pandas和numpy学习

r囧r小猫 2022-01-07 23:05 282阅读 0赞

## 1.导入： ##

1 import numpy as np
    2 import pandas as pd

## 原始文件： ##

![1544387-20190125180524088-181046473.png][]

1 xlsx=pd.ExcelFile('1.xlsx')
    2 df=pd.read_excel(xlsx,0,index_col=None,na_values=['NA'])
    3 print(df.head())

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

## 2.数据筛选和选择： ##

1 print(df.shape)
     2 print("*"*111)
     3 print(df.index)
     4 print("*"*111)
     5 print(df.index.values)
     6 print("*"*111)
     7 print(df.columns)
     8 print("*"*111)
     9 print(df.columns.values)
    10 print("*"*111)
    11 print(df.columns.get_loc("num"))
    12 print("*"*111)
    13 print(df.columns.get_indexer(["num","cell"]))
    14 print("*"*111)
    15 print(df["num"])
    16 print("*"*111)
    17 print(df[["num","cell"]])
    18 print("*"*111)
    19 print(df[["num","cell"]].values)
    20 print("*"*111)
    21 print(df.iloc[2:6])
    22 print("*"*111)
    23 print(df.iloc[2:6,2:4])
    24 print("*"*111)
    25 print(df.iloc[2:6,df.columns.get_indexer(["cell","num"])])
    26 print("*"*111)
    27 print(df.loc[2:6,["cell","num"]])
    28 print("*"*111)
    29 print(df.loc[2:6,"num":"cell"])
    30 print("*"*111)
    31 print(df.loc[2:8:2,["cell","num"]])
    32 print("*"*111)
    33 print(df[(df["cell"]==9)|(df["num"]==9)])
    34 print("*"*111)
    35 print(df[(df["num"]==9)|(df["num"]==1)])
    36 print("*"*111)
    37 print(df.loc[df["num"]<7])
    38 print("*"*111)
    39 print(df.loc[df.num<7,['cell','num']])

结果：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/txt.py
    (12, 4)
    ***************************************************************************************************************
    RangeIndex(start=0, stop=12, step=1)
    ***************************************************************************************************************
    [ 0  1  2  3  4  5  6  7  8  9 10 11]
    ***************************************************************************************************************
    Index(['Name', 'Index', 'num', 'cell'], dtype='object')
    ***************************************************************************************************************
    ['Name' 'Index' 'num' 'cell']
    ***************************************************************************************************************
    2
    ***************************************************************************************************************
    [2 3]
    ***************************************************************************************************************
    0      1
    1      3
    2      5
    3      7
    4      9
    5     11
    6     13
    7     15
    8     17
    9     19
    10    21
    11    23
    Name: num, dtype: int64
    ***************************************************************************************************************
        num  cell
    0     1     1
    1     3     5
    2     5     9
    3     7    13
    4     9    17
    5    11    21
    6    13    25
    7    15    29
    8    17    33
    9    19    37
    10   21    41
    11   23    45
    ***************************************************************************************************************
    [[ 1  1]
     [ 3  5]
     [ 5  9]
     [ 7 13]
     [ 9 17]
     [11 21]
     [13 25]
     [15 29]
     [17 33]
     [19 37]
     [21 41]
     [23 45]]
    ***************************************************************************************************************
      Name  Index  num  cell
    2   C7   GBC4    5     9
    3  C10   GBC6    7    13
    4  C13   GBC8    9    17
    5  C16  GBC10   11    21
    ***************************************************************************************************************
       num  cell
    2    5     9
    3    7    13
    4    9    17
    5   11    21
    ***************************************************************************************************************
       cell  num
    2     9    5
    3    13    7
    4    17    9
    5    21   11
    ***************************************************************************************************************
       cell  num
    2     9    5
    3    13    7
    4    17    9
    5    21   11
    6    25   13
    ***************************************************************************************************************
       num  cell
    2    5     9
    3    7    13
    4    9    17
    5   11    21
    6   13    25
    ***************************************************************************************************************
       cell  num
    2     9    5
    4    17    9
    6    25   13
    8    33   17
    ***************************************************************************************************************
      Name Index  num  cell
    2   C7  GBC4    5     9
    4  C13  GBC8    9    17
    ***************************************************************************************************************
      Name Index  num  cell
    0   C1   GB0    1     1
    4  C13  GBC8    9    17
    ***************************************************************************************************************
      Name Index  num  cell
    0   C1   GB0    1     1
    1   C4  GBC2    3     5
    2   C7  GBC4    5     9
    ***************************************************************************************************************
       cell  num
    0     1    1
    1     5    3
    2     9    5
    
    Process finished with exit code 0

## 按照行列遍历 ##

1 import numpy as np
     2 import pandas as pd
     3 xlsx=pd.ExcelFile('1.xlsx')
     4 df=pd.read_excel(xlsx,0,index_col=None,na_values=['NA'])
     5 print(df)
     6 print("*"*111)
     7 #三种迭代函数，interrows()返回（index，Series）行对
     8 for index,row in df.iterrows():
     9     print(index,'\n',row)
    10 print("*"*111)
    11 #返回本身就是一个series，可以按照series的情况提取具体元素
    12 for index,row in df.iterrows():
    13     print(row[2:3])
    14 print("*"*111)
    15 #interitems()返回（column，series）列对
    16 for index,col in df.iteritems():
    17     print(col[2:3])
    18 print("*"*111)
    19 #以上方式遍历不能对数据进行修改，要对数据进行修改，数据小的话直接按照行数遍历修改
    20 #如果很大，建议使用apply函数或者转为list进行处理
    21 for i in df.index:
    22     print(df.loc[i,:])
    23 print("*"*111)
    24 # 也可以用shape[0]表示行数
    25 for i in range(df.shape[0]):
    26     print(df.loc[i,:])

结果：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/txt.py
       Name  Index  num  cell
    0    C1    GB0    1     1
    1    C4   GBC2    3     5
    2    C7   GBC4    5     9
    3   C10   GBC6    7    13
    4   C13   GBC8    9    17
    5   C16  GBC10   11    21
    6   C19  GBC12   13    25
    7    C4  GBC14   15    29
    8    C7  GBC16   17    33
    9   C10  GBC18   19    37
    10  C13  GBC20   21    41
    11  C16  GBC22   23    45
    ***************************************************************************************************************
    0 
     Name      C1
    Index    GB0
    num        1
    cell       1
    Name: 0, dtype: object
    1 
     Name       C4
    Index    GBC2
    num         3
    cell        5
    Name: 1, dtype: object
    2 
     Name       C7
    Index    GBC4
    num         5
    cell        9
    Name: 2, dtype: object
    3 
     Name      C10
    Index    GBC6
    num         7
    cell       13
    Name: 3, dtype: object
    4 
     Name      C13
    Index    GBC8
    num         9
    cell       17
    Name: 4, dtype: object
    5 
     Name       C16
    Index    GBC10
    num         11
    cell        21
    Name: 5, dtype: object
    6 
     Name       C19
    Index    GBC12
    num         13
    cell        25
    Name: 6, dtype: object
    7 
     Name        C4
    Index    GBC14
    num         15
    cell        29
    Name: 7, dtype: object
    8 
     Name        C7
    Index    GBC16
    num         17
    cell        33
    Name: 8, dtype: object
    9 
     Name       C10
    Index    GBC18
    num         19
    cell        37
    Name: 9, dtype: object
    10 
     Name       C13
    Index    GBC20
    num         21
    cell        41
    Name: 10, dtype: object
    11 
     Name       C16
    Index    GBC22
    num         23
    cell        45
    Name: 11, dtype: object
    ***************************************************************************************************************
    num    1
    Name: 0, dtype: object
    num    3
    Name: 1, dtype: object
    num    5
    Name: 2, dtype: object
    num    7
    Name: 3, dtype: object
    num    9
    Name: 4, dtype: object
    num    11
    Name: 5, dtype: object
    num    13
    Name: 6, dtype: object
    num    15
    Name: 7, dtype: object
    num    17
    Name: 8, dtype: object
    num    19
    Name: 9, dtype: object
    num    21
    Name: 10, dtype: object
    num    23
    Name: 11, dtype: object
    ***************************************************************************************************************
    2    C7
    Name: Name, dtype: object
    2    GBC4
    Name: Index, dtype: object
    2    5
    Name: num, dtype: int64
    2    9
    Name: cell, dtype: int64
    ***************************************************************************************************************
    Name      C1
    Index    GB0
    num        1
    cell       1
    Name: 0, dtype: object
    Name       C4
    Index    GBC2
    num         3
    cell        5
    Name: 1, dtype: object
    Name       C7
    Index    GBC4
    num         5
    cell        9
    Name: 2, dtype: object
    Name      C10
    Index    GBC6
    num         7
    cell       13
    Name: 3, dtype: object
    Name      C13
    Index    GBC8
    num         9
    cell       17
    Name: 4, dtype: object
    Name       C16
    Index    GBC10
    num         11
    cell        21
    Name: 5, dtype: object
    Name       C19
    Index    GBC12
    num         13
    cell        25
    Name: 6, dtype: object
    Name        C4
    Index    GBC14
    num         15
    cell        29
    Name: 7, dtype: object
    Name        C7
    Index    GBC16
    num         17
    cell        33
    Name: 8, dtype: object
    Name       C10
    Index    GBC18
    num         19
    cell        37
    Name: 9, dtype: object
    Name       C13
    Index    GBC20
    num         21
    cell        41
    Name: 10, dtype: object
    Name       C16
    Index    GBC22
    num         23
    cell        45
    Name: 11, dtype: object
    ***************************************************************************************************************
    Name      C1
    Index    GB0
    num        1
    cell       1
    Name: 0, dtype: object
    Name       C4
    Index    GBC2
    num         3
    cell        5
    Name: 1, dtype: object
    Name       C7
    Index    GBC4
    num         5
    cell        9
    Name: 2, dtype: object
    Name      C10
    Index    GBC6
    num         7
    cell       13
    Name: 3, dtype: object
    Name      C13
    Index    GBC8
    num         9
    cell       17
    Name: 4, dtype: object
    Name       C16
    Index    GBC10
    num         11
    cell        21
    Name: 5, dtype: object
    Name       C19
    Index    GBC12
    num         13
    cell        25
    Name: 6, dtype: object
    Name        C4
    Index    GBC14
    num         15
    cell        29
    Name: 7, dtype: object
    Name        C7
    Index    GBC16
    num         17
    cell        33
    Name: 8, dtype: object
    Name       C10
    Index    GBC18
    num         19
    cell        37
    Name: 9, dtype: object
    Name       C13
    Index    GBC20
    num         21
    cell        41
    Name: 10, dtype: object
    Name       C16
    Index    GBC22
    num         23
    cell        45
    Name: 11, dtype: object
    
    Process finished with exit code 0

## 重要： ##

1 #如果数据量较大，可以在list中转下
     2 modify_result=[] #优化遍历速度，使用list进行
     3 column_names=list(df.columns.values)
     4 modify_result.append(column_names)  #添加标题行
     5 for i ,row in df.iterrows():
     6     newrows=list(row)
     7     if i>=4:
     8         newrows[column_names.index("num")]=99
     9     modify_result.append(newrows) #添加每一行数据
    10 df=pd.DataFrame(modify_result[1:],columns=modify_result[0])  #重新变回df
    11 print(df)

结果：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

## 赋值修改： ##

1 for i in df.index:
    2     if i>2:
    3         df.loc[i,"cell"]=28
    4 
    5 print(df)

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

## 矢量运算： ##

1 df['num']=50
    2 print(df)

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

##  组建新的形式 ##

1 for m ,row in df.iterrows():
     2     excel_id="{}-{}-{}".format(row['cell'],row['num'],row['Name'])
     3     print(excel_id)
     4 print("*" * 111)
     5 for i, row in df.iterrows():
     6     print(row[['num','Name']])
     7 print("*"*111)
     8 for t  in df.index:
     9     excel_id="{}-{}-{}".format(df.loc[t,'cell'],df.loc[t,'num'],df.loc[t,'Name'])
    10     print(excel_id)

1 df['Col_sum'] = df.apply(lambda x: x.sum(), axis=1)
    2 
    3 df.loc['Row_sum'] = df.apply(lambda x: x.sum())
    4 print(df)

结果：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/12.py
    1-1-C1
    5-3-C4
    9-5-C7
    13-7-C10
    17-9-C13
    21-11-C16
    25-13-C19
    29-15-C4
    33-17-C7
    37-19-C10
    41-21-C13
    45-23-C16
    ***************************************************************************************************************
    num      1
    Name    C1
    Name: 0, dtype: object
    num      3
    Name    C4
    Name: 1, dtype: object
    num      5
    Name    C7
    Name: 2, dtype: object
    num       7
    Name    C10
    Name: 3, dtype: object
    num       9
    Name    C13
    Name: 4, dtype: object
    num      11
    Name    C16
    Name: 5, dtype: object
    num      13
    Name    C19
    Name: 6, dtype: object
    num     15
    Name    C4
    Name: 7, dtype: object
    num     17
    Name    C7
    Name: 8, dtype: object
    num      19
    Name    C10
    Name: 9, dtype: object
    num      21
    Name    C13
    Name: 10, dtype: object
    num      23
    Name    C16
    Name: 11, dtype: object
    ***************************************************************************************************************
    1-1-C1
    5-3-C4
    9-5-C7
    13-7-C10
    17-9-C13
    21-11-C16
    25-13-C19
    29-15-C4
    33-17-C7
    37-19-C10
    41-21-C13
    45-23-C16
    
    Process finished with exit code 0

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/12.py
             name  index  num  cell  Col_sum
    0           1      0    1     1        3
    1           4      2    3     5       14
    2           7      4    5     9       25
    3          10      6    7    13       36
    4          13      8    9    17       47
    5          16     10    5    21       52
    6          10      8    9     5       32
    7          13     10   13     7       43
    8          16     12   17     9       54
    9          19     14    3     5       41
    10          4     20    5    41       70
    11         16     22    7    45       90
    Row_sum   129    116   84   178      507
    
    Process finished with exit code 0

## 排序： ##

1 1 print(df)
    2 2 print("*"*111)
    3 3 print(df.sort_values(by="cell"))
    4 4 print("*"*111)
    5 5 print(df)
    6 6 print("*"*111)
    7 7 print(df.sort_values(by=['cell','num','name']).reset_index(drop=True))
    8 8 print("*"*111)
    9 9 print(df.sort_values(by=['cell','num','name']).reset_index(drop=False))

结果：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/12.py
        name  index  num  cell
    0      1      0    1     1
    1      4      2    3     5
    2      7      4    5     9
    3     10      6    7    13
    4     13      8    9    17
    5     16     10    5    21
    6     10      8    9     5
    7     13     10   13     7
    8     16     12   17     9
    9     19     14    3     5
    10     4     20    5    41
    11    16     22    7    45
    ***************************************************************************************************************
        name  index  num  cell
    0      1      0    1     1
    1      4      2    3     5
    6     10      8    9     5
    9     19     14    3     5
    7     13     10   13     7
    2      7      4    5     9
    8     16     12   17     9
    3     10      6    7    13
    4     13      8    9    17
    5     16     10    5    21
    10     4     20    5    41
    11    16     22    7    45
    ***************************************************************************************************************
        name  index  num  cell
    0      1      0    1     1
    1      4      2    3     5
    2      7      4    5     9
    3     10      6    7    13
    4     13      8    9    17
    5     16     10    5    21
    6     10      8    9     5
    7     13     10   13     7
    8     16     12   17     9
    9     19     14    3     5
    10     4     20    5    41
    11    16     22    7    45
    ***************************************************************************************************************
        name  index  num  cell
    0      1      0    1     1
    1      4      2    3     5
    2     19     14    3     5
    3     10      8    9     5
    4     13     10   13     7
    5      7      4    5     9
    6     16     12   17     9
    7     10      6    7    13
    8     13      8    9    17
    9     16     10    5    21
    10     4     20    5    41
    11    16     22    7    45
    ***************************************************************************************************************
        level_0  name  index  num  cell
    0         0     1      0    1     1
    1         1     4      2    3     5
    2         9    19     14    3     5
    3         6    10      8    9     5
    4         7    13     10   13     7
    5         2     7      4    5     9
    6         8    16     12   17     9
    7         3    10      6    7    13
    8         4    13      8    9    17
    9         5    16     10    5    21
    10       10     4     20    5    41
    11       11    16     22    7    45
    
    Process finished with exit code 0

## 合并方法：注意表头一致 ##

1 import numpy as np
     2 import pandas as pd
     3 excel1 = pd.ExcelFile('1.xlsx')
     4 df1 = pd.read_excel(excel1 ,0,index_col=None,na_values=['NA'])
     5 excel2 = pd.ExcelFile('2.xlsx')
     6 df2 = pd.read_excel(excel2 ,0,index_col=None,na_values=['NA'])
     7 print(df1)
     8 print("*"*111)
     9 print(df2)
    10 print("*"*111)
    11 print(df1[:3])
    12 print("*"*111)
    13 pieces=[df1[1:4],df1[2:4],df2[2:3]]
    14 print( pd.concat(pieces,sort=True))
    15 print("*"*111)
    16 print( pd.concat(pieces,sort=False))

结果：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/12.py
         N  IN  num  cell
    0    1   0    1     1
    1    4   2    3     5
    2    7   4    5     9
    3   10   6    7    13
    4   13   8    9    17
    5   16  10    5    21
    6   10   8    9     5
    7   13  10   13     7
    8   16  12   17     9
    9   19  14    3     5
    10   4  20    5    41
    11  16  22    7    45
    ***************************************************************************************************************
        name  index  num  cell
    0      1      6    1     1
    1      2      7    3     5
    2      3      8    5     9
    3      4      9    7    13
    4      5     10    9    17
    5      6     11   11    21
    6      7     12   13    25
    7      8     13   15    29
    8      9     14   17    33
    9     10     15   19    37
    10    11     16   21    41
    11    12     17   23    45
    ***************************************************************************************************************
       N  IN  num  cell
    0  1   0    1     1
    1  4   2    3     5
    2  7   4    5     9
    ***************************************************************************************************************
        IN     N  cell  index  name  num
    1  2.0   4.0     5    NaN   NaN    3
    2  4.0   7.0     9    NaN   NaN    5
    3  6.0  10.0    13    NaN   NaN    7
    2  4.0   7.0     9    NaN   NaN    5
    3  6.0  10.0    13    NaN   NaN    7
    2  NaN   NaN     9    8.0   3.0    5
    ***************************************************************************************************************
          N   IN  num  cell  name  index
    1   4.0  2.0    3     5   NaN    NaN
    2   7.0  4.0    5     9   NaN    NaN
    3  10.0  6.0    7    13   NaN    NaN
    2   7.0  4.0    5     9   NaN    NaN
    3  10.0  6.0    7    13   NaN    NaN
    2   NaN  NaN    5     9   3.0    8.0
    
    Process finished with exit code 0

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/12.py
        name  index  num  cell
    0      1      0    1     1
    1      4      2    3     5
    2      7      4    5     9
    3     10      6    7    13
    4     13      8    9    17
    5     16     10    5    21
    6     10      8    9     5
    7     13     10   13     7
    8     16     12   17     9
    9     19     14    3     5
    10     4     20    5    41
    11    16     22    7    45
    ***************************************************************************************************************
        name  index  num  cell
    0      1      6    1     1
    1      2      7    3     5
    2      3      8    5     9
    3      4      9    7    13
    4      5     10    9    17
    5      6     11   11    21
    6      7     12   13    25
    7      8     13   15    29
    8      9     14   17    33
    9     10     15   19    37
    10    11     16   21    41
    11    12     17   23    45
    ***************************************************************************************************************
       name  index  num  cell
    0     1      0    1     1
    1     4      2    3     5
    2     7      4    5     9
    ***************************************************************************************************************
       cell  index  name  num
    1     5      2     4    3
    2     9      4     7    5
    3    13      6    10    7
    2     9      4     7    5
    3    13      6    10    7
    2     9      8     3    5
    ***************************************************************************************************************
       name  index  num  cell
    1     4      2    3     5
    2     7      4    5     9
    3    10      6    7    13
    2     7      4    5     9
    3    10      6    7    13
    2     3      8    5     9
    
    Process finished with exit code 0

第一个表头不一致，第二个一致

##  三种合并的方法： ##

1 import numpy as np
     2 import pandas as pd
     3 excel1 = pd.ExcelFile('1.xlsx')
     4 df1 = pd.read_excel(excel1 ,0,index_col=None,na_values=['NA'])
     5 excel2 = pd.ExcelFile('2.xlsx')
     6 df2 = pd.read_excel(excel2 ,0,index_col=None,na_values=['NA'])
     7 excel3 = pd.ExcelFile('3.xlsx')
     8 df3 = pd.read_excel(excel3 ,0,index_col=None,na_values=['NA'])
     9 excel4 = pd.ExcelFile('4.xlsx')
    10 df4 = pd.read_excel(excel4 ,0,index_col=None,na_values=['NA'])
    11 print(df1)
    12 print(df2)
    13 print(df3)
    14 print(df4)

表格结果如下：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/12.py
       name  index  num  cell
    0     1      1    5     5
    1     2      1    2     4
    2     3      1    3     4
    3     4      2    2     2
    4     5      2    1     1
       name  index  num  cell
    0     6      6    7    10
    1     7      7    8     7
    2     8      8    9     8
    3     9      9    7     9
    4    10      6    9    10
       name  index  num  cell
    0    11     12   13    10
    1    12     13   12    13
    2    13     10   13    12
    3    14     12   10    10
    4    15     14   12    12
       name  index  num  cell
    0    16     20   16    17
    1    17     17   17    17
    2    18     18   18    18
    3    19     19   19    20
    4    20     16   17    16
    
    Process finished with exit code 0

## concat参数 ##

1 """
     2 concat参数
     3 pd.concat(objs, axis=0, join='outer', join_axes=None, 
     4                 ignore_index=False,keys=None,levels=None, 
     5                 names=None,verify_integrity=False,
     6                 copy=True) 
     7 1、objs : #输入,DataFrame格式或list(多个合并).
     8 
     9 2、axis: {0, 1, ...}, default 0. #控制连接的方向,0代表列纵向,1代表行横向
    10 
    11 3、join : {‘inner’, ‘outer’}, default ‘outer’. #控制连接的索引,inner表示取表索引之间的交集,outer表示取索引的并集
    12 
    13 4、ignore_index: boolean, default False. #是否使用原索引，选舍弃便对新表重新进行索引排序。
    14 
    15 5、join_axes : list of Index objects. #设定使用的索引,例以df1的索引为标准,join_axes=[df1.index]
    16 
    17 6、keys : sequence, default None. #类似Multiindex,设立另一层索引
    18 """
    19 pieces=[df1[1:4],df2[2:4],df3[2:],df4[4:]]
    20 print( pd.concat(pieces,axis=0))
    21 print("*"*111)
    22 print( pd.concat(pieces,axis=1))
    23 print("*"*111)
    24 list=(df1,df2,df3,df4)
    25 print( pd.concat(list))
    26 print("*"*111)
    27 print( pd.concat(list,axis=1))
    28 print("*"*111)
    29 print( pd.concat(list,axis =0, ignore_index=False))
    30 print("*"*111)
    31 print( pd.concat(list,axis =0, ignore_index=True))

结果：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/txt.py
       name  index  num  cell
    1     2      1    2     4
    2     3      1    3     4
    3     4      2    2     2
    2     8      8    9     8
    3     9      9    7     9
    2    13     10   13    12
    3    14     12   10    10
    4    15     14   12    12
    4    20     16   17    16
    ***************************************************************************************************************
       name  index  num  cell  name  ...   cell  name  index   num  cell
    1   2.0    1.0  2.0   4.0   NaN  ...    NaN   NaN    NaN   NaN   NaN
    2   3.0    1.0  3.0   4.0   8.0  ...   12.0   NaN    NaN   NaN   NaN
    3   4.0    2.0  2.0   2.0   9.0  ...   10.0   NaN    NaN   NaN   NaN
    4   NaN    NaN  NaN   NaN   NaN  ...   12.0  20.0   16.0  17.0  16.0
    
    [4 rows x 16 columns]
    ***************************************************************************************************************
       name  index  num  cell
    0     1      1    5     5
    1     2      1    2     4
    2     3      1    3     4
    3     4      2    2     2
    4     5      2    1     1
    0     6      6    7    10
    1     7      7    8     7
    2     8      8    9     8
    3     9      9    7     9
    4    10      6    9    10
    0    11     12   13    10
    1    12     13   12    13
    2    13     10   13    12
    3    14     12   10    10
    4    15     14   12    12
    0    16     20   16    17
    1    17     17   17    17
    2    18     18   18    18
    3    19     19   19    20
    4    20     16   17    16
    ***************************************************************************************************************
       name  index  num  cell  name  index  ...   num  cell  name  index  num  cell
    0     1      1    5     5     6      6  ...    13    10    16     20   16    17
    1     2      1    2     4     7      7  ...    12    13    17     17   17    17
    2     3      1    3     4     8      8  ...    13    12    18     18   18    18
    3     4      2    2     2     9      9  ...    10    10    19     19   19    20
    4     5      2    1     1    10      6  ...    12    12    20     16   17    16
    
    [5 rows x 16 columns]
    ***************************************************************************************************************
       name  index  num  cell
    0     1      1    5     5
    1     2      1    2     4
    2     3      1    3     4
    3     4      2    2     2
    4     5      2    1     1
    0     6      6    7    10
    1     7      7    8     7
    2     8      8    9     8
    3     9      9    7     9
    4    10      6    9    10
    0    11     12   13    10
    1    12     13   12    13
    2    13     10   13    12
    3    14     12   10    10
    4    15     14   12    12
    0    16     20   16    17
    1    17     17   17    17
    2    18     18   18    18
    3    19     19   19    20
    4    20     16   17    16
    ***************************************************************************************************************
        name  index  num  cell
    0      1      1    5     5
    1      2      1    2     4
    2      3      1    3     4
    3      4      2    2     2
    4      5      2    1     1
    5      6      6    7    10
    6      7      7    8     7
    7      8      8    9     8
    8      9      9    7     9
    9     10      6    9    10
    10    11     12   13    10
    11    12     13   12    13
    12    13     10   13    12
    13    14     12   10    10
    14    15     14   12    12
    15    16     20   16    17
    16    17     17   17    17
    17    18     18   18    18
    18    19     19   19    20
    19    20     16   17    16
    
    Process finished with exit code 0

merge的参数（暂时没有用到，用到时补充）
    merge(left, right, how='inner', on=None, left_on=None, right_on=None,
          left_index=False, right_index=False, sort=True,
          suffixes=('_x', '_y'), copy=True, indicator=False)
    
    1、on：列名，join用来对齐的那一列的名字，用到这个参数的时候一定要保证左表和右表用来对齐的那一列都有相同的列名。
    
    2、left_on：左表对齐的列，可以是列名，也可以是和dataframe同样长度的arrays。
    
    3、right_on：右表对齐的列，可以是列名，也可以是和dataframe同样长度的arrays。
    
    4、left_index/ right_index: 如果是True的haunted以index作为对齐的key
    
    5、how：数据融合的方法。
    
    6、 sort：根据dataframe合并的keys按字典顺序排序，默认是，如果置false可以提高表现。

Append用法：
    
    df1.append([df2,df3，df4])= pd.concat([df1,df2,df3，df4])

## 判断是否重复： ##

df1 = pd.read_excel(excel1 ,0,index_col=None,na_values=['NA'])
    print(df1)
    print("*"*111)
    print(df1.duplicated())

结果：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/txt.py
       name  index  num  cell
    0     1      1    5     5
    1     2      1    2     4
    2     3      3    3     3
    3     4      2    2     2
    4     4      2    2     2
    ***************************************************************************************************************
    0    False
    1    False
    2    False
    3    False
    4     True
    dtype: bool
    
    Process finished with exit code 0

## 剔除重复： ##

1 print(df1)
    2 print("*"*111)
    3 print(df1.drop_duplicates("index"))

结果：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/txt.py
       name  index  num  cell
    0     1      1    5     5
    1     2      1    2     4
    2     3      3    3     3
    3     4      2    2     2
    4     4      2    2     2
    ***************************************************************************************************************
       name  index  num  cell
    0     1      1    5     5
    2     3      3    3     3
    3     4      2    2     2
    
    Process finished with exit code 0

## 分类汇总： ##

1 import numpy as np
     2 import pandas as pd
     3 excel1 = pd.ExcelFile('1.xlsx')
     4 df1 = pd.read_excel(excel1 ,0,index_col=None,na_values=['NA'])
     5 print("原始数据：",df1)
     6 print("*"*111)
     7 print(df1.groupby(['num']))
     8 print("*"*111)
     9 print(df1.groupby(['num']).groups)
    10 print("*"*111)
    11 for name ,group in df1.groupby(['num']):
    12     print("group:",name)
    13     print(group)
    14 print("*"*111)
    15 print(df1.groupby(['num']).sum())
    16 print("*"*111)
    17 grouped=df1.groupby(['num'])
    18 print(grouped.size())
    19 print("*"*111)
    20 print(len(grouped))
    21 print("*"*111)
    22 print(grouped.first())
    23 print("*"*111)
    24 print(grouped.last())
    25 print("*"*111)
    26 print(grouped.get_group(3))
    27 print("*"*111)
    28 grouped2=df1.groupby(['num','cell'])
    29 print(grouped2.groups)
    30 print("*"*111)
    31 print(grouped2.get_group((2,4)))
    32 print("*"*111)
    33 for (k1,k2),group_t in grouped2:
    34     print("group:",k1,k2)
    35     print(group_t)

结果：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/txt.py
    原始数据：    name  index  num  cell
    0     1      1    5     5
    1     2      1    2     4
    2     3      3    3     3
    3     4      2    2     2
    4     4      2    2     2
    ***************************************************************************************************************
    <pandas.core.groupby.groupby.DataFrameGroupBy object at 0x07746D90>
    ***************************************************************************************************************
    {
          2: Int64Index([1, 3, 4], dtype='int64'), 3: Int64Index([2], dtype='int64'), 5: Int64Index([0], dtype='int64')}
    ***************************************************************************************************************
    group: 2
       name  index  num  cell
    1     2      1    2     4
    3     4      2    2     2
    4     4      2    2     2
    group: 3
       name  index  num  cell
    2     3      3    3     3
    group: 5
       name  index  num  cell
    0     1      1    5     5
    ***************************************************************************************************************
         name  index  cell
    num                   
    2      10      5     8
    3       3      3     3
    5       1      1     5
    ***************************************************************************************************************
    num
    2    3
    3    1
    5    1
    dtype: int64
    ***************************************************************************************************************
    3
    ***************************************************************************************************************
         name  index  cell
    num                   
    2       2      1     4
    3       3      3     3
    5       1      1     5
    ***************************************************************************************************************
         name  index  cell
    num                   
    2       4      2     2
    3       3      3     3
    5       1      1     5
    ***************************************************************************************************************
       cell  index  name
    2     3      3     3
    ***************************************************************************************************************
    {(2, 2): Int64Index([3, 4], dtype='int64'), (2, 4): Int64Index([1], dtype='int64'), (3, 3): Int64Index([2], dtype='int64'), (5, 5): Int64Index([0], dtype='int64')}
    ***************************************************************************************************************
       name  index  num  cell
    1     2      1    2     4
    ***************************************************************************************************************
    group: 2 2
       name  index  num  cell
    3     4      2    2     2
    4     4      2    2     2
    group: 2 4
       name  index  num  cell
    1     2      1    2     4
    group: 3 3
       name  index  num  cell
    2     3      3    3     3
    group: 5 5
       name  index  num  cell
    0     1      1    5     5
    
    Process finished with exit code 0

## 数据透视表 ##

1 import numpy as np
     2 import pandas as pd
     3 excel1 = pd.ExcelFile('1.xlsx')
     4 df1 = pd.read_excel(excel1 ,0,index_col=None,na_values=['NA'])
     5 print(df1)
     6 print("*"*111)
     7 pvt=pd.pivot_table(df1,index=["name"],values=['num','cell'],aggfunc=np.sum)
     8 print(pvt)
     9 print("*"*111)
    10 print(pvt.loc[('A'),'num'])
    11 print("*"*111)
    12 print(pvt.reset_index())
    13 print("*"*111)
    14 print(df1)
    15 print("*"*111)
    16 pvt2=pd.pivot_table(df1,index=["name"],values=['num'],columns=['dex'],aggfunc=np.sum)
    17 print(pvt2)
    18 print("*"*111)
    19 df2=pvt2.reset_index()
    20 print(df2)
    21 print("*"*111)
    22 print(df2.columns.values)
    23 print("*"*111)
    24 print(df2.shape[1])

结果：

![ContractedBlock.gif][] ![ExpandedBlockStart.gif][]

C:\Users\zte\AppData\Local\Programs\Python\Python37-32\python.exe C:/Users/zte/PycharmProjects/radioparametercheck/csvfiles/txt.py
      name  dex  num  cell
    0    A    1    5     5
    1    A    2    2     4
    2    B    3    3     3
    3    B    4    2     2
    4    E    5    2     2
    ***************************************************************************************************************
          cell  num
    name           
    A        9    7
    B        5    5
    E        2    2
    ***************************************************************************************************************
    7
    ***************************************************************************************************************
      name  cell  num
    0    A     9    7
    1    B     5    5
    2    E     2    2
    ***************************************************************************************************************
      name  dex  num  cell
    0    A    1    5     5
    1    A    2    2     4
    2    B    3    3     3
    3    B    4    2     2
    4    E    5    2     2
    ***************************************************************************************************************
          num                    
    dex     1    2    3    4    5
    name                         
    A     5.0  2.0  NaN  NaN  NaN
    B     NaN  NaN  3.0  2.0  NaN
    E     NaN  NaN  NaN  NaN  2.0
    ***************************************************************************************************************
        name  num                    
    dex         1    2    3    4    5
    0      A  5.0  2.0  NaN  NaN  NaN
    1      B  NaN  NaN  3.0  2.0  NaN
    2      E  NaN  NaN  NaN  NaN  2.0
    ***************************************************************************************************************
    [('name', '') ('num', 1) ('num', 2) ('num', 3) ('num', 4) ('num', 5)]
    ***************************************************************************************************************
    6
    
    Process finished with exit code 0

转载于:https://www.cnblogs.com/noplablem-wangzhe0635/p/10321184.html

[1544387-20190125180524088-181046473.png]: /images/20211220/8476f6636e0f4b0da74891c4488ca4fa.png
[ContractedBlock.gif]: https://images.cnblogs.com/OutliningIndicators/ContractedBlock.gif
[ExpandedBlockStart.gif]: /images/20211220/eb8488e2b8e24a228469d140c4f8ec21.png