# Information Systems homework help

**Data Manipulation with Numpy and Pandas in Python**

**Starting with Numpy**

#load the library and check its version, just to make sure we aren’t using an older version

import numpy as np

np.__version__

‘1.12.1’

#create a list comprising numbers from 0 to 9

L = list(range(10))

#converting integers to string – this style of handling lists is known as list comprehension.

#List comprehension offers a versatile way to handle list manipulations tasks easily. We’ll learn about them in future tutorials. Here’s an example.

[str(c) for c in L]

[‘0’, ‘1’, ‘2’, ‘3’, ‘4’, ‘5’, ‘6’, ‘7’, ‘8’, ‘9’]

[type(item) for item in L]

[int, int, int, int, int, int, int, int, int, int]

**Creating Arrays**

Numpy arrays are homogeneous in nature, i.e., they comprise one data type (integer, float, double, etc.) unlike lists.

#creating arrays

np.zeros(10, dtype=’int’)

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

#creating a 3 row x 5 column matrix

np.ones((3,5), dtype=float)

array([[ 1., 1., 1., 1., 1.],

[ 1., 1., 1., 1., 1.],

[ 1., 1., 1., 1., 1.]])

#creating a matrix with a predefined value

np.full((3,5),1.23)

array([[ 1.23, 1.23, 1.23, 1.23, 1.23],

[ 1.23, 1.23, 1.23, 1.23, 1.23],

[ 1.23, 1.23, 1.23, 1.23, 1.23]])

#create an array with a set sequence

np.arange(0, 20, 2)

array([0, 2, 4, 6, 8,10,12,14,16,18])

#create an array of even space between the given range of values

np.linspace(0, 1, 5)

array([ 0., 0.25, 0.5 , 0.75, 1.])

#create a 3×3 array with mean 0 and standard deviation 1 in a given dimension

np.random.normal(0, 1, (3,3))

array([[ 0.72432142, -0.90024075, 0.27363808],

[ 0.88426129, 1.45096856, -1.03547109],

[-0.42930994, -1.02284441, -1.59753603]])

#create an identity matrix

np.eye(3)

array([[ 1., 0., 0.],

[ 0., 1., 0.],

[ 0., 0., 1.]])

#set a random seed

np.random.seed(0)

x1 = np.random.randint(10, size=6) #one dimension

x2 = np.random.randint(10, size=(3,4)) #two dimension

x3 = np.random.randint(10, size=(3,4,5)) #three dimension

print(“x3 ndim:”, x3.ndim)

print(“x3 shape:”, x3.shape)

print(“x3 size: “, x3.size)

(‘x3 ndim:’, 3)

(‘x3 shape:’, (3, 4, 5))

(‘x3 size: ‘, 60)

**Array Indexing**

The important thing to remember is that indexing in python starts at zero.

x1 = np.array([4, 3, 4, 4, 8, 4])

x1

array([4, 3, 4, 4, 8, 4])

#assess value to index zero

x1[0]

4

#assess fifth value

x1[4]

8

#get the last value

x1[-1]

4

#get the second last value

x1[-2]

8

#in a multidimensional array, we need to specify row and column index

x2

array([[3, 7, 5, 5],

[0, 1, 5, 9],

[3, 0, 5, 0]])

#1st row and 2nd column value

x2[2,3]

0

#3rd row and last value from the 3rd column

x2[2,-1]

0

#replace value at 0,0 index

x2[0,0] = 12

x2

array([[12, 7, 5, 5],

[ 0, 1, 5, 9],

[ 3, 0, 5, 0]])

**Array Slicing**

Now, we’ll learn to access multiple or a range of elements from an array.

x = np.arange(10)

x

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

#from start to 4th position

x[:5]

array([0, 1, 2, 3, 4])

#from 4th position to end

x[4:]

array([4, 5, 6, 7, 8, 9])

#from 4th to 6th position

x[4:7]

array([4, 5, 6])

#return elements at even place

x[ : : 2]

array([0, 2, 4, 6, 8])

#return elements from first position step by two

x[1::2]

array([1, 3, 5, 7, 9])

#reverse the array

x[::-1]

array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])

**Array Concatenation**

Many a time, we are required to combine different arrays. So, instead of typing each of their elements manually, you can use array concatenation to handle such tasks easily.

#You can concatenate two or more arrays at once.

x = np.array([1, 2, 3])

y = np.array([3, 2, 1])

z = [21,21,21]

np.concatenate([x, y,z])

array([ 1, 2, 3, 3, 2, 1, 21, 21, 21])

#You can also use this function to create 2-dimensional arrays.

grid = np.array([[1,2,3],[4,5,6]])

np.concatenate([grid,grid])

array([[1, 2, 3],

[4, 5, 6],

[1, 2, 3],

[4, 5, 6]])

#Using its axis parameter, you can define row-wise or column-wise matrix

np.concatenate([grid,grid],axis=1)

array([[1, 2, 3, 1, 2, 3],

[4, 5, 6, 4, 5, 6]])

Until now, we used the concatenation function of arrays of equal dimension. But, what if you are required to combine a 2D array with 1D array? In such situations, np.concatenate might not be the best option to use. Instead, you can use np.vstack or np.hstack to do the task. Let’s see how!

x = np.array([3,4,5])

grid = np.array([[1,2,3],[17,18,19]])

np.vstack([x,grid])

array([[ 3, 4, 5],

[ 1, 2, 3],

[17, 18, 19]])

#Similarly, you can add an array using np.hstack

z = np.array([[9],[9]])

np.hstack([grid,z])

array([[ 1, 2, 3, 9],

[17, 18, 19, 9]])

Also, we can split the arrays based on pre-defined positions. Let’s see how!

x = np.arange(10)

x

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

x1,x2,x3 = np.split(x,[3,6])

print x1,x2,x3

[0 1 2] [3 4 5] [6 7 8 9]

grid = np.arange(16).reshape((4,4))

grid

upper,lower = np.vsplit(grid,[2])

print (upper, lower)

(array([[0, 1, 2, 3],

[4, 5, 6, 7]]), array([[ 8, 9, 10, 11],

[12, 13, 14, 15]]))

In addition to the functions we learned above, there are several other mathematical functions available in the numpy library such as sum, divide, multiple, abs, power, mod, sin, cos, tan, log, var, min, mean, max, etc. which you can be used to perform basic arithmetic calculations. Feel free to refer to numpy documentation for more information on such functions.

**Let’s start with Pandas**

#load library – pd is just an alias. I used pd because it’s short and literally abbreviates pandas.

#You can use any name as an alias.

import pandas as pd

#create a data frame – dictionary is used here where keys get converted to column names and values to row values.

data = pd.DataFrame({‘Country’: [‘Russia’,’Colombia’,’Chile’,’Equador’,’Nigeria’],

‘Rank’:[121,40,100,130,11]})

data

#We can do a quick analysis of any data set using:

data.describe()

Remember, describe() method computes summary statistics of integer / double variables. To get the complete information about the data set, we can use info() function.

#Among other things, it shows the data set has 5 rows and 2 columns with their respective names.

data.info()

<class ‘pandas.core.frame.DataFrame’>

RangeIndex: 5 entries, 0 to 4

Data columns (total 2 columns):

Country 5 non-null object

Rank 5 non-null int64

dtypes: int64(1), object(1)

memory usage: 152.0+ bytes

#Let’s create another data frame.

data = pd.DataFrame({‘group’:[‘a’, ‘a’, ‘a’, ‘b’,’b’, ‘b’, ‘c’, ‘c’,’c’],’ounces’:[4, 3, 12, 6, 7.5, 8, 3, 5, 6]})

data

#Let’s sort the data frame by ounces – inplace = True will make changes to the data

data.sort_values(by=[‘ounces’],ascending=True,inplace=False)

We can sort the data by not just one column but multiple columns as well.

data.sort_values(by=[‘group’,’ounces’],ascending=[True,False],inplace=False)

Often, we get data sets with duplicate rows, which is nothing but noise. Therefore, before training the model, we need to make sure we get rid of such inconsistencies in the data set. Let’s see how we can remove duplicate rows.

#create another data with duplicated rows

data = pd.DataFrame({‘k1’:[‘one’]*3 + [‘two’]*4, ‘k2′:[3,2,1,3,3,4,4]})

data

#sort values

data.sort_values(by=’k2′)

#remove duplicates – ta da!

data.drop_duplicates()

Here, we removed duplicates based on matching row values across all columns. Alternatively, we can also remove duplicates based on a particular column. Let’s remove duplicate values from the k1 column.

data.drop_duplicates(subset=’k1’)

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