[untitled] numpy learning

1. Slicing and indexing （ Select rows and columns ）t[2]    t[3:,:] From the position 3 All lines are selected , Select column   t[:,4:] A series of lines ： t[2:,3] Discontinuous lines t[[1,3],[2,4]] The choice is （1,2）（3,4） Two positions     Indexes t[2,3]
2. assignment t[2:,3]=3
3. Boolean index   t[t>10]=10
4. Ternary operator    np.where(t>10,20,0) The greater than 10 The replacement of is 20 Replace others with 0
5. tailoring t.clip(10,20) The less than 10 The replacement of is 10, about 20 The replacement of is 20
6. Transposition t.transform()     t.T     t.swapaxes(1,0)
7. Read local file np.loadtxt(file_file_path ,delimiter,dtype)
8. nan and inf     inf Express infinite     nan It's not a number np.nan!=np.nan   np.count_nonzero(na.nan！=np.nan)   np.isan(t1)  Effects np.nan！=np.nan identical
9. Common statistical functions    t.sum(axis=0)     np.median(t,axis=0)  t.mean(axis=0)  t.max()  t.mean()  np.mean()  np.ptp  The calculation is very poor   t.std  Standard deviation   Reaction data dispersion

Add country information     Splice two sets of data

np.loadtxt(us_data,delimiter=",",dytepe=int)

Construct all zero data ,zeros_data=np.zeros(）

ones_data=np.data()

Add data

us-data=np.hstack()

Create an array of all zeros , All in one number

np,eyes(5）

Get the maximum , minimum value

np.argmax(t,axis=0)

np.argmin(t,axis=0)

np.random.randint() Given the upper and lower limits, select the random number certificate

.rand()

.randn()

.unifi

np.random.seed()

t=np.random.randint()