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Python - Data Operations

Neha Kumawat

a year ago

Data Operations | Insideaiml
Table of Contents
  • Data Operations in Numpy
  • Data Operations in Pandas
            1. Pandas Series
            2. Pandas DataFrame
            3. Pandas Panel
          Python handles data of various formats through two libraries, Pandas and Numpy. In this chapter, we will see some basic examples from each of the libraries on how to operate on data.

Data Operations in Numpy

        An N-dimensional array type called ndarray is the most important object defined in NumPy. It describes the collection of items of the same type which can be accessed using a zero-based index. by different array creation routines, an instance of ndarray class can be constructed which is described later in the tutorial. The basic ndarray is created using an array function in NumPy as follows −
Following are some examples on Numpy Data handling.

Example 1

# more than one dimensions 
import numpy as np 
a = np.array([[1, 2], [3, 4]]) 
The output is as follows −
[[1, 2] 

[3, 4]]

Example 2

# minimum dimensions 
import numpy as np 
a = np.array([1, 2, 3,4,5], ndmin = 2) 
The output is as follows −
[[1, 2, 3, 4, 5]]

Example 3

# dtype parameter 
import numpy as np 
a = np.array([1, 2, 3], dtype = complex) 
The output is as follows −
[ 1.+0.j, 2.+0.j, 3.+0.j]

Data Operations in Pandas

         Pandas handle data through Series, Data Frame, and Panel. We will see some examples from each of these.

1. Pandas Series

          Series is a one-dimensional labeled array, it is capable of holding data of any type (integer, string, float, python objects, etc.). The collection of axis labels is called index. A pandas Series can be created using the following constructor −
pandas.Series( data, index, dtype, copy)


Here we create a series from a Numpy Array.

#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
s = pd.Series(data)
Its output is as follows −

0   a
1   b
2   c
3   d
dtype: object

2. Pandas DataFrame

        A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. A pandas DataFrame can be created using the following constructor −
pandas.DataFrame( data, index, columns, dtype, copy)
Let us now create an indexed DataFrame using arrays.

import pandas as pd
data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}
df = pd.DataFrame(data, index=['rank1','rank2','rank3','rank4'])
Its output is as follows −

         Age    Name
rank1    28      Tom
rank2    34     Jack
rank3    29    Steve
rank4    42    Ricky

3. Pandas Panel

          A panel is a 3D container of data. The term Panel data is derived from econometrics and is partially responsible for the name pandas − pan(el)-da(ta)-s.
A Panel can be created using the following constructor −
pandas.Panel(data, items, major_axis, minor_axis, dtype, copy)
In the below example we create a panel from dict of DataFrame Objects

#creating an empty panel
import pandas as pd
import numpy as np

data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)), 
        'Item2' : pd.DataFrame(np.random.randn(4, 2))}
p = pd.Panel(data)
Its output is as follows −

Dimensions: 2 (items) x 4 (major_axis) x 5 (minor_axis)
Items axis: 0 to 1
Major_axis axis: 0 to 3
Minor_axis axis: 0 to 4
I hope you enjoyed reading this article and finally, you came to know about Python - Data Operations.
For more such blogs/courses on data science, machine learning, artificial intelligence and emerging new technologies do visit us at InsideAIML.
Thanks for reading…
Happy Learning…

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