Python NumPy Pandas

Import numpya=numpy.array ([1,2,3,4]) b=numpy.array ([[1,2,3],[4,5,6],[7,8,9]])print (A.shape) Print (B.shape)

Creates a one-dimensional vector and a matrix of three rows of hashes

Note: Here the data is required to be the same structure, the shape function: Several rows of columns

Value:

Import numpyb=numpy.array ([[[1,2,3],[4,5,6],[7,8,9]])print (b[:,1])# Here we print the second column of the Matrix  Print(B[:,0:2])# This takes you to the first and second columns

To modify a value in a matrix:

This changes the value of 5 and 7 to 10.

Import numpyb=numpy.array ([[[1,2,3],[4,5,6],[7,8,9]]) b[(b==5) | ( b==7)] = tenprint(b)

Strong Turn Type:

convert int to str type

Import numpyb=numpy.array ([[[1,2,3],[4,5,6],[7,8,9 = b.astype (str)print(c)

Other operations:

Import numpyb=numpy.array ([[[1,2,3],[4,5,6],[7,8,9]])print(B.min ())# to find the minimum value  Print(B.max (Axis=1))# max print(b.sum (Axis=0)) # by line# Sum by Column

Import NumPy as NPA=np.arange (0). Reshape (2,5)print(a)" creates a matrix: [[2] + 3 4] [5 6 7 8 9]] " " Print (A.ndim) # Seeking Dimensions Print (A.shape) # a few rows of columns Print (A.dtype.name) # matrix data type name Print (a.size) # Number of elements

Matrix initialization:

Import NumPy as NP # Matrix initialization method:Np.zeros ((3,4))#3 row 4 column matrix initialized to 0 (default to float type)np.ones ((3,4), Dtype=np.int32)#  3 row 4 column initialize int type with value 1

To create a matrix:

Import NumPy as Npnp.arange (10,30,5) # from 10 to 30, every 5 # Array ([ten, +,]) Np.random.random ((2,3)) " " randomly created: 2 rows 3 columns, 1 to 1 note: Must be two randomarray ([[0.20925672, 0.09790786, 0.00158854],       [0.73711854, 0.83033327, 0.22525092]]"np.linspace (1,3,100)#  Averaging 100 numbers from 1 to 3 (float type)

Operation:

ImportNumPy as NPA=np.array ([[1,2,3],[4,5,6],[7,8,9]])Print(Np.hstack ((a,a)))Print(Np.vstack ((a,a)))Print(A.T)Print(A +a)Print(A *a)Print(A.dot (a))Print(Np.dot (a,a))Print(Np.exp (a))Print(Np.sqrt (a))Print(A.shape)Print(A.ravel ())" "Do not explain, at a glance [[1 2 3 1 2 3] [4 5 6 4 5 6] [7 8 9 7 8 9]][[1 2 3] [4 5 6] [7 8 9] [1 2 3] [4 5] 6 7] (8 4 7) [3 6 9]] [[2 4 6] [8 10 12] [14 16 18]] [[1 4 9] [16 25 36] [49 64 81]] [[30 36 42] [66 81 96] [102 126 150]]  [[30 36 42] [66 81 96] [102 126 150]] [[2.71828183e+00 7.38905610e+00 2.00855369e+01] [5.45981500e+01 1.48413159e+02 4.03428793e+02] [1.09663316e+03         2.98095799E+03 8.10308393e+03]] [[1].         1.41421356 1.73205081] [2.        2.23606798 2.44948974] [2.64575131 2.82842712 3. ] (3, 3) [1 2 3 4 5 6 7 8 9]" "

Import NumPy as NPA=np.array ([[[1,2,3],[4,5,6],[7,8,9]])print(A.argmax (axis=0))#  [2 2 2] column maximum index value print(a.argmin (Axis=1))#[0 0 0] Row min index value

Import NumPy as NPA=np.arange (0,40,10)print(a) b=np.tile (A, (3,2)) C=np.tile ( A, (2,3))print(b)print(c)"[0 10 20 30][[0 10 20 30<  c13/>0 [0] [0] [0] [0] [  [0  ] 0 0 (10)----+------+------ [0 0] [  0] []] ""

Sort:

ImportNumPy as NPA=np.array ([[1,4,6],[2,9,7],[5,3,8]])Print(a)" "[ [1 4 6] [2 9 7] [5 3 8]]" "b=np.sort (A,axis=1)#Arrange By RowPrint(b)" "[ [1 4 6] [2 7 9] [3 5 8]]" "C=np.sort (a,axis=0)#Arrange By ColumnPrint(c)" "[ [1 3 6] [2 4 7] [5 9 8]]" "D=np.argsort (a)#index Value OrderingPrint(d)" "[ [0 1 2] [0 2 1] [1 0 2]]" "

Special attention:

ImportNumPy as NPA=np.array ([[1,2,3],[4,5,6],[7,8,9]]) C=A.view ()Print(c isA#False (C and a point to memory addresses are different)#copy A, assign a value to C#if it is c=a, then C and a are the same (point to the same address)#Print (c is a) in the word, it prints truec[1,2] = 100Print(a)" "[ [1 2 3] [4 5] [7 8 9]]" "#here we find that C has been modified, so a has also been modified.#C and a have different addresses but share a set of dataD=a.copy ()Print(d isA#falsed[1,3] = 100#There's no change here .Print(a)

Read TXT file:

Import NumPy # The first parameter is a path, the second argument is a delimiter, and the third argument is the type of the read # The last parameter means: Do you want to remove the first line a=numpy.genfromtxt ("d:/a.txt", delimiter=",  ", dtype="str", skip_header=1)print(a)

Pandas for data processing:

Examples of Use:

ImportPandasfood= Pandas.read_csv ("D:/a.csv")#Read CSV filePrint(food.dtypes)#field TypePrint(Food.head (4))#get first 4 rows (default = 5)Print(Food.tail (3))#get the following 3 rows (default = 5)Print(Food.shape)#a few rows of columnsPrint(food.columns)#each column name

Print (food.loc[1]) # get the 2nd row of data Print (food["name"]) # get name to column

1.

ImportPandasfood= Pandas.read_csv ("D:/a.csv") List=food.columns.tolist ()Print(list)#convert all column names to listsList1= [] forCinchlist:if(C.endswith ("(MG)")): List1.append (c) a=Food[list1]Print(a)#Add a new list to the end of (MG) and process the complete

2. Sort (default ascending)

Import= pandas.read_csv ("d:/a.csv") food.sort_values ("  Calcium_ (mg)", Inplace=true, ascending=False)# in descending order, first parameter column name, The third argument, whether the third one is ascending, and the default is Trueprint(food["calcium_ (mg)"))

3.

ImportPandasman= Pandas.read_csv ("D:/t.csv")Print(man) age= man[" Age"]#Age ColumnAge_null = Age[pandas.isnull (man[" Age"])]#field age is empty lineAge_null_len =Len (age_null)#The sum of age is empty

Python NumPy Pandas