Pandas – Series object

Creating simple series object:

import pandas
s = pandas.Series()
print(s)

Create Series object from numpy array object:

import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
s = pd.Series(data)
print(s)

• If we store only integers: dtype become int
• If we store integers and Floats: dtype is float
• If we store different types: dtype become Object

import pandas as pd
import numpy as np
data = np.array([10,20,30,2.34,"Python"])
s = pd.Series(data)
print(s)

We can specify the index for elements:

import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
s = pd.Series(data,index=[1,2,3,4])
print(s)

Heterogeneous keys can be used as index for the elements of same series:

import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
s = pd.Series(data,index=[10,2.3,'g',"Key"])
print(s)

Duplicate index is allowed to store values :

import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
s = pd.Series(data,index=[10,20,10,20])
print(s)

We can create series from dictionary :

import pandas as pd
import numpy as np
dictionary = {10:"A" , 20:"B" , 30:"C" , 10:"D"}
s = pd.Series(dictionary)
print(s)

Storing Scalar value in the series:

import pandas as pd
import numpy as np
s = pd.Series(5,index=[1,2,3,4])
print(s)

We can process elements using loops :

import pandas as pd
import numpy as np
List = [10,20,30,40,50]
Array = np.array(List)
series = pd.Series(Array)
for ele in series:
    print(ele)

Access elements using Slicing:

import pandas as pd
import numpy as np
Array = np.array([10,20,30,40,50])
series = pd.Series(Array)
print(series[:4])

Access elements using string type index :

import pandas as pd
import numpy as np
Array = np.array([10,20,30,40,50])
series = pd.Series(Array, index=['a','b','c','d','e'])
print(series['c'])