Python Pandas series creating and accessing elements

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

Python Pandas library Series #02

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

print(s.iloc[:2].index.tolist()) #List of index of first two

print(s.iloc[:2].values.tolist()) #List of values of first two

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

Use this sample series for all examples below.

import pandas as pd 
l1=[52,13,45,39] # List of values 
#l1=['One','Tow','Three','Four'] # List of values
my_id=['x','b','y','p'] # List of index 
s=pd.Series(l1,index=my_id) # create a series

Apply function to increase the value of all elements

s=s.apply(lambda x:x+5) # operation on each element

List of index of the series

print(s.index.tolist()) # List of index

Value based on index

print(s['y']) # Key error if not found

print(s.sum()) # sum of all values / elements

Find the maximum , minimum , median, mode of the series

print(s.min()) # use max(), mean(),median(),mode()

Sorting of values and index, use the options ascending, inplace, kind, na_position,ignore_index, Key

print(s.sort_values()) #

print(s.sort_index())

Delete one element using index. Watch the option inplace

s.drop(labels=['b'],inplace=True)

s.pop('x') # remove element using key

Adding element using index.

s['z']=50 # adding element using key

print(s.filter(items=['x','p'])) # searching based on key

Searching for values in a series.

print(s.isin([50,13])) # Matching True or False
#print(s[s.isin([50,13])]) # Matching row only
#print(s[s.isin([50,13])].tolist()) # Matching rows only
#print(s[s.isin([13,39])].index.tolist()) # Matching index or keys

to_numpy()

print(s.to_numpy()) # value to Numpy 
print(s.index.to_numpy()) # index to Numpy 
print(type(s.to_numpy())) # class 'numpy.ndarray'

Numpy to Pandas Interoperability Guide

Merging series

import pandas as pd
my_dict1={'a':300,'c':400}
my_dict2={'a':500,'c':200,'d':600}

s1=pd.Series(data=my_dict1)
s2=pd.Series(data=my_dict2)
#s=s1.combine(s2,max,fill_value=0)
s=s1.combine(s2,max)
print(s)

Output

a    500.0
c    400.0
d      NaN
dtype: float64

Questions..

How do you create a pandas Series from a list?
How can you access the index of a Series?
How do you calculate the sum of values in a Series?
What method can be used to check if a value exists in a Series?
How can you change the name of a Series?
How do you extract the first three elements from a Series?
What method can be used to sort a Series in descending order?
How do you apply a mathematical function to each element of a Series?
How can you find the maximum value in a Series?
How do you add a new element to a Series?

Think...

Is a Pandas Series one-dimensional?
Can a Pandas Series contain data of different types?
Can you perform mathematical operations on a Pandas Series?
Can you access elements of a Pandas Series by index?
Can you change the index labels of a Pandas Series?
Can you add or remove elements from a Pandas Series?
Can you sort a Pandas Series?
Can you perform element-wise comparisons on a Pandas Series?
Can you apply a function to each element of a Pandas Series?
Can you merge two Pandas Series into a single Series?
Can you convert a Pandas Series to a NumPy array?
Can you convert a Pandas Series to a Python list?
Can you calculate the mean of a Pandas Series?
Can you calculate the maximum value of a Pandas Series?
Can you calculate the minimum value of a Pandas Series?
Can you calculate the sum of all elements in a Pandas Series?
Can you calculate the standard deviation of a Pandas Series?
Can you replace specific values in a Pandas Series?
Can you check if a value exists in a Pandas Series?
Can you slice a Pandas Series to extract a subset of elements?

Pandas Pandas DataFrame
contains() to display and delete row based on Conditions

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Subhendu Mohapatra

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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

to_numpy()

Merging series

Subhendu Mohapatra