A Guide to the Pandas Series Constructor

In this post, I am going to deep dive into the Pandas Series constructor.

 

Following table summarizes the parameters of Series constructor.

 

Parameter	Description
data	Specifies the data that we want to include in the Series. data can be a Python list, NumPy array, dictionary, scalar value, or another Series.
index	Index argument allows you to specify custom labels for the elements of the series.
dtype	Specify the data type of the series
copy	If copy is set to True, then Pandas create a Series from an existing object by copying the data. 'copy' argument is set to Tru by default.   If copy is set to False, then the original data is shared with Series object, If you modify the original object, the it modifies the Series.
name	Set the name to a Pandas Series.

 

data argument

We can specify the value to 'data' parameter directly in Series method.

series_from_list = pd.Series(data=[2, 3, 5, 7])

 

Mentioning the name ‘data’ is optional, following statement do the same.

series_from_list = pd.Series([2, 3, 5, 7])

data_argument.py

import pandas as pd
import numpy as np

# data can be a Python list, NumPy array, dictionary, scalar value, or another Series.
series_from_list = pd.Series([2, 3, 5, 7])
series_from_list_1 = pd.Series(data=[2, 3, 5, 7])
series_from_numpy_array = pd.Series(np.array([2, 3, 5, 7]))
series_from_dict = pd.Series({'FirstPrime' : 2, 'SecondPrime' : 3, 'ThirdPrime' : 5})
series_from_scalar = pd.Series(2)
series_from_another_series = pd.Series(series_from_list)

print('series_from_list:\n',series_from_list)
print('\nseries_from_list_1:\n',series_from_list_1)
print('\nseries_from_numpy_array:\n',series_from_numpy_array)
print('\nseries_from_dict:\n',series_from_dict)
print('\nseries_from_scalar:\n',series_from_scalar)
print('\nseries_from_another_series:\n',series_from_another_series)

Output

series_from_list:
 0    2
1    3
2    5
3    7
dtype: int64

series_from_list_1:
 0    2
1    3
2    5
3    7
dtype: int64

series_from_numpy_array:
 0    2
1    3
2    5
3    7
dtype: int64

series_from_dict:
 FirstPrime     2
SecondPrime    3
ThirdPrime     5
dtype: int64

series_from_scalar:
 0    2
dtype: int64

series_from_another_series:
 0    2
1    3
2    5
3    7
dtype: int64

index argument

Example

data = [2, 3, 5, 7]
index = ['first_prime', 'second_prime', 'third_prime', 'fourth_prime']
series = pd.Series(data, index)

index_argument.py

import pandas as pd

data = [2, 3, 5, 7]
index = ['first_prime', 'second_prime', 'third_prime', 'fourth_prime']
series = pd.Series(data, index)

print(series)

Output

first_prime     2
second_prime    3
third_prime     5
fourth_prime    7
dtype: int64

dtype argument

Example 1: Data type is derived from the data by default

data = [2, 3, 5, 7]
index = ['first_prime', 'second_prime', 'third_prime', 'fourth_prime']
series1 = pd.Series(data, index)

In the above example, Pandas deduce data type as int64

first_prime     2
second_prime    3
third_prime     5
fourth_prime    7
dtype: int64 

Example 2: Specify the data type explicitly.

series2 = pd.Series(data, index, dtype='float64')

In the above example, I set the data type as float64 explicitly.

 

dtype_argument.py

import pandas as pd

data = [2, 3, 5, 7]
index = ['first_prime', 'second_prime', 'third_prime', 'fourth_prime']
series1 = pd.Series(data, index)
series2 = pd.Series(data, index, dtype='float64')

print(series1,'\n')
print(series2)

copy argument

If copy is set to True, then Pandas create a Series from an existing object by copying the data.

If copy is set to False, then the original data is shared with Series object, If you modify the original object, the it modifies the Series.

name argument

name_argument.py

import pandas as pd

series_1 = pd.Series([2, 3, 5, 7, 11], name='five_primes')
print(series_1)

Output

0     2
1     3
2     5
3     7
4    11
Name: five_primes, dtype: int64

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