Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). Labels need not be unique but must be a hashable type. An element in the series can be accessed similarly to that in an ndarray. Elements of a series can be accessed in two ways –
- Accessing Element from Series with Position
- Accessing Element Using Label (index)
In this article, we are using “nba.csv” file, to download the CSV, click here.
Accessing Element from Series with Position
In order to access the series element refers to the index number. Use the index operator [ ] to access an element in a series. The index must be an integer.
In order to access multiple elements from a series, we use Slice operation. Slice operation is performed on Series with the use of the colon(:). To print elements from beginning to a range use [:Index], to print elements from end-use [:-Index], to print elements from specific Index till the end use [Index:], to print elements within a range, use [Start Index:End Index] and to print whole Series with the use of slicing operation, use [:]. Further, to print the whole Series in reverse order, use [::-1].
Code #1: Accessing a first element of series
# import pandas and numpy import pandas as pd import numpy as np # creating simple array data = np.array(['g', 'e', 'e', 'k', 's', 'f', 'o', 'r', 'g', 'e', 'e', 'k', 's']) ser = pd.Series(data) # retrieve the first element print(ser[0]) |
Output :
g
Code #2: Accessing first 5 elements of Series
# import pandas and numpy import pandas as pd import numpy as np # creating simple array data = np.array(['g', 'e', 'e', 'k', 's', 'f', 'o', 'r', 'g', 'e', 'e', 'k', 's']) ser = pd.Series(data) # retrieve the first element print(ser[:5]) |
Output :
Code #3: Accessing last 10 elements of Series
# import pandas and numpy import pandas as pd import numpy as np # creating simple array data = np.array(['g', 'e', 'e', 'k', 's', 'f', 'o', 'r', 'g', 'e', 'e', 'k', 's']) ser = pd.Series(data) # retrieve the first element print(ser[-10:]) |
Output :
Code #4: Accessing first 5 elements of Series in nba.csv file
# importing pandas module import pandas as pd # making data frame df = pd.read_csv("nba.csv") ser = pd.Series(df['Name']) ser.head(10) |
Now we access first 5 elements of series
# get first five names ser[:5] |
Output :
Accessing Element Using Label (index)
In order to access an element from series, we have to set values by index label. A Series is like a fixed-size dictionary in that you can get and set values by index label.
Code #1: Accessing a single element using index label
# import pandas and numpy import pandas as pd import numpy as np # creating simple array data = np.array(['g', 'e', 'e', 'k', 's', 'f', 'o', 'r', 'g', 'e', 'e', 'k', 's']) ser = pd.Series(data, index =[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]) # accessing a element using index element print(ser[16]) |
Output :
o
Code #2: Accessing a multiple element using index label
# import pandas and numpy import pandas as pd import numpy as np # creating simple array data = np.array(['g', 'e', 'e', 'k', 's', 'f', 'o', 'r', 'g', 'e', 'e', 'k', 's']) ser = pd.Series(data, index =[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]) # accessing a multiple element using # index element print(ser[[10, 11, 12, 13, 14]]) |
Output :
Code #3: Access multiple elements by providing label of index
# importing pandas and numpy import pandas as pd import numpy as np ser = pd.Series(np.arange(3, 9), index =['a', 'b', 'c', 'd', 'e', 'f']) print(ser[['a', 'd', 'g', 'l']]) |
Output :
Code #4: Accessing a multiple element using index label in nba.csv file
# importing pandas module import pandas as pd # making data frame df = pd.read_csv("nba.csv") ser = pd.Series(df['Name']) ser.head(10) |
Now we access an multiple element using index label
ser[[0, 3, 6, 9]] |
Output :
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