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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