How to Convert a List to a DataFrame Row in Python?
Last Updated :
28 Dec, 2021
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 as pd
list1 = [ "durga" , "ramya" , "meghana" , "mansa" ]
data = pd.DataFrame(list1).T
data.columns = [ 'student1' , 'student2' ,
'student3' , 'student4' ]
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 as pd
list1 = [[ "durga" , "java" , 90 ], [ "gopi" , "python" , 80 ],
[ "pavani" , "c/cpp" , 94 ], [ "sravya" , "html" , 90 ]]
data = pd.DataFrame(list1)
data.columns = [ 'student1' , 'subject' , 'marks' ]
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 as pd
list1 = [[ "durga" , "java" , 90 ], [ "gopi" , "python" , 80 ],
[ "pavani" , "c/cpp" , 94 ], [ "sravya" , "html" , 90 ]]
data = pd.DataFrame(list1, columns = [ 'student1' ,
'subject' ,
'marks' ])
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 as pd
list1 = [ "durga" , "ramya" , "sravya" ]
list2 = [ "java" , "php" , "mysql" ]
list3 = [ 67 , 89 , 65 ]
data = pd.DataFrame( list ( zip (list1, list2, list3)),
columns = [ 'student' , 'subject' , 'marks' ])
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 as pd
list1 = [ "durga" , "ramya" , "sravya" ]
list2 = [ "java" , "php" , "mysql" ]
list3 = [ 67 , 89 , 65 ]
dictionary = { 'name' : list1, 'subject' : list2,
'marks' : list3}
data = pd.DataFrame(dictionary)
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
- list is an multidimensional list
- columns are the column names
Example:
Python3
import pandas as pd
list1 = [[ "durga" , "java" , 90 ],
[ "gopi" , "python" , 80 ],
[ "pavani" , "c/cpp" , 94 ],
[ "sravya" , "html" , 90 ]]
data = pd.DataFrame(list1, columns = [ 'student1' ,
'subject' ,
'marks' ])
data
|
Output:
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