Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index.
pandas.Series
A pandas Series can be created using the following constructor −
pandas.Series( data, index, dtype, copy)
The parameters of the constructor are as follows −
| S.No | Parameter & Description |
|---|---|
| 1 | data data takes various forms like ndarray, list, constants |
| 2 | index Index values must be unique and hashable, same length as data. Default np.arrange(n) if no index is passed. |
| 3 | dtype dtype is for data type. If None, data type will be inferred |
| 4 | copy Copy data. Default False |
A series can be created using various inputs like −
- Array
- Dict
- Scalar value or constant
Create an Empty Series
A basic series, which can be created is an Empty Series.
Example
#import the pandas library and aliasing as pd import pandas as pd s = pd.Series() print s
Its output is as follows −
Series([], dtype: float64)
Create a Series from ndarray
If data is an ndarray, then index passed must be of the same length. If no index is passed, then by default index will be range(n) where n is array length, i.e., [0,1,2,3…. range(len(array))-1].
Example 1
#import the pandas library and aliasing as pd import pandas as pd import numpy as np data = np.array(['a','b','c','d']) s = pd.Series(data) print s
Its output is as follows −
0 a 1 b 2 c 3 d dtype: object
We did not pass any index, so by default, it assigned the indexes ranging from 0 to len(data)-1, i.e., 0 to 3.
Example 2
#import the pandas library and aliasing as pd import pandas as pd import numpy as np data = np.array(['a','b','c','d']) s = pd.Series(data,index=[100,101,102,103]) print s
Its output is as follows −
100 a 101 b 102 c 103 d dtype: object
We passed the index values here. Now we can see the customized indexed values in the output.
Create a Series from dict
A dict can be passed as input and if no index is specified, then the dictionary keys are taken in a sorted order to construct index. If index is passed, the values in data corresponding to the labels in the index will be pulled out.
Example 1
#import the pandas library and aliasing as pd import pandas as pd import numpy as np data = {'a' : 0., 'b' : 1., 'c' : 2.} s = pd.Series(data) print s
Its output is as follows −
a 0.0 b 1.0 c 2.0 dtype: float64
Observe − Dictionary keys are used to construct index.
Example 2
#import the pandas library and aliasing as pd import pandas as pd import numpy as np data = {'a' : 0., 'b' : 1., 'c' : 2.} s = pd.Series(data,index=['b','c','d','a']) print s
Its output is as follows −
b 1.0 c 2.0 d NaN a 0.0 dtype: float64
Observe − Index order is persisted and the missing element is filled with NaN (Not a Number).
Create a Series from Scalar
If data is a scalar value, an index must be provided. The value will be repeated to match the length of index
#import the pandas library and aliasing as pd import pandas as pd import numpy as np s = pd.Series(5, index=[0, 1, 2, 3]) print s
Its output is as follows −
0 5 1 5 2 5 3 5 dtype: int64
Accessing Data from Series with Position
Data in the series can be accessed similar to that in an ndarray.
Example 1
Retrieve the first element. As we already know, the counting starts from zero for the array, which means the first element is stored at zeroth position and so on.
import pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve the first element print s[0]
Its output is as follows −
1
Example 2
Retrieve the first three elements in the Series. If a : is inserted in front of it, all items from that index onwards will be extracted. If two parameters (with : between them) is used, items between the two indexes (not including the stop index)
import pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve the first three element print s[:3]
Its output is as follows −
a 1 b 2 c 3 dtype: int64
Example 3
Retrieve the last three elements.
import pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve the last three element print s[-3:]
Its output is as follows −
c 3 d 4 e 5 dtype: int64
Retrieve Data Using Label (Index)
A Series is like a fixed-size dict in that you can get and set values by index label.
Example 1
Retrieve a single element using index label value.
import pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve a single element print s['a']
Its output is as follows −
1
Example 2
Retrieve multiple elements using a list of index label values.
import pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve multiple elements print s[['a','c','d']]
Its output is as follows −
a 1 c 3 d 4 dtype: int64
Example 3
If a label is not contained, an exception is raised.
import pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) #retrieve multiple elements print s['f']
Its output is as follows −
… KeyError: 'f' ####################################################
import pandas as pd
import numpy as np
s=pd.Series()
print(s)
# Create Series From List
a=np.array([1,2,3,4])
s=pd.Series(a)
print(s)
s=pd.Series(a,index=['rank1','rank2','rank3','rank4'])
print(s)
print(s[2]) #index
print(s['rank3']) # Labelled index
# Create Series from dictionary
d={'a':21,'b':22,'c':21}
s=pd.Series(d)
print(s)
print(s[2]) #index
print(s['b']) # Labelled index
print(s[['b','c']])