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: