NumPy Array (5)-axis of two-dimensional array

NumPy mean (), STD () and other methods are acting on the entire numpy array, if it is a two-dimensional array, but also the entire array, including all the rows and columns, but we often need it only for rows or columns, rather than the entire two-dimensional array, this time, you can define axis axis:

axis=0 represents the action on the column

axis=1 indicates the action on the line

Take the sum () sum method as an example:

Import= Np.array ([    [1, 2, 3],    [4, 5, 6],    [7, 8, 9]])     Print  a.sum ()#  print a.sum (axis=0)
# Indicates the sum of each column  # [] print a.sum (Axis=1)
# Indicates the sum of each row  # [6]

A comprehensive chestnut:

# Suppose there are 5 subway stations for 10 days of passenger traffic data ridership = Np.array ([    [   0,    0    ,2,    5,    0],    [ 1478, 3877, 3674, 2328, 2539],    [1613, 4088, 3991, 6461, 2691],    [1560, 3392, 3826, 47 2613],    [1608, 4802, 3932, 4477, 2705],    [1576, 3933, 3909, 4979, 2685],    [ c21/>,  229,  255,  496,  201],    [   2,    0,    1,    ,    0],    [1438, 3785, 3589, 4174, 2215],    [1342, 4043, 4009, 4665, 3033]]

The average value of the daily passenger flow is calculated from each station to find the maximum and minimum values:

def Min_and_max_riders_per_day (ridership):     = Ridership.mean (axis=0)    = Mean_ridership_per_station.max ()         = mean_ridership_per_ Station.min ()         return  (max_daily_ridership, min_daily_ridership)    Print  (Min_and_max_riders_per_day (ridership))    #  results (3239.9, 1071.2)

NumPy Array (5)-axis of two-dimensional array