Installation Pandas and Concept Related To Pandas

Python Pandas is a powerful open-source data manipulation and analysis library. It provides easy-to-use data structures and data analysis tools, making it a go-to choice for working with structured data. In this blog, we will find Installation Pandas and Concept Related To Pandas

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# Pandas 

– pandas using Data Manipulation for Tabular Data. 

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Key Features of Pandas 

– Fast and clean data manipulation as well as data analysis. 

– Loading data from multiple formats.  

– Pandas using Slicing, Indexing, and other some operation provided easily. 

– Easily joins two DataFrame and Create Mearge operation using Pandas that’s time better data manipulation. 

 

How to install Pandas Lab’s in your systems. 

– pip install pandas 

 

 

How to use pandas lab / how to import pandas as well as rename pandas lab as pd  import pandas as pd  

# Pandas structure  

1. Series 

– Series is a one-dimensional array. 

– Series is a Homogeneous Data Structure means Same data type of items . 

2. DataFrame 

– DataFrame is a two-dimensional array 

-Data Structure is Heterogeneous means different data type of items . 

3. Panel

– Panel is a three-dimensional data structure 

-Data Structure is Heterogeneous means different data type of items . 

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# 1. Series  

Syntax :- 

 pandas.Series(data, index,dtype) 

#How to create an empty Pandas Series 

series = pd.Series() 

print(series) 

import numpy as np  

data = np.array([121,131,141,151,161,171]) 

series = pd.Series(data) 

print(series) 

print(type(series)) 

#Assign the customer Index to Pandas Series  

series = pd.Series(data, index=[‘a’,’b’,’c’,’d’,’e’,’f’]) 

series 

# Dict to Pandas Series  

Pincode = { 

 “PimpleSaudagar”:411027,

 “Aundh”:411044, 

 “Baner”:411045, 

 “Ravet”:412101, 

 “Thergaon”:411033, 

 “Wakad”:411057, 

 “Chinchwad”:411032, 

 “Bavdhan”:411021 

} 

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series = pd.Series(Pincode) print(series) 

print(type(series)) 

#indexing and slicing  

series[0] 

series[2:5] 

series[“PimpleSaudagar”] 

series[[“PimpleSaudagar”,”Baner”]]

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