Page 230 - AI Ver 1.0 Class 10
P. 230
Sorting the array ARR.sort() a = np.array([12,4,-10,23,29,15, [-14 -10 -1 4 12
-1,45,33,37,-14]) #Creating a 1-D 15 23 29 33 37
Numpy array 45]
print(np.sort(a)) #Printing the [[ -9 5 9 12 18]
sorted numpy array
[-10 -5 3 10 11]]
#We can also sort array row wise
[[ -9 5 3 -5 -10]
and column wise!
b = np.array([[-9,5,18,9,12], [ 10 11 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
Pandas
Panda is an open-source Python library used for data manipulation and data analysis. It provides a very strong
feature of using three important data structures— Series (1-dimensional), DataFrame
(2-dimensional) and Panel (3-dimensional) for smooth processing and analysis of data, regardless of its origin.
The data actually need not be labelled at all to be placed into a Pandas data structure.
Pandas was created by Wes McKinney in 2008 and has derived its name from both “Panel Data”, and “Python
Data Analysis” which means using a statistical method of analysing the data taken from the observations about
different cross sections over the period of time.
Pandas libraries are built on NumPy so to work in Pandas the prerequisite is to get familiar with NumPy and
install it. Data required for Pandas can be taken as:
• Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet.
• Ordered and unordered (not necessarily fixed-frequency) time series data.
• Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels.
• Any other form of observational/statistical data sets.
File Access Using Pandas
Pandas can be accessed by using import:
import pandas as pd
To save the csv file into a variable:
s = pd.read_csv(“Student data.csv”)
To print the first five rows of the file:
print (s.head(5))
228 Touchpad Artificial Intelligence-X

