350 lines
5.1 KiB
Markdown
350 lines
5.1 KiB
Markdown
---
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title: pandas Series
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---
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## Series
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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.
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### Basic syntax of Series
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```python
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pandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)
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```
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`data` : dcit,array or scaler value
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`index` : array-like or range. Should be of same size as the data.Defaults to Rangearray(0,1,2..n)
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`dtype` : numpy.dtype
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`name` : name for the series
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### Creating Series in different ways:
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Let's first import our `pandas` module:
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```python
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import pandas as pd
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```
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### Create an empty Series:
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```python
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pd.Series()
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```
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Series([], dtype: float64)
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### Create Using a dict:
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```python
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input_data = {'a':1,'b':2,'c':3,'d':4}
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s = pd.Series(input_data)
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print(s)
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```
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a 1
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b 2
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c 3
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d 4
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dtype: int64
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### Create using a ndarray:
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```python
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import numpy as np
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input_data = np.array(['a','b','c','d'])
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s = pd.Series(input_data)
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print(s)
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```
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0 a
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1 b
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2 c
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3 d
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dtype: object
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Adding our own index:
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```python
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s1 = pd.Series(data = input_data,index = [5,6,7,8])
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print(s1)
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```
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5 a
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6 b
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7 c
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8 d
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dtype: object
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### Create using a scalar value:
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```python
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s = pd.Series(data = 5,index = range(6),dtype= int)
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print(s)
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```
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0 5
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1 5
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2 5
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3 5
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4 5
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5 5
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dtype: int32
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```python
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s = pd.Series(data = 5,index = range(6),dtype= float)
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print(s)
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```
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0 5.0
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1 5.0
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2 5.0
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3 5.0
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4 5.0
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5 5.0
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dtype: float64
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Now we know how to ceate a pandas series. Next we check how can we access or retrive a specific data from a series.
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### Retriving a data from a Series:
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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.
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### Position based selection:
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```python
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s = pd.Series([1,2,3,4,5,6,7])
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print('Selecting the first element: {}'.format(s[0]))
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print('Slecting the first 3 element: \n{}'.format(s[:3]))
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```
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Selecting the first element: 1
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Slecting the first 3 element:
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0 1
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1 2
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2 3
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dtype: int64
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### Label based selection(index):
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```python
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s = pd.Series([1,2,3,4,5], index = ['a','b','c','d','e'])
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print('Select the value for c: {}'.format(s['c']))
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print('Select the values for b,c,d: \n{}'.format(s[['b','c','d']]))
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```
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Select the value for c: 3
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Select the values for b,c,d:
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b 2
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c 3
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d 4
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dtype: int64
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## Series Basic Functionalities:
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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.
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```python
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s = pd.Series(data = [84,78,88,93,91,84],
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index= ['Mark','Lilly','Ben','Hari','Akbar','Monika'], #Scores of students in Science
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name='Science')
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```
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```python
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s.head() # retruns the top five elements
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```
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Mark 84
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Lilly 78
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Ben 88
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Hari 93
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Akbar 91
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Name: Science, dtype: int64
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```python
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s.tail() # retruns the bottom five elements
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```
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Lilly 78
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Ben 88
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Hari 93
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Akbar 91
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Monika 84
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Name: Science, dtype: int64
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```python
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s.size # returns the total number of elements in the series
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```
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6
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```python
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s.sum() # retruns the sum of all the elements
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```
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518
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```python
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s.mean() # retruns the mean of the elements.
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```
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86.33333333333333
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```python
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s.unique() # retruns all the unique values in the Series.
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```
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array([84, 78, 88, 93, 91], dtype=int64)
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```python
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s.nunique() # retruns total no. of unique values in the series
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```
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5
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```python
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s['Mark'] # get the score of Mark
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```
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84
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```python
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s.axes # returns all the index values
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```
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[Index(['Mark', 'Lilly', 'Ben', 'Hari', 'Akbar', 'Monika'], dtype='object')]
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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
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```python
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s + 5 # adding extra 5 marks to all the students
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```
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Mark 89
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Lilly 83
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Ben 93
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Hari 98
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Akbar 96
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Monika 89
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Name: Science, dtype: int64
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```python
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s[['Mark','Monika']] - 2 # Subtracting 2 marks to Mark and Monika
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```
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Mark 82
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Monika 82
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Name: Science, dtype: int64
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```python
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s.drop(labels= 'Ben') # drop deletes a row based on the index value.
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```
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Mark 84
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Lilly 78
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Hari 93
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Akbar 91
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Monika 84
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Name: Science, dtype: int64
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#### More Information:
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[pandas.Series](https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.Series.html)
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