Pandas实用小功能

程序员文章站 2022-05-18 19:15:39

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Pandas实用小功能

import pandas as pd
import numpy as np

1、读取时抽样百分比

data = pd.read_csv(r'..\test.csv',encoding='utf-8',
                  skiprows = lambda x: x>0 and np.random.rand() > 0.5)
data.head()

	PassengerId	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	894	2	Myles, Mr. Thomas Francis	male	62.0	0	0	240276	9.6875	NaN	Q
1	896	3	Hirvonen, Mrs. Alexander (Helga E Lindqvist)	female	22.0	1	1	3101298	12.2875	NaN	S
2	897	3	Svensson, Mr. Johan Cervin	male	14.0	0	0	7538	9.2250	NaN	S
3	898	3	Connolly, Miss. Kate	female	30.0	0	0	330972	7.6292	NaN	Q
4	900	3	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	female	18.0	0	0	2657	7.2292	NaN	C

2、Pandas 空值检查

哪列有空值

data.isnull().any()

PassengerId    False
Pclass         False
Name           False
Sex            False
Age             True
SibSp          False
Parch          False
Ticket         False
Fare            True
Cabin           True
Embarked       False
dtype: bool

有多少空值

data.isnull().sum()

PassengerId      0
Pclass           0
Name             0
Sex              0
Age             39
SibSp            0
Parch            0
Ticket           0
Fare             1
Cabin          146
Embarked         0
dtype: int64

3、replace 做清洗

data['Name'] = data['Name'].replace('[()]','',regex=True).astype('str')

data.head()

	PassengerId	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	894	2	Myles, Mr. Thomas Francis	male	62.0	0	0	240276	9.6875	NaN	Q
1	896	3	Hirvonen, Mrs. Alexander Helga E Lindqvist	female	22.0	1	1	3101298	12.2875	NaN	S
2	897	3	Svensson, Mr. Johan Cervin	male	14.0	0	0	7538	9.2250	NaN	S
3	898	3	Connolly, Miss. Kate	female	30.0	0	0	330972	7.6292	NaN	Q
4	900	3	Abrahim, Mrs. Joseph Sophie Halaut Easu	female	18.0	0	0	2657	7.2292	NaN	C

4、使用 apply(type) 做类型检查

data['Age'].apply(type)

0      <class 'float'>
1      <class 'float'>
2      <class 'float'>
3      <class 'float'>
4      <class 'float'>
            ...       
189    <class 'float'>
190    <class 'float'>
191    <class 'float'>
192    <class 'float'>
193    <class 'float'>
Name: Age, Length: 194, dtype: object

5、重新排序列

方法1

data1 = data[['Name','Age','Sex']]
data1.head()

	Name	Age	Sex
0	Myles, Mr. Thomas Francis	62.0	male
1	Hirvonen, Mrs. Alexander Helga E Lindqvist	22.0	female
2	Svensson, Mr. Johan Cervin	14.0	male
3	Connolly, Miss. Kate	30.0	female
4	Abrahim, Mrs. Joseph Sophie Halaut Easu	18.0	female

方法2

cols = data.columns[[2,4,3]]
data2 = data[cols]
data2.head()

	Name	Age	Sex
0	Myles, Mr. Thomas Francis	62.0	male
1	Hirvonen, Mrs. Alexander Helga E Lindqvist	22.0	female
2	Svensson, Mr. Johan Cervin	14.0	male
3	Connolly, Miss. Kate	30.0	female
4	Abrahim, Mrs. Joseph Sophie Halaut Easu	18.0	female

6、标签和位置选择数据

iloc

# 选择前两行
data.iloc[:2]

	PassengerId	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	894	2	Myles, Mr. Thomas Francis	male	62.0	0	0	240276	9.6875	NaN	Q
1	896	3	Hirvonen, Mrs. Alexander Helga E Lindqvist	female	22.0	1	1	3101298	12.2875	NaN	S

# 选择两列
data.iloc[:,1:3]

	Pclass	Name
0	2	Myles, Mr. Thomas Francis
1	3	Hirvonen, Mrs. Alexander Helga E Lindqvist
2	3	Svensson, Mr. Johan Cervin
3	3	Connolly, Miss. Kate
4	3	Abrahim, Mrs. Joseph Sophie Halaut Easu
...	...	...
189	3	Riordan, Miss. Johanna Hannah""
190	3	Peacock, Miss. Treasteall
191	3	Naughton, Miss. Hannah
192	1	Minahan, Mrs. William Edward Lillian E Thorpe
193	3	Henriksson, Miss. Jenny Lovisa

194 rows × 2 columns

# 特定区域
data.iloc[2:5,2:5]

	Name	Sex	Age
2	Svensson, Mr. Johan Cervin	male	14.0
3	Connolly, Miss. Kate	female	30.0
4	Abrahim, Mrs. Joseph Sophie Halaut Easu	female	18.0

7、找到出现频率最高的值TOPn–nlargest(n)

data['Title'] = data['Name'].str.split(',',expand=True)[1].str.split('.',expand=True)[0]

data['Title'].value_counts().nlargest(3)

 Mr      107
 Miss     40
 Mrs      34
Name: Title, dtype: int64

8、结合使用 where 和 isin

称谓名称top3

top3 = data['Title'].value_counts().nlargest(3).index
top3

Index([' Mr', ' Miss', ' Mrs'], dtype='object')

其他的改为【others】

data1 = data.where(data['Title'].isin(top3),other='others')

data1['Title'].value_counts()

 Mr       107
 Miss      40
 Mrs       34
others     13
Name: Title, dtype: int64

9、小分类值的替换

统计频次，并归一化

dt = data['Title'].value_counts(normalize=True)
dt

 Mr        0.551546
 Miss      0.206186
 Mrs       0.175258
 Master    0.051546
 Ms        0.005155
 Rev       0.005155
 Dr        0.005155
Name: Title, dtype: float64

flag = 0.05
dt_others = dt[dt<flag].index
dt_others

Index([' Ms', ' Rev', ' Dr'], dtype='object')

替换频次低的类别

data['Title'] = data['Title'].replace(dt_others,'others')

data['Title'].value_counts()

 Mr        107
 Miss       40
 Mrs        34
 Master     10
others       3
Name: Title, dtype: int64

10、生成时间序列的数据集

pd.util.testing.makeTimeDataFrame(10)

	A	B	C	D
2000-01-03	-1.640177	2.286963	0.663883	0.730500
2000-01-04	-0.962963	0.468783	0.420607	-2.049439
2000-01-05	1.176148	0.336326	0.275701	-1.546738
2000-01-06	-0.346942	0.988084	0.638690	2.542611
2000-01-07	1.464977	-0.425312	0.452835	0.555940
2000-01-10	1.468564	0.102388	-1.694136	0.758442
2000-01-11	-0.741469	-0.037244	0.422585	-0.300642
2000-01-12	-0.749106	0.286984	-0.023601	0.093908
2000-01-13	-2.053304	0.408734	-0.602061	2.245386
2000-01-14	0.287270	1.010302	1.319785	0.667926

11、时间数据下采样

df = pd.DataFrame(np.random.randint(1,10,size=(100000,3)),columns = ['商品编码','商品销量','商品库存'])
df.index = pd.util.testing.makeDateIndex(100000,freq='H')
df.head()

	商品编码	商品销量	商品库存
2000-01-01 00:00:00	7	9	1
2000-01-01 01:00:00	9	6	7
2000-01-01 02:00:00	6	8	1
2000-01-01 03:00:00	7	6	1
2000-01-01 04:00:00	3	2	5

使用 resample 方法，合并为天（D）：

day_df = df.resample("D")["商品销量"].sum().to_frame()
day_df.head()

	商品销量
2000-01-01	135
2000-01-02	117
2000-01-03	124
2000-01-04	106
2000-01-05	121

m_df = df.resample("M")['商品销量'].sum().to_frame()
m_df.head()

	商品销量
2000-01-31	3767
2000-02-29	3473
2000-03-31	3818
2000-04-30	3538
2000-05-31	3674

y_df = df.resample("Y")['商品销量'].sum().to_frame()
y_df.head()

	商品销量
2000-12-31	43720
2001-12-31	43705
2002-12-31	43642
2003-12-31	44268
2004-12-31	43867

12、转 datetime

df['datetime'] = pd.to_datetime(df.index,format='%Y/%m/%d')
df.head()

	商品编码	商品销量	商品库存	datetime
2000-01-01 00:00:00	7	9	1	2000-01-01 00:00:00
2000-01-01 01:00:00	9	6	7	2000-01-01 01:00:00
2000-01-01 02:00:00	6	8	1	2000-01-01 02:00:00
2000-01-01 03:00:00	7	6	1	2000-01-01 03:00:00
2000-01-01 04:00:00	3	2	5	2000-01-01 04:00:00

df.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 100000 entries, 2000-01-01 00:00:00 to 2011-05-29 15:00:00
Freq: H
Data columns (total 4 columns):
 #   Column    Non-Null Count   Dtype         
---  ------    --------------   -----         
 0   商品编码      100000 non-null  int32         
 1   商品销量      100000 non-null  int32         
 2   商品库存      100000 non-null  int32         
 3   datetime  100000 non-null  datetime64[ns]
dtypes: datetime64[ns](1), int32(3)
memory usage: 2.7 MB

13、map 做特征工程

d = {
"sex":["male", "female", "male","female"], 
"color":["red", "green", "blue","green"], 
"age":[25, 30, 15, 32]
}

df = pd.DataFrame(d)
df

	sex	color	age
0	male	red	25
1	female	green	30
2	male	blue	15
3	female	green	32

d = {"red": 0, "green": 1,'blue':2}
d

{'red': 0, 'green': 1, 'blue': 2}

df['color2'] = df['color'].map(d) 
df

	sex	color	age	color2
0	male	red	25	0
1	female	green	30	1
2	male	blue	15	2
3	female	green	32	1

相关标签：数据分析 python 数据挖掘 python 机器学习

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Pandas实用小功能

Pandas实用小功能

1、读取时抽样百分比

2、Pandas 空值检查

3、replace 做清洗

4、使用 apply(type) 做类型检查

5、重新排序列

6、标签和位置选择数据

7、找到出现频率最高的值TOPn–nlargest(n)

8、结合使用 where 和 isin

9、小分类值的替换

10、生成时间序列的数据集

11、时间数据下采样

12、转 datetime

13、map 做特征工程

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