 B 9 9
0 A 2 0
1 A 1 1
df = pd.DataFrame(np.random.randn(8, 4),
index = ['a','b','c','d','e','f','g','h'], columns = ['A', 'B', 'C', 'D'])
df
|
A |
B |
C |
D |
| a |
-0.235229 |
0.214813 |
0.838116 |
1.081632 |
| b |
0.530365 |
0.600021 |
0.753903 |
-1.958886 |
| c |
1.760542 |
-1.027882 |
-0.053263 |
0.299710 |
| d |
-0.241942 |
0.455707 |
-0.684968 |
-0.513217 |
| e |
0.866758 |
1.035051 |
-0.451651 |
-0.987964 |
| f |
1.620520 |
0.236408 |
0.478373 |
-1.012238 |
| g |
-0.236978 |
0.352751 |
-0.514737 |
-0.195936 |
| h |
-0.046064 |
0.129530 |
-0.874676 |
1.740141 |
df.loc[:, 'A']
a 2.539530
b -0.278140
c 1.291831
d -0.231592
e -2.047005
f -0.720743
g -0.995131
h 0.190029
Name: A, dtype: float64
df.loc[:, ['A', 'C']]
|
A |
C |
| a |
2.539530 |
-0.290170 |
| b |
-0.278140 |
1.575699 |
| c |
1.291831 |
0.038547 |
| d |
-0.231592 |
0.117562 |
| e |
-2.047005 |
-0.569768 |
| f |
-0.720743 |
0.321223 |
| g |
-0.995131 |
1.530757 |
| h |
0.190029 |
-0.068202 |
df.loc['a':'h']
|
A |
B |
C |
D |
| a |
2.539530 |
0.046380 |
-0.290170 |
-1.540302 |
| b |
-0.278140 |
1.420046 |
1.575699 |
0.533353 |
| c |
1.291831 |
2.595299 |
0.038547 |
-0.488134 |
| d |
-0.231592 |
-0.162497 |
0.117562 |
1.452291 |
| e |
-2.047005 |
-0.046110 |
-0.569768 |
1.328672 |
| f |
-0.720743 |
0.339251 |
0.321223 |
-0.310041 |
| g |
-0.995131 |
0.831769 |
1.530757 |
0.975214 |
| h |
0.190029 |
1.056606 |
-0.068202 |
-1.127776 |
df.loc[df.loc[:, 'A'] > 0, 'A']
b 0.530365
c 1.760542
e 0.866758
f 1.620520
Name: A, dtype: float64
iloc()
df.iloc[:4]
|
A |
B |
C |
D |
| a |
2.539530 |
0.046380 |
-0.290170 |
-1.540302 |
| b |
-0.278140 |
1.420046 |
1.575699 |
0.533353 |
| c |
1.291831 |
2.595299 |
0.038547 |
-0.488134 |
| d |
-0.231592 |
-0.162497 |
0.117562 |
1.452291 |
df.iloc[1:5, 2:4]
|
C |
D |
| b |
1.575699 |
0.533353 |
| c |
0.038547 |
-0.488134 |
| d |
0.117562 |
1.452291 |
| e |
-0.569768 |
1.328672 |
import collections
p = collections.defaultdict(int)
p['A'] += 1
p['B'] += 1
p
defaultdict(int, {'A': 1, 'B': 1})
df = pd.DataFrame({'thing':['A', 'A', 'B', 'A', 'B', 'A', 'C', 'C', 'C']})
for row in df.loc[df.loc[:, 'thing']== 'D'].iterrows():
print(1)
x = set([1, 2, 3, 1])
x
{1, 2, 3}
df2 = df.copy()
df2
|
A |
D |
| a |
-0.235229 |
1.081632 |
| b |
0.530365 |
-1.958886 |
| c |
1.760542 |
0.299710 |
| d |
-0.241942 |
-0.513217 |
| e |
0.866758 |
-0.987964 |
| f |
1.620520 |
-1.012238 |
| g |
-0.236978 |
-0.195936 |
| h |
-0.046064 |
1.740141 |
df2.loc['a'][0] = 3
df3 = df['A']
df3
a -0.235229
b 0.530365
c 1.760542
d -0.241942
e 0.866758
f 1.620520
g -0.236978
h -0.046064
Name: A, dtype: float64
df3['a'] = 3
df3
a 3.000000
b 0.530365
c 1.760542
d -0.241942
e 0.866758
f 1.620520
g -0.236978
h -0.046064
Name: A, dtype: float64
df3 = df.iloc[:4]
df3
|
A |
D |
| a |
3.000000 |
1.081632 |
| b |
0.530365 |
-1.958886 |
| c |
1.760542 |
0.299710 |
| d |
-0.241942 |
-0.513217 |
df3.pop('A')
a 0.000000
b 0.530365
c 1.760542
d -0.241942
Name: A, dtype: float64
d = {1:1, 2:2}
u = {3:3}
d.update(u)
d
{1: 1, 2: 2, 3: 3}
Wroking with Text Data
s = pd.Series(['Tom', 'William Rick', 'John', 'Alber@t', np.nan, '1234','SteveSmith'])
s
0 Tom
1 William Rick
2 John
3 Alber@t
4 NaN
5 1234
6 SteveSmith
dtype: object
s.str.lower() s.str.upper()
list(s.str)
[0 T
1 W
2 J
3 A
4 NaN
5 1
6 S
dtype: object, 0 o
1 i
2 o
3 l
4 NaN
5 2
6 t
dtype: object, 0 m
1 l
2 h
3 b
4 NaN
5 3
6 e
dtype: object, 0 NaN
1 l
2 n
3 e
4 NaN
5 4
6 v
dtype: object, 0 NaN
1 i
2 NaN
3 r
4 NaN
5 NaN
6 e
dtype: object, 0 NaN
1 a
2 NaN
3 @
4 NaN
5 NaN
6 S
dtype: object, 0 NaN
1 m
2 NaN
3 t
4 NaN
5 NaN
6 m
dtype: object, 0 NaN
1
2 NaN
3 NaN
4 NaN
5 NaN
6 i
dtype: object, 0 NaN
1 R
2 NaN
3 NaN
4 NaN
5 NaN
6 t
dtype: object, 0 NaN
1 i
2 NaN
3 NaN
4 NaN
5 NaN
6 h
dtype: object, 0 NaN
1 c
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
dtype: object, 0 NaN
1 k
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
dtype: object]
s.str.lower() #因此這一步實際上就是把s.str的第一個部分所有改成小寫？
0 tom
1 william rick
2 john
3 alber@t
4 NaN
5 1234
6 stevesmith
dtype: object
s.str.upper()
0 TOM
1 WILLIAM RICK
2 JOHN
3 ALBER@T
4 NaN
5 1234
6 STEVESMITH
dtype: object
s.str.len()
s.str.len() #實際上就是把每個元素的長度弄出來
0 3.0
1 12.0
2 4.0
3 7.0
4 NaN
5 4.0
6 10.0
dtype: float64
s.str.strip()
s = pd.Series(['Tom ', ' William Rick', 'John', 'Alber@t'])
s
0 Tom
1 William Rick
2 John
3 Alber@t
dtype: object
s.str.strip()
0 Tom
1 William Rick
2 John
3 Alber@t
dtype: object
s.str.strip('k')
0 Tom
1 William Ric
2 John
3 Alber@t
dtype: object
s.str.spilt()
s.str.split('o')
0 [T, m ]
1 [ William Rick]
2 [J, hn]
3 [Alber@t]
dtype: object
s.str.split()
0 [Tom]
1 [William, Rick]
2 [John]
3 [Alber@t]
dtype: object
s.str.split(' ')
0 [Tom, , , , , , , , , , , , ]
1 [, William, Rick]
2 [John]
3 [Alber@t]
dtype: object
s.str.cat()
s.str.cat()
'Tom William RickJohnAlber@t'
s.str.cat(sep='A')
'Tom A William RickAJohnAAlber@t'
s.str.cat(sep='_____')
'Tom _____ William Rick_____John_____Alber@t'
help(s.str.cat) #因此若是不是series之間相鏈接，須要經過關鍵詞sep來傳入分隔符
Help on method cat in module pandas.core.strings:
cat(others=None, sep=None, na_rep=None, join=None) method of pandas.core.strings.StringMethods instance
Concatenate strings in the Series/Index with given separator.
If `others` is specified, this function concatenates the Series/Index
and elements of `others` element-wise.
If `others` is not passed, then all values in the Series/Index are
concatenated into a single string with a given `sep`.
Parameters
----------
others : Series, Index, DataFrame, np.ndarrary or list-like
Series, Index, DataFrame, np.ndarray (one- or two-dimensional) and
other list-likes of strings must have the same length as the
calling Series/Index, with the exception of indexed objects (i.e.
Series/Index/DataFrame) if `join` is not None.
If others is a list-like that contains a combination of Series,
np.ndarray (1-dim) or list-like, then all elements will be unpacked
and must satisfy the above criteria individually.
If others is None, the method returns the concatenation of all
strings in the calling Series/Index.
sep : string or None, default None
If None, concatenates without any separator.
na_rep : string or None, default None
Representation that is inserted for all missing values:
- If `na_rep` is None, and `others` is None, missing values in the
Series/Index are omitted from the result.
- If `na_rep` is None, and `others` is not None, a row containing a
missing value in any of the columns (before concatenation) will
have a missing value in the result.
join : {'left', 'right', 'outer', 'inner'}, default None
Determines the join-style between the calling Series/Index and any
Series/Index/DataFrame in `others` (objects without an index need
to match the length of the calling Series/Index). If None,
alignment is disabled, but this option will be removed in a future
version of pandas and replaced with a default of `'left'`. To
disable alignment, use `.values` on any Series/Index/DataFrame in
`others`.
.. versionadded:: 0.23.0
Returns
-------
concat : str or Series/Index of objects
If `others` is None, `str` is returned, otherwise a `Series/Index`
(same type as caller) of objects is returned.
See Also
--------
split : Split each string in the Series/Index
Examples
--------
When not passing `others`, all values are concatenated into a single
string:
>>> s = pd.Series(['a', 'b', np.nan, 'd'])
>>> s.str.cat(sep=' ')
'a b d'
By default, NA values in the Series are ignored. Using `na_rep`, they
can be given a representation:
>>> s.str.cat(sep=' ', na_rep='?')
'a b ? d'
If `others` is specified, corresponding values are concatenated with
the separator. Result will be a Series of strings.
>>> s.str.cat(['A', 'B', 'C', 'D'], sep=',')
0 a,A
1 b,B
2 NaN
3 d,D
dtype: object
Missing values will remain missing in the result, but can again be
represented using `na_rep`
>>> s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-')
0 a,A
1 b,B
2 -,C
3 d,D
dtype: object
If `sep` is not specified, the values are concatenated without
separation.
>>> s.str.cat(['A', 'B', 'C', 'D'], na_rep='-')
0 aA
1 bB
2 -C
3 dD
dtype: object
Series with different indexes can be aligned before concatenation. The
`join`-keyword works as in other methods.
>>> t = pd.Series(['d', 'a', 'e', 'c'], index=[3, 0, 4, 2])
>>> s.str.cat(t, join=None, na_rep='-')
0 ad
1 ba
2 -e
3 dc
dtype: object
>>>
>>> s.str.cat(t, join='left', na_rep='-')
0 aa
1 b-
2 -c
3 dd
dtype: object
>>>
>>> s.str.cat(t, join='outer', na_rep='-')
0 aa
1 b-
2 -c
3 dd
4 -e
dtype: object
>>>
>>> s.str.cat(t, join='inner', na_rep='-')
0 aa
2 -c
3 dd
dtype: object
>>>
>>> s.str.cat(t, join='right', na_rep='-')
3 dd
0 aa
4 -e
2 -c
dtype: object
For more examples, see :ref:`here <text.concatenate>`.
s.str.get_dummies()
s.str.get_dummies()
|
William Rick |
Alber@t |
John |
Tom |
| 0 |
0 |
0 |
0 |
1 |
| 1 |
1 |
0 |
0 |
0 |
| 2 |
0 |
0 |
1 |
0 |
| 3 |
0 |
1 |
0 |
0 |
s = pd.Series(['Tom ', ' William Rick', 'Alber@t', 'John'])
s.str.get_dummies()
|
William Rick |
Alber@t |
John |
Tom |
| 0 |
0 |
0 |
0 |
1 |
| 1 |
1 |
0 |
0 |
0 |
| 2 |
0 |
1 |
0 |
0 |
| 3 |
0 |
0 |
1 |
0 |
因此是以DataFrame的形式來表示各個字符出現的順序？
s.str.contains()
s.str.contains(' ')
0 True
1 True
2 False
3 False
dtype: bool
s.str.contains('o')
0 True
1 False
2 False
3 True
dtype: bool
s.str.replace()
s.str.replace('@', '$')
0 Tom
1 William Rick
2 Alber$t
3 John
dtype: object
s.str.repeat()
s = pd.Series(['Tom ', ' William Rick', 'Alber@t', 'John', np.nan])
s.str.repeat(3)
0 Tom Tom Tom
1 William Rick William Rick William Rick
2 Alber@tAlber@tAlber@t
3 JohnJohnJohn
dtype: object
s.str.repeat([1, 2, 3, 4])
0 Tom
1 William Rick William Rick
2 Alber@tAlber@tAlber@t
3 JohnJohnJohnJohn
dtype: object
s.str.count(s2)
統計s2在每一個元素中出現的次數(不必定要相等，被包含也是能夠的)
s.str.count('m')
0 1
1 1
2 0
3 0
dtype: int64
s.str.startswith()
s.str.startswith('To')
0 True
1 False
2 False
3 False
4 NaN
dtype: object
s.str.startswith('To', na=False)
0 True
1 False
2 False
3 False
4 False
dtype: bool
s.str.find()
s.str.find('e') # -1表示不存在, 其它的數字表示其位置
0 -1.0
1 -1.0
2 3.0
3 -1.0
4 NaN
dtype: float64
s.str.findall()
s.str.findall('e') #？這樣感受還不如上面的好用啊
0 []
1 []
2 [e]
3 []
4 NaN
dtype: object
s.str.islower()
s.str.islower()
0 False
1 False
2 False
3 False
4 NaN
dtype: object
s.str.isupper()
s.str.isupper()
0 False
1 False
2 False
3 False
4 NaN
dtype: object
s.str.isnumeric()
s.str.isnumeric()
0 False
1 False
2 False
3 False
4 NaN
dtype: object
Option & Customization
get_option()
set_option()
reset_option()
describe_option()
option_context()
display.max_rows
display.max_columns
display.expand_frame_repr
display.max_colwidth
display.precision #精確度
get_option()
pd.get_option("display.max_rows") #可以顯示的最大行數
60
pd.get_option("display.max_columns") #可以顯示的最大列數
20
set_option()
pd.set_option("display.max_rows", 10) #設置可以顯示的最大行數爲10
pd.get_option("display.max_rows")
10
s = pd.Series(np.arange(11))
s
0 0
1 1
2 2
3 3
4 4
..
6 6
7 7
8 8
9 9
10 10
Length: 11, dtype: int32
pd.set_option("display.max_columns", 2) #設置可以顯示最大列數爲2
pd.get_option("display.max_columns")
2
data = {1:np.arange(10), 2:np.arange(1, 11), 3:np.arange(2, 12)}
df = pd.DataFrame(data)
df
|
1 |
... |
3 |
| 0 |
0 |
... |
2 |
| 1 |
1 |
... |
3 |
| 2 |
2 |
... |
4 |
| 3 |
3 |
... |
5 |
| 4 |
4 |
... |
6 |
| 5 |
5 |
... |
7 |
| 6 |
6 |
... |
8 |
| 7 |
7 |
... |
9 |
| 8 |
8 |
... |
10 |
| 9 |
9 |
... |
11 |
10 rows × 3 columns
reset_option()
reset_option 接受一個參數，設置其屬性爲默認的屬性
pd.reset_option("display.max_rows")
pd.reset_option("display.max_columns")
s
0 0
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
dtype: int32
describe_option()
打印對參數的說明
pd.describe_option("display.max_rows")
display.max_rows : int
If max_rows is exceeded, switch to truncate view. Depending on
`large_repr`, objects are either centrally truncated or printed as
a summary view. 'None' value means unlimited.
In case python/IPython is running in a terminal and `large_repr`
equals 'truncate' this can be set to 0 and pandas will auto-detect
the height of the terminal and print a truncated object which fits
the screen height. The IPython notebook, IPython qtconsole, or
IDLE do not run in a terminal and hence it is not possible to do
correct auto-detection.
[default: 60] [currently: 60]
option_context()
with pd.option_context("display.max_rows", 10):
print(pd.get_option("display.max_rows"))
print(pd.get_option("display.max_rows"))
10
60
因此其實這就是一個上下文，在上下文管理器中，參數會被暫時性地調整，離開控制以後，便會回到原先的狀態
統計函數
percent_change pct_change
比較每個元素與其以前的元素的變化率
s = pd.Series([1,2,3,4,5,4])
s.pct_change()
0 NaN
1 1.000000
2 0.500000
3 0.333333
4 0.250000
5 -0.200000
dtype: float64
df = pd.DataFrame(np.random.randn(5, 2))
df
|
0 |
1 |
| 0 |
0.855450 |
-0.673131 |
| 1 |
0.610321 |
0.389186 |
| 2 |
0.386450 |
-0.209481 |
| 3 |
-0.159426 |
1.941561 |
| 4 |
0.692407 |
0.332914 |
df.pct_change()
|
0 |
1 |
| 0 |
NaN |
NaN |
| 1 |
-0.286549 |
-1.578173 |
| 2 |
-0.366809 |
-1.538254 |
| 3 |
-1.412541 |
-10.268438 |
| 4 |
-5.343109 |
-0.828533 |
help(df.pct_change)
Help on method pct_change in module pandas.core.generic:
pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs) method of pandas.core.frame.DataFrame instance
Percentage change between the current and a prior element.
Computes the percentage change from the immediately previous row by
default. This is useful in comparing the percentage of change in a time
series of elements.
Parameters
----------
periods : int, default 1
Periods to shift for forming percent change.
fill_method : str, default 'pad'
How to handle NAs before computing percent changes.
limit : int, default None
The number of consecutive NAs to fill before stopping.
freq : DateOffset, timedelta, or offset alias string, optional
Increment to use from time series API (e.g. 'M' or BDay()).
**kwargs
Additional keyword arguments are passed into
`DataFrame.shift` or `Series.shift`.
Returns
-------
chg : Series or DataFrame
The same type as the calling object.
See Also
--------
Series.diff : Compute the difference of two elements in a Series.
DataFrame.diff : Compute the difference of two elements in a DataFrame.
Series.shift : Shift the index by some number of periods.
DataFrame.shift : Shift the index by some number of periods.
Examples
--------
**Series**
>>> s = pd.Series([90, 91, 85])
>>> s
0 90
1 91
2 85
dtype: int64
>>> s.pct_change()
0 NaN
1 0.011111
2 -0.065934
dtype: float64
>>> s.pct_change(periods=2)
0 NaN
1 NaN
2 -0.055556
dtype: float64
See the percentage change in a Series where filling NAs with last
valid observation forward to next valid.
>>> s = pd.Series([90, 91, None, 85])
>>> s
0 90.0
1 91.0
2 NaN
3 85.0
dtype: float64
>>> s.pct_change(fill_method='ffill')
0 NaN
1 0.011111
2 0.000000
3 -0.065934
dtype: float64
**DataFrame**
Percentage change in French franc, Deutsche Mark, and Italian lira from
1980-01-01 to 1980-03-01.
>>> df = pd.DataFrame({
... 'FR': [4.0405, 4.0963, 4.3149],
... 'GR': [1.7246, 1.7482, 1.8519],
... 'IT': [804.74, 810.01, 860.13]},
... index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
FR GR IT
1980-01-01 4.0405 1.7246 804.74
1980-02-01 4.0963 1.7482 810.01
1980-03-01 4.3149 1.8519 860.13
>>> df.pct_change()
FR GR IT
1980-01-01 NaN NaN NaN
1980-02-01 0.013810 0.013684 0.006549
1980-03-01 0.053365 0.059318 0.061876
Percentage of change in GOOG and APPL stock volume. Shows computing
the percentage change between columns.
>>> df = pd.DataFrame({
... '2016': [1769950, 30586265],
... '2015': [1500923, 40912316],
... '2014': [1371819, 41403351]},
... index=['GOOG', 'APPL'])
>>> df
2016 2015 2014
GOOG 1769950 1500923 1371819
APPL 30586265 40912316 41403351
>>> df.pct_change(axis='columns')
2016 2015 2014
GOOG NaN -0.151997 -0.086016
APPL NaN 0.337604 0.012002
s.pct_change(2)
0 NaN
1 NaN
2 2.000000
3 1.000000
4 0.666667
5 0.000000
dtype: float64
covariance
計算協方差，會自動省略NA
s1 = pd.Series(np.random.randn(10))
s2 = pd.Series(np.random.randn(10))
s1.cov(s2)
0.05172017259428779
當cov做用於DataFrame的時候，會計算列之間的協方差
frame = pd.DataFrame(np.random.randn(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
frame.cov()
|
a |
b |
c |
d |
e |
| a |
1.737924 |
0.114498 |
0.279058 |
-0.325639 |
-0.224395 |
| b |
0.114498 |
0.543846 |
0.421225 |
-0.103112 |
-0.373280 |
| c |
0.279058 |
0.421225 |
1.326316 |
-0.635491 |
-0.573974 |
| d |
-0.325639 |
-0.103112 |
-0.635491 |
0.978824 |
0.764530 |
| e |
-0.224395 |
-0.373280 |
-0.573974 |
0.764530 |
0.856515 |
frame['a'].cov(frame.loc[:, 'b'])
0.11449787431144376
corrlation
計算相關係數
frame = pd.DataFrame(np.random.randn(10, 5), columns=['a', 'b', 'c', 'd', 'e'])
frame.corr()
|
a |
b |
c |
d |
e |
| a |
1.000000 |
0.312138 |
0.173514 |
0.247459 |
-0.389265 |
| b |
0.312138 |
1.000000 |
-0.410371 |
0.104755 |
-0.480116 |
| c |
0.173514 |
-0.410371 |
1.000000 |
0.124817 |
-0.230226 |
| d |
0.247459 |
0.104755 |
0.124817 |
1.000000 |
0.259208 |
| e |
-0.389265 |
-0.480116 |
-0.230226 |
0.259208 |
1.000000 |
frame['a'].corr(frame.iloc[:, 1])
0.3121378592734573
Ranking
排序
s = pd.Series(np.random.randn(5), index=list('abcde'))
s['d'] = s['b'] # so there's a tie
s
a -2.179226
b 0.614786
c 1.801039
d 0.614786
e -1.604162
dtype: float64
s.rank()
a 1.0
b 3.5
c 5.0
d 3.5
e 2.0
dtype: float64
window function 一塊一塊來，蠻有用的
.rolling()
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2000', periods=10),
columns = ['A', 'B', 'C', 'D'])
df
|
A |
B |
C |
D |
| 2000-01-01 |
-0.602802 |
0.294063 |
-0.803316 |
-0.500838 |
| 2000-01-02 |
-0.968835 |
-1.745470 |
0.027664 |
-1.012092 |
| 2000-01-03 |
-0.047073 |
0.440166 |
-0.338257 |
1.551372 |
| 2000-01-04 |
0.136861 |
0.357544 |
-0.370691 |
-0.312876 |
| 2000-01-05 |
1.257872 |
-1.126768 |
-0.539122 |
-0.478309 |
| 2000-01-06 |
-0.954518 |
-0.067380 |
0.139257 |
-0.908213 |
| 2000-01-07 |
1.501658 |
-1.189674 |
0.794113 |
-0.155611 |
| 2000-01-08 |
0.400153 |
0.291841 |
-0.450429 |
1.044665 |
| 2000-01-09 |
-0.797415 |
0.346594 |
-0.107653 |
-0.605027 |
| 2000-01-10 |
-0.532034 |
1.296260 |
0.303357 |
-0.056933 |
df.rolling(window=3).mean()
|
A |
B |
C |
D |
| 2000-01-01 |
NaN |
NaN |
NaN |
NaN |
| 2000-01-02 |
NaN |
NaN |
NaN |
NaN |
| 2000-01-03 |
-0.539570 |
-0.337081 |
-0.371303 |
0.012814 |
| 2000-01-04 |
-0.293015 |
-0.315920 |
-0.227095 |
0.075468 |
| 2000-01-05 |
0.449220 |
-0.109686 |
-0.416023 |
0.253396 |
| 2000-01-06 |
0.146739 |
-0.278868 |
-0.256852 |
-0.566466 |
| 2000-01-07 |
0.601671 |
-0.794607 |
0.131416 |
-0.514044 |
| 2000-01-08 |
0.315764 |
-0.321738 |
0.160980 |
-0.006386 |
| 2000-01-09 |
0.368132 |
-0.183746 |
0.078677 |
0.094676 |
| 2000-01-10 |
-0.309765 |
0.644898 |
-0.084908 |
0.127569 |
注意到, window=3, 因此每次的做用因而3行，後面跟的函數是mean, 因此第n行的結果是n, n-1, n-2三行的平均值，前倆行天然而然是NaN
.expanding()
df.expanding(min_periods=3).mean()
|
A |
B |
C |
D |
| 2000-01-01 |
NaN |
NaN |
NaN |
NaN |
| 2000-01-02 |
NaN |
NaN |
NaN |
NaN |
| 2000-01-03 |
-0.539570 |
-0.337081 |
-0.371303 |
0.012814 |
| 2000-01-04 |
-0.370462 |
-0.163425 |
-0.371150 |
-0.068609 |
| 2000-01-05 |
-0.044795 |
-0.356093 |
-0.404744 |
-0.150549 |
| 2000-01-06 |
-0.196416 |
-0.307974 |
-0.314078 |
-0.276826 |
| 2000-01-07 |
0.046166 |
-0.433932 |
-0.155765 |
-0.259510 |
| 2000-01-08 |
0.090415 |
-0.343210 |
-0.192598 |
-0.096488 |
| 2000-01-09 |
-0.008233 |
-0.266565 |
-0.183159 |
-0.152992 |
| 2000-01-10 |
-0.060613 |
-0.110283 |
-0.134508 |
-0.143386 |
df['A'][:4].mean()
-0.37046210746336367
注意到 2000-01-03是同樣的，後面的就不同了，這是由於, min_periods=3限制了做用域最小爲3行，因此n=4的時候，其實是會把前面的4行取平均
.ewm()
滑動平均
實現方式
df
|
A |
B |
C |
D |
| 2000-01-01 |
-0.602802 |
0.294063 |
-0.803316 |
-0.500838 |
| 2000-01-02 |
-0.968835 |
-1.745470 |
0.027664 |
-1.012092 |
| 2000-01-03 |
-0.047073 |
0.440166 |
-0.338257 |
1.551372 |
| 2000-01-04 |
0.136861 |
0.357544 |
-0.370691 |
-0.312876 |
| 2000-01-05 |
1.257872 |
-1.126768 |
-0.539122 |
-0.478309 |
| 2000-01-06 |
-0.954518 |
-0.067380 |
0.139257 |
-0.908213 |
| 2000-01-07 |
1.501658 |
-1.189674 |
0.794113 |
-0.155611 |
| 2000-01-08 |
0.400153 |
0.291841 |
-0.450429 |
1.044665 |
| 2000-01-09 |
-0.797415 |
0.346594 |
-0.107653 |
-0.605027 |
| 2000-01-10 |
-0.532034 |
1.296260 |
0.303357 |
-0.056933 |
df.ewm(com=0.5, adjust=True).mean()
|
A |
B |
C |
D |
| 2000-01-01 |
-0.602802 |
0.294063 |
-0.803316 |
-0.500838 |
| 2000-01-02 |
-0.877327 |
-1.235587 |
-0.180081 |
-0.884278 |
| 2000-01-03 |
-0.302535 |
-0.075451 |
-0.289588 |
0.801941 |
| 2000-01-04 |
-0.005943 |
0.216821 |
-0.344332 |
0.049439 |
| 2000-01-05 |
0.840082 |
-0.682606 |
-0.474729 |
-0.303847 |
| 2000-01-06 |
-0.357961 |
-0.271892 |
-0.064843 |
-0.707311 |
| 2000-01-07 |
0.882352 |
-0.884027 |
0.508056 |
-0.339343 |
| 2000-01-08 |
0.560837 |
-0.099995 |
-0.131032 |
0.583470 |
| 2000-01-09 |
-0.344710 |
0.197746 |
-0.115445 |
-0.208901 |
| 2000-01-10 |
-0.469595 |
0.930101 |
0.163761 |
-0.107587 |
a = 1 / (1+0.5)
x1 = df.iloc[0, 0]
x2 = df.iloc[1, 0]
(x2 + (1-a) * x1) / (1 + 1 - a)
-0.8773265770527067
df.mean()
A -0.060613
B -0.110283
C -0.134508
D -0.143386
dtype: float64
Aggregations 花哨
df = pd.DataFrame(np.random.randn(10, 4),
index = pd.date_range('1/1/2000', periods=10),
columns = ['A', 'B', 'C', 'D'])
df
|
A |
B |
C |
D |
| 2000-01-01 |
0.371455 |
-0.191824 |
-0.146096 |
-1.347259 |
| 2000-01-02 |
-1.571376 |
0.061927 |
0.149302 |
-0.507093 |
| 2000-01-03 |
0.015607 |
1.637870 |
-0.642065 |
-0.228584 |
| 2000-01-04 |
-0.236157 |
0.366852 |
-0.117198 |
1.373123 |
| 2000-01-05 |
-0.390561 |
-0.670603 |
-2.022454 |
0.964826 |
| 2000-01-06 |
-0.309272 |
1.234031 |
-0.383297 |
0.234326 |
| 2000-01-07 |
-0.925264 |
0.417228 |
-0.432956 |
-1.331263 |
| 2000-01-08 |
0.223505 |
0.160549 |
-0.247965 |
0.262888 |
| 2000-01-09 |
-2.442173 |
0.757845 |
-0.704929 |
0.037361 |
| 2000-01-10 |
-0.936853 |
-0.479592 |
-0.274561 |
0.146732 |
r = df.rolling(window=3, min_periods=1)
r
Rolling [window=3,min_periods=1,center=False,axis=0]
r.aggregate(np.sum)
|
A |
B |
C |
D |
| 2000-01-01 |
0.371455 |
-0.191824 |
-0.146096 |
-1.347259 |
| 2000-01-02 |
-1.199921 |
-0.129897 |
0.003207 |
-1.854353 |
| 2000-01-03 |
-1.184314 |
1.507972 |
-0.638858 |
-2.082937 |
| 2000-01-04 |
-1.791925 |
2.066648 |
-0.609961 |
0.637446 |
| 2000-01-05 |
-0.611110 |
1.334119 |
-2.781717 |
2.109366 |
| 2000-01-06 |
-0.935989 |
0.930280 |
-2.522949 |
2.572276 |
| 2000-01-07 |
-1.625096 |
0.980656 |
-2.838707 |
-0.132111 |
| 2000-01-08 |
-1.011030 |
1.811808 |
-1.064218 |
-0.834049 |
| 2000-01-09 |
-3.143932 |
1.335622 |
-1.385850 |
-1.031014 |
| 2000-01-10 |
-3.155521 |
0.438802 |
-1.227455 |
0.446981 |
r['A'].aggregate(np.sum)
2000-01-01 0.371455
2000-01-02 -1.199921
2000-01-03 -1.184314
2000-01-04 -1.791925
2000-01-05 -0.611110
2000-01-06 -0.935989
2000-01-07 -1.625096
2000-01-08 -1.011030
2000-01-09 -3.143932
2000-01-10 -3.155521
Freq: D, Name: A, dtype: float64
r[['A', 'B']].aggregate(np.sum)
|
A |
B |
| 2000-01-01 |
0.371455 |
-0.191824 |
| 2000-01-02 |
-1.199921 |
-0.129897 |
| 2000-01-03 |
-1.184314 |
1.507972 |
| 2000-01-04 |
-1.791925 |
2.066648 |
| 2000-01-05 |
-0.611110 |
1.334119 |
| 2000-01-06 |
-0.935989 |
0.930280 |
| 2000-01-07 |
-1.625096 |
0.980656 |
| 2000-01-08 |
-1.011030 |
1.811808 |
| 2000-01-09 |
-3.143932 |
1.335622 |
| 2000-01-10 |
-3.155521 |
0.438802 |
r['A'].aggregate([np.sum, np.mean])
|
sum |
mean |
| 2000-01-01 |
0.371455 |
0.371455 |
| 2000-01-02 |
-1.199921 |
-0.599960 |
| 2000-01-03 |
-1.184314 |
-0.394771 |
| 2000-01-04 |
-1.791925 |
-0.597308 |
| 2000-01-05 |
-0.611110 |
-0.203703 |
| 2000-01-06 |
-0.935989 |
-0.311996 |
| 2000-01-07 |
-1.625096 |
-0.541699 |
| 2000-01-08 |
-1.011030 |
-0.337010 |
| 2000-01-09 |
-3.143932 |
-1.047977 |
| 2000-01-10 |
-3.155521 |
-1.051840 |
r.aggregate({'A':np.sum, 'B':np.mean})
|
A |
B |
| 2000-01-01 |
0.371455 |
-0.191824 |
| 2000-01-02 |
-1.199921 |
-0.064949 |
| 2000-01-03 |
-1.184314 |
0.502657 |
| 2000-01-04 |
-1.791925 |
0.688883 |
| 2000-01-05 |
-0.611110 |
0.444706 |
| 2000-01-06 |
-0.935989 |
0.310093 |
| 2000-01-07 |
-1.625096 |
0.326885 |
| 2000-01-08 |
-1.011030 |
0.603936 |
| 2000-01-09 |
-3.143932 |
0.445207 |
| 2000-01-10 |
-3.155521 |
0.146267 |
Missing Data
df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])
df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
df
|
one |
two |
three |
| a |
0.560200 |
0.244413 |
-1.814612 |
| b |
NaN |
NaN |
NaN |
| c |
0.210847 |
0.014889 |
-0.711094 |
| d |
NaN |
NaN |
NaN |
| e |
-0.340756 |
1.657751 |
0.419182 |
| f |
-0.699982 |
0.258028 |
1.324182 |
| g |
NaN |
NaN |
NaN |
| h |
-1.271993 |
1.477846 |
0.488302 |
NaN: Not a Number
isnull() notnull()
df.isnull()
|
one |
two |
three |
| a |
False |
False |
False |
| b |
True |
True |
True |
| c |
False |
False |
False |
| d |
True |
True |
True |
| e |
False |
False |
False |
| f |
False |
False |
False |
| g |
True |
True |
True |
| h |
False |
False |
False |
df.notnull()
|
one |
two |
three |
| a |
True |
True |
True |
| b |
False |
False |
False |
| c |
True |
True |
True |
| d |
False |
False |
False |
| e |
True |
True |
True |
| f |
True |
True |
True |
| g |
False |
False |
False |
| h |
True |
True |
True |
關於缺失值的計算
當數據求和的時候，缺失值視爲0，若是數據全爲NA，那麼結果也是NA, 額。。。好像仍是0，修改了？
df['one'].sum()
-1.541684617991043
df = pd.DataFrame(index=[0,1,2,3,4,5],columns=['one','two'])
df
|
one |
two |
| 0 |
NaN |
NaN |
| 1 |
NaN |
NaN |
| 2 |
NaN |
NaN |
| 3 |
NaN |
NaN |
| 4 |
NaN |
NaN |
| 5 |
NaN |
NaN |
df['one'].sum()
0
清理，替換缺失值
df = pd.DataFrame(np.random.randn(3, 3), index=['a', 'c', 'e'],columns=['one',
'two', 'three'])
df = df.reindex(['a', 'b', 'c'])
df
|
one |
two |
three |
| a |
0.326460 |
1.529225 |
1.230027 |
| b |
NaN |
NaN |
NaN |
| c |
1.296313 |
0.032379 |
2.182915 |
df.fillna(0)
|
one |
two |
three |
| a |
0.326460 |
1.529225 |
1.230027 |
| b |
0.000000 |
0.000000 |
0.000000 |
| c |
1.296313 |
0.032379 |
2.182915 |
df #看來上面是返回一個副本
|
one |
two |
three |
| a |
0.326460 |
1.529225 |
1.230027 |
| b |
NaN |
NaN |
NaN |
| c |
1.296313 |
0.032379 |
2.182915 |
df.fillna('haha')
|
one |
two |
three |
| a |
0.32646 |
1.52922 |
1.23003 |
| b |
haha |
haha |
haha |
| c |
1.29631 |
0.0323788 |
2.18292 |
前向後向替換
df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f',
'h'],columns=['one', 'two', 'three'])
df = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])
df
|
one |
two |
three |
| a |
2.218769 |
-0.742408 |
-1.068846 |
| b |
NaN |
NaN |
NaN |
| c |
0.467651 |
0.372357 |
1.387020 |
| d |
NaN |
NaN |
NaN |
| e |
-0.868840 |
-0.648827 |
-2.261319 |
| f |
-0.755799 |
0.159130 |
-0.129401 |
| g |
NaN |
NaN |
NaN |
| h |
0.703744 |
-1.665470 |
0.166229 |
df.fillna(method='pad') # 或者 method='ffill'
|
one |
two |
three |
| a |
2.218769 |
-0.742408 |
-1.068846 |
| b |
2.218769 |
-0.742408 |
-1.068846 |
| c |
0.467651 |
0.372357 |
1.387020 |
| d |
0.467651 |
0.372357 |
1.387020 |
| e |
-0.868840 |
-0.648827 |
-2.261319 |
| f |
-0.755799 |
0.159130 |
-0.129401 |
| g |
-0.755799 |
0.159130 |
-0.129401 |
| h |
0.703744 |
-1.665470 |
0.166229 |
能夠看到，會用前面的元素來替換
df.fillna(method="backfill") #或者 method='bfill'
|
one |
two |
three |
| a |
2.218769 |
-0.742408 |
-1.068846 |
| b |
0.467651 |
0.372357 |
1.387020 |
| c |
0.467651 |
0.372357 |
1.387020 |
| d |
-0.868840 |
-0.648827 |
-2.261319 |
| e |
-0.868840 |
-0.648827 |
-2.261319 |
| f |
-0.755799 |
0.159130 |
-0.129401 |
| g |
0.703744 |
-1.665470 |
0.166229 |
| h |
0.703744 |
-1.665470 |
0.166229 |
help(df.fillna)
Help on method fillna in module pandas.core.frame:
fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs) method of pandas.core.frame.DataFrame instance
Fill NA/NaN values using the specified method
Parameters
----------
value : scalar, dict, Series, or DataFrame
Value to use to fill holes (e.g. 0), alternately a
dict/Series/DataFrame of values specifying which value to use for
each index (for a Series) or column (for a DataFrame). (values not
in the dict/Series/DataFrame will not be filled). This value cannot
be a list.
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
Method to use for filling holes in reindexed Series
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use NEXT valid observation to fill gap
axis : {0 or 'index', 1 or 'columns'}
inplace : boolean, default False
If True, fill in place. Note: this will modify any
other views on this object, (e.g. a no-copy slice for a column in a
DataFrame).
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled. Must be greater than 0 if not None.
downcast : dict, default is None
a dict of item->dtype of what to downcast if possible,
or the string 'infer' which will try to downcast to an appropriate
equal type (e.g. float64 to int64 if possible)
See Also
--------
interpolate : Fill NaN values using interpolation.
reindex, asfreq
Returns
-------
filled : DataFrame
Examples
--------
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
... [3, 4, np.nan, 1],
... [np.nan, np.nan, np.nan, 5],
... [np.nan, 3, np.nan, 4]],
... columns=list('ABCD'))
>>> df
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 NaN 4
Replace all NaN elements with 0s.
>>> df.fillna(0)
A B C D
0 0.0 2.0 0.0 0
1 3.0 4.0 0.0 1
2 0.0 0.0 0.0 5
3 0.0 3.0 0.0 4
We can also propagate non-null values forward or backward.
>>> df.fillna(method='ffill')
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 3.0 4.0 NaN 5
3 3.0 3.0 NaN 4
Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
2, and 3 respectively.
>>> values = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
>>> df.fillna(value=values)
A B C D
0 0.0 2.0 2.0 0
1 3.0 4.0 2.0 1
2 0.0 1.0 2.0 5
3 0.0 3.0 2.0 4
Only replace the first NaN element.
>>> df.fillna(value=values, limit=1)
A B C D
0 0.0 2.0 2.0 0
1 3.0 4.0 NaN 1
2 NaN 1.0 NaN 5
3 NaN 3.0 NaN 4
丟棄缺失數據
咱們能夠利用dropna來捨棄缺失數據所在的軸(默認爲行)
df.dropna()
|
one |
two |
three |
| a |
2.218769 |
-0.742408 |
-1.068846 |
| c |
0.467651 |
0.372357 |
1.387020 |
| e |
-0.868840 |
-0.648827 |
-2.261319 |
| f |
-0.755799 |
0.159130 |
-0.129401 |
| h |
0.703744 |
-1.665470 |
0.166229 |
df = pd.DataFrame({'one':np.arange(10), 'two':np.arange(10), 'three':np.arange(10)})
df.iloc[1, 2] = np.nan
df
|
one |
two |
three |
| 0 |
0 |
0 |
0.0 |
| 1 |
1 |
1 |
NaN |
| 2 |
2 |
2 |
2.0 |
| 3 |
3 |
3 |
3.0 |
| 4 |
4 |
4 |
4.0 |
| 5 |
5 |
5 |
5.0 |
| 6 |
6 |
6 |
6.0 |
| 7 |
7 |
7 |
7.0 |
| 8 |
8 |
8 |
8.0 |
| 9 |
9 |
9 |
9.0 |
df.dropna(axis=1)
|
one |
two |
| 0 |
0 |
0 |
| 1 |
1 |
1 |
| 2 |
2 |
2 |
| 3 |
3 |
3 |
| 4 |
4 |
4 |
| 5 |
5 |
5 |
| 6 |
6 |
6 |
| 7 |
7 |
7 |
| 8 |
8 |
8 |
| 9 |
9 |
9 |
替換數據 .replace()
df = pd.DataFrame({'one':[10,20,30,40,50,2000], 'two':[1000,0,30,40,50,60]})
df
|
one |
two |
| 0 |
10 |
1000 |
| 1 |
20 |
0 |
| 2 |
30 |
30 |
| 3 |
40 |
40 |
| 4 |
50 |
50 |
| 5 |
2000 |
60 |
df.replace({1000:10, 2000:66})
|
one |
two |
| 0 |
10 |
10 |
| 1 |
20 |
0 |
| 2 |
30 |
30 |
| 3 |
40 |
40 |
| 4 |
50 |
50 |
| 5 |
66 |
60 |
help(df.replace)
Help on method replace in module pandas.core.frame:
replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad') method of pandas.core.frame.DataFrame instance
Replace values given in `to_replace` with `value`.
Values of the DataFrame are replaced with other values dynamically.
This differs from updating with ``.loc`` or ``.iloc``, which require
you to specify a location to update with some value.
Parameters
----------
to_replace : str, regex, list, dict, Series, int, float, or None
How to find the values that will be replaced.
* numeric, str or regex:
- numeric: numeric values equal to `to_replace` will be
replaced with `value`
- str: string exactly matching `to_replace` will be replaced
with `value`
- regex: regexs matching `to_replace` will be replaced with
`value`
* list of str, regex, or numeric:
- First, if `to_replace` and `value` are both lists, they
**must** be the same length.
- Second, if ``regex=True`` then all of the strings in **both**
lists will be interpreted as regexs otherwise they will match
directly. This doesn't matter much for `value` since there
are only a few possible substitution regexes you can use.
- str, regex and numeric rules apply as above.
* dict:
- Dicts can be used to specify different replacement values
for different existing values. For example,
``{'a': 'b', 'y': 'z'}`` replaces the value 'a' with 'b' and
'y' with 'z'. To use a dict in this way the `value`
parameter should be `None`.
- For a DataFrame a dict can specify that different values
should be replaced in different columns. For example,
``{'a': 1, 'b': 'z'}`` looks for the value 1 in column 'a'
and the value 'z' in column 'b' and replaces these values
with whatever is specified in `value`. The `value` parameter
should not be ``None`` in this case. You can treat this as a
special case of passing two lists except that you are
specifying the column to search in.
- For a DataFrame nested dictionaries, e.g.,
``{'a': {'b': np.nan}}``, are read as follows: look in column
'a' for the value 'b' and replace it with NaN. The `value`
parameter should be ``None`` to use a nested dict in this
way. You can nest regular expressions as well. Note that
column names (the top-level dictionary keys in a nested
dictionary) **cannot** be regular expressions.
* None:
- This means that the `regex` argument must be a string,
compiled regular expression, or list, dict, ndarray or
Series of such elements. If `value` is also ``None`` then
this **must** be a nested dictionary or Series.
See the examples section for examples of each of these.
value : scalar, dict, list, str, regex, default None
Value to replace any values matching `to_replace` with.
For a DataFrame a dict of values can be used to specify which
value to use for each column (columns not in the dict will not be
filled). Regular expressions, strings and lists or dicts of such
objects are also allowed.
inplace : boolean, default False
If True, in place. Note: this will modify any
other views on this object (e.g. a column from a DataFrame).
Returns the caller if this is True.
limit : int, default None
Maximum size gap to forward or backward fill.
regex : bool or same types as `to_replace`, default False
Whether to interpret `to_replace` and/or `value` as regular
expressions. If this is ``True`` then `to_replace` *must* be a
string. Alternatively, this could be a regular expression or a
list, dict, or array of regular expressions in which case
`to_replace` must be ``None``.
method : {'pad', 'ffill', 'bfill', `None`}
The method to use when for replacement, when `to_replace` is a
scalar, list or tuple and `value` is ``None``.
.. versionchanged:: 0.23.0
Added to DataFrame.
See Also
--------
DataFrame.fillna : Fill NA values
DataFrame.where : Replace values based on boolean condition
Series.str.replace : Simple string replacement.
Returns
-------
DataFrame
Object after replacement.
Raises
------
AssertionError
* If `regex` is not a ``bool`` and `to_replace` is not
``None``.
TypeError
* If `to_replace` is a ``dict`` and `value` is not a ``list``,
``dict``, ``ndarray``, or ``Series``
* If `to_replace` is ``None`` and `regex` is not compilable
into a regular expression or is a list, dict, ndarray, or
Series.
* When replacing multiple ``bool`` or ``datetime64`` objects and
the arguments to `to_replace` does not match the type of the
value being replaced
ValueError
* If a ``list`` or an ``ndarray`` is passed to `to_replace` and
`value` but they are not the same length.
Notes
-----
* Regex substitution is performed under the hood with ``re.sub``. The
rules for substitution for ``re.sub`` are the same.
* Regular expressions will only substitute on strings, meaning you
cannot provide, for example, a regular expression matching floating
point numbers and expect the columns in your frame that have a
numeric dtype to be matched. However, if those floating point
numbers *are* strings, then you can do this.
* This method has *a lot* of options. You are encouraged to experiment
and play with this method to gain intuition about how it works.
* When dict is used as the `to_replace` value, it is like
key(s) in the dict are the to_replace part and
value(s) in the dict are the value parameter.
Examples
--------
**Scalar `to_replace` and `value`**
>>> s = pd.Series([0, 1, 2, 3, 4])
>>> s.replace(0, 5)
0 5
1 1
2 2
3 3
4 4
dtype: int64
>>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4],
... 'B': [5, 6, 7, 8, 9],
... 'C': ['a', 'b', 'c', 'd', 'e']})
>>> df.replace(0, 5)
A B C
0 5 5 a
1 1 6 b
2 2 7 c
3 3 8 d
4 4 9 e
**List-like `to_replace`**
>>> df.replace([0, 1, 2, 3], 4)
A B C
0 4 5 a
1 4 6 b
2 4 7 c
3 4 8 d
4 4 9 e
>>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
A B C
0 4 5 a
1 3 6 b
2 2 7 c
3 1 8 d
4 4 9 e
>>> s.replace([1, 2], method='bfill')
0 0
1 3
2 3
3 3
4 4
dtype: int64
**dict-like `to_replace`**
>>> df.replace({0: 10, 1: 100})
A B C
0 10 5 a
1 100 6 b
2 2 7 c
3 3 8 d
4 4 9 e
>>> df.replace({'A': 0, 'B': 5}, 100)
A B C
0 100 100 a
1 1 6 b
2 2 7 c
3 3 8 d
4 4 9 e
>>> df.replace({'A': {0: 100, 4: 400}})
A B C
0 100 5 a
1 1 6 b
2 2 7 c
3 3 8 d
4 400 9 e
**Regular expression `to_replace`**
>>> df = pd.DataFrame({'A': ['bat', 'foo', 'bait'],
... 'B': ['abc', 'bar', 'xyz']})
>>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
A B
0 new abc
1 foo new
2 bait xyz
>>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True)
A B
0 new abc
1 foo bar
2 bait xyz
>>> df.replace(regex=r'^ba.$', value='new')
A B
0 new abc
1 foo new
2 bait xyz
>>> df.replace(regex={r'^ba.$':'new', 'foo':'xyz'})
A B
0 new abc
1 xyz new
2 bait xyz
>>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
A B
0 new abc
1 new new
2 bait xyz
Note that when replacing multiple ``bool`` or ``datetime64`` objects,
the data types in the `to_replace` parameter must match the data
type of the value being replaced:
>>> df = pd.DataFrame({'A': [True, False, True],
... 'B': [False, True, False]})
>>> df.replace({'a string': 'new value', True: False}) # raises
Traceback (most recent call last):
...
TypeError: Cannot compare types 'ndarray(dtype=bool)' and 'str'
This raises a ``TypeError`` because one of the ``dict`` keys is not of
the correct type for replacement.
Compare the behavior of ``s.replace({'a': None})`` and
``s.replace('a', None)`` to understand the pecularities
of the `to_replace` parameter:
>>> s = pd.Series([10, 'a', 'a', 'b', 'a'])
When one uses a dict as the `to_replace` value, it is like the
value(s) in the dict are equal to the `value` parameter.
``s.replace({'a': None})`` is equivalent to
``s.replace(to_replace={'a': None}, value=None, method=None)``:
>>> s.replace({'a': None})
0 10
1 None
2 None
3 b
4 None
dtype: object
When ``value=None`` and `to_replace` is a scalar, list or
tuple, `replace` uses the method parameter (default 'pad') to do the
replacement. So this is why the 'a' values are being replaced by 10
in rows 1 and 2 and 'b' in row 4 in this case.
The command ``s.replace('a', None)`` is actually equivalent to
``s.replace(to_replace='a', value=None, method='pad')``:
>>> s.replace('a', None)
0 10
1 10
2 10
3 b
4 b
dtype: object
df = pd.DataFrame({'one':np.arange(10), 'two':np.arange(2, 12)})
df
|
one |
two |
| 0 |
0 |
2 |
| 1 |
1 |
3 |
| 2 |
2 |
4 |
| 3 |
3 |
5 |
| 4 |
4 |
6 |
| 5 |
5 |
7 |
| 6 |
6 |
8 |
| 7 |
7 |
9 |
| 8 |
8 |
10 |
| 9 |
9 |
11 |
df.replace(r'4', 'haha', regex=True)
|
one |
two |
| 0 |
0 |
2 |
| 1 |
1 |
3 |
| 2 |
2 |
4 |
| 3 |
3 |
5 |
| 4 |
4 |
6 |
| 5 |
5 |
7 |
| 6 |
6 |
8 |
| 7 |
7 |
9 |
| 8 |
8 |
10 |
| 9 |
9 |
11 |
s = pd.Series(['1', '2', '3'])
s
0 1
1 2
2 3
dtype: object
s.replace(r'3', 4, regex=True)
0 1
1 2
2 4
dtype: object
df = pd.DataFrame({'A': ['bat', 'foo', 'bait'],
'B': ['abc', 'bar', 'xyz']})
df
|
A |
B |
| 0 |
bat |
abc |
| 1 |
foo |
bar |
| 2 |
bait |
xyz |
df.replace(r'ba', 'new', regex=True)
|
A |
B |
| 0 |
newt |
abc |
| 1 |
foo |
newr |
| 2 |
newit |
xyz |
看來只有str才能用regex
