14. Pandas的DataFrame行操作

本章主要围绕对dataframe行的各项操作展开。

14.1 append增加行

append函数可以将某dataframe添加到另一个dataframe的尾部组成一个新的dataframe,如果列不同,没有数据的对应填充NaN数据,append不影响原dataframe。

import pandas as pd
import numpy as np
val1 = np.arange(10, 40).reshape(10, 3)
val2 = np.arange(50, 80).reshape(10, 3)
col1 = ["ax", "bx", "cx"]
col2 = ["cx", "dx", "ex"]
idx = list("abcdefghij")
df1 = pd.DataFrame(val1, columns = col1, index = idx)
df2 = pd.DataFrame(val2, columns = col2, index = idx)
print "*" * 21
print df1[:3]
print "*" * 21
print df2[:3]
print "*" * 21
df3 = df1.append(df2)
print df1
print "*" * 21
print df3

程序执行的结果:

   ax  bx  cx
a  10  11  12
b  13  14  15
c  16  17  18
*********************
   cx  dx  ex
a  50  51  52
b  53  54  55
c  56  57  58
*********************
   ax  bx  cx
a  10  11  12
b  13  14  15
....
i  34  35  36
j  37  38  39
*********************
   ax  bx  cx  dx  ex
a  10  11  12 NaN NaN
b  13  14  15 NaN NaN
....
i  34  35  36 NaN NaN
j  37  38  39 NaN NaN
a NaN NaN  50  51  52
b NaN NaN  53  54  55
....
i NaN NaN  74  75  76
j NaN NaN  77  78  79

14.2 concat连接多行

pandas的concat可以多列连接dataframe也可多行连接dataframe,区别在于axis的指定,当axis为0时是行连接。

import pandas as pd
import numpy as np
val1 = np.arange(10, 40).reshape(10, 3)
val2 = np.arange(50, 80).reshape(10, 3)
col1 = ["ax", "bx", "cx"]
col2 = ["cx", "dx", "ex"]
idx = list("abcdefghij")
df1 = pd.DataFrame(val1, columns = col1, index = idx)
df2 = pd.DataFrame(val2, columns = col2, index = idx)
print "*" * 21
print df1[:3]
print "*" * 21
print df2[:3]
print "*" * 21
df3 = pd.concat([df1,df2], axis = 0)
print df1[:3]
print "*" * 21
print df3[:2]
print df3[-2:]

程序的结果:

   ax  bx  cx
a  10  11  12
b  13  14  15
c  16  17  18
*********************
   cx  dx  ex
a  50  51  52
b  53  54  55
c  56  57  58
*********************
   ax  bx  cx
a  10  11  12
b  13  14  15
c  16  17  18
*********************
   ax  bx  cx  dx  ex
a  10  11  12 NaN NaN
b  13  14  15 NaN NaN
   ax  bx  cx  dx  ex
i NaN NaN  74  75  76
j NaN NaN  77  78  79

14.3 行内容替换

通过dataframe对象名[label]赋值的方式修改对应行的数据。

import pandas as pd
import numpy as np
val1 = np.arange(10, 40).reshape(10, 3)
val2 = np.arange(50, 80).reshape(10, 3)
col1 = ["ax", "bx", "cx"]
col2 = ["cx", "dx", "ex"]
idx = list("abcdefghij")
df1 = pd.DataFrame(val1, columns = col1, index = idx)
df2 = pd.DataFrame(val2, columns = col2, index = idx)
print "*" * 21
print df1[:3]
print "*" * 21
print df2[:3]
print "*" * 21
df1.loc["a"] = df2.loc["c"]
print df1[:3]
df1.loc["a"] = [11, 22, 33]
print df1[:3]

程序的执行结果:

   ax  bx  cx
a  10  11  12
b  13  14  15
c  16  17  18
*********************
   cx  dx  ex
a  50  51  52
b  53  54  55
c  56  57  58
*********************
   ax  bx  cx
a NaN NaN  56
b  13  14  15
c  16  17  18
   ax  bx  cx
a  11  22  33
b  13  14  15
c  16  17  18

14.4 删除行

删除dataframe的行和删除列一样有很多的方式。

  • drop删除指定的各个行,用列表给出行的信息数据,返回值是原dataframe删除后的数据,原dataframe不受影响。
import pandas as pd
import numpy as np
val1 = np.arange(10, 40).reshape(10, 3)
val2 = np.arange(50, 80).reshape(10, 3)
col1 = ["ax", "bx", "cx"]
col2 = ["cx", "dx", "ex"]
idx = list("abcdefghij")
df1 = pd.DataFrame(val1, columns = col1, index = idx)
df2 = pd.DataFrame(val2, columns = col2, index = idx)
print "*" * 21
print df1[:3]
print "*" * 21
print df2[:3]
print "*" * 21
df3 = df1.drop(["a", "c", "f"])
print df1[:3]
print df3[:3]

程序的执行结果:

   ax  bx  cx
a  10  11  12
b  13  14  15
c  16  17  18
*********************
   cx  dx  ex
a  50  51  52
b  53  54  55
c  56  57  58
*********************
   ax  bx  cx
a  10  11  12
b  13  14  15
c  16  17  18
   ax  bx  cx
b  13  14  15
d  19  20  21
e  22  23  24
  • 利用切片的结果赋值给新的dataframe也是一种变相的删除。

  • 利用布尔选择的结果也是一种变相的删除。

import pandas as pd
import numpy as np
val = np.arange(10, 40).reshape(10, 3)
col = ["ax", "bx", "cx"]
idx = list("abcdefghij")
df1 = pd.DataFrame(val, columns = col, index = idx)
print "*" * 21
print df1
print "*" * 21
bs = df1["ax"] > 33
df2 = df1[bs]
print df2
df2 = df1[:3]
print df2

程序的执行结果:

   ax  bx  cx
a  10  11  12
b  13  14  15
c  16  17  18
d  19  20  21
e  22  23  24
f  25  26  27
g  28  29  30
h  31  32  33
i  34  35  36
j  37  38  39
*********************
   ax  bx  cx
i  34  35  36
j  37  38  39
   ax  bx  cx
a  10  11  12
b  13  14  15
c  16  17  18

感谢Klang(金浪)智能数据看板klang.org.cn鼎力支持!