What is the difference between a pandas Series and a single-column DataFrame?
Question:
Why does pandas make a distinction between a Series and a single-column DataFrame?
In other words: what is the reason of existence of the Series class?
I’m mainly using time series with datetime index, maybe that helps to set the context.
Answers:
Quoting the Pandas docs
pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)
Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.
So, the Series is the data structure for a single column of a DataFrame, not only conceptually, but literally, i.e. the data in a DataFrame is actually stored in memory as a collection of Series.
Analogously: We need both lists and matrices, because matrices are built with lists. Single row matricies, while equivalent to lists in functionality still cannot exist without the list(s) they’re composed of.
They both have extremely similar APIs, but you’ll find that DataFrame methods always cater to the possibility that you have more than one column. And, of course, you can always add another Series (or equivalent object) to a DataFrame, while adding a Series to another Series involves creating a DataFrame.
from the pandas doc http://pandas.pydata.org/pandas-docs/stable/dsintro.html
Series is a one-dimensional labeled array capable of holding any data type.
To read data in form of panda Series:
import pandas as pd
ds = pd.Series(data, index=index)
DataFrame is a 2-dimensional labeled data structure with columns of potentially different types.
import pandas as pd
df = pd.DataFrame(data, index=index)
In both of the above index is list
for example: I have a csv file with following data:
,country,popuplation,area,capital
BR,Brazil,10210,12015,Brasile
RU,Russia,1025,457,Moscow
IN,India,10458,457787,New Delhi
To read above data as series and data frame:
import pandas as pd
file_data = pd.read_csv("file_path", index_col=0)
d = pd.Series(file_data.country, index=['BR','RU','IN'] or index = file_data.index)
output:
>>> d
BR Brazil
RU Russia
IN India
df = pd.DataFrame(file_data.area, index=['BR','RU','IN'] or index = file_data.index )
output:
>>> df
area
BR 12015
RU 457
IN 457787
Series is a one-dimensional object that can hold any data type such as integers, floats and strings e.g
import pandas as pd
x = pd.Series([A,B,C])
0 A
1 B
2 C
The first column of Series is known as index i.e 0,1,2
the second column is your actual data i.e A,B,C
DataFrames is two-dimensional object that can hold series, list, dictionary
df=pd.DataFrame(rd(5,4),['A','B','C','D','E'],['W','X','Y','Z'])
Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the index. The basic method to create a Series is to call:
s = pd.Series(data, index=index)
DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects.
d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
Here is how the cars.csv file looks.
Print out drives_right column as Series:
print(cars.loc[:,"drives_right"])
US True
AUS False
JAP False
IN False
RU True
MOR True
EG True
Name: drives_right, dtype: bool
The single bracket version gives a Pandas Series, the double bracket version gives a Pandas DataFrame.
Print out drives_right column as DataFrame
print(cars.loc[:,["drives_right"]])
drives_right
US True
AUS False
JAP False
IN False
RU True
MOR True
EG True
Adding a Series to another Series creates a DataFrame.
A DataFrame is often described as
a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table
Because of this definition, we may look at the data as cells, like in an Excel spreadsheet, which has rows with a row number and and columns with a column header. Because of this simplistic view, the underlying data structure may be a bit surprising.
The DataFrame actually consists of Index objects for the axes labels (row and column labels) and Series objects for the column data.
The Series object provides encapsulation for each of the column data (which is held in a one dimensional numpy.ndarray), with row labels and the column label.
So a single column DataFrame will have one underlying Series object for the column data.
See DataFrame data structure for a simple example with a visual representation of the underlying data structure.
Why does pandas make a distinction between a Series and a single-column DataFrame?
In other words: what is the reason of existence of the Series class?
I’m mainly using time series with datetime index, maybe that helps to set the context.
Quoting the Pandas docs
pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.
So, the Series is the data structure for a single column of a DataFrame, not only conceptually, but literally, i.e. the data in a DataFrame is actually stored in memory as a collection of Series.
Analogously: We need both lists and matrices, because matrices are built with lists. Single row matricies, while equivalent to lists in functionality still cannot exist without the list(s) they’re composed of.
They both have extremely similar APIs, but you’ll find that DataFrame methods always cater to the possibility that you have more than one column. And, of course, you can always add another Series (or equivalent object) to a DataFrame, while adding a Series to another Series involves creating a DataFrame.
from the pandas doc http://pandas.pydata.org/pandas-docs/stable/dsintro.html
Series is a one-dimensional labeled array capable of holding any data type.
To read data in form of panda Series:
import pandas as pd
ds = pd.Series(data, index=index)
DataFrame is a 2-dimensional labeled data structure with columns of potentially different types.
import pandas as pd
df = pd.DataFrame(data, index=index)
In both of the above index is list
for example: I have a csv file with following data:
,country,popuplation,area,capital
BR,Brazil,10210,12015,Brasile
RU,Russia,1025,457,Moscow
IN,India,10458,457787,New Delhi
To read above data as series and data frame:
import pandas as pd
file_data = pd.read_csv("file_path", index_col=0)
d = pd.Series(file_data.country, index=['BR','RU','IN'] or index = file_data.index)
output:
>>> d
BR Brazil
RU Russia
IN India
df = pd.DataFrame(file_data.area, index=['BR','RU','IN'] or index = file_data.index )
output:
>>> df
area
BR 12015
RU 457
IN 457787
Series is a one-dimensional object that can hold any data type such as integers, floats and strings e.g
import pandas as pd
x = pd.Series([A,B,C])
0 A
1 B
2 C
The first column of Series is known as index i.e 0,1,2
the second column is your actual data i.e A,B,C
DataFrames is two-dimensional object that can hold series, list, dictionary
df=pd.DataFrame(rd(5,4),['A','B','C','D','E'],['W','X','Y','Z'])
Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the index. The basic method to create a Series is to call:
s = pd.Series(data, index=index)
DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dict of Series objects.
d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),
two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
Here is how the cars.csv file looks.
Print out drives_right column as Series:
print(cars.loc[:,"drives_right"])
US True
AUS False
JAP False
IN False
RU True
MOR True
EG True
Name: drives_right, dtype: bool
The single bracket version gives a Pandas Series, the double bracket version gives a Pandas DataFrame.
Print out drives_right column as DataFrame
print(cars.loc[:,["drives_right"]])
drives_right
US True
AUS False
JAP False
IN False
RU True
MOR True
EG True
Adding a Series to another Series creates a DataFrame.
A DataFrame is often described as
a 2-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table
Because of this definition, we may look at the data as cells, like in an Excel spreadsheet, which has rows with a row number and and columns with a column header. Because of this simplistic view, the underlying data structure may be a bit surprising.
The DataFrame actually consists of Index objects for the axes labels (row and column labels) and Series objects for the column data.
The Series object provides encapsulation for each of the column data (which is held in a one dimensional numpy.ndarray), with row labels and the column label.
So a single column DataFrame will have one underlying Series object for the column data.
See DataFrame data structure for a simple example with a visual representation of the underlying data structure.