In this discussion, we will illustrate the process of creating a Pandas DataFrame with the two-dimensional list. Python is widely recognized for its effectiveness in data analysis, thanks to its robust ecosystem of data-centric packages. Among these packages, Pandas stands out, streamlining the import and analysis of data. There are various methods to achieve a Pandas DataFrame, and in this article, we will focus on creating one using a two-dimensional list.
Pandas DataFrame with Two-dimensional List
There are several methods for creating a Pandas DataFrame with the two-dimensional list. In this context, we will explain some commonly used approaches.
-
Using
pd.DataFrame()
-
Using
pd.DataFrame.from_records()
-
Using
pd.DataFrame.from_dict()
- Using Specifying Data Types
Create Pandas Dataframe from 2D List using pd.DataFrame()
In this example below code creates a Pandas DataFrame (‘df’) from a two-dimensional list (‘lst’) with specified column names (‘Tag’ and ‘number’) and prints the resulting DataFrame.
# import pandas as pd import pandas as pd
# List1 lst = [[ 'Geek' , 25 ], [ 'is' , 30 ],
[ 'for' , 26 ], [ 'Geeksforgeeks' , 22 ]]
# creating df object with columns specified df = pd.DataFrame(lst, columns = [ 'Tag' , 'number' ])
print (df )
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Output :
Tag number 0 Geek 25 1 is 30 2 for 26 3 Geeksforgeeks 22
Create Pandas Dataframe from 2D List using pd.DataFrame.from_records()
In this example below code uses the pandas library in Python to create a DataFrame from a two-dimensional list (data
). The DataFrame has columns with names ‘Name’, ‘Age’, and ‘Occupation’. The print(df)
statement will display the DataFrame. Here’s the expected output:
import pandas as pd
# Two-dimensional list data = [[ 'Geek1' , 28 , 'Analyst' ],
[ 'Geek2' , 35 , 'Manager' ],
[ 'Geek3' , 29 , 'Developer' ]]
# Column names columns = [ 'Name' , 'Age' , 'Occupation' ]
# Creating DataFrame using pd.DataFrame.from_records() df = pd.DataFrame.from_records(data, columns = columns)
# Displaying the DataFrame print (df)
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Output:
Name Age Occupation 0 Geek1 28 Analyst 1 Geek2 35 Manager 2 Geek3 29 Developer
Create Pandas Dataframe from 2D List using pd.DataFrame.from_dict()
In this example below code uses the pandas library in Python to create a DataFrame from a two-dimensional list (data
). Instead of using pd.DataFrame.from_records()
, this time it uses pd.DataFrame.from_dict()
along with the zip
function to transpose the data.
import pandas as pd
# Two-dimensional list data = [[ 'Geek1' , 26 , 'Scientist' ],
[ 'Geek2' , 31 , 'Researcher' ],
[ 'Geek3' , 24 , 'Engineer' ]]
# Column names columns = [ 'Name' , 'Age' , 'Occupation' ]
# Creating DataFrame using pd.DataFrame.from_dict() df = pd.DataFrame.from_dict( dict ( zip (columns, zip ( * data))))
# Displaying the DataFrame print (df)
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Output:
Name Age Occupation 0 Geek1 26 Scientist 1 Geek2 31 Researcher 2 Geek3 24 Engineer
Create Pandas Dataframe from 2D List using Specifying Data Types
In this example below code uses the pandas library in Python to create a DataFrame from a two-dimensional list (data
). The DataFrame has columns with names ‘FName’, ‘LName’, and ‘Age’. The specified data types for all columns are set to float using the dtype
parameter.
import pandas as pd
# Two-dimensional list data = [[ 'Geek1' , 'Reacher' , 25 ],
[ 'Geek2' , 'Pete' , 30 ],
[ 'Geek3' , 'Wilson' , 26 ],
[ 'Geek4' , 'Williams' , 22 ]]
# Column names columns = [ 'FName' , 'LName' , 'Age' ]
# Creating DataFrame with specified data types df = pd.DataFrame(data, columns = columns, dtype = float )
# Displaying the DataFrame print (df)
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Output :
FName LName Age 0 Geek1 Reacher 25.0 1 Geek2 Pete 30.0 2 Geek3 Wilson 26.0 3 Geek4 Williams 22.0