pandas 基础操作 更新
- 创建一个Series,同时让pandas自动生成索引列
- 创建一个DataFrame数据框
- 查看数据
- 数据的简单统计
- 数据的排序
- 选择数据(类似于数据库中sql语句)
- 数据合并
- 分组操作Groupby操作
- reshape操作
- pivot_table 透视表
- 至此,pandas的基础的使用介绍也就结束了,后续会有专题性质的分析,包括(字符串处理,apply的使用,数据合并,透视表,时间序列的分析)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
创建一个Series,同时让pandas自动生成索引列
s = pd.Series([1,3,5,np.nan,6,8])
# 查看s
s
0 1.0
1 3.0
2 5.0
3 NaN
4 6.0
5 8.0
dtype: float64
创建一个DataFrame数据框
### 创建一个DataFrame ,可以传入一个numpy array 可以自己构建索引以及列标
dates = pd.date_range('2018-11-01',periods=7)
#### 比如说生成一个时间序列,以20181101 为起始位置的,7个日期组成的时间序列,数据的类型为datetime64[ns]
dates
DatetimeIndex(['2018-11-01', '2018-11-02', '2018-11-03', '2018-11-04',
'2018-11-05', '2018-11-06', '2018-11-07'],
dtype='datetime64[ns]', freq='D')
df = pd.DataFrame(np.random.randn(7,4),index= dates,columns=list('ABCD'))
df
# 产生随机正态分布的数据,7行4列,分别对应的index的长度以及column的长度
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A | B | C | D | |
---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 0.660073 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | -1.913573 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 0.712624 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 |
### 同时用可以使用dict的实行创建DataFrame
df2 = pd.DataFrame({"A":1,
"B":"20181101",
'C':np.array([3]*4,dtype='int32'),
'D':pd.Categorical(['test','train','test','train']),
"E":1.5},
)
df2
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A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 20181101 | 3 | test | 1.5 |
1 | 1 | 20181101 | 3 | train | 1.5 |
2 | 1 | 20181101 | 3 | test | 1.5 |
3 | 1 | 20181101 | 3 | train | 1.5 |
df2.dtypes
### 查看数据框中的数据类型,常见的数据类型还有时间类型以及float类型
A int64
B object
C int32
D category
E float64
dtype: object
查看数据
# 比如说看前5行
df.head()
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A | B | C | D | |
---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 0.660073 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | -1.913573 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 0.712624 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 |
# 后4行
df.tail(4)
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A | B | C | D | |
---|---|---|---|---|
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 |
# 查看DataFrame的索引
df.index
DatetimeIndex(['2018-11-01', '2018-11-02', '2018-11-03', '2018-11-04',
'2018-11-05', '2018-11-06', '2018-11-07'],
dtype='datetime64[ns]', freq='D')
# 查看DataFrame的列索引
df.columns
Index(['A', 'B', 'C', 'D'], dtype='object')
# 查看DataFrame的数据,将DataFrame转化为numpy array 的数据形式
df.values
array([[-0.1703643 , -0.23754121, 0.52990284, 0.66007285],
[-0.15844565, -0.48853537, 0.08296043, -1.91357255],
[-0.51842554, 0.73086567, -1.03382969, 0.71262388],
[ 1.01352712, 0.27016714, 0.08180539, 0.17819344],
[-0.89749689, -0.01627937, -0.23499323, 0.08120819],
[-0.03058032, 0.54556063, 1.09112723, -0.13157934],
[-0.31334198, -0.68817881, -0.41775393, 0.85502652]])
数据的简单统计
# 可以使用describe函数对DataFrame中的数值型数据进行统计
df.describe()
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A | B | C | D | |
---|---|---|---|---|
count | 7.000000 | 7.000000 | 7.000000 | 7.000000 |
mean | -0.153590 | 0.016580 | 0.014174 | 0.063139 |
std | 0.590144 | 0.527860 | 0.680939 | 0.945526 |
min | -0.897497 | -0.688179 | -1.033830 | -1.913573 |
25% | -0.415884 | -0.363038 | -0.326374 | -0.025186 |
50% | -0.170364 | -0.016279 | 0.081805 | 0.178193 |
75% | -0.094513 | 0.407864 | 0.306432 | 0.686348 |
max | 1.013527 | 0.730866 | 1.091127 | 0.855027 |
df2.describe()
### 对于其他的数据类型的数据describe函数会自动过滤掉
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A | C | E | |
---|---|---|---|
count | 4.0 | 4.0 | 4.0 |
mean | 1.0 | 3.0 | 1.5 |
std | 0.0 | 0.0 | 0.0 |
min | 1.0 | 3.0 | 1.5 |
25% | 1.0 | 3.0 | 1.5 |
50% | 1.0 | 3.0 | 1.5 |
75% | 1.0 | 3.0 | 1.5 |
max | 1.0 | 3.0 | 1.5 |
### DataFrame 的转置,将列索引与行索引进行调换,行数据与列数进行调换
df.T
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2018-11-01 00:00:00 | 2018-11-02 00:00:00 | 2018-11-03 00:00:00 | 2018-11-04 00:00:00 | 2018-11-05 00:00:00 | 2018-11-06 00:00:00 | 2018-11-07 00:00:00 | |
---|---|---|---|---|---|---|---|
A | -0.170364 | -0.158446 | -0.518426 | 1.013527 | -0.897497 | -0.030580 | -0.313342 |
B | -0.237541 | -0.488535 | 0.730866 | 0.270167 | -0.016279 | 0.545561 | -0.688179 |
C | 0.529903 | 0.082960 | -1.033830 | 0.081805 | -0.234993 | 1.091127 | -0.417754 |
D | 0.660073 | -1.913573 | 0.712624 | 0.178193 | 0.081208 | -0.131579 | 0.855027 |
df
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A | B | C | D | |
---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 0.660073 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | -1.913573 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 0.712624 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 |
数据的排序
df.sort_index(ascending=False)
### 降序,按照列进行降序,通过该索引列
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A | B | C | D | |
---|---|---|---|---|
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 0.712624 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | -1.913573 |
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 0.660073 |
print(df.sort_values(by=['B','A']))
# 默认是升序,可以选择多指排序,先照B,后排A,如果B中的数据一样,则按照A中的大小进行排序
df.sort_values(by='B')
A B C D
2018-11-07 -0.313342 -0.688179 -0.417754 0.855027
2018-11-02 -0.158446 -0.488535 0.082960 -1.913573
2018-11-01 -0.170364 -0.237541 0.529903 0.660073
2018-11-05 -0.897497 -0.016279 -0.234993 0.081208
2018-11-04 1.013527 0.270167 0.081805 0.178193
2018-11-06 -0.030580 0.545561 1.091127 -0.131579
2018-11-03 -0.518426 0.730866 -1.033830 0.712624
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A | B | C | D | |
---|---|---|---|---|
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | -1.913573 |
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 0.660073 |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 0.712624 |
选择数据(类似于数据库中sql语句)
df['A']
# 取出单独的一列数据,等价于df.A
2018-11-01 -0.170364
2018-11-02 -0.158446
2018-11-03 -0.518426
2018-11-04 1.013527
2018-11-05 -0.897497
2018-11-06 -0.030580
2018-11-07 -0.313342
Freq: D, Name: A, dtype: float64
# 通过[]进行行选择切片
df[0:3]
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A | B | C | D | |
---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 0.660073 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | -1.913573 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 0.712624 |
# 同时对于时间索引而言,可以直接使用比如
df['2018-11-01':'2018-11-04']
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A | B | C | D | |
---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 0.660073 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | -1.913573 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 0.712624 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 |
另外可以使用标签来选择
df.loc['2018-11-01']
A -0.170364
B -0.237541
C 0.529903
D 0.660073
Name: 2018-11-01 00:00:00, dtype: float64
#### 通过标签来进行多个轴上的进行选择
df.loc[:,["A","B"]] # 等价于df[["A","B"]]
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A | B | |
---|---|---|
2018-11-01 | -0.170364 | -0.237541 |
2018-11-02 | -0.158446 | -0.488535 |
2018-11-03 | -0.518426 | 0.730866 |
2018-11-04 | 1.013527 | 0.270167 |
2018-11-05 | -0.897497 | -0.016279 |
2018-11-06 | -0.030580 | 0.545561 |
2018-11-07 | -0.313342 | -0.688179 |
df.loc["2018-11-01":"2018-11-03",["A","B"]]
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A | B | |
---|---|---|
2018-11-01 | -0.170364 | -0.237541 |
2018-11-02 | -0.158446 | -0.488535 |
2018-11-03 | -0.518426 | 0.730866 |
#### 获得一个标量数据
df.loc['2018-11-01','A']
-0.17036430076617162
通过位置获取数据
df.iloc[3] # 获得第四行的数据
A 1.013527
B 0.270167
C 0.081805
D 0.178193
Name: 2018-11-04 00:00:00, dtype: float64
df.iloc[1:3,1:4] # 与numpy中的ndarray类似
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B | C | D | |
---|---|---|---|
2018-11-02 | -0.488535 | 0.08296 | -1.913573 |
2018-11-03 | 0.730866 | -1.03383 | 0.712624 |
# 可以选取不连续的行或者列进行取值
df.iloc[[1,3],[1,3]]
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B | D | |
---|---|---|
2018-11-02 | -0.488535 | -1.913573 |
2018-11-04 | 0.270167 | 0.178193 |
# 对行进行切片处理
df.iloc[1:3,:]
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A | B | C | D | |
---|---|---|---|---|
2018-11-02 | -0.158446 | -0.488535 | 0.08296 | -1.913573 |
2018-11-03 | -0.518426 | 0.730866 | -1.03383 | 0.712624 |
# 对列进行切片
df.iloc[:,1:4]
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B | C | D | |
---|---|---|---|
2018-11-01 | -0.237541 | 0.529903 | 0.660073 |
2018-11-02 | -0.488535 | 0.082960 | -1.913573 |
2018-11-03 | 0.730866 | -1.033830 | 0.712624 |
2018-11-04 | 0.270167 | 0.081805 | 0.178193 |
2018-11-05 | -0.016279 | -0.234993 | 0.081208 |
2018-11-06 | 0.545561 | 1.091127 | -0.131579 |
2018-11-07 | -0.688179 | -0.417754 | 0.855027 |
# 获取特定的值
df.iloc[1,3]
-1.9135725473596013
布尔值索引
# 使用单列的数据作为条件进行筛选
df[df.A>0]
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A | B | C | D | |
---|---|---|---|---|
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 |
#很少用到,很少使用这种大范围的条件进行筛选
df[df>0]
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A | B | C | D | |
---|---|---|---|---|
2018-11-01 | NaN | NaN | 0.529903 | 0.660073 |
2018-11-02 | NaN | NaN | 0.082960 | NaN |
2018-11-03 | NaN | 0.730866 | NaN | 0.712624 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 |
2018-11-05 | NaN | NaN | NaN | 0.081208 |
2018-11-06 | NaN | 0.545561 | 1.091127 | NaN |
2018-11-07 | NaN | NaN | NaN | 0.855027 |
# 使用isin()方法过滤
df2.head()
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A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 20181101 | 3 | test | 1.5 |
1 | 1 | 20181101 | 3 | train | 1.5 |
2 | 1 | 20181101 | 3 | test | 1.5 |
3 | 1 | 20181101 | 3 | train | 1.5 |
df2[df2['D'].isin(['test'])]
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A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 20181101 | 3 | test | 1.5 |
2 | 1 | 20181101 | 3 | test | 1.5 |
设定数值(类似于sql update 或者add)
- 设定一个新的列
df['E'] = [1,2,3,4,5,6,7]
df
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 0.660073 | 1 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | -1.913573 | 2 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 0.712624 | 3 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 | 4 |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 | 5 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 | 6 |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 | 7 |
- 通过标签设定新的值
df.loc['2018-11-01','E']= 10 # 第一行,E列的数据修改为10
df
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 0.660073 | 10 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | -1.913573 | 2 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 0.712624 | 3 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 | 4 |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 | 5 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 | 6 |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 | 7 |
df.iloc[1,4]=5000 # 第二行第五列数据修改为5000
df
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 0.660073 | 10 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | -1.913573 | 5000 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 0.712624 | 3 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 | 4 |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 | 5 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 | 6 |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 | 7 |
df3 =df.copy()
df3[df3<0]= -df3
df3 # 都变成非负数
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | 0.170364 | 0.237541 | 0.529903 | 0.660073 | 10 |
2018-11-02 | 0.158446 | 0.488535 | 0.082960 | 1.913573 | 5000 |
2018-11-03 | 0.518426 | 0.730866 | 1.033830 | 0.712624 | 3 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 | 4 |
2018-11-05 | 0.897497 | 0.016279 | 0.234993 | 0.081208 | 5 |
2018-11-06 | 0.030580 | 0.545561 | 1.091127 | 0.131579 | 6 |
2018-11-07 | 0.313342 | 0.688179 | 0.417754 | 0.855027 | 7 |
缺失值处理
df
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 0.660073 | 10 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | -1.913573 | 5000 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 0.712624 | 3 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 | 4 |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 | 5 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 | 6 |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 | 7 |
df['E']=[1,np.nan,2,np.nan,4,np.nan,6]
df.loc['2018-11-01':'2018-11-03','D']=np.nan
df
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | NaN | 1.0 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | NaN | NaN |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | NaN | 2.0 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 | NaN |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 | 4.0 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 | NaN |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 | 6.0 |
- 去掉缺失值的行
df4 = df.copy()
df4.dropna(how='any')
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 | 4.0 |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 | 6.0 |
df4.dropna(how='all')
# """DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)"""
# aixs 轴0或者1 index或者columns
# how 方式
# thresh 超过阈值个数的缺失值
# subset 那些字段的处理
# inplace 是否直接在原数据框中的替换
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | NaN | 1.0 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | NaN | NaN |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | NaN | 2.0 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 | NaN |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 | 4.0 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 | NaN |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 | 6.0 |
- 对缺失值就行填充
df4.fillna(1000)
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | 1000.000000 | 1.0 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | 1000.000000 | 1000.0 |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | 1000.000000 | 2.0 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 | 1000.0 |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 | 4.0 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 | 1000.0 |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 | 6.0 |
- 对数据进行布尔值进行填充
pd.isnull(df4)
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | False | False | False | True | False |
2018-11-02 | False | False | False | True | True |
2018-11-03 | False | False | False | True | False |
2018-11-04 | False | False | False | False | True |
2018-11-05 | False | False | False | False | False |
2018-11-06 | False | False | False | False | True |
2018-11-07 | False | False | False | False | False |
数据操作
#统计的工作一般情况下都不包含缺失值,
df4.mean()
# 默认是对列进行求平均,沿着行方向也就是axis=0
A -0.153590
B 0.016580
C 0.014174
D 0.245712
E 3.250000
dtype: float64
df4.mean(axis=1)
# 沿着列方向求每行的平均
2018-11-01 0.280499
2018-11-02 -0.188007
2018-11-03 0.294653
2018-11-04 0.385923
2018-11-05 0.586488
2018-11-06 0.368632
2018-11-07 1.087150
Freq: D, dtype: float64
# 对于拥有不同维度,需要对齐的对象进行操作。Pandas会自动的沿着指定的维度进行广播:
s = pd.Series([1,3,4,np.nan,6,7,8],index=dates)
s
2018-11-01 1.0
2018-11-02 3.0
2018-11-03 4.0
2018-11-04 NaN
2018-11-05 6.0
2018-11-06 7.0
2018-11-07 8.0
Freq: D, dtype: float64
df4.sub(s,axis='index')
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | -1.170364 | -1.237541 | -0.470097 | NaN | 0.0 |
2018-11-02 | -3.158446 | -3.488535 | -2.917040 | NaN | NaN |
2018-11-03 | -4.518426 | -3.269134 | -5.033830 | NaN | -2.0 |
2018-11-04 | NaN | NaN | NaN | NaN | NaN |
2018-11-05 | -6.897497 | -6.016279 | -6.234993 | -5.918792 | -2.0 |
2018-11-06 | -7.030580 | -6.454439 | -5.908873 | -7.131579 | NaN |
2018-11-07 | -8.313342 | -8.688179 | -8.417754 | -7.144973 | -2.0 |
df4
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | NaN | 1.0 |
2018-11-02 | -0.158446 | -0.488535 | 0.082960 | NaN | NaN |
2018-11-03 | -0.518426 | 0.730866 | -1.033830 | NaN | 2.0 |
2018-11-04 | 1.013527 | 0.270167 | 0.081805 | 0.178193 | NaN |
2018-11-05 | -0.897497 | -0.016279 | -0.234993 | 0.081208 | 4.0 |
2018-11-06 | -0.030580 | 0.545561 | 1.091127 | -0.131579 | NaN |
2018-11-07 | -0.313342 | -0.688179 | -0.417754 | 0.855027 | 6.0 |
df4.apply(np.cumsum)
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A | B | C | D | E | |
---|---|---|---|---|---|
2018-11-01 | -0.170364 | -0.237541 | 0.529903 | NaN | 1.0 |
2018-11-02 | -0.328810 | -0.726077 | 0.612863 | NaN | NaN |
2018-11-03 | -0.847235 | 0.004789 | -0.420966 | NaN | 3.0 |
2018-11-04 | 0.166292 | 0.274956 | -0.339161 | 0.178193 | NaN |
2018-11-05 | -0.731205 | 0.258677 | -0.574154 | 0.259402 | 7.0 |
2018-11-06 | -0.761786 | 0.804237 | 0.516973 | 0.127822 | NaN |
2018-11-07 | -1.075128 | 0.116059 | 0.099219 | 0.982849 | 13.0 |
df4.apply(lambda x: x.max()-x.min())
A 1.911024
B 1.419044
C 2.124957
D 0.986606
E 5.000000
dtype: float64
统计个数与离散化
s = pd.Series(np.random.randint(0,7,size=15))
s
0 5
1 4
2 1
3 2
4 1
5 0
6 2
7 6
8 4
9 3
10 1
11 1
12 1
13 3
14 2
dtype: int32
s.value_counts()
# 统计元素的个数,并按照元素统计量进行排序,未出现的元素不会显示出来
1 5
2 3
4 2
3 2
6 1
5 1
0 1
dtype: int64
s.reindex(range(0,7))
# 按照固定的顺序输出元素的个数统计
0 5
1 4
2 1
3 2
4 1
5 0
6 2
dtype: int32
s.mode()
# 众数
0 1
dtype: int32
- 离散化
# 连续值转化为离散值,可以使用cut函数进行操作(bins based on vlaues) qcut (bins based on sample
# quantiles) 函数
arr = np.random.randint(0,20,size=15) # 正态分布
arr
array([ 5, 18, 13, 16, 16, 1, 15, 11, 0, 17, 16, 18, 15, 12, 13])
factor = pd.cut(arr,3)
factor
[(-0.018, 6.0], (12.0, 18.0], (12.0, 18.0], (12.0, 18.0], (12.0, 18.0], ..., (12.0, 18.0], (12.0, 18.0], (12.0, 18.0], (6.0, 12.0], (12.0, 18.0]]
Length: 15
Categories (3, interval[float64]): [(-0.018, 6.0] < (6.0, 12.0] < (12.0, 18.0]]
pd.value_counts(factor)
(12.0, 18.0] 10
(-0.018, 6.0] 3
(6.0, 12.0] 2
dtype: int64
factor1 = pd.cut(arr,[-1,5,10,15,20])
pd.value_counts(factor1)
(15, 20] 6
(10, 15] 6
(-1, 5] 3
(5, 10] 0
dtype: int64
factor2 = pd.qcut(arr,[0,0.25,0.5,0.75,1])
pd.value_counts(factor2)
(11.5, 15.0] 5
(-0.001, 11.5] 4
(16.0, 18.0] 3
(15.0, 16.0] 3
dtype: int64
pandas 处理字符串(单独一个大的章节,这人不做详述)
数据合并
- concat
- merge(类似于sql数据库中的join)
- append
首先看concat合并数据框
df = pd.DataFrame(np.random.randn(10,4)) # 10行列的标准正态分布数据框
df
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0 | 1 | 2 | 3 | |
---|---|---|---|---|
0 | 0.949746 | -0.050767 | 1.478622 | -0.239901 |
1 | -0.297120 | -0.562589 | 0.371837 | 1.180715 |
2 | 0.953856 | 0.492295 | 0.821156 | -0.323328 |
3 | 0.016153 | 1.554225 | -1.166304 | -0.904040 |
4 | 0.204763 | -0.951291 | -1.317620 | 0.672900 |
5 | 2.241006 | -0.925746 | -1.961408 | 0.853367 |
6 | 2.217133 | -0.430812 | 0.518926 | 1.741445 |
7 | -0.571104 | -0.437305 | -0.902241 | 0.786231 |
8 | -2.511387 | 0.523760 | 1.811622 | -0.777296 |
9 | 0.252690 | 0.901952 | 0.619614 | -0.006631 |
d1,d2,d3 = df[:3],df[3:7],df[7:]
d1,d2,d3
( 0 1 2 3
0 0.949746 -0.050767 1.478622 -0.239901
1 -0.297120 -0.562589 0.371837 1.180715
2 0.953856 0.492295 0.821156 -0.323328,
0 1 2 3
3 0.016153 1.554225 -1.166304 -0.904040
4 0.204763 -0.951291 -1.317620 0.672900
5 2.241006 -0.925746 -1.961408 0.853367
6 2.217133 -0.430812 0.518926 1.741445,
0 1 2 3
7 -0.571104 -0.437305 -0.902241 0.786231
8 -2.511387 0.523760 1.811622 -0.777296
9 0.252690 0.901952 0.619614 -0.006631)
pd.concat([d1,d2,d3])
#合并三个数据框,数据结构相同,通常合并相同结构的数据,数据框中的字段一致,类似于数据添加新的数据来源
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0 | 1 | 2 | 3 | |
---|---|---|---|---|
0 | 0.949746 | -0.050767 | 1.478622 | -0.239901 |
1 | -0.297120 | -0.562589 | 0.371837 | 1.180715 |
2 | 0.953856 | 0.492295 | 0.821156 | -0.323328 |
3 | 0.016153 | 1.554225 | -1.166304 | -0.904040 |
4 | 0.204763 | -0.951291 | -1.317620 | 0.672900 |
5 | 2.241006 | -0.925746 | -1.961408 | 0.853367 |
6 | 2.217133 | -0.430812 | 0.518926 | 1.741445 |
7 | -0.571104 | -0.437305 | -0.902241 | 0.786231 |
8 | -2.511387 | 0.523760 | 1.811622 | -0.777296 |
9 | 0.252690 | 0.901952 | 0.619614 | -0.006631 |
merge方式合并(数据库中的join)
left = pd.DataFrame({'key':['foo','foo'],"lval":[1,2]})
right = pd.DataFrame({'key':['foo','foo'],'rval':[4,5]})
left
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key | lval | |
---|---|---|
0 | foo | 1 |
1 | foo | 2 |
right
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key | rval | |
---|---|---|
0 | foo | 4 |
1 | foo | 5 |
pd.merge(left,right,on='key')
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key | lval | rval | |
---|---|---|---|
0 | foo | 1 | 4 |
1 | foo | 1 | 5 |
2 | foo | 2 | 4 |
3 | foo | 2 | 5 |
left = pd.DataFrame({'key':['foo','bar'],"lval":[1,2]})
right = pd.DataFrame({'key':['foo','bar'],'rval':[4,5]})
pd.merge(left,right,on='key')
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key | lval | rval | |
---|---|---|---|
0 | foo | 1 | 4 |
1 | bar | 2 | 5 |
left
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key | lval | |
---|---|---|
0 | foo | 1 |
1 | bar | 2 |
right
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key | rval | |
---|---|---|
0 | foo | 4 |
1 | bar | 5 |
Append方式合并数据
# 与concat 类似,常用的方法可以参考一下日子
df = pd.DataFrame(np.random.randn(8,4),columns=['A','B','C','D'])
df
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A | B | C | D | |
---|---|---|---|---|
0 | 1.825997 | -0.331086 | -0.067143 | 0.747226 |
1 | -0.027497 | 0.861639 | 0.928621 | -2.549617 |
2 | -0.546645 | -0.072253 | -0.788483 | 0.484140 |
3 | -0.472240 | -1.776993 | -1.647407 | 0.170596 |
4 | -0.099453 | 0.380143 | -0.890510 | 1.233741 |
5 | 0.351915 | 0.137522 | -1.165938 | 1.128146 |
6 | 0.558442 | -1.047060 | -0.598197 | -1.979876 |
7 | 0.067321 | -1.037666 | -1.140675 | -0.098562 |
##
d1 = df.iloc[3]
df.append(d1,ignore_index= True)
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A | B | C | D | |
---|---|---|---|---|
0 | 1.825997 | -0.331086 | -0.067143 | 0.747226 |
1 | -0.027497 | 0.861639 | 0.928621 | -2.549617 |
2 | -0.546645 | -0.072253 | -0.788483 | 0.484140 |
3 | -0.472240 | -1.776993 | -1.647407 | 0.170596 |
4 | -0.099453 | 0.380143 | -0.890510 | 1.233741 |
5 | 0.351915 | 0.137522 | -1.165938 | 1.128146 |
6 | 0.558442 | -1.047060 | -0.598197 | -1.979876 |
7 | 0.067321 | -1.037666 | -1.140675 | -0.098562 |
8 | -0.472240 | -1.776993 | -1.647407 | 0.170596 |
分组操作Groupby操作
df = pd.DataFrame({"A":['foo','bar','foo','bar'],
"B":['one','one','two','three'],
"C":np.random.randn(4),
"D":np.random.randn(4)})
df
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A | B | C | D | |
---|---|---|---|---|
0 | foo | one | 0.938910 | 0.505163 |
1 | bar | one | 0.660543 | 0.353860 |
2 | foo | two | 0.520309 | 1.157462 |
3 | bar | three | -1.054927 | 0.290693 |
df.groupby('A').sum()
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C | D | |
---|---|---|
A | ||
bar | -0.394384 | 0.644553 |
foo | 1.459219 | 1.662625 |
df.groupby('A').size()
A
bar 2
foo 2
dtype: int64
df.groupby(['A',"B"]).sum()
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C | D | ||
---|---|---|---|
A | B | ||
bar | one | 0.660543 | 0.353860 |
three | -1.054927 | 0.290693 | |
foo | one | 0.938910 | 0.505163 |
two | 0.520309 | 1.157462 |
df.groupby(['A',"B"]).size()
A B
bar one 1
three 1
foo one 1
two 1
dtype: int64
reshape操作
tuples = list(zip(*[['bar','bar','baz','baz','foo','foo','qux','qux'],
['one','two','one','two','one','two','one','two']]))
index = pd.MultiIndex.from_tuples(tuples,names=['first','second'])
df = pd.DataFrame(np.random.randn(8,2),index= index,columns=['A','B'])
df2 = df[:4]
df2
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A | B | ||
---|---|---|---|
first | second | ||
bar | one | 0.510758 | 0.641370 |
two | 0.481230 | -0.470894 | |
baz | one | -0.076294 | 0.121247 |
two | 0.378507 | -1.358932 |
df
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A | B | ||
---|---|---|---|
first | second | ||
bar | one | 0.510758 | 0.641370 |
two | 0.481230 | -0.470894 | |
baz | one | -0.076294 | 0.121247 |
two | 0.378507 | -1.358932 | |
foo | one | -0.873012 | 0.531595 |
two | 0.266968 | -0.393124 | |
qux | one | 0.981866 | 1.205994 |
two | 0.265772 | 0.132489 |
stack 与unstack 方法
df2_stacked = df2.stack()
# 将column也作为index
df2_stacked
first second
bar one A 0.510758
B 0.641370
two A 0.481230
B -0.470894
baz one A -0.076294
B 0.121247
two A 0.378507
B -1.358932
dtype: float64
df2_stacked.unstack() # 回复到原来的状态
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A | B | ||
---|---|---|---|
first | second | ||
bar | one | 0.510758 | 0.641370 |
two | 0.481230 | -0.470894 | |
baz | one | -0.076294 | 0.121247 |
two | 0.378507 | -1.358932 |
df2_stacked
first second
bar one A 0.510758
B 0.641370
two A 0.481230
B -0.470894
baz one A -0.076294
B 0.121247
two A 0.378507
B -1.358932
dtype: float64
df2_stacked.unstack(1)
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second | one | two | |
---|---|---|---|
first | |||
bar | A | 0.510758 | 0.481230 |
B | 0.641370 | -0.470894 | |
baz | A | -0.076294 | 0.378507 |
B | 0.121247 | -1.358932 |
df2_stacked.unstack(0)
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first | bar | baz | |
---|---|---|---|
second | |||
one | A | 0.510758 | -0.076294 |
B | 0.641370 | 0.121247 | |
two | A | 0.481230 | 0.378507 |
B | -0.470894 | -1.358932 |
pivot_table 透视表
df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3, 'B' : ['A', 'B', 'C'] * 4,
'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,
'D' : np.random.randn(12),
'E' : np.random.randn(12)})
df
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A | B | C | D | E | |
---|---|---|---|---|---|
0 | one | A | foo | 0.006247 | -0.894827 |
1 | one | B | foo | 1.653974 | -0.340107 |
2 | two | C | foo | -1.627485 | -1.011403 |
3 | three | A | bar | -0.716002 | 1.533422 |
4 | one | B | bar | 0.422688 | -0.807675 |
5 | one | C | bar | 0.264818 | 0.249770 |
6 | two | A | foo | 0.643288 | -1.166616 |
7 | three | B | foo | 0.348041 | -0.659099 |
8 | one | C | foo | 1.593486 | -1.098731 |
9 | one | A | bar | -0.389344 | 0.919528 |
10 | two | B | bar | -1.407450 | 1.269716 |
11 | three | C | bar | -0.172672 | 0.883970 |
pd.pivot_table(df,values='D',index=['A','B'],columns=['C'],aggfunc=np.mean)
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C | bar | foo | |
---|---|---|---|
A | B | ||
one | A | -0.389344 | 0.006247 |
B | 0.422688 | 1.653974 | |
C | 0.264818 | 1.593486 | |
three | A | -0.716002 | NaN |
B | NaN | 0.348041 | |
C | -0.172672 | NaN | |
two | A | NaN | 0.643288 |
B | -1.407450 | NaN | |
C | NaN | -1.627485 |
pd.pivot_table(df,values='D',index=['A','B'],columns=['C'],aggfunc=np.sum)
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C | bar | foo | |
---|---|---|---|
A | B | ||
one | A | -0.389344 | 0.006247 |
B | 0.422688 | 1.653974 | |
C | 0.264818 | 1.593486 | |
three | A | -0.716002 | NaN |
B | NaN | 0.348041 | |
C | -0.172672 | NaN | |
two | A | NaN | 0.643288 |
B | -1.407450 | NaN | |
C | NaN | -1.627485 |
pd.pivot_table(df,values='D',index=['A','B'],columns=['C'],aggfunc=np.mean,fill_value=0)
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C | bar | foo | |
---|---|---|---|
A | B | ||
one | A | -0.389344 | 0.006247 |
B | 0.422688 | 1.653974 | |
C | 0.264818 | 1.593486 | |
three | A | -0.716002 | 0.000000 |
B | 0.000000 | 0.348041 | |
C | -0.172672 | 0.000000 | |
two | A | 0.000000 | 0.643288 |
B | -1.407450 | 0.000000 | |
C | 0.000000 | -1.627485 |
df1 = pd.pivot_table(df,values='D',index=['A','B'],columns=['C'],aggfunc=np.mean,fill_value=0)
df1.index
MultiIndex(levels=[['one', 'three', 'two'], ['A', 'B', 'C']],
labels=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]],
names=['A', 'B'])
df1.stack()
A B C
one A bar -0.389344
foo 0.006247
B bar 0.422688
foo 1.653974
C bar 0.264818
foo 1.593486
three A bar -0.716002
foo 0.000000
B bar 0.000000
foo 0.348041
C bar -0.172672
foo 0.000000
two A bar 0.000000
foo 0.643288
B bar -1.407450
foo 0.000000
C bar 0.000000
foo -1.627485
dtype: float64
df1.unstack()
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C | bar | foo | ||||
---|---|---|---|---|---|---|
B | A | B | C | A | B | C |
A | ||||||
one | -0.389344 | 0.422688 | 0.264818 | 0.006247 | 1.653974 | 1.593486 |
three | -0.716002 | 0.000000 | -0.172672 | 0.000000 | 0.348041 | 0.000000 |
two | 0.000000 | -1.407450 | 0.000000 | 0.643288 | 0.000000 | -1.627485 |
df1.unstack(1)
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C | bar | foo | ||||
---|---|---|---|---|---|---|
B | A | B | C | A | B | C |
A | ||||||
one | -0.389344 | 0.422688 | 0.264818 | 0.006247 | 1.653974 | 1.593486 |
three | -0.716002 | 0.000000 | -0.172672 | 0.000000 | 0.348041 | 0.000000 |
two | 0.000000 | -1.407450 | 0.000000 | 0.643288 | 0.000000 | -1.627485 |
df1.unstack(0)
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C | bar | foo | ||||
---|---|---|---|---|---|---|
A | one | three | two | one | three | two |
B | ||||||
A | -0.389344 | -0.716002 | 0.00000 | 0.006247 | 0.000000 | 0.643288 |
B | 0.422688 | 0.000000 | -1.40745 | 1.653974 | 0.348041 | 0.000000 |
C | 0.264818 | -0.172672 | 0.00000 | 1.593486 | 0.000000 | -1.627485 |
至此,pandas的基础的使用介绍也就结束了,后续会有专题性质的分析,包括(字符串处理,apply的使用,数据合并,透视表,时间序列的分析)
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