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Data Operations using Python
Neha Kumawat
5 months ago
Table of Contents
- Introduction
- Operations on data using Numpy
1. How to create ndarray?
- Data Operations in Pandas
1. Series in Pandas
2. DataFrame in Pandas
Introduction
In this article, we will try to see how we can
perform some operations on our data frame using python libraries like Pandas and
Numpy. We all know how important and useful these python libraries are while we
are performing an Exploratory Data Analysis or any Data Science projects.
Here we will try to learn some basic examples
from each of the two libraries and see how they help us to operate on the
data.
Operations on data using Numpy
Numpy package is a very important library
provided by python used for any mathematical operation is to be done. This
Package is very important for performing any Machine learning and Deep Learning
tasks.
Numpy stands for N-dimensional array called
ndarray. It describes the collection of items of
the same type. Items in any collections can be accessed using a zero-based
index. An instance of ndarray class can be constructed by different array
creation.
How to create ndarray?
Let’s
see with some examples, how basic ndarray is created using an array function in
Numpy.
numpy.array
Below are some examples of Numpy Data handling.
Example 1
# more than one dimensions
import numpy as np
arr = np.array([[4, 6], [7, 8]])
print(arr)Output
[[4 6]
[7 8]]
Example 2
# minimum dimensions
import numpy as np
arr = np.array([1, 2, 3, 4, 5], ndmin = 2)
print(arr)Output
[[1 2 3 4 5]]Example 3
# with dtype parameter
import numpy as np
arr = np.array([1, 2, 3], dtype = complex)
print(arr)Output
[1.+0.j 2.+0.j 3.+0.j]Data Operations in Pandas
Python pandas library provides many functions
to handles data through Series, Data Frame,
and Panel.
Series in Pandas
Pandas Series is a one-dimensional labeled array
which is capable of holding data of any type (integer, string, float, python
objects, etc.). The axis labels are collectively known as index.
Let’s see how we can create a pandas series
pandas.Series( data, index, dtype, copy)
Now let’s take an
example and understand it in a better way
Example:
How
to 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(['P','Q','R','S'])
series = pd.Series(data)
print(series)Output
0 P
1 Q
2 R
3 S
dtype: objectDataFrame in Pandas
Pandas data frame is a two-dimensional data
structure in python. Here, data is aligned in a tabular form in rows and
columns.
Let’s see how we can create pandas DatFrame.
pandas.DataFrame( data, index, columns, dtype, copy)
Let’s see an example
Example:
import
pandas as pd
data =
{'Name':['Ram', 'Mohan', 'Amit', 'Ricky'],'Age':[23,25,29,36]}
df1 =
pd.DataFrame(data, index=['rank1','rank2','rank3','rank4'])
print(df1)Output
Name Age
rank1 Ram 23
rank2 Mohan 25
rank3 Amit 29
rank4 Ricky 36
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