Pandas : Convert a DataFrame into a list of rows or columns in python | (list of lists)

Converting a DataFrame into a list of rows or columns in python | (list of lists)

In this article, we will discuss how we can convert a dataframe into a list, by converting each row or column into a list and creating a python lists from them.

Let’s first, create a dataframe,

import pandas as pd
#The List of Tuples
students = [('Arun', 23, 'Chennai', 127),
            ('Priya', 31, 'Delhi', 174.5),
            ('Ritik', 24, 'Mumbai', 181),
            ('Kimun', 37, 'Hyderabad', 125),
            ('Sinvee', 16, 'Delhi', 175.5),
            ('Kunu', 28, 'Mumbai', 115),
            ('Lisa', 31, 'Pune', 191)
            ]
# Creating DataFrame object
studentId = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Score'])
print(studentId)

Output :
     Name     Age       City           Score
0    Arun      23     Chennai         127.0
1   Priya       31      Delhi            174.5
2   Ritik        24     Mumbai         181.0
3   Kimun     37    Hyderabad     125.0
4   Sinvee    16      Delhi              175.5
5    Kunu    28     Mumbai           115.0
6    Lisa      31       Pune              191.0

Convert a Dataframe into a list of lists – Rows Wise :

In the dataframe created above, we must fetch each line as a list and create a list of these lists.

Let’s see how we can do this

import pandas as pd
#The List of Tuples
students = [('Arun', 23, 'Chennai', 127),
            ('Priya', 31, 'Delhi', 174.5),
            ('Ritik', 24, 'Mumbai', 181),
            ('Kimun', 37, 'Hyderabad', 125),
            ('Sinvee', 16, 'Delhi', 175.5),
            ('Kunu', 28, 'Mumbai', 115),
            ('Lisa', 31, 'Pune', 191)
            ]
# Creating DataFrame object
studentId = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Score'])

# By Converting a dataframe to the list of rows (list of lists)
listOfRows = studentId.to_numpy().tolist()
print(listOfRows)
print(type(listOfRows))

Output :
[['Arun', 23, 'Chennai', 127.0], ['Priya', 31, 'Delhi', 174.5], ['Ritik', 24, 'Mumbai', 181.0], ['Kimun', 37, 'Hyderabad', 125.0], ['Sinvee', 16, 'Delhi', 175.5], ['Kunu', 28, 'Mumbai', 115.0], ['Lisa', 31, 'Pune', 191.0]]
<class 'list'>

It Converted the data name into a sequential target list, that is, each linked list contains a line of data names. But what happened in one line ?

How did it work?

Let’s divide one line above into several lines to understand the concept behind it.

Step 1: Convert the Dataframe to a nested Numpy array using DataFrame.to_numpy() :

import pandas as pd
#The List of Tuples
students = [('Arun', 23, 'Chennai', 127),
            ('Priya', 31, 'Delhi', 174.5),
            ('Ritik', 24, 'Mumbai', 181),
            ('Kimun', 37, 'Hyderabad', 125),
            ('Sinvee', 16, 'Delhi', 175.5),
            ('Kunu', 28, 'Mumbai', 115),
            ('Lisa', 31, 'Pune', 191)
            ]
# Creating DataFrame object
studentId = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Score'])


# By getting rows of a dataframe as a nested numpy array
numpy_2d_array = studentId.to_numpy()
print(numpy_2d_array)
print(type(numpy_2d_array))

Output :
[['Arun' 23 'Chennai' 127.0]
['Priya' 31 'Delhi' 174.5]
['Ritik' 24 'Mumbai' 181.0]
['Kimun' 37 'Hyderabad' 125.0]
['Sinvee' 16 'Delhi' 175.5]
['Kunu' 28 'Mumbai' 115.0]
['Lisa' 31 'Pune' 191.0]]
<class 'numpy.ndarray'>

Actually DataFrame.to_numpy() converts data name into Numpy array. So we have a 2D Numpy array here. We have confirmed that by printing the type of returned item.

Step 2: Convert 2D Numpy array into a list of lists :

Numpy provides a function tolist(), which converts Numpy Array into a list. Let’s call that function in the object built above 2D Numpy,

import pandas as pd
#The List of Tuples
students = [('Arun', 23, 'Chennai', 127),
            ('Priya', 31, 'Delhi', 174.5),
            ('Ritik', 24, 'Mumbai', 181),
            ('Kimun', 37, 'Hyderabad', 125),
            ('Sinvee', 16, 'Delhi', 175.5),
            ('Kunu', 28, 'Mumbai', 115),
            ('Lisa', 31, 'Pune', 191)
            ]
# Creating DataFrame object
studentId = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Score'])


# By getting rows of a dataframe as a nested numpy array
numpy_2d_array = studentId.to_numpy()

# By Converting 2D numpy array to the list of lists
listOfRows = numpy_2d_array.tolist()
print(listOfRows)
print(type(listOfRows))

Output :
[['Arun', 23, 'Chennai', 127.0], ['Priya', 31, 'Delhi', 174.5], ['Ritik', 24, 'Mumbai', 181.0], ['Kimun', 37, 'Hyderabad', 125.0], ['Sinvee', 16, 'Delhi', 175.5], ['Kunu', 28, 'Mumbai', 115.0], ['Lisa', 31, 'Pune', 191.0]]
<class 'list'>

It converted 2D Numpy Array into a list.

So, this is how we changed the dataframe to 2D Numpy Array and then List of Lists, where each nested list represents a dataframe line.

Convert a Dataframe into a list of lists – Column Wise :

Now turn each column into a list and create a list of these lists,

import pandas as pd
#The List of Tuples
students = [('Arun', 23, 'Chennai', 127),
            ('Priya', 31, 'Delhi', 174.5),
            ('Ritik', 24, 'Mumbai', 181),
            ('Kimun', 37, 'Hyderabad', 125),
            ('Sinvee', 16, 'Delhi', 175.5),
            ('Kunu', 28, 'Mumbai', 115),
            ('Lisa', 31, 'Pune', 191)
            ]
# Creating DataFrame object
studentId = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Score'])


# Convert a dataframe to the list of columns i.e. list of lists
listOfRows = studentId.transpose().values.tolist()
print(listOfRows)
print(type(listOfRows))

Output :
[['Arun', 'Priya', 'Ritik', 'Kimun', 'Sinvee', 'Kunu', 'Lisa'], [23, 31, 24, 37, 16, 28, 31], ['Chennai', 'Delhi', 'Mumbai', 'Hyderabad', 'Delhi', 'Mumbai', 'Pune'], [127.0, 174.5, 181.0, 125.0, 175.5, 115.0, 191.0]]

<class 'list'>

How did it work?

It works on the same concept we discussed above, just one more step here i.e.

Step 1: Transpose the dataframe to convert rows as columns and columns as rows :

import pandas as pd
#The List of Tuples
students = [('Arun', 23, 'Chennai', 127),
            ('Priya', 31, 'Delhi', 174.5),
            ('Ritik', 24, 'Mumbai', 181),
            ('Kimun', 37, 'Hyderabad', 125),
            ('Sinvee', 16, 'Delhi', 175.5),
            ('Kunu', 28, 'Mumbai', 115),
            ('Lisa', 31, 'Pune', 191)
            ]
# Creating DataFrame object
studentId = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Score'])



# Transposing the dataframe, rows are now columns and columns are now rows
transposedObj = studentId.transpose()
print(transposedObj)

Output :
0      1       2          3       4       5      6
Name      Arun  Priya   Ritik      Kimun  Sinvee    Kunu   Lisa
Age         23     31      24         37      16      28     31
City   Chennai  Delhi  Mumbai  Hyderabad   Delhi  Mumbai   Pune
Score    127.0  174.5   181.0      125.0   175.5   115.0  191.0

transposedObj is a transpose of the original data i.e. lines in studentId with columns in transposedObj and columns in studentId are lines in transposedObj.

Step 2: Convert the Dataframe to a nested Numpy array using DataFrame.to_numpy() :

import pandas as pd
#The List of Tuples
students = [('Arun', 23, 'Chennai', 127),
            ('Priya', 31, 'Delhi', 174.5),
            ('Ritik', 24, 'Mumbai', 181),
            ('Kimun', 37, 'Hyderabad', 125),
            ('Sinvee', 16, 'Delhi', 175.5),
            ('Kunu', 28, 'Mumbai', 115),
            ('Lisa', 31, 'Pune', 191)
            ]
# Creating DataFrame object
studentId = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Score'])


# Transposing the dataframe, rows are now columns and columns are now rows
transposedObj = studentId.transpose()


# By getting rows of a dataframe as a nested numpy array
numpy_2d_array = transposedObj.to_numpy()
print(numpy_2d_array)
print(type(numpy_2d_array))

Output :
[['Arun' 'Priya' 'Ritik' 'Kimun' 'Sinvee' 'Kunu' 'Lisa']
[23 31 24 37 16 28 31]
['Chennai' 'Delhi' 'Mumbai' 'Hyderabad' 'Delhi' 'Mumbai' 'Pune']
[127.0 174.5 181.0 125.0 175.5 115.0 191.0]]
<class 'numpy.ndarray'>

Step 3: Convert 2D Numpy array into a list of lists. :

import pandas as pd
#The List of Tuples
students = [('Arun', 23, 'Chennai', 127),
            ('Priya', 31, 'Delhi', 174.5),
            ('Ritik', 24, 'Mumbai', 181),
            ('Kimun', 37, 'Hyderabad', 125),
            ('Sinvee', 16, 'Delhi', 175.5),
            ('Kunu', 28, 'Mumbai', 115),
            ('Lisa', 31, 'Pune', 191)
            ]
# Creating DataFrame object
studentId = pd.DataFrame(students, columns=['Name', 'Age', 'City', 'Score'])


# Transposing the dataframe, rows are now columns and columns are now rows
transposedObj = studentId.transpose()


# By getting rows of a dataframe as a nested numpy array
numpy_2d_array = transposedObj.to_numpy()

#By Converting 2D numpy array to the list of lists
listOfRows = numpy_2d_array.tolist()
print(listOfRows)
print(type(listOfRows))

Output :
[['Arun', 'Priya', 'Ritik', 'Kimun', 'Sinvee', 'Kunu', 'Lisa'], [23, 31, 24, 37, 16, 28, 31], ['Chennai', 'Delhi', 'Mumbai', 'Hyderabad', 'Delhi', 'Mumbai', 'Pune'], [127.0, 174.5, 181.0, 125.0, 175.5, 115.0, 191.0]]
<class 'list'>

Want to expert in the python programming language? Exploring Python Data Analysis using Pandas tutorial changes your knowledge from basic to advance level in python concepts.

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