Creating a Pandas dataframe using list of tuples
Pandas is famous for data manipulation in Python. We can create a DataFrame from a list of simple tuples, and can even choose the specific elements of the tuples we want to use.
Using pd.DataFrame() function
Here we will create a Pandas Dataframe using a list of tuples with the pd.DataFrame()
function.
Example 1: In this example, we will simply pass the tuple to the DataFrame constructor which will return a pandas dataframe.
Python3
import pandas as pd
data = [( 'Peter' , 18 , 7 ),
( 'Riff' , 15 , 6 ),
( 'John' , 17 , 8 ),
( 'Michel' , 18 , 7 ),
( 'Sheli' , 17 , 5 ) ]
df = pd.DataFrame(data, columns = [ 'Name' , 'Age' , 'Score' ])
print (df)
|
Output:
Name Age Score
0 Peter 18 7
1 Riff 15 6
2 John 17 8
3 Michel 18 7
4 Sheli 17 5
Using from_records()
Here we will create a Pandas Dataframe using a list of tuples using the from_records()
method.
Example 2: In this example, we will use the df.from_records() to create the dataframe from the list of tuples.
Python3
import pandas as pd
data = [( 'Peter' , 18 , 7 ),
( 'Riff' , 15 , 6 ),
( 'John' , 17 , 8 ),
( 'Michel' , 18 , 7 ),
( 'Sheli' , 17 , 5 ) ]
df = pd.DataFrame.from_records(data, columns = [ 'Team' , 'Age' , 'Score' ])
print (df)
|
Output:
Team Age Score
0 Peter 18 7
1 Riff 15 6
2 John 17 8
3 Michel 18 7
4 Sheli 17 5
Using df.pivot() function
Here we will create a Pandas Dataframe using a list of tuples using the df.pivot() function method.
Example 3: Creating pivot table by using three columns
Python3
import pandas as pd
data = [( 'Peter' , 18 , 7 ),
( 'Riff' , 15 , 6 ),
( 'John' , 17 , 8 ),
( 'Michel' , 18 , 7 ),
( 'Sheli' , 17 , 5 ) ]
df = pd.DataFrame(data, columns = [ 'Team' , 'Age' , 'Score' ])
a = df.pivot( 'Team' , 'Score' , 'Age' )
print (a)
|
Output:
Score 5 6 7 8
Team
John NaN NaN NaN 17.0
Michel NaN NaN 18.0 NaN
Peter NaN NaN 18.0 NaN
Riff NaN 15.0 NaN NaN
Sheli 17.0 NaN NaN NaN
Last Updated :
25 Aug, 2023
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