Pandas DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. It is generally the most commonly used pandas object.
Pandas DataFrame can be created in multiple ways. Let’s discuss how to create a Pandas dataframe using list of lists.
Using pd.DataFrame() function
Example #1:
In this example, we will create a list of lists and then pass it to the Pandas DataFrame function. Also, we will add the parameter of columns which will contain the column names.
Python3
# Import pandas libraryimport pandas as pd# initialize list of listsdata = [['Geeks', 10], ['for', 15], ['Lazyroar', 20]]# Create the pandas DataFramedf = pd.DataFrame(data, columns = ['Name', 'Age'])# print dataframe.print(df) |
Output:
Name Age
0 Geeks 10
1 for 15
2 Lazyroar 20
Example #2:
Let’s see another example with the same implementation as above.
Python3
# Import pandas libraryimport pandas as pd# initialize list of listsdata = [['DS', 'Linked_list', 10], ['DS', 'Stack', 9], ['DS', 'Queue', 7], ['Algo', 'Greedy', 8], ['Algo', 'DP', 6], ['Algo', 'BackTrack', 5], ]# Create the pandas DataFramedf = pd.DataFrame(data, columns = ['Category', 'Name', 'Marks'])# print dataframe.print(df) |
Output:
Category Name Marks
0 DS Linked_list 10
1 DS Stack 9
2 DS Queue 7
3 Algo Greedy 8
4 Algo DP 6
5 Algo BackTrack 5
Defining column names using Dataframe.columns() function
Doing some operations on dataframe like transpose. And also defining the Dataframe without column parameters and using df.columns() for the same.
Python3
# Import pandas libraryimport pandas as pd# initialize list of listsdata = [[1, 5, 10], [2, 6, 9], [3, 7, 8]]# Create the pandas DataFramedf = pd.DataFrame(data)# specifying column namesdf.columns = ['Col_1', 'Col_2', 'Col_3']# print dataframe.print(df, "\n")# transpose of dataframedf = df.transpose()print("Transpose of above dataframe is-\n", df) |
Output:
Col_1 Col_2 Col_3
0 1 5 10
1 2 6 9
2 3 7 8
Transpose of above dataframe is-
0 1 2
Col_1 1 2 3
Col_2 5 6 7
Col_3 10 9 8
