Creating series using pandas library
3 min readMay 17, 2020
Series can be considered as columns in a data set.
Creating series using python list
#Import pandas library
import pandas as pd#Creating a python list
fruit_price_1 = [100, 200, 300, 400, 500]#Creating a series
fruit_series_1 = pd.Series(data=fruit_price_1)
print(fruit_series)
0 100
1 200
2 300
3 400
4 500
dtype: int64
# 0, 1, 2, 3, 4 are default index lables#Assigning custom labels
fruit_labels = [‘apple’, ‘banana’, ‘orange’, ‘grape’, ‘mango’]
fruit_series_1 = pd.Series(data=fruit_price_1, index=fruit_labels)print(fruit_series_1)
apple 100
banana 200
orange 300
grape 400
mango 500
dtype: int64
Creating series using python array
import numpy as np #Import numpy library
import pandas as pd #Import pandas library#Custom labels
fruit_labels = ['apple', 'banana', 'orange', 'grape', 'mango']#Creating python array
sales_array_1 = np.array([10, 15, 30, 9, 20])#Creating series from array
sales_series_1 = pd.Series(data = sales_array_1, index=fruit_labels)print(sales_series_1)
apple 10
banana 15
orange 30
grape 9
mango 20
dtype: int64
Artithematic operations on series
The below sample code explains addition(+), subtraction(-), multiplication(*), division(/), and modulo(%) operation on series.
import pandas as pd#Creating first series
sales_series_1 = pd.Series(data = [10, 15, 30, 9, 20], index=['apple', 'banana', 'orange', 'grape', 'mango'])#Creating second series
sales_series_2 = pd.Series(data = [5, 10, 20, 90, 25], index=['apple', 'banana', 'orange', 'grape', 'mango'])#Addition
print(sales_series_1 + sales_series_2)
apple 15
banana 25
orange 50
grape 99
mango 45#Addition to a constant value
print(sales_series_1 + 2)
apple 12
banana 17
orange 32
grape 11
mango 22
dtype: int64#Substraction
print(sales_series_1 - sales_series_2)
apple 5
banana 5
orange 10
grape -81
mango -5
dtype: int64#Substraction from a constant value
print(sales_series_1 - 2)
apple 8
banana 13
orange 28
grape 7
mango 18
dtype: int64#Multiplication
print(sales_series_1 * sales_series_2)
apple 50
banana 150
orange 600
grape 810
mango 500
dtype: int64#Multiplication with a constant value
print(sales_series_1 * 2)
apple 20
banana 30
orange 60
grape 18
mango 40
dtype: int64#Division
print(sales_series_1 / sales_series_2)
apple 2.0
banana 1.5
orange 1.5
grape 0.1
mango 0.8
dtype: float64#Division with a constant value
print(sales_series_1 / 2)
apple 5.0
banana 7.5
orange 15.0
grape 4.5
mango 10.0
dtype: float64#Modulo operation
print(sales_series_1 % 2)
apple 0
banana 1
orange 0
grape 1
mango 0
dtype: int64#Arithematic operation on series with different labels.
#The below example uses addition operation#Creating first series
sales_series_3 = pd.Series(data = [10, 15, 30, 9, 20], index=['apple', 'banana', 'orange', 'grape', 'mango'])#Creating second series. This series has lime
sales_series_4 = pd.Series(data = [5, 10, 20, 90, 200, 25], index=['apple', 'banana', 'orange', 'grape', 'lime', 'mango'])#Addition
#Lime has no matching cell in sales_series_3.
#Adding lime sale value 200 to an unknown value in
#sales_series_3 is unknown and it will be displayed as NaN print(sales_series_3 + sales_series_4)
apple 15.0
banana 25.0
grape 99.0
lime NaN
mango 45.0
orange 50.0
dtype: float64
Comparison operations
The below sample code explains the usage of >, >=, <, <=, == and != comparision operatiors on series.
import pandas as pd#Creating first series
sales_series_1 = pd.Series(data = [10, 15, 30, 9, 20], index=['apple', 'banana', 'orange', 'grape', 'mango'])# >
print(sales_series > 10)
apple False
banana True
orange True
grape False
mango True
dtype: bool# >=
print(sales_series >= 15)
apple False
banana True
orange True
grape False
mango True
dtype: bool#Listing all elements >10 and <=20
print(sales_series[sales_series >= 15])
banana 15
orange 30
mango 20
dtype: int64# <
print(sales_series < 15)
apple True
banana False
orange False
grape True
mango False
dtype: bool# <=
print(sales_series <= 20)
apple True
banana True
orange False
grape True
mango True
dtype: bool# ==
print(sales_series == 20)
apple False
banana False
orange False
grape False
mango True
dtype: bool# !=
print(sales_series != 20)
apple True
banana True
orange True
grape True
mango False
dtype: bool#Listing all elements != 20
print(sales_series[sales_series != 20])
apple 10
banana 15
orange 30
grape 9
dtype: int64
