numpy和pandas axis的差异

1.numpy

numpy.sum(a, axis=None, dtype=None, out=None, keepdims=<class 'numpy._globals._NoValue'>)[source]

Sum of array elements over a given axis.

 

Parameters:

a : array_like Elements to sum. axis : None or int or tuple of ints, optional Axis or axes along which a sum is performed. The default, axis=None, will sum all of the elements of the input array. If axis is negative it counts from the last to the first axis. New in version 1.7.0. If axis is a tuple of ints, a sum is performed on all of the axes specified in the tuple instead of a single axis or all the axes as before.

 

arr = np.random.randn(5,4)#正态分布数据
print(arr)
print(arr.sum()) # 数组/矩阵中所有元素求和,等价于np.sum(arr)
print(np.sum(arr, axis=0))# 按列去求和; 
print(arr.sum(axis=1))  # 按行去求和;

[[ 0.04154798  0.25697028  2.36239272 -1.72886735]
 [ 0.50448843 -0.63285194  2.9090727   0.61004107]
 [ 0.10730241 -0.13162546 -0.67925053  0.12864452]
 [ 0.04125252 -0.03968486 -0.60453958  0.94637586]
 [ 1.65060502 -0.18266035 -1.06259085  0.18515147]]
4.681774035077944
[ 2.34519635 -0.72985234  2.92508445  0.14134557]
[ 0.93204362  3.39075026 -0.57492907  0.34340393  0.59050528]
2.pandas　

DataFrame.sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs)[source]
axis : {index (0), columns (1)}
df

one    two
a    1.40    NaN
b    7.10    -4.5
c    NaN    NaN
d    0.75    -1.3

df.sum(axis=1)

a    1.40
b    2.60
c    0.00
d   -0.55
dtype: float64

 

pandas.DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)[source]

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

Determine if rows or columns which contain missing values are removed.

• 0, or ‘index’ : Drop rows which contain missing values.
• 1, or ‘columns’ : Drop columns which contain missing value.

Deprecated since version 0.23.0:: Pass tuple or list to drop on multiple

axes.

how : {‘any’, ‘all’}, default ‘any’

df

	id	date	city	category	age	price
one	1001	2013-01-02	Beijing	100-A	23	1200.0
two	1002	2013-01-03	NaN	100-B	44	NaN
three	1003	2013-01-04	guangzhou	110-A	54	2133.0
four	1004	2013-01-05	Shenzhen	110-C	32	5433.0
five	1005	2013-01-06	shanghai	210-A	34	NaN
six	1006	2013-01-07	BEIJING	130-F	32	4432.0

 

 

 

 

 

 

 

df.dropna(axis=1)#针对列向有nan值的情况

	id	date	category	age
one	1001	2013-01-02	100-A	23
two	1002	2013-01-03	100-B	44
three	1003	2013-01-04	110-A	54
four	1004	2013-01-05	110-C	32
five	1005	2013-01-06	210-A	34
six	1006	2013-01-07	130-F	32