How to Convert a List to a DataFrame Row in Python?

In this article, we will discuss how to convert a list to a dataframe row in Python.

Method 1: Using T function

This is known as the Transpose function, this will convert the list into a row. Here each value is stored in one column.

Syntax: pandas.DataFrame(list).T

Example:

Python3

# import pandas module
import pandas as pd

# consider a list
list1 = ["durga", "ramya", "meghana", "mansa"]

# convert the list into dataframe row
data = pd.DataFrame(list1).T

# add columns
data.columns = ['student1', 'student2',
                'student3', 'student4']

# display
data

Output:

Method 2: Creating from multi-dimensional list to dataframe row

Here we are converting a list of lists to dataframe rows

Syntax: pd.DataFrame(list)

where list is the list of lists

Example:

Python3

# import pandas module
import pandas as pd

# consider a list
list1 = [["durga", "java", 90], ["gopi", "python", 80],
         ["pavani", "c/cpp", 94], ["sravya", "html", 90]]

# convert the list into dataframe row
data = pd.DataFrame(list1)

# add columns
data.columns = ['student1', 'subject', 'marks']

# display
data

Output:

Method 3: Using a list with index and columns

Here we are getting data (rows ) from the list and assigning columns to these values from columns 

Syntax: pd.DataFrame(list, columns, dtype )

where

• list is  the list of input values
• columns are the column names for list of values
• dtype is the column data type

Example:

Python3

# import pandas module
import pandas as pd

# consider a list
list1 = [["durga", "java", 90], ["gopi", "python", 80],
         ["pavani", "c/cpp", 94], ["sravya", "html", 90]]

# convert the list into dataframe row by adding columns
data = pd.DataFrame(list1, columns=['student1',
                                    'subject',
                                    'marks'])


# display
data

Output:

Method 4: Using zip() function

Here  we are taking separate lists as input such that each list will act as one column, so the number of lists = n columns in the dataframe, and using zip function we are combining the lists.

Syntax pd.DataFrame(list(zip(list1,list2,.,list n)),columns)

where

• columns is the column for the list values
• list1.list n represent number of input lists for columns

Example:

Python3

# import pandas module
import pandas as pd

# consider a list
list1 = ["durga", "ramya", "sravya"]
list2 = ["java", "php", "mysql"]
list3 = [67, 89, 65]

# convert the list into dataframe row by
# using zip()
data = pd.DataFrame(list(zip(list1, list2, list3)),
                    columns=['student', 'subject', 'marks'])


# display
data

Output:

Method 5: Using a list of dictionary

Here we are passing the individual lists which act as columns in the data frame to keys to the dictionary, so by passing the dictionary into dataframe() we can convert list to dataframe.

Syntax: pd.DataFrame{‘key’: list1, ‘key’: list2, ……..,’key’: listn}

These keys will be the column names in the dataframe.

Example:

Python3

# import pandas module
import pandas as pd

# consider a list
list1 = ["durga", "ramya", "sravya"]

list2 = ["java", "php", "mysql"]

list3 = [67, 89, 65]

# convert the list into dataframe row by
# using dictionary
dictionary = {'name': list1, 'subject': list2,
              'marks': list3}

data = pd.DataFrame(dictionary)

# display
data

Output:

Method 6: Creating from  multi-dimensional list to dataframe row with columns

Here we are taking input from multi-dimensional lists and assigning column names in the DataFrame() function

Syntax: pd.DataFrame(list,columns)

where

1. list is an multidimensional list
2. columns are the column names

Example:

Python3

# import pandas module
import pandas as pd

# consider a list
list1 = [["durga", "java", 90],
         ["gopi", "python", 80],
         ["pavani", "c/cpp", 94],
         ["sravya", "html", 90]]

# convert the list into dataframe
# row using columns from multi lists
data = pd.DataFrame(list1, columns=['student1',
                                    'subject',
                                    'marks'])

# display
data

Output: