Python Pandas series creating and accessing elements

Pandas

Series is one dimensional labelled data structure. ( DataFrame is two dimensional )

Series can store string, integer and other python objects. It is like one column in a table. Syntax

pandas.Series(data=None, index=None, dtype=None, name=None, 
	copy=False, fastpath=False)

data : array like , iterable , dictionary, list, tuple , scalar
index : have the same length as data, Not necessarily unique,must be a hashable type
dtype : Data type of output series
name: Name given to series
copy: Default is false, Copy input data

Creating Series

Using List we can create one series

import pandas as pd
my_list=['Ravi','Raju','Alex']
s=pd.Series(data=my_list)
print(s)

Output

0    Ravi
1    Raju
2    Alex

We can add our index to this

my_list=['Ravi','Raju','Alex']
my_id=['p','q','r']
s=pd.Series(data=my_list,index=my_id)
print(s)

Output

p    Ravi
q    Raju
r    Alex

Using set as data source we can't create series as set is unordered.

Using tuple.

my_list=('Ravi','Raju','Alex')
s=pd.Series(data=my_list)
print(s)

Using dictionary as data source. Here dictionary keys are used as index.

my_dict={'a':'Ravi','b':'Raju','c':'Alex'}
s=pd.Series(data=my_dict)
print(s)

Output

a    Ravi
b    Raju
c    Alex

Only matching index are used.

my_dict={'a':'Ravi','b':'Raju','c':'Alex'}
s=pd.Series(data=my_dict,index=['a','c'])
print(s)

Output

a    Ravi    
c    Alex

All unmatching index

my_dict={'a':'Ravi','b':'Raju','c':'Alex'}
s=pd.Series(data=my_dict,index=['x','y','z'])
print(s)

Output

x    NaN
y    NaN
z    NaN

Using Scalar value

s=pd.Series(data='Alex',index=['x','y','z'])
print(s)

Output

x    Alex
y    Alex
z    Alex

Using Numpy

We can create one dimensional numpy array.

import numpy as np
ar=np.random.rand(4) # Numpy array with random numbers 
s=pd.Series(ar)
print(s)

From DataFrame to Series

From DataFrame we can create Series by using columns. Read more on our sample student DataFrame. This sample DataFrame has 35 rows.

import pandas as pd 
df= pd.read_excel('D:\\my_data\\student.xlsx') # excel file 
df= pd.read_csv('D:\\my_data\\student.csv') # csv  file 
s=pd.Series(df['name']) # using name column data
print(s)

We can create a Series by using all columns of a DataFrame by converting the DataFrame to a list by using tolist()

s=pd.Series(df.values.tolist())

Sample output is here ( few records are shown, actual output will contain 35 records )

0        [1, John Deo, Four, 75, female]
1         [2, Max Ruin, Three, 85, male]
2           [3, Arnold, Three, 55, male]
3      [4, Krish Star, Four, 60, female]
----
----
31    [32, Binn Rott, Seven, 90, female]
32      [33, Kenn Rein, Six, 96, female]
33       [34, Gain Toe, Seven, 69, male]
34     [35, Rows Noump, Six, 88, female]

Accessing elements of a Series

Using head() and tail() ( above code with 35 records to be used )

print(s.head()) # First five records 
print(s.head(2)) # last  five records 
print(s.tail()) # last  five records 
print(s.tail(2)) # last  two records 
print(s[:5]) # first 5 records

Using iloc

print(s.iloc[3:7])

Output

3     Krish Star
4      John Mike
5      Alex John
6    My John Rob

Getting alternate rows

print(s.iloc[::2]) # alternate rows

Using index position

my_dict={'a':'Ravi','b':'Raju','c':'Alex'}
s=pd.Series(data=my_dict)
print(s['b']) # Output:  Raju

Updating elements of series

my_dict={'a':'Ravi','b':'Raju','c':'Alex'}
s=pd.Series(data=my_dict)
s['b']='King' # updating using index 
print(s)

Output

a    Ravi
b    King
c    Alex

Converting Data type of Series

By default this will be inferred from data. Here the id column is integer so by default the dtype is integer (dtype: int64) . Here we have specified the dtype to be string. Check the output by removing dtype='string'.
How to create sample student DataFrame?

df= pd.read_csv('D:\\my_data\\student.csv') # csv  file 
s=pd.Series(df['id'],dtype='string')
print(s) # Name : id , dtype string

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Pandas Series

Creating Series

Using Numpy

From DataFrame to Series

Accessing elements of a Series

Updating elements of series

Converting Data type of Series

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