Pandas Data Structure-series

Pandas contain 2 important data structures.

• Series
• dataFrame
  

1.Series:

Series is one dimensional , labeled array, capable of storing any type of data (integers, strings, floating point numbers, Python objects, etc.)

import pandas as pd

my_data=["one","dimension","array",1,"D",["array"]]          #list

my_series=pd.Series(my_data)        #converting list to series

 

variable_name=pd.Series(data) is the syntax to create a series.

my_series=pd.Series(my_data)

.Series(my_data) also takes index as a parameter.

If index parameter is not used, the index positions of data are used as label.

The passed index is a list of axis labels. 

 

Here, data can be many different things:

• List
  

• a Python dict

• an ndarray

• a scalar value (like 5)

1)Series from list:

 

2)Series from dictionary:

my_dic={"name":"priya","id":1001,"dept":"ECE"}
series=pd.Series(my_dic)
print(series)

Index, if given while working with dictionary series, will try to pull the value from the dictionary. If the element in index is not present in dictionary, it returns NaN(not a number)

3)Series from ndarray:

my_series=pd.Series(np.random.rand(5), index=["a","b","c","d","e"])          

np is numpy library, random.rand(5) generates five random value.

 

 

4)Series from scalar value:

Series can also be creating using scalar value.

my_series=pd.Series(5,index=["a","b","c"])

If the data is scalar, then index must be provided.

The data will be repeated to match the index.