5.1 KiB
title
| title |
|---|
| pandas Series |
Series
In this section we will go through one of the important pandas object Series. Series is a one dimensional ndarray. It is similar to a column in a table. It can have a custom axis label or by default pandas adds a range value as index. It is mainly used to represent the values of a single column. Example the scores of students in science subject from a marksheet.
Basic syntax of Series
pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)
data : dcit,array or scaler value
index : array-like or range. Should be of same size as the data.Defaults to Rangearray(0,1,2..n)
dtype : numpy.dtype
name : name for the series
Creating Series in different ways:
Let's first import our pandas module:
import pandas as pd
Create an empty Series:
pd.Series()
Series([], dtype: float64)
Create Using a dict:
input_data = {'a':1,'b':2,'c':3,'d':4}
s = pd.Series(input_data)
print(s)
a 1
b 2
c 3
d 4
dtype: int64
Create using a ndarray:
import numpy as np
input_data = np.array(['a','b','c','d'])
s = pd.Series(input_data)
print(s)
0 a
1 b
2 c
3 d
dtype: object
Adding our own index:
s1 = pd.Series(data = input_data,index = [5,6,7,8])
print(s1)
5 a
6 b
7 c
8 d
dtype: object
Create using a scalar value:
s = pd.Series(data = 5,index = range(6),dtype= int)
print(s)
0 5
1 5
2 5
3 5
4 5
5 5
dtype: int32
s = pd.Series(data = 5,index = range(6),dtype= float)
print(s)
0 5.0
1 5.0
2 5.0
3 5.0
4 5.0
5 5.0
dtype: float64
Now we know how to ceate a pandas series. Next we check how can we access or retrive a specific data from a series.
Retriving a data from a Series:
Retriving data from a Series is similar to ndaray in numpy or the basic list in python. It supports all the similar python list retrival methods like position based, label based as well as slicing.
Position based selection:
s = pd.Series([1,2,3,4,5,6,7])
print('Selecting the first element: {}'.format(s[0]))
print('Slecting the first 3 element: \n{}'.format(s[:3]))
Selecting the first element: 1
Slecting the first 3 element:
0 1
1 2
2 3
dtype: int64
Label based selection(index):
s = pd.Series([1,2,3,4,5], index = ['a','b','c','d','e'])
print('Select the value for c: {}'.format(s['c']))
print('Select the values for b,c,d: \n{}'.format(s[['b','c','d']]))
Select the value for c: 3
Select the values for b,c,d:
b 2
c 3
d 4
dtype: int64
Series Basic Functionalities:
Now we will see some of the basic functionalities or methods available with pandas Series. This methods come handy when performing some operations with the a Series.
s = pd.Series(data = [84,78,88,93,91,84],
index= ['Mark','Lilly','Ben','Hari','Akbar','Monika'], #Scores of students in Science
name='Science')
s.head() # retruns the top five elements
Mark 84
Lilly 78
Ben 88
Hari 93
Akbar 91
Name: Science, dtype: int64
s.tail() # retruns the bottom five elements
Lilly 78
Ben 88
Hari 93
Akbar 91
Monika 84
Name: Science, dtype: int64
s.size # returns the total number of elements in the series
6
s.sum() # retruns the sum of all the elements
518
s.mean() # retruns the mean of the elements.
86.33333333333333
s.unique() # retruns all the unique values in the Series.
array([84, 78, 88, 93, 91], dtype=int64)
s.nunique() # retruns total no. of unique values in the series
5
s['Mark'] # get the score of Mark
84
s.axes # returns all the index values
[Index(['Mark', 'Lilly', 'Ben', 'Hari', 'Akbar', 'Monika'], dtype='object')]
Scalar arithimatic operations are supported in Series. It is generally called as brodacasting as it applies to all the elements in the series. Example as follow
s + 5 # adding extra 5 marks to all the students
Mark 89
Lilly 83
Ben 93
Hari 98
Akbar 96
Monika 89
Name: Science, dtype: int64
s[['Mark','Monika']] - 2 # Subtracting 2 marks to Mark and Monika
Mark 82
Monika 82
Name: Science, dtype: int64
s.drop(labels= 'Ben') # drop deletes a row based on the index value.
Mark 84
Lilly 78
Hari 93
Akbar 91
Monika 84
Name: Science, dtype: int64