Pandas--DataFrame

创建DataFrame

import pandas as pd
import numpy as np

a = np.arange(50).reshape(10,5)
# print a
test_df = pd.DataFrame(
    data=a,
    index=['index0', 'index1', 'index2', 'index3', 'index4',
           'index5', 'index6', 'index7', 'index8', 'index9'],
    columns=['L1', 'L2', 'L3', 'L4', 'L5']
)

        L1  L2  L3  L4  L5
index0   0   1   2   3   4
index1   5   6   7   8   9
index2  10  11  12  13  14
index3  15  16  17  18  19
index4  20  21  22  23  24
index5  25  26  27  28  29
index6  30  31  32  33  34
index7  35  36  37  38  39
index8  40  41  42  43  44
index9  45  46  47  48  49

将列的名称映射为值

test_df1 = pd.DataFrame({'A': [0, 1, 2,3], 'B': [ 4, 5,6,7]})
print test_df1


   A  B
0  0  4
1  1  5
2  2  6
3  3  7

获取元素

#获取行
print test_df.iloc[0]
print test_df.loc['index3']

L1    0
L2    1
L3    2
L4    3
L5    4
Name: index0, dtype: int32
L1    15
L2    16
L3    17
L4    18
L5    19
Name: index3, dtype: int32

#获取列
print test_df['L3']

index0     2
index1     7
index2    12
index3    17
index4    22
index5    27
index6    32
index7    37
index8    42
index9    47
Name: L3, dtype: int32

#获取某个元素
print test_df.iloc[1, 3] #8


#获取多行
print test_df.iloc[1:4]

        L1  L2  L3  L4  L5
index1   5   6   7   8   9
index2  10  11  12  13  14
index3  15  16  17  18  19

#获取多列
print test_df[['L1', 'L2']]

        L1  L2
index0   0   1
index1   5   6
index2  10  11
index3  15  16
index4  20  21
index5  25  26
index6  30  31
index7  35  36
index8  40  41
index9  45  46

pandas axis 

#列的sum值
print test_df.sum()

L1    225
L2    235
L3    245
L4    255
L5    265

#行的sum值
print test_df.sum(axis=1)

index0     10
index1     35
index2     60
index3     85
index4    110
index5    135
index6    160
index7    185
index8    210
index9    235

#总的sum值，其中test_df.values返回一个numpy形式的数组
print test_df.values.sum() #1225