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This article will show you how to convert items in a list into a Pandas DataFrame.

Convert List to Pandas DataFrame in Python

DataFrame, in general, is a two-dimensional labeled data structure. Pandas is an open source Python package that is very useful for data science.

Here, we will first import the pandas package. We will define the pandas package in this particular program as pd. We will then create a list my_listto store the list values Tom​​, Mark, and Tony, which are just random names. We will then pd.DataFrame(my_list)assign to the variable df. DataFrame(my_list)The method takes my_listthe value of and creates a DataFrame with it. In the last line of the program, we called dfprint DataFrame stored in the variable . Note that we could have also just written dfinstead of print(df)to see our DataFrame.

Sample code:

# python 3.x
import pandas as pd

my_list = ["Tom", "Mark", "Tony"]
df = pd.DataFrame(my_list)
print(df)

Output:

    0
0   Tom
1   Mark
2   Tony

We can see that the elements we provided in the list are now in one column of the above output.

Storing Lists in Pandas DataFrame Columns in Python

We can convert the list to a pandas DataFrame by creating a column in the DataFrame and storing the converted data in that column.

To convert a list into data for a Pandas DataFrame column, we will create a list my_listand give the list some random names as values. Our goal is to ensure that the list elements become Namesentries of a column titled . To do this, we will use pass columns = ['Names']the variable my_listto pd.DataFrame(), as shown below. We then print dfthe variable and run our code to see the output.

Sample code:

# python 3.x
import pandas as pd

my_list = ["Tom", "Mark", "Tony"]
df = pd.DataFrame(my_list, columns=["Names"])
print(df)

Output:

    Names
0   Tom
1   Mark
2   Tony

After we add an extra attribute columns = ['Names'], we see my_listthe names in as Namesthe values ​​of the columns in the DataFrame .

Convert a list to an indexed DataFrame in Python

We can also index the list items while converting them to a DataFrame.

We will create a list my_list. Our goal is to ensure that the list elements become column entries with predefined row indices titled 名称. To do this, we will create a list indexand fill it with i, ii, and iii. We can use a list as pd.DataFrame()the second argument in . The first and third arguments are my_listand columns =['Names']. We will then print the variables that store the expressions we wrote df.

Sample code:

# python 3.x
import pandas as pd

my_list = [" Tom", "Mark", "Tony"]
df = pd.DataFrame(my_list, index=["i.", "ii.", "iii."], columns=["Names"])
print(df)

Output:

     Names
i.    Tom
ii.   Mark
iii.  Tony

We can see that indexthe values ​​in the list have replaced the default Pandas index. We can put any value into indexand generate the result accordingly.

zip()Zip two lists into a single DataFrame in Python using

zip()The function merges the values ​​of two different lists into one by combining the values ​​of the lists with the same index. Before we create a DataFrame, let's see zip()how it works.

Sample code:

# python 3.x
a = ["1", "2", "3"]
b = ["4", "5", "6"]
c = zip(a, b)
list1 = list(c)
print(list1)

Output:

[('1', '4'), ('2', '5'), ('3', '6')]

We can see zip()that the function helps us combine the lists aand bwith grouped similar index items. We store the compressed state of the lists aand into and then create and store the compressed list into it. In the following example, we will create a Pandas DataFrame using .bclist1czip()

We will create two different lists, name_listand height_list, and store some names and heights respectively. Then we use zip(name_list, height_list)zip name_listand height_listto create a Pandas DataFrame.

Note that we can also index our data by simply putting another attribute index = [ 'index1', 'index2', 'index3' ], where the items in the index list can be anything.

Sample code:

# python 3.x
import pandas as pd

name_list = ["Tom", "Mark", "Tony"]
height_list = ["150", "151", "152"]
df = pd.DataFrame((zip(name_list, height_list)), columns=["Name", "Height"])
print(df)

Output:

    Name    Height
0   Tom     150
1   Mark    151
2   Tony    152

We can see that the resulting DataFrame consists of the values ​​of name_listand height_listin the correct order.

We can also use this technique to zip more than two lists.

Convert a Multidimensional List to a Pandas DataFrame in Python

We can even convert a multidimensional list to a Pandas DataFrame. We can set column names for the list items in a multidimensional list. We will demonstrate this method with a two-dimensional list.

To convert a multidimensional list to a Pandas DataFrame, we need to first create a list of lists. Therefore, we will first import Pandas and then create a list where infowe will store the names and ages of three different individuals in three separate lists. We will then call pd.DataFrame()and process the lists into it and specify two column headers for our data, , Nameand Age.

Sample code:

# python 3.x
import pandas as pd

info = [["Tom", 18], ["Mark", 25], ["Tony", 68]]
df = pd.DataFrame(info, columns=["Name", "Age"])
print(df)

Output:

    Name    Age
0   Tom     18
1   Mark    25
2   Tony    68

We have two columns as output, name and age in their respective order. We can infoadd additional values ​​to the individual lists in and assign column headers to them to get more columns in our DataFrame.