Page 293 - Ai_C10_Flipbook
P. 293
Type Function Example
Row wise & ARR.
column wise max(axis=1) [1]: import numpy as np
maximum value for row ARR = np.array([[11,2,13,4],[3,4,5,6]])
print("Rowwise max :",ARR.max(axis=1))
print("Column wise max :",ARR.max(axis=0))
ARR. Rowwise max : [13 6]
max(axis=0) Column wise max : [11 4 13 6]
for column
Row wise & ARR.
[1]: import numpy as np
column wise min(axis=1)
ARR = np.array([[11,2,13,4],[3,4,5,6]])
minimum value for row print("Rowwise min :",ARR.min(axis=1))
ARR. print("Column wise min :",ARR.min(axis=0))
min(axis=0) Rowwise min : [2 3]
for column Column wise min : [3 2 5 4]
Sum of all values ARR.sum()
[1]: import numpy as np
in the given array
ARR = np.array([[11,2,13,4],[3,4,5,6]])
print("Row Wise sum :",ARR.sum(axis=1))
print("Column wise sum :",ARR.sum(axis=0))
print("sum is :",ARR.sum())
Rowwise sum : [30 18]
Column wise sum : [14 6 18 10]
sum is : 48
Sorting the array ARR.sort()
[1]: a = np.array([12,4,-10,23,29,15, -1,45,33,37,-14])
#Creating a 1-D Numpy array
print(np.sort(a)) #Printing the sorted numpy array
#We can also sort array row wise and column wise!
b = np.array([[-9,5,18,9,12], [10,11,3,-5,-10]])
#Creating a 2-D Numpy array
print(np.sort(b, axis = 1)) #Axis = 1performs the
sorting function row-wise
print(np.sort(b, axis = 0)) #Axis = 0 performs the
sorting function columns-wise
[-14 -10 -1 4 12 15 23 29 33 37 45]
[[ -9 5 9 12 18]
[-10 -5 3 10 11]]
[[ -9 5 3 -5 -10]
[ 10 11 18 9 12]]
Pandas (PANel DAta)
Pandas is an open-source Python library used for data manipulation and analysis. It provides strong features for
working with three key data structures: Series (1-dimensional), DataFrame (2-dimensional), and Index (used for
label-based indexing). These structures allow smooth processing and analysis of data, regardless of its origin. In
Pandas, the data need not be labelled to be placed into a data structure.
Advance Python (Practical) 291

